Behavioural and energetic constraints of reproduction: Distinguishing breeding from non-breeding northern fulmars at their colony
Bibliographic record
Abstract
During the breeding season, seabird colonies are attended by active breeders, failed breeders, and non-breeding birds. Determining breeding status of some seabirds can be challenging, but tracking non-breeder attendance can provide important information on the health of the colony or the local marine environment. We used behavioural and energetic data to distinguish between breeding and non-breeding northern fulmars (Fulmarus glacialis) at a colony in the Canadian High Arctic, a region where global warming is rapidly changing the marine environment in which these birds feed. Breeding fulmars could be distinguished from non-breeders based on their larger fat reserves, although body mass and morphometrics taken in the field were not sufficient to distinguish breeding classes reliably. Behavioural cues also signaled fulmar breeding status, but observing these cues required a considerable amount of observation time. As environmental conditions in the Arctic continue to change, monitoring and population modeling efforts will require detailed observation periods rather than rapid assessments to reliably assess proportions of non-breeding fulmars at Arctic colonies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".