The hidden value of artisanal fisheries in Honduras
Bibliographic record
Abstract
Abstract Declining fisheries catches are a global trend, with management failing to keep pace with growth in fishing effort and technological advances. The economic value of Honduras’ catches was estimated within the industrial and artisanal sectors. Catches were found to be 2.9 times greater than the official statistics between 1950 and 2015. The merging of industrial and artisanal catch data masked the decline in industrial catches and hid the strong growth of artisanal fisheries. In 1996, annual artisanal fisheries landed catches surpassed the industrial fishery sector, and in 2000, the annual net value of artisanal fisheries eclipsed the value of the industrial fisheries. These data highlight the importance of artisanal fisheries in Honduras and challenge the long‐held belief that the industrial sector contributes more to the national economy. The global paucity of fisheries data highlights the need for comprehensive strategies to collect more detailed and accurate fisheries data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".