A reconstruction of the Ukraine's marine fisheries catches, 1950-2010
Bibliographic record
Abstract
AbstractUkraine’s marine fisheries catches were re-estimated for the 1950-2010 time period using a reconstruction approach which estimated all unreported fisheries removals, i.e., catches from the industrial, artisanal, recreational, and subsistence sectors, as well as discards from major fisheries. The reconstructed total catch for the 1950-2010 time period is 1.4 times the data we deemed officially reported on behalf of Ukraine to the FAO, which included only industrial landings. Reconstructed catches consisted to 71% of industrial, 11% artisanal, 8% recreational and 7% subsistence landings, while discards accounted for 3%. Total catches increased from about 50,000 tin 1950 to a peak of about 175,000 t in 1988, then declined with the collapse of the Soviet Union to about 55,000 t in 1991, also due to an invasion of ctenophores in the Black Sea. In 2010, total reported marine landings for Ukraine were about 70,000 t, while the reconstructed total catch was just over 110,000 t. Major unreported species were Mediterranean horse mackerel (Trachurus mediterraneus), gobies (Gobiidae), whiting (Merlangius merlangus), and bluefish (Pomatomus saltatrix). Accounting for all fisheries removals should help to establish a reliable baseline, better understand the fisheries, and thus assist management.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".