Seychelles tuna bulletin : third quarter 1989
Bibliographic record
Abstract
The number of purse seiners active in the Western Indian Ocean during the third quarter of 1989 increased from 46 vessels in June to 48 in July reaching an all-time high of 51 in September. The highest number of vessels previously recorded for this fishery was 49, between December 1984 to February 1985, and in early 1989. During the same period last year a maximum of 46 vessels were in operation.Poor fishing results obtained during the second quarter continued throughout July and August, with daily catch rates averaging 11-13 MT. In September, however, fishing success improved considerably with catch rates rising to 26.5 MT/day, nearly reaching the level obtained (30 MT/day during the same period last year. Skipjack remained the predominant species in the catch whereas only a slight improvement in the proportion of yellow fin was obtained, from 12% in July to 19% in September. The fishing pattern was similar to that observed in previous years; fishing activity was concentrated towards the northern end of the Seychelles EEZ, north or latitude 5 degrees south. As determined from seiner logbooks received by the 30th of September 1989 the cumulative catch by purse seiners in the Western Indian Ocean now stands at 163,252 tones for 1989.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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) | 0.043 | 0.016 |
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".