XPost: alt.ufo.reports

In the movie "Failsafe" -- unfortunately coming out in the same year
as another movie -- Walter Matthau plays a provocative character -- a
govt expert of some kind -- who keeps making what appears to be
totally ridiculous claims... but then proceeding to prove them.
You are now in that movie. You were always in the Twilight Zone.

Executive Summary:
- We build a predictive model for mass animal deaths. If seems UFO
sightings are relevant to an increase in mass deaths.
- Dividing animal deaths into categories we find water-based animals
are in increased danger but birds are generally not in danger from
increased UFO activity.
- Lake animals seem to be in more danger than ocean animals.
- UFO activity seems to have a slight protective effect on mass animal
deaths due to bird flu.
- When dividing mass deaths up into countries we get a measure of how
connected UFO sightings are with each. The pattern of country
attributes and this connection allow us to get an idea of which
countries UFO's may be "more interested in" and why. Such interest
may reflect something about UFO's themselves.

In prev posts we've seen the objects representing the interesting part
of UFO sightings across N Am seem to have a light footprint. From the
areal density of sightings vis a vis military bases they seem to fly defensively. There seems to be no association (i.e. "0" is included
in the relevant 5% confidence interval) between UFO sightings and US
power outages. And there seems to be no association between UFO
sightings and plane crashes.

So it's time to look at things where UFO's *do* appear to leave
traces. This and a following post will look at animal mass deaths.
Another post will look at UK crop circles (chosen simply because I
easily found a fairly good database for that country) and the
possibility we might have clues to a "UFO language" given there is a
stat relevant association between certain UFO sightings with certain
features (mostly alleged interactions between UFO's and aircraft,
particularly military aircraft) and crop circles with certain elements.

The mass deaths data I'm using is collated by an "end of the world" (<http://www.end-times-prophecy.org>) church group. While I'm less
interested in their beliefs I certainly appreciate the diligence they
show collecting newspaper articles reporting mass animal deaths
between 2011 and 2019. Thank you. :)

As usual I'm using the UFO sighting data from NUFORC. I lightly
twiddle that month-to-month numbers to remove some biases (e.g.
sightings seem to increase Mon through Sat which I'm assuming is due
to "alertness" of observers rather than an indication UFO's operate on
the basis of a 7d work week) and a radical methodology change in early
2006 (introduction of a web reporting form).

Using a simple "AI program" the method involves building the best
predictive model for different types of mass animal deaths and then
determining whether adding UFO sightings data to the model improves
the predictions with a statistical certainty. If so we then find out
how much and in what way UFO's may be influencing the frequency of
different types of animal deaths. Or we might prove with a statistical certainty (defined here as "0" falls inside the 5% confidence interval
-- i.e. we are 95% certain there is no association; not the same as
sometimes presented as the same thing "95% unsure whether there is an association or not").

I've run a slew of these things for the different types of animals in
the church data. Much of this was informed by intermediate results.
The s/w I use tends to run off at the mouth when I leave it running
for days on end. It's intended to find "interesting relationships" in
data and given one interesting relationship make an hypothesis that
might reveal something else interesting and then go and test it.
After a day or 2 you tend to end up with a slew of interesting things
but then be stuck trying to spot a pattern in all the results that are
(a) easy to understand yourself, and (b) easy to explain to someone
else that is not a data geek (there are whole conferences on that kind
of thing, these days ;).

But the first one run was the "all" versus "all" case. Do UFO
sightings overall seem to influence or have a statistically
interesting relationship with mass animal deaths overall.

Here is the model the s/w found:

REWEIGHTED LEAST SQUARES BASED ON THE LMS
*****************************************
VARIABLE COEFFICIENT STAND. ERROR T - VALUE P - VALUE
----------------------------------------------------------------------
date -3.35855 0.69790 -4.81236 0.00001
x1 0.03738 0.00708 5.28028 0.00000
x2 10.06353 1.03562 9.71736 0.00000
x3 26.75816 5.68531 4.70654 0.00001
x4 -6.18689 1.19655 -5.17061 0.00000
x5 -28.28455 5.09218 -5.55451 0.00000
x6 -13.41708 4.30274 -3.11826 0.00241
x7 7.05270 2.15929 3.26621 0.00152
x8 16.27486 4.55065 3.57638 0.00055
CONSTANT 3485.39136 1263.27832 2.75901 0.00697
WEIGHTED SUM OF SQUARES = 12816.51172
DEGREES OF FREEDOM = 94
SCALE ESTIMATE = 11.67672
COEFFICIENT OF DETERMINATION (R SQUARED) = 0.77333
THE F-VALUE = 35.632 (WITH 9 AND 94 DF) P - VALUE = 0.00000
THERE ARE 104 POINTS WITH NON-ZERO WEIGHT.
AVERAGE WEIGHT = 0.96296

In the model, "x1" represents the twiddled UFO sighting data. The
other variables x2..x8 are various "weather related" data I've
uploaded over the years or the s/w has decided to upload itself after
running some google queries. Using just what I have on HD there are
almost 9000 data series from air pressures region-to-region, sea
temperatures, pH, salinity from the surface down to 10 km, reports
from robots floating around nr the Antarctic or in the Caribbean, to
neutron counts from a long-established Russian cosmic ray network.
(And, yes, UFO's seem to know something about cosmic rays -- and they
don't like them).

