Survey of Climate-Related Risks to Tilapia Pond Farms in Northern Thailand
Bibliographic record
Abstract
Climate is an important factor for aquaculture production. This study aimed to understand how farmers that culture tilapia in earthen ponds perceive and respond to climate impacts. Important climate-related risks identified include extreme temperatures (hot and cold), excessive rainfall, prolonged cloud cover, flood and drought. Site visits and data collection using questionnaires were conducted to identify important factors influencing farm profits, losses, decision to increase or decrease ponds. Special attention was given to knowledge about fish markets and weather and the impacts of weather and climate on fish growth, disease outbreaks and water quality in ponds. Altogether 585 fish farms in four provinces in northern part of Thailand selected to cover a range of elevations above sea level and thus climate were surveyed (Nakornsawan 500 m). Fish farms at different elevations reported different climate and weather-related impacts. In the area where elevation above sea level is 500 were impacted mainly by drought and cold weather. These conditions also caused disease outbreaks and reduced feeding rates. Farmers responded by reducing the amount of feed supplied and considering non-fish pond or non-farm supplementary occupations as an adaptation strategy. Among non-climate related factors high prices of feed were most commonly identified as a key issue by farmers. The differences among sites at higher and lower elevation provided insights into the kind of changes in risks farmers may face as climate changes that could be helpful in developing adaptation strategies for individual farmers and the sector as a whole.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".