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Record W2762368186 · doi:10.1139/cjz-2016-0321

The effects of climate on annual variation in reproductive output in Snapping Turtles (<i>Chelydra</i> <i>serpentina</i>)

2017· article· en· W2762368186 on OpenAlexvenueno aff
Ashley R. Hedrick, Hanna Klondaris, L.C. Corichi, Michael J. Dreslik, John B. Iverson

Bibliographic record

VenueCanadian Journal of Zoology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicTurtle Biology and Conservation
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsChelydraAvian clutch sizeCarapaceTurtle (robot)BiologyReproductionClutchEcologyZoologyCrustacean

Abstract

fetched live from OpenAlex

Reptiles are highly dependent on climatic patterns to regulate their behavior and physiology, and studies of the effects of climate on the biology of organisms are increasingly important given expected climate change. Our study examined the effects of climate variation over 15 of the 26 years between 1990 and 2015 on the reproductive output of the Snapping Turtle (Chelydra serpentina (Linnaeus, 1758)). Egg mass, clutch size, and clutch mass (relative to body size) were significantly higher in years following warmer temperatures in September and October of the year before reproduction, but not related to temperatures in April and May just before reproduction. Of the above life-history traits, egg mass varied the least across years, and after warm autumns small turtles (225–285 mm carapace length) increased clutch mass by increasing clutch size but not egg mass. In contrast, under the same conditions, large turtles increased clutch mass by increasing egg mass but not clutch size. Our data suggest optimal egg mass may vary with female size. Climate change may already have impacted reproductive output in Snapping Turtles at the site because temperatures during September and October have increased about 0.5 °C each decade for the last 45 years.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.208
Teacher spread0.202 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations15
Published2017
Admission routes1
Has abstractyes

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