Postrelease mortality in istiophorid billfish
Bibliographic record
Abstract
Meta-analysis (inverse-variance, random-effects model) involving 46 studies was used to estimate the effect size of postrelease mortality (Fr) in six istiophorid billfish species (black marlin (Istiompax indica), blue marlin (Makaira nigricans), longbill spearfish (Tetrapturus pfluegeri), sailfish (Istiophorus platypterus), striped marlin (Kajikia audax), and white marlin (Kajikia albida)) following release from recreational, longline, and harpoon fishing gears. The studies involved 400 reporting pop-up satellite archival tags and 64 reporting acoustic (ultrasonic) tags. Despite fish being captured, tagged, and released under widely disparate conditions, locations, and gear types, Fr was homogeneous among species. Variability in Fr was principally due to random sampling error within studies with no evident patterns. Fifteen studies (33% of tags) indicated no mortality, and the overall summary effect size for Fr was 13.5% (95% CI: 10.3%–17.6%). Since the random-effects model decomposed to a fixed-effect model when the between-studies variance T2 = 0.00, results were confirmed using exact nonparametric inferential tests and sensitivity analyses. Our results support earlier findings in the Atlantic and substantiate the majority of istiophorid billfish survive when released from recreational and longline fishing gear, clearly implying catch-and-release as a viable management option that permits fishing activity while protecting parental biomass and the fishery.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.027 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".