Effects of selective fishing on a small scale multi-species and multi-gear freshwater fishery in the Magdalena River Basin (Colombia)
Bibliographic record
Abstract
Classical management has not been able to stop the 65% decrease in fishery production during the last 40 years in the Magdalena–Cauca River Basin. To analyze the effects of selective fishing of multiple species and small scale fisheries we addressed temporal changes at fishing level and the response of fishermen. The fishery reduced production and CPUE (catch per unit effort standardized), decreased the large sizes and growth rates of Prochilodus magdalenae and Pseudoplatystoma magdaleniatum, changed the abundance of trophic levels (decreased carnivores and increase of detritivores, omnivores), and increased exploitation rates. The fishermen have responded by implementing self-control measures, diversifying fishing gear and mesh size, including new species and sizes in the catch with a higher CPUE of small sized fish, adjusting the fishing effort to the abundance. We conclude that selective fishing has had ecological effects and fishermen have empirically self-regulated to optimize the cost–benefit ratio of their activity, developing a fishery that is more in line with ecosystemic structures. We address the balanced harvest strategy as a management alternative.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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 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".