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Record W2118399858 · doi:10.1093/czoolo/58.6.805

Familiarity with a female does not affect a male’s courtship intensity in garter snakes Thamnophis sirtalis parietalis

2012· article· en· W2118399858 on OpenAlexaboutno aff
Richard Shine, Jonathan K. Webb, Amanda Lane, Robert T. Mason

Bibliographic record

VenueCurrent Zoology · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsThamnophis sirtalisCourtshipMatingMate choiceBiologyLizardZoologySexual selectionOphidiaCourtship displayEcology

Abstract

fetched live from OpenAlex

Abstract In many animal species, males direct more intense courtship towards females they have not previously encountered, than towards females with which they have previously mated. To test the factors responsible for this “Coolidge Effect”, we need studies on a wide range of taxa – including those with mating systems in which we would not expect (based on current theory) that such an effect would be evident. The Coolidge Effect has been documented in several lizard species, but has not been looked for (and would not be expected) in snakes. We conducted experimental trials with red-sided garter snakes Thamnophis sirtalis pa-rietalis at a communal den in Manitoba, to see whether previous exposure to a female (either courting, or courting plus mating) modified male mate choice or courtship intensity. In keeping with prediction from theory (but contrary to an early anecdotal report), male garter snakes did not modify their courtship behaviour based upon their familiarity (or lack thereof) with a specific female. At least in large courting aggregations, male snakes may maximize their fitness by basing mate-choice upon immediate attributes of the female (body size, condition, mated status) and the intensity of competition (numbers and sizes of rival males) rather than information derived from previous sexual encounters.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.385

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 studies0.0000.000
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 head0.049
GPT teacher head0.270
Teacher spread0.221 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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