Asymmetry across international borders: Research, fishery and management trends and economic value of the giant sea bass (<i>Stereolepis gigas</i>)
Bibliographic record
Abstract
Abstract Co‐operation in the management of shared fish stocks is often necessary to achieve sustainability and reduce uncertainty. The United States of America (USA) and Mexico share a number of fish stocks and marine ecosystems, while there is some binational co‐operation in scientific research, unilateral management decisions are generally the rule. We present a case study using the giant sea bass (Stereolepis gigas, Polyprionidae) to highlight how these management and research asymmetries can skew national perceptions of population status for a fully transboundary species. Scientific publications and annual funding related to giant sea bass are 7x and 25x higher in the USA, respectively, despite the fact that 73% of the species’ range occurs in Mexico. Conversely, annual fishery production and consumptive value of giant sea bass in Mexico are 19x and 3.5x higher than in the USA, respectively, while the non‐consumptive value related to dive ecotourism is 76x higher in the USA. These asymmetries have generated a distorted view of the population status of the giant sea bass across its entire range. This and other factors related to historical fishery dynamics and policy must be accounted for when assessing population status, and subsequent appropriate management responses, across geopolitical boundaries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".