Climate Change Impacts and Adaptation of Households in U-Tapao River Sub-Basin, Thailand
Bibliographic record
Abstract
Flood, storm, and drought have frequently impacted households in the U-Tapao River Sub-basin, Songkhla Province, Thailand, as a result of climate change. Studying how to assist them in better adapting to the effects of climate change is a critical mission that researchers should strive to achieve. The goals of this study are to (1) investigate the effects of climate change on households in the U-Tapao River Sub-basin, (2) examine the adaptation strategies they used, (3) the challenges they faced when attempting to adapt, and (4) provide recommendations for future adaptation. The study was carried out in the jurisdictions of three local government authorities. A survey questionnaire was used to collect quantitative data from 300 households, which was then analyzed using frequency, percentage, mean, and standard deviation. To supplement survey data, qualitative data were collected from 50 key informants via in-depth interviews and focus-group discussions. Climate change impacted households in four ways, according to the findings: health, housing, agriculture, and livelihood activities. Although households can take general measures to mitigate the effects of climate change on their health and livelihood, they do not appear to be able to take preventive measures to minimize flooding impacts on their house and property, nor do they appear to be able to adopt on-farm adaptation strategies to prevent income loss. The main impediment to taking more effective measures is a lack of funds, knowledge, and technical assistance. As a result, practical recommendations are provided at the end of this paper to help overcome such challenges and encourage households to adopt more adaptation strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".