Fine-scale behaviour and population estimates suggest low exposure but do not exclude high sensitivity to bycatch for Endangered sooty albatrosses
Bibliographic record
Abstract
Recent developments in assessing species-specific seabird bycatch risks demonstrated that fine-scale approaches are essential tools to quantify interactions with fishing vessels and understand attraction and attendance behaviours. Matching boats movements with birds tracking data specifically allows to investigate seabird-fishery interaction for cryptic species for which on-board information is critically lacking. The sooty albatross (Phoebetria fusca) overlaps with fisheries throughout its range and is known to be vulnerable to incidental bycatch. Combining GPS and behaviour data from individuals from Crozet Islands and boat locations during the incubation period, we investigated interactions of sooty albatrosses with fisheries in the southern Indian Ocean. Individuals foraged mostly in sub-tropical international waters, where they only encountered a small number of boats. The low interaction rate during this period may suggests that sooty albatrosses are not strongly attracted towards fishing vessels. However, this result should be interpreted with caution due to the low sample size and fishing effort during the study period, as these observations may conceal a higher bycatch risk during intense fishing effort and/or energetically demanding periods. The species conservation status requires further data to be collected throughout the annual cycle to provide an accurate assessment of the threat.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.001 |
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".