Impacts of Ocean Warming on China's Fisheries Catches: An Application of “Mean Temperature of the Catch” Concept
Bibliographic record
Abstract
Ocean warming can strongly impact marine fisheries; notably, it can cause the “mean temperature of the catch” (MTC) to increase, an indicator of the tropicalization of fisheries catches. In this contribution, we explore MTC changes in three large marine ecosystems (LMEs) along China’s coasts, i.e., the Yellow Sea, East China Sea, and South China Sea LMEs, and their relationships to shifts of the sea surface temperature (SST). The results show that, while the MTCs began to increase in 1962 in the East China Sea and in 1968 in the Yellow Sea, there was no detectable increase in the South China Sea. There also was a strong relationship between MTC and SST in the Yellow and East China Seas from 1950 to 2010, especially when taking a 3-year time-lag into account. The lack of change of the MTC in the South China Sea is attributed to the relatively small increase in SST over the time period considered, and the fact that the MTC of tropical ecosystems such as the South China Sea is not predicted to increase in the first place, given that their fauna cannot be replaced by another, adapted to higher temperature. Overall, these results suggest that ocean warming is already having an impact on China’s marine fisheries, and that policies to curtail greenhouse gas emissions are urgently needed to minimize the increase of these impacts on fisheries.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".