Predicting Postrelease Survival in Large Pelagic Fish
Bibliographic record
Abstract
Abstract Sharks, turtles, billfish, and marine mammals are frequently caught accidentally in commercial fisheries. Although conservationists and fisheries managers encourage the release of these nontarget species, the long-term outcome of released animals is uncertain. Using blue sharks Prionace glauca, we developed a model to predict the long-term survival of released animals based on analysis of small blood samples. About 5% of the sharks were landed in obviously poor condition (lethargic and unresponsive to handling); these moribund sharks were sampled and euthanized. A subset of the remaining sharks was sampled and tagged with pop-up satellite archival tags (PSATs). Each of the PSATs that reported data (11 tags) showed that the sharks roamed at sea for at least 3 weeks postrelease. Five variables differentiated moribund sharks from survivors: Plasma Mg2+ (moribund, 1.57 ± 0.08 mM; survivor, 0.98 ± 0.05 mM; P < 0.00001), plasma lactate (moribund, 27.7 ± 4.1 mM; survivor, 5.80 ± 2.96 mM; P < 0.001), erythrocyte heat shock protein 70 (Hsp70) mRNA (relative levels: Moribund, 3.96 ± 0.53; survivor, 1.00 ± 0.29; P < 0.005), plasma Ca2+ (moribund, 3.70 ± 0.14 mM; survivor, 3.13 ± 0.11; P < 0.005), and plasma K+ (moribund, 7.01 ± 0.66 mM; survivor, 5.12 ± 0.44 mM; P < 0.05). These analyses were used to develop logistic regression models that could “predict” the long-term survival of captured sharks, including a larger group of sharks that we sampled but did not tag. The best logistic model, which incorporated Mg2+ and lactate, successfully categorized 95% of fish of known outcome (19 of 20). These analyses suggest that sharks landed in an apparently healthy condition are likely to survive long term if released (95% survival based on biochemical analyses; 100% based on PSATs).
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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) | 0.001 | 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 | 0.000 | 0.000 |
| 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".