Historical dynamics of the demersal fish community in the East and South China Seas
Bibliographic record
Abstract
Taiwan has a long history of fishery operations and contributes significantly to the global fishery harvest. The East and South China seas are important fishing grounds for which publicly available data are very limited. More efforts are needed to digitise and analyse historical catch rate data to illuminate species and community changes in this region. In this study we digitised historical records of catch and effort from government fishery reports for nine commercial species caught by otter trawl, and reported quarterly from 1970 to 2001, from the East and South China seas. We analysed the four seasons and present abundance indices, distributions and among-species correlations for nine commercially important species from 1970 to 1988 (a period with high fishing effort) using a multispecies spatiotemporal model that estimates both covariation in multispecies catch rates, attributed to spatial habitat preferences and environmental responses, and indices representing trends in abundance and distribution. We found substantial spatial, temporal and spatiotemporal variation in the distribution of fishes and season-specific patterns. We recommend collaborative work from various adjacent countries to digitise historical records of fishing catch rates, because more records would potentially address scientific disagreements regarding trends in the abundance and distribution of commercial fishes in this region through comparative studies.
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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 | 0.001 | 0.001 |
| 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".