A non-invasive method during routine handling indicates docility in a wild, crevice-nesting seabird
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".