Effects of indiscriminate fisheries on a group of small data-poor species in Thailand
Bibliographic record
Abstract
Abstract As catches of economically valuable target fishes decline, indiscriminate fisheries are on the rise, where commercial and small-scale fishers retain and sell an increasing number of marine species. Some of these catches are destined for international markets and subject to international trade regulations. Many of these species are considered “data-poor” in that there are limited data on their biology, ecology, and exploitation, which poses a serious management challenge for sustainable fisheries and trade. Our research explores the relative pressure exerted by such indiscriminate fisheries on a data-poor marine fish genus—seahorses (Hippocampus spp.)—whose considerable international trade is regulated globally. Our focus is Thailand, a dominant fishing nation and the world‘s largest exporter of seahorses, where we gathered data by interviewing commercial and small-scale fishers and through port sampling of landed catch. We estimate that annual catches were more than threefold larger than previously documented, approximating 29 million individuals from all gears. Three fishing gears–two commercial (otter and pair trawl) and one small-scale (gillnet)–caught the most individuals. Results from port sampling and our vulnerability analysis confirmed that H. kelloggi, H. kuda, and H. trimaculatus were the three species (of seven found in Thai waters) most susceptible to fishing. Small-scale gillnets captured the majority of specimens under length at maturity, largely due to catches of juvenile H. kuda and H. trimaculatus. This research indicates a role for vulnerability analysis to initiate precautionary management plans while more extensive studies can be conducted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".