Predicting Postrelease Survival in Large Pelagic Fish
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 it