Seychelles tuna bulletin : second quarter 1987
Bibliographic record
Abstract
The number of purse seiners active in the Western Indian Ocean during the second quarter of 1987 decreased from 33 vessels during the previous quarter to a minimum of 29 in May, increasing to 30 in June. Good catch rates were obtained during this quarter averaging 16MT/day; the highest catch rate ever recorded during this period for this fishery. The proportion of skipjack in the catch increased in April as vessels moved south to fish around the Mozambique Channel. Although this fishing pattern is somewhat similar to that observed during the same period last year, it would appear from data obtained from logbooks that this year the purse seiners moved north one month earlier i.e. in June. This resulted in an increase in the catch rate of yellow fin. \nAs determined from logbooks and transshipment data received by the 30th of June 1987, the cumulative catch by purse seiners in the Western Indian Ocean for the first half of 1987 has been estimated at 42,980 tones. Since most of the purse seiners have been operating away from Seychelles, data for the second quarter is still incomplete with only 14 out of 30 vessels having submitted catch returns for the months of May and June. A new fishing Agreement was signed in June with a Soviet fishing company that will take effect on the 1st of September 1987. The agreement grants fishing rights to a maximum of four purse seiners and two long liners to fish for tuna in Seychelles waters. In return Seychelles will benefit from a substantial percentage of the fish caught in its waters.
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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 | 0.000 | 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) | 0.120 | 0.073 |
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".