Climate Smart Village Report: Htee Pu Village, Myanmar
Bibliographic record
Abstract
Htee Pu village in the Dry Zone was designated as Climate-Smart Village, where participatory action research was undertaken from 2018 to 2020 to find solutions to climate change's challenges to local farmers' lives and livelihoods. A Dry Zone is typically characterized by a lack of water, thin vegetation cover, and severe soil erosion. Nyaung U Township has the highest temperature in Myanmar's dry zone regions. With support from the International Development Research Centre (IDRC), Canada, the research project was implemented in Myanmar from July 2020 to July 2022. Htee Pu village was also one of the research areas in Myanmar to investigate the potential contributions of CSVs and CSA in enriching local food systems for better nutrition, enhancing livelihoods, increasing household resilience, and enhancing gender equity and inclusion. This brief aims to describe the updated profile of Htee Pu CSV in the Dry Zone, Myanmar, from 2018 to 2022.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".