Modelling the Mediterranean Sea ecosystem at high spatial resolution to inform the ecosystem-based management in the region
Bibliographic record
Abstract
Cumulative pressures are rapidly expanding in the Mediterranean Sea with consequences for marine biodiversity and marine resources, and the services they provide. Policy makers urge for a marine ecosystem assessment of the region in space and time. This study evaluates how the whole Mediterranean food web may have responded to historical changes in the climate, environment and fisheries, through the use of an ecosystem modelling over a long time span (decades) at high spatial resolution (8 × 8 km), to inform regional and sub-regional management. Results indicate coastal and shelf areas to be the sites with highest marine biodiversity and marine resources biomass, which decrease towards the south-eastern regions. High levels of total catches and discards are predicted to be concentrated in the Western sub-basin and the Adriatic Sea. Mean spatial-temporal changes of total and commercial biomass show increases in offshore waters of the region, while biodiversity indicators show marginal changes. Total catches and discards increase greatly in offshore waters of the Western and Eastern sub-basins. Spatial patterns and temporal mean changes of marine biodiversity, community biomasses and trophic indices, assessed in this study, aim at identifying areas and food web components that show signs of deterioration with the overall goal of assisting policy makers in designing and implementing spatial management actions for the region.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".