A risk assessment for Pacific leatherback turtles (<i>Dermochelys coriacea</i>)
Bibliographic record
Abstract
Leatherback turtles (Dermochelys coriacea) are critically endangered in the eastern and western Pacific Ocean. Here, I estimate the magnitude of two likely causes of their decline: (i) bycatch by longline fishing vessels and (ii) coastal sources of mortality. I calculate point estimates of longline bycatch based on turtle catch rates from the US Hawaii-based fleet and effort data for the international Pacific longline fleet. I estimate the intrinsic growth rate of the population and the magnitude of coastal mortality by fitting a simple logistic model. In the western and central Pacific, coastal sources lead to a 13% annual mortality rate, compared with a point estimate of 12% from longlining. In the eastern Pacific, coastal sources account for a 28% annual mortality rate, compared with a point estimate of only 5% from longlining. A Bayesian risk assessment reveals the importance of reducing coastal sources of mortality, as well as longline bycatch, if the populations are to avoid extinction. International efforts to protect the leatherback should expand beyond focusing solely on longline bycatch and should attempt to reduce coastal harvest of adult females and eggs, as well as reduce bycatch by inshore gears such as gillnets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".