MétaCan
Menu
Back to cohort
Record W4403231890 · doi:10.1080/11956860.2024.2400850

The Age of Snakes: a comparison of three Canadian species at their northern peripheries

2024· article· fr· W4403231890 on OpenAlexafffundvenueabout
Jennifer Petersen, Alycia L Aird, Dylan Bégin, Patrick T. Gregory, Karl W. Larsen

Bibliographic record

VenueEcoscience · 2024
Typearticle
Languagefr
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of VictoriaThompson Rivers University
FundersParks Canada
KeywordsGeographyEcologyBiologyZoology

Abstract

fetched live from OpenAlex

Aging animals enables an understanding of life-history strategies, population dynamics, and conservation concerns. Such information is particularly wanting for reptiles at their range peripheries where life-history trade-offs make body size an unreliable surrogate for age. Using skeletochronology we quantified the ages of three species sampled near their northern range limits (Common gartersnake – Thamnophis sirtalis, Western Rattlesnake – Crotalus oreganus, Great Basin Gophersnake – Pituophis catenifer). Considerable range in length was seen for snakes of the same age, and the relationship between age and size was stronger in males. Male gartersnakes reached a size associated with sexual maturity in about half the time of females, but for the other two species there was no significant difference between sexes. The oldest snakes sampled were 10–12 years, with one outlier gartersnake estimated at 15 years. Given that females in northern populations cannot reproduce annually, our estimated ages of these snakes suggest reproductive bouts occur only a small number of times during their lifespan, reflecting the precarious existence of northern snake populations. Age data improves our understanding of how life-history varies with locations (including latitude), and further studies that collect contemporary and widespread data on this basic population parameter are required.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient 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 score0.906
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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

Citations1
Published2024
Admission routes4
Has abstractyes

Explore more

Same venueEcoscienceSame topicWildlife Ecology and ConservationFrench-language works237,207