River-Based Cage Aquaculture of Tilapia in Northern Thailand: Sustainability of Rearing and Business Practices
Bibliographic record
Abstract
Cage-based aquaculture in rivers raises issues of natural resource management more familiar to fisheries management than does aquaculture in fish ponds on private land. Hybrid red and black Nile tilapias (Oreochromis niloticus L) are reared for 4 - 5 months in cages in the upper Ping River in northern Thailand. Observed mean stocking density was 49 ± 16 fish·m-3, feed conversion ratio 1.47 ± 0.43 kg feed per kg fish and yield density 26.6 ± 8.1 kg·m-3. Input costs were dominated by feed (70%) and stock (16%). Most farms borrowed money and participated in contracts. Fish farming was usually a component of a portfolio of household activities but for some a core business. To succeed fish farmers must manage a combination of market, climate and environmental-related risks. Cage-based aquaculture in rivers faces many challenges; further research on farm practices and vulnerabilities, river and water management, and the commodity-chain are needed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".