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Record W4225154173 · doi:10.22621/cfn.v135i4.2709

Apparent winterkill of Painted Turtle (<i>Chrysemys picta</i>)

2022· article· en· W4225154173 on OpenAlexaffvenueabout
David C. Seburn, Mackenzie Burns, Iyanuoluwa Akinrinola, Sara Cecile, Thomas Farquharson, Charlotte Hung, Payton McIntyre

Bibliographic record

VenueThe Canadian Field-Naturalist · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsCanadian Wildlife Federation
Fundersnot available
KeywordsOverwinteringPainted turtleTemperate climateShoreEcologySnowHabitatFisheryTurtle (robot)BiologyDisjunctGeographyDemographyPopulationMeteorology

Abstract

fetched live from OpenAlex

Around the margin of an artificial pond in Ottawa, Ontario, we found 25 Painted Turtles (Chrysemys picta) that appeared to have died over the course of two winters (17 during the first winter and eight during the second). We examined meteorological data to try to determine the cause of the mortality. Summer and fall rains were only slightly below normal in both years, suggesting water levels should have been close to normal. The winter air temperature was warmer than normal and winter snowfall was slightly above normal in both years. Unseasonable weather does not appear to be responsible for the winter mortality and the pond’s maximum depth of 1.7 m should prevent freezing to the bottom. It is possible that the artificial nature of the pond creates suboptimal overwintering habitat, rendering the site an ecological trap; however, there is no direct evidence to support this theory. It is also possible that winter mortality of turtles is widespread at temperate wetlands, but that dead turtles were more detectable at this site because of the bare shoreline around the pond. Winter mass mortality events, if common, may represent an additional threat to turtle populations, which are declining from various anthropogenic threats.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0150.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.204
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2022
Admission routes3
Has abstractyes

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