How do farmers' perceptions and attitudes toward agricultural water consumption behaviors can lead to unsustainability; evidence from Mahabad plain, Lake Urmia, Iran
Bibliographic record
Abstract
<title>Abstract</title> There has been much attention paid to Lake Urmia's catastrophic desiccation by researchers and the government. An in-depth semi-structured interview and thematic analysis were used in this study to examine irrigation behavior and crop type selection decisions. 73% of farmers believe that there is no need to reduce their water consumption, 87% do not look for rain forecasts since they regard the government as responsible for water supply or have very few crop alternatives to choose from. In choosing the type of product, 77% only consider economics and do not consider environmental objectives, and 71% do not think drought conditions affect irrigation decisions. Educating farmers and increasing their collaboration role are therefore necessary. Therefore, these variables are the basis for extending psychological theories such as TPB to predict farmers' behavior to a much greater extent. While this study focused on one region, its findings are applicable to similar circumstances worldwide.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".