Fitting in with the crowd: the role of prospecting in seabird behavioural trends
Bibliographic record
Abstract
Prebreeding, subadult seabirds have been documented prospecting or visiting multiple sites throughout the breeding season to gather information on colony reproductive success, identify suitable habitat, evaluate prey abundance, and locate potential partners; however, many aspects of prospector biology remain unknown. We explored prospector behaviour as a means of furthering our understanding of postnatal seabird dispersal and colony attendance using Least Auklets (Aethia pusilla (Pallas, 1811)) and Crested Auklets (Aethia cristatella (Pallas, 1769)) breeding at Gareloi Island, Alaska, in 2014 and 2015. We recorded age class, length of time spent on the colony, and behaviour for individuals attending a study plot over the course of two breeding seasons. Although prospectors typically spent more time on the colony surface than adults, prospectors rarely socialized with conspecifics during their visits to the colony, possibly due to the absence of a citrus-like feather odour used in olfactory communication. Additionally, we found substantial differences between observed and predicted data between years, demonstrating that other factors (likely prey abundance or quality) influenced behaviour in 2015. Our results suggest that the collective knowledge of seabird prospecting behaviour is not necessarily transferable between taxa and there may be a range of strategies employed by prospectors when assessing 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".