Interpretation of Ensemble Forecasting Study on the Response of Fish Distribution in the Yellow and Bohai Seas of China to Climate Change
Bibliographic record
Abstract
This article provides an academic review of the research titled "Ensemble projections of fish distribution in response to climate changes in the Yellow and Bohai Seas, China" Ensemble prediction of fish distribution in response to climate change in the Yellow and Bohai Seas. In order to determine the geographical distribution pattern and potential suitable habitats for fish in the Yellow and Bohai Seas, this study established a spatial distribution set model for 22 important fish species using 3185 valid distribution records extracted from multiple databases and 9 environmental variables. The research results provide a theoretical basis for predicting climate driven changes in the range of fish activity in one of the most severely affected marine ecosystems in the world, and can be extended to developing climate adaptive management strategies. This review mainly summarizes the main contents and innovations of the study, puts forward academic suggestions for the future research direction of the study, and quantifies the impact of climate change on marine Species distribution.
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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 | 0.002 |
| 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 | 0.000 | 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".