Fisheries impact on the South China Sea large marine ecosystem: a preliminary analysis using spatially-explicit methodology
Bibliographic record
Abstract
A multiple regression model is derived, based on biomass estimates in 16 massbalance food web (Ecopath) models, which explains 68 % of the variation in the data at hand, and shows that the abundance of fish with trophic levels of 3.0 or more in the South China Sea area had declined, by 2000, to less than half its value in 1960. This is worrisome, as this generalizes to the entire region declining trends observed in local areas within the South China Sea. Moreover this estimate is almost surely too conservative, given the method we used. This declining trend is compatible however with the fishing ædown marine food websÆ, reported from well studied parts of the South China Sea, notably the Gulf of Thailand, where the mean trophic levels of landings have declined, indicating gradual replacement in the underlying ecosystems of large, long lived, high-trophic level fishes by small, short-lived, low trophic level species often described as ætrashfishÆ. The only exception to these trends is Brunei, whose offshore oilrigs have led to regulations precluding trawling across much of the shelf, thus in effect creating a marine reserve. We conclude by pointing out that marine reserves are indeed one approach that will have to be used if the present declining trends are to be reversed, along with a rollback of excessive fishing effort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".