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Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 33
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Statistical analysis and prospective
application of the GM-scale,
a semi-harmonic EMF scale proposed to
discriminate between ‘coherent’ and
‘decoherent’ EM frequencies
on life conditions
Trudi Sonderkamp*, Hans (J H) Geesink** and Dirk K F Meijer***
Abstract
The Generalized Music (GM)-scale is an acoustic (octave–like) algorithm of 12 tones
that describes the electromagnetic (EM) frequency band pattern discovered from a
meta-analysis of in total 468 biomedical research papers. These studies reported
either beneficial or detrimental effects of electromagnetic frequencies (EMF) on
biological tissues/cells in vitro or whole organisms in vivo. The apparent quantized
pattern of EM frequency bands was postulated to represent a potential ‘quantum
algorithm of life’. In the present paper a statistical analysis is made of the overall
data underlying this patterned EM frequency distribution. Data were sorted
according to their features to be either beneficial, ‘coherent’ frequencies or
detrimental, ‘decoherent’ frequencies and grouped around the theoretical 12 GM-
scale values. A Wilcoxon rank sum test was used to discriminate between these d ata
populations and this test showed that the difference between the ‘coherent’ and
‘decoherent’ data sets is indeed statistically significant (p<0.0025) for all of the 12
GM-scale groups. The mean values of the groups correspond very well with the
postulated GM-scale values (difference <0,9%). To analyze the fit of the biomedical
EM-frequency data to the GM-scale algorithm values, 24 alternating and
“decoherent’ frequency bands were defined and the life data were plotted in these
bands. This test showed that 89.4% of ‘coherent’ data and 83.4% of ‘decoherent’
data corresponded to their respective frequency bands. The particular band widths,
and consequently the related error margins, are very small (2.6%-3.3%). A
prospective method is demonstrated to apply the GM-scale algorithm to identify
(label) experimental or already published EM frequency data as potential “coherent’
or “decoherent’. These and future analyses of experimental data with respect to the
fit of their EM frequencies to the GM-scale will help to further validate this algorithm
as a new biophysical principle.
Key Words: Generalized Music-scale, GM-scale, electromagnetic radiation, EMF, statistics,
statistical analysis, EM frequency distribution patterns, coherence/decoherence balance,
electromagnetic frequencies
Quantum Biosystems 2019; 10 (2): 32-51
* Ir. Biochemist, Sonderkamp Research, Eindhoven, email:
t.sonderkamp@kpnmail.nl ** Previous: Ir, Project leader
mineral Nanotechnology, DSM-Research, the Netherlands ***
Em. Professor of Pharmacokinetics and Drug
Targeting,University of Groningen, the Netherlands
Address: Groningen, Parklaan 17, 9724 AN, The Netherlands
e-mail: meij6076@planet.nl
Introduction
Over the years evidence has been
accumulating that electromagnetic
radiation plays a role in intercellular and
intracellular communication and probably
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 34
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
also in communication between
organisms. For a review see Fields of the
Cell, edited by D. Fels, M. Cifra and F.
Scholkmann (2015).
In search for an underlying
mechanism for such effects of
electromagnetic frequencies (EMF),
(Geesink and Meijer, 2015) found a
striking, band-like, distribution pattern of
frequencies of electromagnetic radiation
that either affect life systems in a life
sustaining way or a life-endangering
manner (Geesink & Meijer, 2016).
[1]
En : Energy distribution,
ωref : Reference frequency = 1 Hz,
ℏ : Reduced Planck’s constant,
n : Series of integers: 0, 0.5, 2, 4, 5, 7, 8, -1, -3, -4,-6,
-7,
m : series of integers: 0, 1, 2, 3, 4, 5, -1, -2, -3, -4, -5,
p : Series of integers: <-4, -4, -3, -2, -1, 0, 1, 2, 3, 4,
5, 6, > +52
A mathematical algorithm for the
beneficial (so-called coherent) frequencies
pattern was revealed and shown to be
analogous to a tempered Pythagorean
acoustic scale that therefore was called the
GM-scale. A literature survey of initially
175 independent biological studies showed
that 97 electromagnetic frequencies (EMF)
that were reported to exhibit beneficial
effects fitted on this logarithmic GM-scale,
whereas 5 frequencies with detrimental
effects did not (Geesink & Meijer, 2016).
