Seychelles tuna bulletin : fourth quarter 1988
Bibliographic record
Abstract
The number of purse seiners active in the Western Indian Ocean during the fourth quarter of 1988 increased gradually from 45 in October to 48 in December. This represents an increase over the same period last year, when 41 vessels were present. \nThere was also a considerable improvement in the performance of the fishing fleet compared to the two previous years. Catch rates for 1988 were estimated at 29 MT/day compared to 18 MT/day in 1987 and 15 MT/day in 1986. The proportion of yellow fin in the catch moreover increased from 38% in 1987 to 47% in 1988 with a corresponding decline in the percentage of skipjack, from 61 to 52 percent. \nThe fishing pattern during 1988 however followed an almost similar trend as previous years with yellow fin catch rates improving from January to March followed by a decline in April. This year however, unlike previous ones, there was an unexpected increase in yellow fin catch rates in June due to vessels moving north from Mozambique Channel early. From June the proportion of yellow fin decreased slightly until November when it recovered again. As in the preceding years. \nAs determined from seiner logbooks received by the 31st of December 1988 the cumulative catch by purse seiners in the Western Indian Ocean for 1988, now stands at 216,227 tones. The estimated total catch for 1988 should be around 230,000 tones compared to 160,000 tones in 1987.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.084 | 0.022 |
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".