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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 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".