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Record W2153578778 · doi:10.1139/z09-114

Factors influencing the emergence of a northern population of Eastern Ribbon Snakes (Thamnophis sauritus) from artificial hibernacula

2009· article· en· W2153578778 on OpenAlexafffundvenue
J. Nick Todd, Joshua J. Amiel, Richard J. Wassersug

Bibliographic record

VenueCanadian Journal of Zoology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsDalhousie University
FundersParks CanadaAcadia University
KeywordsBiologyHibernation (computing)EcologyDesiccationPopulationZoologyDemography

Abstract

fetched live from OpenAlex

We investigated whether Eastern Ribbon Snakes ( Thamnophis sauritus (L., 1766)) use a rise in water level as a cue for emergence from hibernation. We also examined the hypotheses that snakes use temperature gradients or endogenous signals as emergence cues. Twelve artificial hibernacula were used to house 15 Ribbon Snakes. Water level and temperature were regulated. Four Ribbon Snakes emerged from hibernation without any manipulation of water level or temperature. Eight snakes emerged after thermal conditions in their hibernacula changed. Of these, one emerged after the hibernaculum was made warmer on the surface than at depth, four emerged after the room temperature was increased to 9 °C, and three emerged after incandescent lights were shone on the surface of each hibernaculum. Three snakes died during hibernation. Eight snakes chose to hibernate fully submerged in water. Although the sample size is too small to draw conclusions that are statistically significant at α = 0.05, our observations collectively suggest that Ribbon Snakes do not use a rise in water level as a cue to emerge. While water-level rise does not appear to be an emergence cue, hibernation below the water table may lead to increased survivorship by decreased metabolism and elimination of the risk of desiccation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.217
Teacher spread0.199 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2009
Admission routes3
Has abstractyes

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