Report of the Seminar on Fishery and Aquaculture Information Systems in Southeast Asia, Bangkok, Thailand, 7-10 February 1989
Bibliographic record
Abstract
The need to improve fisheries information management in the region was recognized as early as 1982 when SEAFDEC, in collaboration with IDRC, organized the Seminar on Fisheries Information Science in Southeast Asia. During the Seminar, existing fisheries information systems at the national, international, and regional levels were identified, and corresponding problems and constraints were discussed. The recommendation to strengthen the national information services and to promote regional cooperation/collaboration with a view to improving effective transfer of fishery information within and outside the region was considered. As a sequel to the 1982 Seminar, the SEAFDEC Secretariat organized the SEAFIS Regional Seminar on Fishery and Aquaculture Information Systems in Southeast Asia, held in Bangkok, Thailand, from 7 to 10 February 1989. The list of participants and observers, and the Agenda appear as Annexes 1 and 2. The objectives of the Seminar were to review the current status of fishery and aquaculture information systems in the region, and to determine future activities in order to strengthen collaboration between various information sources in Southeast Asia. The Seminar also aimed to determine the appropriate training programs which could enhance development of fishery information systems in the region as well as improve information management.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.112 | 0.039 |
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".