Newsgroups: alt.ufo.reports,alt.paranet.ufo,alt.paranet
From: MrPostingRobot@kymhorsell.com
Subject: water monsters

[uploaded 3 times; last 16/10/2024]

After some scouring of web resources I've managed to gather together a
big list of monster sightings in lakes and oceans.  The longish history
of the sightings makes a tantalising study possible -- can we link
these data to the movements of planets and asteroids going back
centuries? Of particular interest are the movements of Pluto. Normally
it's very long orbit -- almost 250y -- means it can't reliably
compared with any phenomena that does not go back to at least the 16th cent.
With sea monsters, it becomes possible.

The only small problem we find now is the movement of Pluto itself
is not very well known prior to the 18th cent. I had managed to
snaffle some data from JPL that purported to show the orbital position
of Pluto back to the 17th cent. Just checking today is seems that
model has been withdrawn and the present Horizons database only tracks
Pluto back to 1800.  But I still have the older version of the data,
so let's forge ahead!

The AI programs have been churning through the problem for several
days now, examining many ways of massaging the available data and
performing robust analysis thereof, and they seem to be settling down
to a less than interesting conclusion the data is just too poor at
this point to drawn any detailed conclusions. However they are quite
sure there is SOME link between planetary movements (we've also looked
at key asteroids going back 100s of years, too, but that I'll leave
for a follow-up article) and lake/sea monsters, but exactly which
planets have the best link(s) depends on exactly what you do in the
analysis.

An outline of how the calculations proceed is as follows.  We assemble
a "total sightings" series based on data going back to the 16th
cent. The data-set we're using today consists of sightings for 3 lake
monsters -- Caddy, Nessie, and Champ -- plus historical sightings of
"sea serpents" in the Atlantic and Pacific. The Champ data consists of
2 sources -- a set of roadside signs in Pt Henry, NY, and a 2023 paper
by F. Foxon.  The net result is an estimate of the rate of sightings
per year going back to 1635.

(Random sample of 1/2 the data-points:)
1635 24.7375 1636 6.7261 1638 13.3755 1639 13.6528 1643 13.5462 1645 14.9221
1650 26.4682 1651 19.5114 1652 16.1528 1655 12.2257 1660 2.73077 1661 6.95687
1665 23.1477 1666 21.7421 1668 19.1825 1671 25.9682 1674 20.7421 1676 24.7375
1677 9.87545 1679 20.5114 1682 23.7375 1684 6.7261 1685 12.3755 1690 22.3319
1691 22.3319 1695 10.9564 1699 5.13636 1702 26.2375 1703 9.78571 1704 20.6011
1705 20.6011 1707 15.4518 1709 7.2261 1710 3.82051 1711 7.2261 1714 1.58974
1719 17.9518 1720 24.7375 1722 22.1011 1727 20.5114 1730 6.36713 1731 9.28571
1732 12.4221 1734 13.8154 1735 3.82051 1738 23.6477 1739 23.1477 1740 12.8154
1744 17.4518 1748 20.5114 1749 13.8154 1753 16.6825 1754 12.3755 1755 7.86713
1757 12.4221 1759 12.1062 1762 13.8154 1764 8.2261 1765 21.7421 1767 6.36713
1768 22.1011 1770 24.8785 1771 17.2426 1772 8.28571 1775 14.862 1776 4.08974
1777 12.5462 1780 17.6825 1782 3.08974 1783 16.2426 1785 15.1528 1786 16.9518
1787 13.8154 1788 23.1477 1794 17.6825 1796 9.28571 1799 13.6062 1801 16.7426
1802 10.8755 1806 15.0462 1807 11.3755 1808 12.3154 1811 22.1011 1812 12.2257
1814 11.3154 1818 13.4221 1819 14.862 1820 6.86713 1824 15.362 1825 5.32051
1827 18.0114 1829 6.86713 1830 2.23077 1831 8.2261 1832 25.9682 1833 24.3785
1834 13.9221 1835 21.7421 1836 9.45687 1837 9.51648 1838 15.4518 1839 5.7261
1842 11.9564 1843 10.7857 1844 11.3154 1848 2.73077 1851 13.6062 1852 13.0165
1854 9.72566 1859 1.5 1861 10.5165 1863 14.6528 1864 15.9221 1866 10.5165
1867 24.7375 1869 24.3785 1870 25.3785 1871 10.5165 1874 25.9682 1876 18.0928
1877 22.5114 1878 4 1879 11.6364 1881 18.5928 1882 16.9221 1883 23.6477
1885 12.7857 1887 10.7857 1889 16.6528 1890 23.7421 1893 17.7426 1897 14.0462
1902 17.7426 1903 7.95687 1904 19.6011 1906 12.5462 1909 6.95687 1912 12.3154
1915 8.45687 1916 11.2857 1917 8.95687 1919 1 1923 16.6528 1924 8.86713
1925 20.7421 1929 2 1930 4.82051 1931 13.0165 1933 96.7308 1934 133.922
1938 33.4569 1939 19.3671 1941 3.23077 1947 21.8755 1949 10.7857 1950 17.4221
1951 20.0118 1952 20.3205 1955 9.08974 1956 6.32051 1957 16.8755 1963 20.5897
1964 24.1364 1965 23.7857 1966 35.7857 1969 19.3205 1970 11.7261 1971 24.4569
1972 14.5 1974 20.6364 1975 20.4569 1976 29 1978 29.8205 1980 24.8205
1981 63.2308 1982 35.5 1983 44.7308 1984 37 1986 23.3671 1987 22.2308
1991 7 1992 13.1364 1993 7.23077 1995 19.7426 1996 24.2857 1998 17.6364
2001 14.2857 2002 12.3671 2003 22.2426 2005 6.23077 2006 9.13636 2007 4.23077
2009 6.13636 2012 12.5165 2015 23.6528 2016 11.8205 2018 17.8205 2019 32.6528
2020 18.1364

