The effect of regional sea surface temperature rise on fisheries along the Portuguese Iberian Atlantic coast
Bibliographic record
Abstract
Abstract The environmental effects of climate change are expected to impact fisheries, and related economies, and represent a significant challenge for the development of government policy. The impact of ocean warming on fisheries yields is of particular concern. The effect of sea surface temperature (SST) on fisheries in three distinct biogeographic areas (north‐western, NW; south‐western, SW; and south, S) and different fleet sectors (trawl, seine, and multi‐gear) of the Portuguese coast was examined. The mean temperature of the catch (MTC) was applied to the official landings statistics to assess the effect of global warming on the exploited marine communities. MTC increased from 16.9, 16.7, and 17.4°C in 1989 to 17.9, 18.1, and 18.3°C in 2009, whereas the linear rate of MTC increase was 0.54, 0.49, and 0.70°C per decade in the NW, SW, and S regions, respectively. The increase of warmer species in fisheries landings is regional‐specific. The percentage increases in landings of warmer water species increased significantly from north to south: 5.1, 6.7, and 18% per decade in the NW, SW, and S, respectively. The results confirmed that ocean warming has affected the composition of fisheries landings (of warmer and colder species) in the three regions of the Portuguese coast. The results highlight the importance and urgency of considering the temperature‐induced shift in species distribution in fisheries management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".