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Record W2159862367 · doi:10.1086/429591

Do Female Garter Snakes Evade Males to Avoid Harassment or to Enhance Mate Quality?

2005· article· en· W2159862367 on OpenAlexaboutno aff
Richard Shine, Michael Wall, Tracy Langkilde, Robert T. Mason

Bibliographic record

VenueThe American Naturalist · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsThamnophis sirtalisCourtshipMatingHarassmentSexual conflictMate choiceBiologyBiological dispersalZoologyCourtship displayEcologyDemographySocial psychologyPsychologyPopulation

Abstract

fetched live from OpenAlex

Females of many species behave in ways that make it difficult for males to locate, court, and inseminate them. Two hypotheses have been advanced to explain such behavior: either a female thereby minimizes costs of harassment (sexual conflict model) or by playing "hard to get" she discourages inferior suitors (indirect mate choice model). Our studies of garter snakes (Thamnophis sirtalis parietalis) at a communal den in Manitoba support an interpretation of sexual conflict rather than indirect mate choice. Female snakes dispersed rapidly from the den through areas with relatively few males rather than waiting for additional courtship. Many females dispersed without mating. Experimental (pheromonal) manipulation of the intensity of courtship accelerated rates of female dispersal rather than delaying dispersal, as would be predicted if females wait to obtain matings. The behaviors of females escaping from courting groups were maximally effective in losing their suitors regardless of the number of courting males or whether or not the female was capable of mating (recently mated females cannot mate again because of a mating plug). In total, our data are most consistent with the hypothesis that female garter snakes at communal dens evade males to escape harassment rather than to enhance mate quality.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.350
Teacher spread0.312 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

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