Shrimp Poly-Culture Development and Local Livelihoods in Tam Giang-Cau Hai Lagoon, Vietnam
Bibliographic record
Abstract
This research was conducted to evaluate the efficiency level of shrimp poly-culture farms by using Data Envelopment Analysis. It also identified factors affecting the inefficiencies of shrimp poly-culture farms using Tobit regression model. The empirical results indicated that the mean of Technical, Allocative and Economic Efficiency were at 84.01%, 64.16%, 55.32%, respectively, hence there is a substantial room for improving the efficiency. DEA results recommended that inefficient farms need to minimize overfeeding of stocks in order to avoid the accumulation of uneaten feeds that further contributes to water pollution. The optimal stocking density ratio should be 8.15 for shrimps, 1.59 for crabs and 2.46 for fish per square meter of pond. The results also showed the presence of scale inefficiency. Smaller farms tend to be more efficient than larger farms. The optimal farm size should be less than 0.5 hectares. The results of Tobit regression model suggested that farm personal characteristics, farm ability to access to institutions, and water environment have significant effect on the efficiency of farms.
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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 | 0.000 | 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) | 0.001 | 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".