In this model the P-VAL shows x1 is relevant. UFO sightings *do* have
a statistically certain relationship with mass animal deaths. For
each 1000 UFO sightings in a month over mostly N America there are an additional 37+-7 mass animal deaths that same month.

It seems zipping around the atm at several km/sec and pulling high-g
turns apparently at random SEEMS to have an effect on the local
wildlife. Who knew?

But then we might ask "what kind of wildlife is being affected"?

So the A/I was off again breaking the mass deaths data down into
subsets and running the same analysis again for each subset. When one
subset was found to YES or NO be associated with UFO sightings the s/w
then has more info what to look at next, extract another component
from the mass deaths data and run that.

The reader can probably guess at the order the following models were
created. It was found initially that "birds" did not seem to be
associated with UFO's but FISH were. The program then went off to
check similar "FISH" like "DOLPHIN" and "TURTLE" which also turned out
to be associated. It then decided to check mass deaths found nr
"LAKES". Then it checked "SHORES". Then it expanded its ideas to
"WATER" and "NOT WATER". Belatedly it found there was a keyword in
the mass deaths description that threw a spanner in a simple way to
combine all these results. It turned out many mass deaths in the
dataset were associated with "FLU". So the AI started testing FLU and
"NOT FLU". It then decided to go off and check "BIRDS and NOT
FLU". Finally, it then decided there were mass deaths dealing with
generic "birds" but also there were mass deaths dealing with
specifically ravens, chickens and other types of birds. So it had to
go and re-run a few things using an expanded definition of "birds".

Here is the summary table for all of that:

Ordered by R2

Type of animal R2 Beta_X1 Stderr T-val P-val
BIRDS 0.85793 0.00055 0.00160 0.34418 0.73160
FLU 0.83704 -0.00673 0.00122 -5.51783 0.00000
FISH 0.81240 0.02584 0.00455 5.67285 0.00000
ALL 0.77333 0.03738 0.00708 5.28028 0.00000
NOT-FLU 0.76042 0.02930 0.00589 4.97514 0.00000
NOT-WATER 0.75867 0.01378 0.00595 2.31443 0.02282
LAKE 0.73622 0.00269 0.00132 2.03721 0.04467
WHALE 0.70574 -0.00023 0.00061 -0.37948 0.70561
BIRDS2NOTFLU 0.69718 -0.00126 0.00090 -1.40809 0.16303
DOLPHIN 0.67136 0.00192 0.00075 2.54939 0.01245
TURTLE 0.65099 0.00148 0.00090 1.63853 0.10574
SHORE 0.62745 0.00123 0.00101 1.22069 0.22532
BIRDS2 0.58220 0.00029 0.00249 0.11509 0.90863
BIRDSNOTFLU 0.49857 -0.00024 0.00084 -0.28013 0.78003

Ordered by \beta:

Type of animal R2 Beta_X1 Stderr T-val P-val
ALL 0.77333 0.03738 0.00708 5.28028 0.00000
NOT-FLU 0.76042 0.02930 0.00589 4.97514 0.00000
FISH 0.81240 0.02584 0.00455 5.67285 0.00000
NOT-WATER 0.75867 0.01378 0.00595 2.31443 0.02282
LAKE 0.73622 0.00269 0.00132 2.03721 0.04467
DOLPHIN 0.67136 0.00192 0.00075 2.54939 0.01245
TURTLE 0.65099 0.00148 0.00090 1.63853 0.10574
SHORE 0.62745 0.00123 0.00101 1.22069 0.22532
BIRDS 0.85793 0.00055 0.00160 0.34418 0.73160
BIRDS2 0.58220 0.00029 0.00249 0.11509 0.90863
WHALE 0.70574 -0.00023 0.00061 -0.37948 0.70561
BIRDSNOTFLU 0.49857 -0.00024 0.00084 -0.28013 0.78003
BIRDS2NOTFLU 0.69718 -0.00126 0.00090 -1.40809 0.16303
FLU 0.83704 -0.00673 0.00122 -5.51783 0.00000


I've ordered the table in 2 ways. Ordering by R2 shows at the top
which models are "most useful". (As distinct from "most certain"; in
this report all models are 95% certain to be "something" and only 5%
likely due to just luck or spurious data). Ordering by BETA of the x1
variable shows how strong the association with UFO sightings that
relationship is. E.g. for "ALL" deaths the R2 is 77% meaning the model
overall (i.e. weather+UFO sightings) predicts 77% of mass animal
deaths month by month. But the \beta is .037+-.007 meaning for a month
with ~1000 UFO sightings there is also expected to be 37 mass animal
deaths that would not be present if there were 0 UFO sightings that month.

