ASSESSING SEABIRD MORTALITY FROM CHRONIC OIL DISCHARGES AT SEA
Bibliographic record
Abstract
Chronic marine oil pollution is an ongoing global problem, yet no model currently exists to assess seabird mortality from continuous low-level inputs of oil. Taking into account persistence and detection rates of birds on beaches, and the wind-dependent proportion of birds lost at sea, we present a general mathematical Oiled Seabird Mortality Model (OSMM) to assess seabird mortality due to chronic oil pollution along a given coastline, using birds counted during systematic beached-bird surveys. We applied our OSMM to Newfoundland, Canada, where the incidence of chronic oil pollution is among the highest in world. We estimated that between 1998 and 2000, an average of 315,000 ± 65,000 murres (common [Uria aalge] and thick-billed [U. lomvia]) and dovekies (Alle alle) were killed annually in southeastern Newfoundland due to illegal discharges of oil from ships. Thick-billed murres that overwinter on the Grand Banks made up 67% of this kill. This species already is subject to extensive summer and winter hunting in Greenland, as well as winter hunting in Newfoundland, which harvests an additional 250,000–300,000 birds/year. Although populations remain stable, these levels of sustained mortality make thick-billed murre populations vulnerable to pulse perturbations and ocean regime shifts and hamper our ability to set harvest at sustainable levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".