EXPLORING THE INFLUENCE OF ECONOMIC AND ENVIRONMENTAL KNOWLEDGE ON FISH PRODUCTION IN RURAL-BANGLADESH
Bibliographic record
Abstract
Abstract Aquaculture is a buzzword for rural economy as well as regional development. In this research, the author considers five individual villages where 200 farmers have been identified randomly. In this paper, the author divides this research into two segments. Firstly, most of the local farmers consider basic factors, where variable costs that is daily basis expenses affect fish production variables for farmers. Secondly, the authors try to identify the environmental knowledge index (EKI) for farmers affecting fish production, the EKI is measured by table 2. In Table 3, most of the expenses are calculated for human labor, fish feed and fingerlings purposes. Marginal farmers face higher variable costs compared to the other two categories of farmers. The authors run the Cobb-Douglas multiple regression model to investigate the effect of independent variables on fish production hectare-wise, while human labor cost, feed and manure cost, water supply, and Sustainability knowledge index have positive and significant relationships with fish production. To justify the 2nd research question, EKI has a significant connection with fish production for farmers, because EKI helps farmers to lead production at a lower cost while maintaining a hygienic production system. The green economy is the upcoming challenge for the future, which leads to sustainable production and consumption behavior for producers and consumers. Moreover, sustainable and effective marketing channel creation are challenging factors for fish production and supply.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".