MétaCan
Menu
← Back to cohort
Record W4394254272 · doi:10.6084/m9.figshare.14988579

Animal personality: a comparison of standardized assays and focal observations in North American red squirrels - data, meta-data, and code

2021· dataset· en· W4394254272 on OpenAlexaffabout
April Robin Martinig, Hayley J Karst, Erin R. Siracusa, Emily K. Studd, Andrew G. McAdam, Ben Dantzer, David M. Delaney, Jeffrey E. Lane, Prashanna Pokharel, Stan Boutin

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCode (set theory)PersonalityMeta-analysisPsychologyComputer scienceSocial psychologyProgramming languageMedicineInternal medicine

Abstract

fetched live from OpenAlex

The data, meta-data, and code for whether standardized behavioural assays and focal-animal sampling measure similar behavioural types in North American red squirrels (Tamiasciurus hudsonicus) in Yukon, Canada. Data obtained with funding from the Natural Sciences and Engineering Council of Canada, Northern Scientific Training Program, the National Science Foundation, University of Alberta Northern Research, American Society of Mammalogists, and Arctic Institute of North America. Martinig, A. R., H. J. Karst, E. R. Siracusa, E. K. Studd, A. G. McAdam, B. Dantzer, D. M. Delaney, J. E. Lane, P. Pokharel, and S. Boutin. 2022. Animal personality: a comparison of standardized assays and focal observations in North American red squirrels. Animal Behaviour.

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.079
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0050.010
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.007

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 head0.263
GPT teacher head0.375
Teacher spread0.113 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueFigshare→Same topicAnimal Ecology and Behavior Studies→French-language works237,207→