Re-estimation of small-scale fishery catches for U.S. flag-associated island areas in the western Pacific: the last 50 years
Bibliographic record
Abstract
Nearshore fisheries in the tropical Pacific play an important role, both culturally and as a reliable source of food security, but often remain under-reported in statistics, leading to undervaluation of their importance to communities. We re-estimated nonpelagic catches for Guam and the Commonwealth of the Northern Mariana Islands (CNMI), and summarize previous work for American Samoa for 1950−2002. For all islands combined, catches declined by 77%, contrasting with increasing trends indicated by reported data. For individual island entities, re-estima-tion suggested declines of 86%, 54%, and 79% for Guam, CNMI, and American Samoa, respectively. Except for Guam, reported data primarily represented commercial catches, and hence under-represented contributions by subsistence and recreational fisheries. Guam’s consistent use of creel surveys for data collection resulted in the most reliable reported catches for any of the islands considered. Our re-estimation makes the scale of under-reporting of total catches evident, and provides valuable baselines of likely historic patterns in fisheries catches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| 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) | 0.000 | 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 teacher head, 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".