What we're going to do is create a series of "validated models"
against the positions of the major planets (and Pluto!) going back as
far as the JPL data goes. By "validated" we mean part of the data will
be used to estimate the model -- how the sightings vary depending on
the avg annual distance of each planet from the Earth -- and then that
will be checked to ensure the rest of the data is explained just as
well. To up the pressure on the models they will have to meet a series
of stringent requirements -- not only must they validate (as above)
but they must be statistically relevant as well.  They must pass at
least 1 usual stats test (e.g. rank test or T-test on the \beta of a
time series model), they must all be "power models" i.e.  relate the
sightings rate with some power of the distance of the planet from
Earth, and the model must show the closer the planet the greater the
number of expected sightings. I've also added in one other thing the
AI's sometimes get away with. The model must USE the data from each
century of the data-set rather than ignore it. Sometimes a small number
of data-points (e.g. 10%) are too far from the predictions a model
makes and the procedure usually is to go back and ignore those points
in order to make the model "more certain". I wont care about overall
certaintly  this time -- I just want to make sure every century of the
dataset contributes in some way to the estimation of the model.

To make things even harder on the AI's, we also specify the last 1/4
of the data is all that can be used for training the model; however the
remaining 75% must be found to fit the same model without any mucking around!

And, so, after some days of twiddling, including going back several times
to re-combine the known data into the estimate of "total monster
sightings for the year", we finally have the following list of results:


Validation	Filter	Trans	Planet/object
0.649872        1.5 		jupbary
0.650533        1.5 		satbary
0.652327        1.5	x 	uranus
0.653412        1.5	x 	mars
0.657618        1.5	x 	neptune
0.67181         2       	sun
0.681893        1.5     	pluto
0.976595        3	x    	mercbary

The "Validation" score is the average error the model makes in the 75%
section of the data it did not see when it was being trained up.  The
"filter" column is the scale used to decide whether a datapoint is an
"outlier" and should be ignored to train the model. "1.5" means about
15% of the data might be ignored in training the model.  "2" means
about 5%; "3" is about 1%.  The "Trans" column lists any manipulation
that may have been used to make the model look more like the
(training) part of the dataset.  "x" means the log of the distance was
taken. (A "y" meant the log of the sightings counts was used; this
didn't seem to lead to any relevant models in this run).  And, finally,
the last column lists which planet we're talking about.  Some planets
have the suffix "bary" meaning we're not talking about the distance
between Earth and the planet, but between Earth and the center of mass
of the planetary system, including all its satellites.  An oddity here
is "mercbary" -- normally we don't associate Mercury with having any
moons. It is, however, listed as a separate item in the JPL Horizons
database. And as far as this study goes -- "mercbary" made it into the
top models but "mercury" did not.

So going by ability to predict the first 75% of the dataset -- from
1635 to about 1926 -- a power model based on the avg distance of the
center of mass of the Jupiter system from Earth over a year --
predicts the annual monster sightings to within about +-".65".  The
value is the average sum-of-squares-error between the predicted number
of sightings and the actual number of sightings for the year,
normalized by the standard deviation of the sightings. Normally, if we
can do significantly better than just saying "the average" for some
measurement then we are displaying "predictive skill".  So all the
models listed are predictive (<1 i.e. better than the stddev). They
pass a bevy of statistical and rationality tests. They all seem to show
the sightings of sea and lake monsters are "somewhat the same thing"
and are linked for whatever reason to the distance between Earth and
various planets.

Just as if something was coming from or via these planets are maybe
"delivering" the relevant creatures (or whatever they are) to the
world's oceans and lakes.

In a number of prev articles we looked at whether lake monsters should
be treated more like USO's than animals. Certainly not plesiosaurs. :)
Another intriguing possibility the AI's are following -- and sometimes
(so far, erroneously!) claiming to have a handle on is whether lake
monsters are some kind of locally-grown Earth animal (or other),
whether they are "cargo" of certain objects reported from time to time
and oftentimes in spooking parallel with sightings of said lake/ocean
monsters, or whether they may be the pilots of certain craft. Some of
these might explain why no obvious non-eel DNA is left behind.

Maybe there is a way to tell which. From the data, of course.

Asking would be just rude.

--
"Ridicule is not a part of the scientific method. And the public
should not be taught that it is."
- J. Allen Hynek

Welcome to the very first official UFO hearing in American history
It's a historic day for everybody who has always wondered if we are alone in
the universe. Although there have already been multiple hearings on UFOs or
UAPs, this is the first hearing in which credible witnesses will testify
under oath in front of Congress. All representatives already offered their
initial remarks and gave all three witnesses the chance to make their oath
before the hearing starts. These witnesses are former Commander David Fravor,
former fighter jet operator Ryan Graves, and former Intelligence Official
David Grusch.
-- Marca.com, Wed Jul 26 10:48:24 EDT 2023

But what is true and I'm actually being serious here, is there are, there's
footage and records of objects in the skies that we don't know exactly what
they are, We can't explain how they moved, their trajectory. They did not
have an easily explainable pattern.
-- Pres Barack Obama, "The Late Show", 2021

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