From bonito to anchovy: a reconstruction of Turkey’s marine fisheries catches (1950-2010)
Bibliographic record
Abstract
Turkey’s marine fisheries catches were estimated for the 1950-2010 time period using a reconstruction approach, which estimated all fisheries removals, including unreported landings, recreational landings and discards. We added these estimates to the ‘official’ data, as reported in TURKSTAT, which are also available from the United Nation’s Food and Agriculture Organization (FAO). The total reconstructed catch for the 1950-2010 time period (inclusive of the reported data) is approximately 32 million t, or 74% more than the 18.4 million t of reported data. This added approximately 13.6 million t to the reported data, consisting of 6.9 million t of unreported landings, 2.6 million t of discards, 2.4 million t of recreational catches, and 1.7 million t of subsistence catches. In 2010, total reported marine landings for Turkey were 445,680 t and the total reconstructed catch was 763,760 t, or 73% more than the reported data. The main unreported taxon by tonnage was European anchovy (Engraulis encrasicolus) due to its sheer high proportion of catch. The major reasons for underreporting include a general distrust fishers have towards the taxing system combined with inefficient fisheries monitoring and surveillance capabilities. Accounting for all fisheries components is crucial in understanding the development of fisheries resources, improving management, and reducing threats to the domestic food security of Turkey.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.002 | 0.001 |
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".