Red snapper management in the Gulf of Mexico: science- or faith-based?
Bibliographic record
Abstract
The most controversial fishery in U.S. waters of the Gulf of Mexico (Gulf) is for northern red snapper Lutjanus campechanus, which collapsed in the late 1980s when stock biomass became too low to be fished commercially in the eastern Gulf. Red snapper management began in 1989; the stock is now showing signs of recovery. The Gulf of Mexico Fishery Management Council has been slow to sufficiently reduce catches of the directed fisheries to rebuild the stock in a timely fashion, although compliance with the Magnuson-Stevens Reauthorization Act of 2006 (MSRA) required substantial cuts in the harvest of red snapper beginning in 2007. In our opinion, this could have been avoided if conservative management practices had been adopted earlier. We believe that ‘faith-based fisheries’ arguments have been used to defer effective management of red snapper in the Gulf, which in turn has strained the relationship between science, management, and stakeholders there. We provide a simple empirical argument and alternate interpretations of a recently published perspective on the historical fishery of red snapper in the Gulf to conclude that the preponderance of evidence used in the agency stock assessment process, and the simple arguments made here, do not support the perspective that the red snapper stock has increased in size sufficiently to defer compliance with the MSRA.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".