Bibliographic record
Abstract
Although the reservoir area in south-east Asia is considerable, the fish production is low in the majority of reservoirs. Although high fish production has been recorded in a number of reservoirs in Sri Lanka and Indonesia, high fish production in reservoirs has with few exceptions resulted from the introduction of typical lake species from Africa. Addition of further lake fish species especially into deeper reservoirs will probably increase present fish yields. Diversification of the lake fish fauna can be achieved by introduction of species indigenous to the region like Etroplus suratensis. In this introduction of fish species, three points may be noted with specific reference to Tilapia spp.: (a) Lakes without indigenous cyprinids show marked fluctuation in fish catch with the introduction of Tilapia, and this could be stabilised by the introduction of predators and more diverse species. (b) Lakes with a moderate diversity of fish fauna show a high stabilised production with the introduction of Tilapia, presumably because of the effect of predator pressure on excessive Tilapia breeding. (c) Lowland lakes with a rich diversity of indigenous fish species colonising lakes may not require the introduction of Tilapia. Introduction of invertebrates into reservoirs in south east-Asia has not been done deliberately so far. However, the experience in other parts of the world, notably the Soviet Union, indicates that such introductions are likely to prove beneficial and to increase fish production. The potential for a considerable increase in fish production from reservoirs exists in south-east Asia. Proper management is likely to cost less than what is required for fish culture. Rural areas are likely to benefit most from increased fish production in reservoirs, and this might be a way to provide cheap protein where it is most urgently needed.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".