Bibliographic record
Abstract
Aquaculture is necessary to produce more fish in the face of decreasing supply from marine fisheries and increasing demand from the burgeoning population.The challenge is how to make the best possible use of coastal and inland waters for aquaculture at low levels of costly inputs and without adverse environmental and socioeconomic changes.Since its establishment more than 20 years ago, the Aquaculture Department of the Southeast Asian Fisheries Development Center (SEAFDEC/ AQD) has generated technologies that contributed significantly to the development of aquaculture in the region.Aquaculture technologies must of course keep abreast with the present and future needs and challenges of the industry.Thus the first Seminar on Aquaculture Development in Southeast Asia (ADSEA '87) assessed aquaculture research and development in the region.In the past 10-15 years, the aquaculture industry was confronted with many problems, particularly, the environmental and socioeconomic effects of intensive culture.Thus, ADSEA '91 was convened in Iloilo City, on 19-23 August 1991 to review recent developments in aquaculture and to redirect SEAFDEC/AQD's efforts toward environment, friendly and socially equitable aquaculture.Prospects for seafarming and searanching were therefore discussed.The idea is that aquaculture must enhance, not degrade coastal resources, and improve, not take away, the livelihood of small-scale fisherfolk and fish farmers.The technologies developed by SEAFDEC/AQD must contribute to sustainable development of the region's aquatic resources.ADSEA '91 established a set of priority species for aquaculture and identified problems for further research as well as strategies for seafarming and searanching.These guided the research and development program of SEAFDEC / AQD from 1992 to 1994.ADSEA '91 would not have been a success without the active participation of the representatives of the participating countries and cooperating agencies, and the efforts of the Workshop Committee members.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.088 |
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; both teacher heads agree on what is shown here.
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".