Enhancing single-species stock assessments with diverse ecosystem perspectives: a case study for Gulf of Mexico red grouper (<i>Epinephelus morio</i>) and red tides
Bibliographic record
Abstract
Impacts of Karenia brevis red tide blooms have been an increasing cause of concern for fisheries management in the Gulf of Mexico (Gulf). The 2019 Gulf red grouper (Epinephelus morio) stock assessment was confronted with the challenges of quantifying and parameterizing red tides during both historical and projection time periods. Red tide mortality was estimated for each age class in the model solely in 2005 and 2014 during severe events. Given the considerable uncertainty surrounding the 2018 red tide and its substantial implications on the status of the population, several projection scenarios were evaluated. Under the assumption of no 2018 red tide mortality, near-term catches were projected to nearly double, a predicted outcome that appeared to be in contrast with recent record low catches and fishing industry perceptions of major stock depletion. In the event that the 2018 red tide caused mortality, but was not accounted for in projections, the recommended catch levels would lead to high probabilities of overfishing and potentially stock collapse. Collectively, these results highlight how consideration of uncertainty in projections can help avoid unintended consequences.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".