Exploring tropical fisheries through fishers’ perceptions: Fishing down the food web in the Tonlé Sap, Cambodia
Bibliographic record
Abstract
Abstract Tropical fisheries are among the most productive fisheries in the world, often providing the primary source of protein for the local population. Despite their importance, data on these systems are relatively limited, thus hampering management and policy development. Here, the implications of increasing fishing pressure are explored by critically evaluating the perceptions of the fishers who rely on these ecosystems to survive. A total of 169 fishers in 26 different fish‐dependent communities in the Tonlé Sap Lake, Cambodia, were surveyed to understand their perceptions of the impact that fishing has had on the ecosystem. The Tonlé Sap is one of the largest, yet poorest studied, freshwater fisheries in the world. Consistent with “fishing down the food web” theory of fisheries, survey data revealed that although fishers observed the total size of fish catch remaining consistent over recent years there has been a drastic decline in the size of individual fish, as well as a reduction in the diversity of species caught. These perceptions are examined with reference to food web theories that explore how fishing pressure leads to ecosystem change, including the more recent “indiscriminate fisheries” theory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".