Seychelles tuna bulletin : third quarter 1986
Bibliographic record
Abstract
The number of purse seiners active in the Western Indian Ocean during the third quarter of 1986 stabilized at 33 vessels. This is comparable to the 34 vessels licensed in September of last year. As determined from purse seiner logbooks collected up to the end of September, catch rates improved considerably from the first and second quarter 1986 though not reaching the level of the first quarter of 1984. The proportion of yellow fin in the catch was also lower than the previous years. \nIn the first half of 1986, the seasonal variation in the distribution of fishing effort and in the species composition of the catch differed from previous years. In the past, yellow fin was the dominant species from November through July, at this period most of the fleet operated inside the EEZ. \nDuring January and February 1986, the proportion of yellow fin was high, but fell sharply from April to June as vessels, moved to the area south of the EEZ and to the north of the Mozambique Canal. In the beginning of September, as vessels moved North East of the EEZ, catch rates improved considerable but skipjack continued to the dominant species, comprising 85% of the catch. \nThe total catch by purse seiners up to the third quarter 1986 has been estimated at 88,137 tones. As most vessels have been operating away from Seychelles for the second quarter as well for part of the third quarter 1986 the lag in data collection has been longer than usual and statistic for the third quarter are still incomplete. \n \n \n•Seychelles \n•Western Indian Ocean
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.111 | 0.068 |
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".