Fisheries impacts on China's coastal ecosystems: Unmasking a pervasive ‘fishing down’ effect
Bibliographic record
Abstract
Intensive fishing can strongly impact marine ecosystems; among other things, it usually causes the mean trophic level of the catches to decline, an indicator of the occurrence of the 'fishing down' (FD) phenomenon. Although FD occurs throughout the world oceans, it can easily be masked by diverse factors, which has misled authors as to its generality. In this contribution, which uses the East China Sea as an example, we explore the masking effect on FD of the taxonomic coarseness of catch data, of assuming that individual sizes remain constant after intensive fishing, and the geographic expansion of fisheries. The result showed that all of these masking factors occur in the East China Sea, where only a few species are reported separately and the bulk of the catch is pooled into non-informative 'mixed fishes'. Also, the small mesh sizes and intensive fishing have reduced the sizes of fish and their trophic levels, while the fisheries have expanded offshore. Overall, taking the masking factors into account, the fishing down effect, i.e., the decline of the mean trophic level of the catch between 1979 and 2014 is in the order of 0.15 TL per decade, i.e., one of the highest estimates of FD in the world. Some ecological implications are presented.
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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 | 0.001 |
| 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 | 0.000 |
| 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".