In later work this survey was extended
to 468 biomedical papers including work
on cancer-related effects of
electromagnetic radiation and spanning a
range of Hz to PHz (Geesink & Meijer,
2018c) and the GM-scale pattern was
further defined and generalized.
After normalization to the same
octave (by dividing or multiplying by
powers of 2; (2p) in equation [1]),
experimental EMF with beneficial effects
generally fitted the theoretical GM-scale
very well, whereas frequencies with
detrimental effects generally fell in
between GM-scale ‘coherent’ scale values,
see Figure 1 (Geesink & Meijer, 2018c).
Figure 1: Measured frequency data of living cells systems that are life-sustaining (coherent data: green points)
and detrimental for life (decoherent data: red squares) versus calculated normalized frequencies. Biological effects
measured following exposures or endogenous effects of living cells in vitro and in vivo at frequencies in the bands of
Hz, kHz, MHz, GHz, THz, PHz. Green triangles plotted on a logarithmic x-axis represent calculated life-sustaining
frequencies; red triangles represent calculated life-destabilizing frequencies. Each point indicated in the graph is taken
from published biological data and are a typical frequency for a biological experiment(s). For clarity, points are
randomly distributed along the Y-axis.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 35
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
The particular GM-scale interference
pattern has been postulated to be
interpreted as a quantized pilot wave
steering pattern, which could represent the
supposed potential wave frequencies of the
‘hidden variable’ quantum theory of Bohm
(1952), and thus be related to quantum
coherence and entanglement. (Geesink &
Meijer, 2018c, 2018a, 2018b).
The pattern was shown to have
similarity with the typical geometric forms
that are induced by sound on particle
covered vibrating plates in the
experiments of Chladni (Chladni, 1787;
Geesink & Meijer, 2016, 2017b), as later on
mathematically and numerically expressed
by Ritz (1909). The pattern turned out to
be valid for a variety of other inanimate
systems, including EMF promoting
entanglement in EPR-experiments
(Geesink & Meijer, 2018d) and in
superconductivity (Geesink & Meijer,
2019). It was therefore hypothesized as a
potential ‘quantum algorithm of life’ and
generalized as a novel biophysical
principle, having a predictive value in life
systems and beyond. (Geesink & Meijer,
2017b, 2017a, Meijer & Geesink, 2016,
2018).
In the present work the
discriminating power of the GM-scale is
tested through a statistical analysis of
the original experimental data, with
regard to their distribution and fit to
the GM-scale. The Wilcoxon rank sum
test was used to discriminate between
populations (Gibbons & Chakraborti,
2011; Hollander & Wolfe, 1999;
Wilcoxon, 1945). Based on the
observations in the current study
recommendations for prospective
application of the GM-scale are made.
Table 1. Generalized GM-scale of ‘coherent’ frequencies (See equation 1 above)
2. Methods
Data were obtained from reference
(Geesink & Meijer, 2018c), from which a
few incidental data were corrected to
arrive at the definite list of frequencies
(see Appendix 1) .
Calculations were performed using
Microsoft Excel 2016 and MATLAB
Statistics Toolbox R2017b, The
MathWorks, Inc., Natick, Massachusetts,
United States.
All data were transposed to frequency
data between 1 and 2 Hz, by dividing by
2m, according to the generalized GM-scale
of ‘coherent’ frequencies, shown in Table
1.
From these formulae the 12 values of
the ‘coherent’ GM-scale between 1 and 2
Hz can be derived as well as 12 values of
the ‘decoherent’ scale, that are positioned
in the middle between 2 ‘coherent’ steps
on a logarithmical scale. This pattern is
shown in Table 2.
Tests were performed to answer
following questions:
1. What is the significance of the
difference between “coherent’ and
“decoherent’ data sets?
2. What is the congruency of
“coherent’ and “decoherent’ data
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 36
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
with the GM-scale values, based on
frequency intervals?
