Throwing light on straddling stocks of <i>Illex argentinus</i>: assessing fishing intensity with satellite imagery
Bibliographic record
Abstract
Marine fisheries provide around 20% of animal protein consumed by man worldwide, but ineffective management can lead to commercial extinction of exploited stocks. Fisheries that overlap nationally controlled and high seas waters cause particular problems, as few management data are available for the high seas. The Argentinean short-finned squid, Illex argentinus, exemplifies such a "straddling stock". Here we demonstrate that light emitted by fishing vessels to attract squid can be detected via remote-sensing. Unlike conventional fisheries data, which are restricted by political boundaries, satellite imagery can provide a synoptic view of fishing activity in both regulated and unregulated areas. By using known levels of fishing effort in Falkland Islands waters to calibrate the images, we are able to estimate effort levels on the high seas, providing a more comprehensive analysis of the overall impact of fishing on the stock. This innovative tool for quantifying fishing activity across management boundaries has wide-ranging applications to squid fisheries worldwide.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".