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Record W4406547485 · doi:10.1163/1568539x-bja10298

A non-invasive method during routine handling indicates docility in a wild, crevice-nesting seabird

2025· article· en· W4406547485 on OpenAlex
Matthew J. Legard, Gail K. Davoren

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBehaviour · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsSeabirdNesting (process)EcologyGeographyFisheryBiologyEngineeringPredation

Abstract

fetched live from OpenAlex

Abstract Personality traits have been identified in many animals but species that are hard to observe in the wild present unique challenges. We aimed to determine an appropriate method for identifying docility in a crevice-nesting seabird (razorbill, Alca torda ) by conducting three tests associated with this trait. Two tests used quantitative behavioural coding (crevice extraction, restraint), while the other used qualitative observer ratings (routine handling). Chick-rearing razorbills ( ) in Newfoundland, Canada were tested across two years (2021, 2022), with 16 tested in both years. Observer ratings during routine handling had the highest repeatability ( , 95% CI = 0.007–0.831), compared to quantified scores during extraction ( , 95% CI = 0–0.399) and restraint ( , 95% CI = 0–0.294) tests. Overall, findings suggest that observer ratings may be a good method to quantify personality traits in species that are hard to observe in the wild.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.283
Teacher spread0.272 · 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