The Impacts of Environmental and Socio-Economic Risks on the Fisheries in the Mediterranean Region
Bibliographic record
Abstract
The objective of this study is to investigate the impacts of the environmental and socio-economic risks on the fisheries in the Mediterranean region from an economic point of view. A balanced panel of 21 Mediterranean countries for 2001–2018 has been estimated by the GLS method, considering heteroskedasticity and correlation among cross sections. The volume of fish landed and landed values have been considered in two models. The results show that increases in sea bottom and surface temperature, H+ ion concentration and salinity threaten the fisheries in the Mediterranean region for the volume of fish landed and that sea surface temperature and salinity negatively influence landed values. In addition, there is an inverse U-shaped relationship between human population and fisheries. Moreover, the Human Development Index (HDI), an indicator of countries’ adaptive capacity, has a positive impact on fisheries and indicates that countries can safeguard fisheries by improving their adaptive capacity. Finally, our results strongly show the risk of climate change for the fisheries in the Mediterranean region and that fisheries are adversely impacted by climate change as well as worsening socio-economic conditions in the absence of adaptation plans.
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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 | 0.003 |
| 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 | 0.001 |
| 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".