3. What is the congruency of
“coherent’ and “decoherent’ data
with GM-scale values based on
population equality tests?
4. How to apply the GM-scale to
evaluate experimental data in a
prospective manner?
Table 2. ‘Coherent’ and ‘Decoherent’ frequency values of the GM-scale algorithm of 12 tones between 1 and 2
Hz. ‘Coherent’ values are depicted in green (Hz) and ‘decoherent’ values in orange (Hz)
Ad 1.
‘Coherent’ data were grouped in intervals
around the steps of the ‘coherent’ scale
with edges in the middle between 2 steps
on a logarithmic scale (edges=decoherent
values), resulting in 12 ‘coherent’
datasets, C1 to C12, see Table 2.
‘Decoherent’ data were grouped around
the steps of the ‘decoherent’ scale with
edges in the middle between 2 steps on a
logarithmic scale (edges=coherent
values), resulting in 12 “decoherent’
datasets, D1 to D12, Table 2. Per dataset
mean, median, standard deviation and
probability distribution was calculated. A
Wilcoxon rank sum test between these
groups was performed in Matlab with
alpha=0.01. The Wilcoxon rank sum test
(Gibbons & Chakraborti, 2011; Hollander
& Wolfe, 1999; Wilcoxon, 1945) is a
nonparametric test for two populations
when samples are independent. This test
is equivalent to a Mann-Whitney U-test
(Mann & Whitney, 1947) and can be
applied to samples of different size. This
test was chosen because the analyzed
data are obtained from a literature survey
of articles that are not related to each
other and therefore independent.
Furthermore data populations are
relatively small (N≤31) and of unequal
size.
Ad 2.
Frequency intervals between ‘coherent’
and ‘decoherent’ steps are positioned in
the middle between each step on a
logarithmic scale (edges in between
‘coherent’ and ‘decoherent’ scale values,
Table 5), resulting in 24 alternating
‘coherent’ and ‘decoherent’ intervals. The
percentage of points falling in the correct
intervals were subsequently calculated.
Ad 3.
Theoretical GM-scale populations, that
precisely fit GM-scale values, were
generated in Matlab, using the functions
‘makedist’ and ‘random’. The theoretical
populations exist of 12 x 100 points
normally distributed around each GM-
scale value (X1 to X12), with standard
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 37
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
deviation (SD) based on GM-scale band
widths. Band widths calculated in test 1 are
taken as a measure for the SD of each
population. The SD is calculated assuming
each interval to be a 90% probability area
and consequently each interval=2 x 1.64 x
SD, SD=interval width/(2x1.64).
A Wilcoxon rank sum test with
alpha=0.05 between the ‘coherent’
datasets C1 to C12 and the GM-datasets X1
to X12 was performed using Matlab. In a
similar way random sets were produced
around the ‘decoherent’ scale values with
SD as specified in Table 8. A Wilcoxon
rank sum test with alpha=0.05 between
the ‘decoherent’ datasets D1 to D12 and the
GM-datasets (X’1 to X’12) was performed
using Matlab.
Ad 4.
Based on the results of tests 2 and 3
the method of test 2 is selected to
demonstrate the prospective application of
the GM-scale. Frequency data published
by Cosic et al. (2016) were used as an
example to show how the GM-scale can be
applied to evaluate such data in order to
label experimental or published EMF
frequencies as potential ‘coherent’ or
‘decoherent’ .
3. Results
1. What is the significance of the
difference between ‘coherent’ and
‘decoherent’ data sets?
The distribution of the frequency
data, transposed to the GM-scale of 1-2
Hz is presented in Table 3 and Figure
2.
The results of the Wilcoxon rank
sum test are presented in Table 4 and
in a boxplot representation in Figure
3.
All “coherent’ groups were shown
to be significantly different from the
“decoherent’ groups with a significance
of p<0.0025.
2. What is the congruency of
‘coherent’ and ‘decoherent’ data with
the GM-scale values, based on
frequency intervals?
The ‘coherent’ and ‘decoherent’
intervals are presumed to be equally
wide on a logarithmic scale, resulting in
the boundaries around the GM-values
presented in Table 5.
