XPost: alt.ufo.reports

Executive Summary:
- We split animal mass deaths up between the countries where they have
been reported.
- We determine which "weather" data and which keywords in UFO
sightings best predict mass animal deaths in each country.
- We order countries by the proportion of predicting factors that are
UFO-related (versus weather related). If animal mass deaths are a
proxy for how much UFO activity is happening in each country then we
should be interested in which countries are seeing more and which
less activity.
- We find the list of national demographics -- drawn from the complete
list of from a couple different encyclopedias -- that predict the
ordering above suggests "UFOs" are "interested in" countries that
resemble Scandinavian countries. Perhaps this reflects something
about UFO's themselves.
- Has anyone checked with the Swedish Air Force to find out if they
have a solid alibi for 1930-2020?


We've seen that UFO activity seems to be linked with certain animal
mass deaths, in particular sea and lake animals.

We established the link to a statistical certainty (99%) by building
validated predictive models for different groups of animal deaths and
showing that adding in UFO sighting information improved the model and
a statistical test verified the information from the sighting data
"could not be ignored". The models were constructed from a large
corpus of mostly physical data from satellites aka "weather data" in
such a way the model was "optimal". The boost from adding sighting
data showed some information inside that data was needed to further
explain (in a statistical sense) why mass animal deaths were happening.

But the s/w that did all that work also proceeded to find "patterns in
the patterns". It asked itself how many weather variables were
relevant to various mass deaths, versus how many "features" relevant
to UFO activity were relevant.

In a series of experiments it tried to build models predicting mass
deaths in various countries from weather data and keywords found in
the description of UFO sightings.

E.g. for India it found the mass death database had 93 entries from
2011 to 2019. It found the best 20 correlates with the India mass
animal death data were:

variable/keyword R2 against India's mass-death data
gavdiffgoing 0.209667 *
world-30 0.168622
gavdifffast 0.150387 *
world-20 0.150253
gavuah_globe6NoPol 0.146567
gavcntyellow 0.13853 *
arc-30 0.134785
gavlat-10 0.133826
arc-40 0.132713
gavworld-40 0.132666
cntwhite 0.132229 *
gavstorms-50 0.132039
world0 0.1296
arc0 0.128071
cntirregular 0.12483 *
world-50 0.122995
russia 0.122629
gavstormseg-150 0.122367
canada 0.121937
gavworld-120 0.120564

The naming of variables/keywords is somewhat inscrutable given it's
selected by the s/w using its down criteria. But the first line is
related to UFO sightings that contain the word "going". It seems of
single variables whether weather-related or UFO sighting-related it is
the best, predicting about 21% of India's animal mass deaths.

The 2nd line is the AI's name for "avg monthly sea surface
temperatures down longitude 25W+-5deg". It finds this "weather" data
predicts about 17% of India's animal mass deaths.

Likewise the other variables are either weather variables or
"features" derived from keywords in UFO sighting data for each month.
I've marked lines corresponding with UFO sighting features with a (*).
So it seems out of the top20 simple models for India's animal deaths
5/20 are related to keywords in UFO sightings for the relevant months.

We can proceed for each country where animal deaths have been noted
and compile the top20 variables (as above) and determine how many
relate to UFO's.

We will then have a new dataset relating countries to a number
representing "how many ways" UFO sightings "interact" with country
mass deaths.

If we do that we get this data table:

Country #UFO keywords in top 20 simple models for mass deaths
in that country
argentina 7
australia 7
brazil 18
bulgaria 15
canada 16
chile 8
china 13
colombia 16
france 16
germany 17
greece 13
india 5
indonesia 16
ireland 17
italy 14
japan 9
malaysia 10
mexico 7
nepal 19
netherlands 14
norway 9
peru 15
philippines 5
russia 12
s_korea 18
sweden 14
vietnam 9

This looks like a total irrelevant jumble, but it actually contains
very interesting information, no matter how noisy.

We can ask -- "what national characteristics are most similar to this
table of ``UFO keyword counts''?".

We might posit that the more UFO features displace weather variables
the more UFO's "interact" with animals in each country. Perhaps it is
a proxy for the number of sightings that SHOULD be reported from each
country, if only those countries were seriously collecting and
publishing that data.

It also might give us an idea what the UFO's are doing. Why do they
fly more often over some countries than others? It's been speculated,
e.g., UFO's are "interested in nuclear power", or one thing or another.

Luckily we have a program that can search ~9000 data sets and figure
out which national demographics "looks like" the above table.

Here's that list:

Correlation with above "UFO links" table.
Demographic R2 Beta

ren 0.683946 0.162265
femwork 0.593184 0.364934
urban 0.532009 -0.236026
manuf 0.45975 0.681975
unemp 0.447461 0.883491
hh 0.436024 -2.07093
gdpcap 0.402594 0.00023103
colcap 0.37143 -0.0335775
tothel 0.35881 1.39157
coalcp 0.351187 -0.0344364
prihel 0.349597 3.69688
pricon 0.34589 -0.261624
saltcp 0.330953 -0.0108056
murders 0.318431 -0.435983
prheex 0.303282 2.86779
ag 0.298916 -0.15751
infectdi 0.28345 -1.46826
wind 0.280375 -1.87441e-05
zfert 0.271938 -1.98775
PSUI 0.269113 -0.211441
zunder15 0.268728 -0.187557
wmuncp 0.261356 -0.0238411
comin5 0.2609 5.83367
fordebt 0.246415 -0.0858908
impcap 0.245439 0.000524649
trdcap 0.241422 0.000245041
incgdp 0.239886 0.233291
wmun 0.23891 -0.00011705
colpro 0.230377 -1.3297e-05
wagcp 0.22799 0.000642778

From this 2nd table we can see line 1 says variable "ren" seems to be
68% like the UFO links table. "Ren" is the% of renewable power
in a country around 2000. The beta column predicts for each 1 pct
point of renewables in a country there will be 16% more UFO keywords
in the top 20 that explain mass animal deaths than weather variables.

It seems the Number One criteria UFO's have for flying over a country
is whether or not it has a lot of renewable power!

In line 2 we find "femwork". This is the% of females in the
labor force. The variable explains 60% of the UFO links table. For
each pct point of women in the workforce 36% more UFO keywords are to
be found in the list of top 20 explanatory variables for mass animal
deaths in that country.

It seems the Number Two reason a UFO has with flying over a country
and bumping into its animals is whether or not there is equal
opportunity for female employment.

In similar fashion other lines in the above table indicate UFO
interactions favor LESS urbanization, MORE manufacturing, MORE
unemployment, FEWER people per household, MORE GDP/cap, LESS coal
production per capita, MORE health spending, and MORE private
consumption. They favor LESS salt consumption/cap, FEWER murders, LESS agriculture, LESS infectious disease, LESS wind-power, LESS fertility,
FEWER people under 15, LESS municipal waste, LESS foreign debt. The
\beta's for trade data indicate UFO's are interested in countries that
have low net trade -- ideally exports match imports.

Many features seem to relate to social equity and zero growth.

It's tempting to think this list of national attributes that arises
from an apparently observable effect of UFO activity (i.e. mass animal
deaths in various countries) not only indicate "interest" but also may
relate to attributes of an hypothetical "UFO society".


These posts are archived at <kymhorsell.com/UFO/Archive>.

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--- SoupGate-Win32 v1.05
* Origin: www.darkrealms.ca (1:229/2)