Favorites and leftovers on the menu of scavenging seabirds: modelling spatiotemporal variation in discard consumption
Bibliographic record
Abstract
Fishery discards subsidise the food supply of a large community of scavenging seabirds, thus substantially influencing seabird ecology. Seabird preference for certain types of discards determines the number and composition of discards available for non-avian marine scavengers. To quantify both portions of discards temporally as well as spatially, we have used a modelling framework that integrates the spatial and temporal variation in seabird distribution, seabird attraction to fishing vessels, and discard distribution. The framework was applied to a case study in the Bay of Biscay, where a wide variation in discard consumption was observed across seabird foraging guilds, discard types, periods, and locations. Seabirds removed about one-quarter of the Bay of Biscay discards. The remaining sinking discards have limited potential to subsidize scavenging benthic communities on a large scale, but they may contribute substantially to scavenger diets on a local scale. Changes in food subsidies caused by discard mitigation measures, such as the “landing obligation” in the European Common Fisheries Policy, are likely to have ecosystem effects on both scavenging seabirds and non-avian marine scavengers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".