Comparison of the 2 datasets with
respect to these intervals is shown in
Figure 4 and 5 and Table 6 and 7.
It can be seen that almost 90% of
the data presumed to be ‘coherent’
actually fall in the ‘coherent’ intervals of
the GM-scale and 83.4% of the
‘decoherent’ data are located in the
‘decoherent’ intervals.
3. What is the congruency of
‘coherent’ and ‘decoherent’ data with
GM-scale values based on population
equality tests?
Theoretical GM-scale groups of 100
points each were created having a
normal distribution around each GM-
scale value for both the ‘coherent’ GM-
scale (X1-X12) and the ‘decoherent’
GM-scale (X’1-X’12). The SD for each
data group is calculated based on the
assumption of 90% probability in each
interval as shown in Table 8.
The ‘coherent’ data groups, C1 to
C12 (Table 3) were compared with the
‘coherent’ theoretical GM-scale groups
(X1-X12) and the ‘decoherent’ data
groups (D1-D12, Table 3) with the
‘decoherent’ theoretical GM-scale groups
(X’1-X’12). Results of the Wilcoxon
rank sum test (p<0.05) are presented in
Table 9.
4. How to apply the GM-scale to
evaluate experimental data in a
prospective manner?
Test 2 was selected to demonstrate
how the GM-scale can be applied to
evaluate the frequency data published
by Cosic et al. (2016).
In this publication Cosic presents
Characteristic Resonant Recognition
Model (RRM) frequencies for different
biological functions of protein- and
DNA-macromolecules, that she
converted to the corresponding EM-
radiation wavelengths.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 38
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Table 3. ‘Coherent’ and ‘Decoherent’ datapoints from reference (Geesink & Meijer, 2018c) grouped to the
GM-scale values.
Each group (‘coherent’C1-C12, ‘decoherent’ D1-D12) consists of N datapoints around the ‘coherent’ (green) or
‘decoherent’ (orange) GM-scale values, using the middle between 2 values on a logarithmic scale as borders to
determine to which group a data point belongs. For each group the mean, median, standard deviation is calculated
as well as the difference between mean and GM-scale value, represented in absolute value and as percentage of
the GM-scale value
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 39
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Figure 2. Distribution of data compared to GM-scale value boundaries. Each dot is a frequency obtained from
literature transposed to a value between 1 and 2 Hz. “Coherent’ frequencies are represented as green dots
‘decoherent’ frequencies as red dots. The y-axis is a vertically spreading axis to visualize the particular points.
Table 4. Results Wilcoxon rank sum test, “coherent’ vs “decoherent’ and vv.
Columns 1 and 3 show groups compared (Table 3). Columns 2 and 4 the results of the Wilcoxon rank sum test. A
value of p<0.01 is taken as statistically different. For the last comparison, the left sided comparison of C12 with
D1, all values of D1 were multiplied with 2.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 40
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Figure 3 a b. Boxplots of ‘coherent’ and ‘decoherent’ dataset 1-6, vs GM-scale value (green*). On each box, the
central mark (red) indicates the median, and the bottom and top edges of the box indicate the 25th and 75th
percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the
outliers are plotted individually using the '+' symbol. If the notches in the box plot do not overlap, you can
conclude, with 95% confidence, that the true medians do differ.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 41
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Figure 3 c d. Boxplots of ‘coherent’ and ‘decoherent’ dataset 6-12, vs GM-scale value (green*). On each box, the
central mark (red) indicates the median, and the bottom and top edges of the box indicate the 25th and 75th
percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers, and the
outliers are plotted individually using the '+' symbol. If the notches in the box plot do not overlap, you can
conclude, with 95% confidence, that the true medians do differ.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 42
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Table 5. Frequency intervals and boundaries of the GM-scale between 1 and 2 Hz.
‘Coherent’ GM-scale values are represented in green, ‘Decoherent’ GM-scale values in orange, interval boundary values
are represented in white. The first ‘decoherent’ interval is thus from 0.9617 to 0.9871 Hz; the first ‘coherent’ interval is
from 0.9871 to 1.0131 Hz; the second ‘decoherent’ interval is from 1.0131 to 1.0399 Hz; the second ‘coherent’ interval is
from 1.0399 to 1.0709 Hz; and so on. To transpose to other frequency ranges the values in the Table can be multiplied by
2m (see Table 1) or alternatively, experimental data can be transposed to a value between 1 and 2 Hz by dividing by 2m.
