‘Reported’ versus ‘likely’ fisheries catches of four Mediterranean countries
Bibliographic record
Abstract
The fisheries catch statistics that member countries report annually to the Food and Agriculture Organization of the United Nations were compared, for the years 1950 to 2010, with ‘reconstructed’, and more likely catch data from the Mediterranean coasts of mainland Spain, France, Italy and Turkey. Reconstructed catches were 2.6 times higher than those submitted to the FAO by these countries in the 1950s, and 1.8 times higher since 2000. If discarded by-catch is ignored they were 2.3 and 1.6 times higher, respectively. The contributors to the reconstructed catch from 1950 to 2010 were large-scale industrial fisheries (46%), discards (29%), artisanal fisheries (10%), recreational fisheries (9%) and subsistence fisheries (6%). The non-reported catch was high in all fishing sectors, including industrial, artisanal and recreational fisheries. The non-inclusion of discards in national and FAO statistics undermines the transition to ecosystem-based fisheries management, but needs to be overcome, as discards must be tracked before discarding itself is eliminated. The systematic underestimation of small-scale fisheries is part of a global phenomenon that will have to be overcome if the potential of these fisheries for sustainable exploitation of coastal systems is to be realized, perhaps in the context of reducing overall fishing capacity, which is excessive in the Mediterranean Sea as elsewhere in the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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 teacher head, 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".