An illicit artisanal fishery for North Pacific white sharks indicates frequent occurrence and high mortality in the Gulf of California
Bibliographic record
Abstract
Abstract Large sharks shape ecosystems across their geographic ranges and have become a top research and conservation priority. Eastern North Pacific (ENP) white shark ( Carcharodon carcharias ) aggregations off the United States and Mexico are well described, but their population status is currently uncertain. Population assessments of ENP white sharks are complicated by migrations across international boundaries, vulnerability at aggregation sites, and undetermined mortality levels. While protective legislation exists both in the United States and Mexico, ongoing incidental and unreported catch may undermine assessments and management. Here, access to a clandestine artisanal fishery provides evidence for white shark abundance and mortality in the Gulf of California that has been underestimated by other methods (e.g., satellite telemetry, [by]catch data). Shark size estimates based on tooth measurements suggest abundance of both juvenile and mature sharks in the region, and updated population models indicate the potential for substantial impacts of this fishery on ENP population viability. The data here, fisher‐provided information, and anecdotal evidence suggest potentially high abundance at two specific regions, making directed future research efforts feasible in the Gulf. These data demonstrate that cryptic life histories and geopolitical boundaries can still limit fundamental understanding of megafauna distribution, necessitating international cooperation for both research and management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".