Managing fisheries from space: Google Earth improves estimates of distant fish catches
Bibliographic record
Abstract
Abstract Global fisheries are overexploited worldwide, yet crucial catch statistics reported to the Food and Agriculture Organization (FAO) by member countries remain unreliable. Recent advances in remote-sensing technology allow us to view fishing practices from space and mitigate gaps in catch reporting. Here, we use Google Earth to count intertidal fishing weirs off the coast of six countries in the Persian Gulf, otherwise known as the Arabian Gulf. Although the name of this body of water remains contentious, we use the name used in Google Earth. Combining, in a Monte Carlo procedure, the number of weirs (after correcting for poor resolution and imagery availability) with assumptions about daily catch and fishing season lengths, we estimate that 1900 (±79) weirs contribute to a regional catch up to six times higher than the officially reported catches of 5260 t. These results, which speak to the unreliability of officially reported fisheries statistics, provide the first example of fisheries catch estimates from space, and point to the potential for remote-sensing approaches to validate catch statistics and fisheries operations in general.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".