Bandwidth is the difference between interval boundaries (white)/GM-scale value (green or orange), e.g. for GM-scale (1)
(1.0131-0.9871)/1.0000=2.6%.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 43
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
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Figure 4. Distribution of ‘coherent’ data, including ‘coherent’ frequencies that are able to inhibit and retard cancer, over
the GM-scale intervals of Table 5. Values are presented as percentage of total, absolute values can be found in Table 6.
Table 6. Distribution of the ‘coherent’ data over the intervals of Table 5.
For each GM-scale step or tone, the number of data points falling in the ‘Coherent’ (Coh int) or ‘Decoherent’ (Decoh int)
interval are represented. This is done for the original ‘coherent’ data set of reference (Geesink & Meijer, 2018c) (Coh
dataset), as well as the frequency data from literature that are able to inhibit and retard cancer (Cancer Coh) (Geesink &
Meijer, 2018c). Sum Coh is the sum of both Coh dataset and Cancer Coh per step and interval.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 44
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Figure 5. Distribution of ‘decoherent’ data, including ‘decoherent’ frequencies that can initiate and promote cancer, over
the GM-scale intervals of Table 5. Values are presented as percentage of total, absolute values can be found in Table 7.
Table 7. Distribution of ‘decoherent’ data over the intervals of Table 5.
For each GM-scale step or tone, the number of data points falling in the ‘Coherent’ (Coh int) or ‘Decoherent’ (Decoh int)
interval are represented. This is done for the original ‘decoherent’ data set of reference (Geesink & Meijer, 2018c)
(Decoh dataset), as well as the frequency data from literature that can initiate and promote cancer (Cancer Decoh)
(Geesink & Meijer, 2018c). Sum Decoh is the sum of both Decoh dataset and Cancer Decoh per step and interval.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 45
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
Table 8. Values used to generate theoretical GM-scale populations
‘Decoherent’ values are depicted in orange, ‘Coherent’ values in green. A ‘coherent’ population on the first ‘coherent’
GM-scale value (1.000) is generated assuming that values are normally distributed and 90% of the values are positioned
between 0.974 and 1.026 equaling an SD of SD(1)=(1.026-0.974)/(2*1.64)=0.0159 . A ‘decoherent’ population on the
first ‘decoherent’ GM-scale value (0.974) is generated assuming that values are normally distributed and 90% of the
values are positioned between 0.9492 (Table 5) and 1.000 equaling an SD of SD(1’)=(1.000-0.949)/(2*1.64) =0.0155.
Other SD’s are calculated in the same way.
A. “Coherent’ B. “Decoherent’
Table 9. Result of Wilcoxon rank sum experimental data comparison with theoretical GM-scale populations.
A. Comparison of theoretical generated populations X1-X12 with data groups C1-C12 (Table 3) per GM-scale step, p-
value, and hypothesis based on p<0,05 for non-equality. Hypothesis 0 is equal, 1 is non-equal
B. Comparison of theoretical generated populations X’1-X’12 with data groups D1-D12 per GM-scale step (Table 3), p-
value, and hypothesis based on p<0,05 for non-equality. Hypothesis 0 is equal, 1 is non-equal
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 46
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
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Figure 6. Distribution of Cosic data compared to GM-scale value boundaries x-axis (Table 5). Each dot is a
frequency obtained from (Cosic et al., 2016), see Table 10. These values were transposed to a value between 1 and
2 Hz. The y-axis is an arbitrary value to visually discriminate between points. Color coding is taken from Cosic’s
distribution of functional groups over super families (Cosic et al., 2016).
Using the velocity of light
(c=299792458 m/s) these wavelengths (λ)
can easily be converted to frequencies
(f=c/λ). Frequencies are then transposed
to a value between 1 and 2 Hz, by dividing
by 2m. Frequency data and their converted
GM-scale values are presented in Table
10.
