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Record W2153056191 · doi:10.1139/z08-049

Feeding ecology of the Great Basin Rattlesnake (Crotalus lutosus, Viperidae)

2008· article· en· W2153056191 on OpenAlexvenueno aff
Xavier Glaudas, Tereza Ježková, Javier A. Rodríguez‐Robles

Bibliographic record

VenueCanadian Journal of Zoology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsPredationBiologyEcologyViperidaeCrotalusZoologyPredatorDiel vertical migrationOntogeny

Abstract

fetched live from OpenAlex

Documenting variation in organismal traits is essential to understanding the ecology of natural populations. We relied on stomach contents of preserved specimens and literature records to assess ontogenetic, intersexual, temporal, and geographic variations in the feeding ecology of the North American Great Basin Rattlesnake ( Crotalus lutosus Klauber, 1930). Snakes preyed mainly on rodents, occasionally on lizards, and less frequently on birds; squamate eggs and frogs were rarely eaten. There was a positive relationship between predator and prey size. The best predictors of this relationship were prey diameter as a function of snake body length and head size, underscoring the importance of prey diameter for gape-limited predators such as snakes. Crotalus lutosus displayed ontogenetic, sexual, and seasonal variations in diet. Smaller rattlesnakes fed predominantly on lizards, whereas larger individuals mostly fed on mammals. Females fed on lizards more often than males. The proportion of mammals in the diet was highest during the summer, a temporal variation that may be related to behavioral shifts in the diel activity and prey selectivity of C. lutosus, and (or) to differential abundance of rodents between seasons. Great Basin Rattlesnakes also displayed geographic variation in feeding habits, with snakes from the Great Basin Desert eating a higher proportion of lizards than serpents from the more northern Columbia Plateau.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.996

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 studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.190
Teacher spread0.178 · 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 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

Citations53
Published2008
Admission routes1
Has abstractyes

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