The impact of environmental pollution on aquaculture development: The case of Vietnam
Bibliographic record
Abstract
This research is conducted to analyze and point out the relationship between environmental pollution and aquaculture development in the coastal areas in Vietnam. In this study, we use the binary Logit model (Y = 1/0) where the variable, Y, reflects the risks and losses in the household's aquaculture; and X is a vector of factors affecting the event. The results show that, among determinants, household's aquaculture experience (X1), the water pollution degree of the household (X4), intensive farming mode of the household (D1) influence household losses, while other variables such as educational attainment of the household head (X2) and farming area (X3) are not correlated with household losses. Based on the findings, some recommendations are given in the aspect that aquaculture households in the region to use new, more effective methods to protect the aquaculture environment in developing countries, including Vietnam.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".