Looking through the list we see there is some evidence birds are not
affected by UFO's. A bit surprising given the we-thought mostly "in
the atmosphere" character of UFO/UAP's. But surprise, animal species
like FISH, TURTLE and DOLPHIN show there is a 90% likely association
with UFO sightings and, moreover, the more sightings the more mass
deaths of that animal. Confusingly, WHALE deaths show a \beta that is
NEGATIVE. More UFO sightings associated with lowering of mass whale
deaths. But the P-VAL for that is much higher than 0.1 -- meaning it
might be a spurious finding and just down to some peculiarity of this
dataset and not justified.

At the same time as the air/water schism there is the wrinkle of bird
deaths due to flu. Both FLU and NOTFLU mass deaths seem to be
influenced by UFO's. The P-VAL is near 0. Meaning we are almost sure
the \beta is not 0. Confusingly, more UFO's is associated with LESS
mass deaths from flu but MORE deaths from things other than flu.

When FLU is taken into account we still find UFO's and bird deaths
don't seem to be related. The P-VAL for the "birds" group (i.e.
specifically "birds" in the report description) is .78 -- so it's 78%
likely not related; and the P-VAL for "birds2" (i.e. merging in other
types of birds that are listed by their common names) has a P-VAL of
16%. Most experts judge this level of P-VAL to mean "just luck". So no
matter which way we define birds there is little to no association.

So UFO's seem to be bumping into fish (or whatever) but not bumbling into birds.

Even more interesting, the results for "LAKE" and "SHORE" models shows
both are stat certain but the "LAKE" mass deaths have 2x the
association (\beta) than "SHORE". To mass deaths of animals living in
lakes is around "twice as much" to mass deaths in the oceans. This may
be due to mass deaths of ocean animals being harder to spot, or maybe
UFO's are mostly hanging around lakes rather than the ocean. But I'll
plug for the "harder" explanation.

In an up-coming post we'll split the mass death data up by country. I
was not much interested in this aspect, although it seemed to suggest
UFO's actually were possibly present over or "in" some countries for
which no other UFO data was easily available (e.g. Kazakhstan,
Mongolia, Pakistan, etc).

But the A/I's didn't leave it there, given a list of countries with
varying strength of association they proceeded to mine that pattern
for interesting patterns and came up with a list of attributions that
explained why some countries appear to have a bigger association with
UFO sightings than others.

Spoiler alert: UFO's seem to have interests in public health and
female workforce participation. They don't "like" coal but do like
renewable energy and nuclear power. They like wealthy countries that
have "low productivity" (i.e. value of imports and exports
balances). They like low population growth, older populations and low
levels of municipal waste.

I've often found in analyzing data associated with human beings that
what interests them reflects something about the people concerned.

So what are the UFO "people" like? They sound like Swedes!

--
Air Force Spacecraft Will Beam Solar Power to Earth
Named Arachne, this new satellite will transfer solar energy wirelessly back
to any location on the planet.
The Debrief, 8 Jan 2021
The Arachne spacecraft is scheduled for launch in 2024. It is built to
capture solar energy, convert it and then transmit that power to Earth.

[Worst since 1970!]
Rare Blizzard Buries Spain in Snow
Newser, 09 Jan 2021 12:31Z
An unusual and persistent blizzard that Madrid's mayor is calling "the worst storm in 80 years" has blanketed large parts of Spain with snow, freezing traffic and ...

Bat warning issued after officials detect 3 cases of lyssavirus in
Queensland in one month
ABC News, 09 Jan 2021 09:01Z
Health officials urge Queenslanders to stay away from bats to avoid
contracting the potentially deadly disease.

Huge sinkhole in parking lot of Italian hospital forces relocation of COVID-
19 patients
New York Post, 08 Jan 2021 19:27Z
A giant sinkhole opened Fri in the parking lot of a hospital in Naples,
Italy, forcing the temporary closure of a nearby residence for recovering COVID-19 ...

'Remarkable' rare drone footage captures dugongs mating
ABC Radio Brisbane, 09 Jan 2021 01:22Z
A beachgoer's sighting of 2 dugongs close to the shore may be a sign that
the population of the usually solitary marine mammals, considered vulnerable
in Queensland, may be recovering.

Nasa's Curiosity rover: 3,000 days on Mars
BBC News, 09 Jan 2021 03:15Z
Three 1000 days and counting: Nasa's Curiosity rover continues its extraordinary exploration of Mars.

Kauai to Hit 80% Renewable Power With Solar-Charged Hydro Storage
Greentech Media News, 08 Jan 2021 17:12Z

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