The transposed frequency values can be
plotted in a GM-scale diagram as shown in
Figure 6 and frequency values in
‘coherent’ and ‘decoherent’ intervals can
be determined. As shown in Figure 6 and
7 and Table 10 the published frequency
data of Cosic seem to be about equally
distributed over ‘coherent’ and
‘decoherent’ intervals.
4 . Discussion
Division of data between ‘coherent’
and ‘decoherent’ and grouping according
to the GM-scale values, resulted in 2 x 12
groups of data, C1-C12 and D1-D12.
Statistical analysis shows that all of the so-
called ‘coherent’ data groups are
statistically different from the ‘decoherent’
groups (p<0,0025). This strongly
supports the hypothesis of the assumed
division between ‘coherent’ and
‘decoherent’ frequencies, as proposed by
Geesink and Meijer.
Considering the fact that the data is
obtained from such a wide variety of
biological studies and spans ranges of Hz
to THz, this truly is a remarkable
discovery.
With respect to the fit to the
theoretical algorithm it can be seen that
mean values of the data groups are almost
equal to the GM-scale values
(difference<0,9%, Table 3, Figure 3).
As can be seen in Figure 1 and 2,
there is some overlap between ‘coherent’
and ‘decoherent’ datapoints.
This is quantified by introducing
alternating ‘coherent’ and ‘decoherent’
intervals, assuming boundaries in the
middle between each interval. 11% of
‘coherent’ data were found in ‘decoherent’
intervals and 17% of ‘decoherent’ data
were found in ‘coherent’ intervals, hinting
at a prediction accuracy of almost 90%,
based on current data.
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Table 10. Resonant Frequency Data of Cosic (Cosic et al., 2016) vs GM-scale (Continued on next page)
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 48
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Table 10. Resonant Frequency Data of Cosic (Cosic et al., 2016) vs GM-scale (Continued)
The data are obtained from reference (Cosic et al., 2016) “Table 1. Characteristic Resonant Recognition Model
(RRM) frequencies for different biological functions of protein and DNA macromolecules.” Column 1 represents
name of functional group of proteins and DNA. Column 2 represents the numerical RRM frequency. Column 3
represents the corresponding electromagnetic radiation in nm. Column 4 is the corresponding frequency of the
electromagnetic radiation of Column 2 in Hz. Column 5 is the transposed frequency between 1 and 2 Hz of the
frequency of Column 4. Column 6 is the assignment of the frequency based on their position in the intervals of
Table 5, 1= ‘coherent’ (green), 0=decoherent (white). E.g. Hemoglobin has RRM-frequency 0.0234 corresponding
to 20,000 nm or 1.5*1013Hz, transposed by division /243=1.7041, which is in the ‘coherent’ interval 10 (1.660-
1.710), and thus assigned ‘coherent’=1 (green).
Figure 7. Distribution of Cosic’s data over the GM-scale intervals of Table 5.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 49
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It should be noted, when using these
scale intervals (Table 5) , that the band
widths of the particular GM-scale band
values are very small (2,6%-3,3% of scale
values). This has implications for the
accuracy of frequency values to be
evaluated since relatively small error
margins could already lead to a false
assignments. A considerable amount of
points are found on, or near, interval
boundaries, which suggests that
optimization of interval boundaries or
small alterations in GM-scale value
definitions could improve results.
An alternative way to test how well
the ‘coherent’ data groups C1-C12 fitted
the GM-scale, was designed by creating
theoretical GM-scale groups around each
GM-scale value and evaluating
congruency of experimental data with
these theoretical data groups. In creating
these theoretical groups a normal
distribution was assumed and SD was
calculated based on 90% probability
around the ‘coherent’ GM-value, using
‘decoherent’ values as boundaries.
Despite the close fit of data group mean
values with GM-scale values, difference
was apparently still such that only 9
‘coherent’ groups were found equal to
their respective theoretical GM-scale
groups. This indicates that differences
are very subtle and emphasizes
importance of accuracy of data.
