Albatrosses Following Fishing Vessels: How Badly Hooked Are They on an Easy Meal?
Bibliographic record
Abstract
Fisheries have major impacts on seabirds, both by changing food availability and by causing direct mortality of birds during trawling and longline setting. However, little is known about the nature and the spatial-temporal extent of the interactions between individual birds and vessels. By studying a system in which we had fine-scale data on bird movements and activity, and near real-time information on vessel distribution, we provide new insights on the association of a threatened albatross with fisheries. During early chick-rearing, black-browed albatrosses Thalassarche melanophris from two different colonies (separated by only 75 km) showed significant differences in the degree of association with fisheries, despite being nearly equidistant to the Falklands fishing fleet. Most foraging trips from either colony did not bring tracked individuals close to vessels, and proportionally little time and foraging effort was spent near ships. Nevertheless, a few individuals repeatedly visited fishing vessels, which may indicate they specialise on fisheries-linked food sources and so are potentially more vulnerable to bycatch. The evidence suggests that this population has little reliance on fisheries discards at a critical stage of its nesting cycle, and hence measures to limit fisheries waste on the Patagonian shelf that also reduce vessel attractiveness and the risk of incidental mortality, would be of high overall conservation benefit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".