Prospective use of the frequency
band spectrum to label EM
frequencies as being in the range of
‘coherency’ or ‘decoherency’
The GM-scale algorithm can be
applied to evaluate EMF data, or
prospectively choose EM frequencies, on
their beneficial (‘coherent’) or
detrimental (‘decoherent’) effects.
Based on current test results,
frequencies can best be evaluated using
the alternating ‘coherent’/’decoherent’
scale intervals (bands). How to apply the
GM-scale in this analysis is demonstrated
on the frequency data published by Cosic
et al.(2016) In this work she states that
her Resonant Recognition Model (RRM)
proposes to identify characteristic EM
frequencies involved in tumor regulation
(Murugan, Rouleau, Karbowski, &
Persinger, 2017), cell growth control
(Cosic, Drummond, Underwood, &
Hearn, 1994), vaccine development
(Krsmanovic et al., 1998), and
interference with infections such as
malaria (Cosic, Caceres, & Cosic, 2015)
and Ebola (Murugan, Karbowski, &
Persinger, 2015).
Unfortunately, no distinction was
made by Cosic between potential harmful
and beneficial effects of the frequency
values obtained. Based on the GM-scale
analysis 50% of frequencies are predicted
‘coherent’, having beneficial effects and
50% are predicted ‘decoherent’ having
harmful effects. A possible explanation
could be that Cosic’s goal is to identify
frequencies that effect certain proteins,
regardless of whether the effect on life
conditions is positive or negative.
For example, proteins that promote
inflammation, immune reactions or
programmed cell death can both be
interpreted as life sustaining and
detrimental for life.
An alternative explanation for this
ambivalent result could lie in the
required accuracy for a prediction. In the
conversion of theoretical RRM-
frequencies to real wavelengths
[2]
an experimentally derived factor (K=201)
is used which has a standard deviation of
15% (Cosic, 1994), far greater than the
interval band widths used in the GM-
scale.
In this article, apart from the
statistical analysis, a prospective method
is presented to further substantiate the
pattern of ‘coherent’ and ‘decoherent’
frequencies and the GM-scale algorithm.
Using this method, predictions with
respect to harmful or beneficial effects of
EMF can be made and evaluated.
Outliers can be identified and further
examined in detail to find explanations
for the discrepancy, which can lead to
new insights in biophysical mechanisms.
Quantum Biosystems | 2019 | Vol 10 | Issue 2 | Page 33 – 51 50
Trudi Sonderkamp, Hans J H Geesink and Dirk K F Meijer
ISSN 1970-223X
www.quantumbiosystems.org
In general, we realize that further
analysis of more experimental data with
respect to the fit to GM-scale values will
help to further validate this potentially
new biophysical principle. The ultimate
test will be a dedicated experimental
study set up that systematically evaluates
the effects of carefully selected individual
or combined series of GM-scale
frequencies on well standardized in-vivo
and in-vitro test systems.
5. Conclusions
The GM-scale presented in this
study is an attractive EMF distribution
pattern with the potential to distinguish
between beneficial (‘coherent’) and
detrimental (‘decoherent’) EM-
frequencies for life conditions.
A Wilcoxon rank sum population
analysis confirms that ‘coherent’ and
‘decoherent’ data groups obtained from
literature are statistically different for all
12 GM-scale groups (p<0,0025).
The mean of the data groups almost
perfectly fit to the GM-scale values
(difference<0,9%). Based on 24
alternating coherent-decoherent GM-
scale frequency intervals 90% of
‘coherent’ data and 87% of ‘decoherent’
data are correctly predicted. The
difference between ‘coherent’ and
‘decoherent’ frequencies is quite subtle;
band widths and therewith respective
error margins, are very small (2,6%-
3,3%).
A method is presented for
prospective application of the GM-scale
using these alternating frequency
intervals. These, and future, analyses of
experimental data with respect to the fit
of their EM frequencies to the GM-scale
will help to further validate this
algorithm as a new biophysical principle.
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I. Appendix 1. Literature data
from ref (Geesink & Meijer, 2018c)
Appendix Tabel 1 : The complete list
of EMF frequency data analyzed in the
present publication can be
directly provided by the first author
(please mail:
t.sonderkamp@kpnmail.nl).
