Quantifying the adoption, usage patterns, and air pollution concentrations from a novel household energy package in the Tibetan Plateau
Why this work is in the frame
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Bibliographic record
Abstract
Cooking with traditional biomass stoves impacts climate and human health. High adoption and use of low-polluting stoves and fuels have the potential to reduce household air pollution and improve population health. Quantification of stove-fuel adoption, usage patterns, and resulting household air pollution concentrations is needed to assess intervention scalability and performance before larger-scale implementation. In Sept 2015, we placed temperature sensors on each stove and a wall (control) in 10 homes before and after receiving an energy package (semi-gasifer cookstove, water heater, & supply of processed biomass fuel) from a government energy demonstration project in the Tibetan Plateau, and conducted 5 months of continuous monitoring. In March 2016 we began monitoring 21 more homes when they received the same energy package. In addition, 48h stove-use and air pollution concentrations are being collecting in all study homes in 2016 (n=200). Cooking events are identified with a smoothing peak detection algorithm. Population-level metrics of stove-use include the total and proportion of days in use and the mean number of meals cooked per day with each stove. The new stove was used on 64% [95% CI 61-67] of stove-days (s-d) monitored (%=561/881 s-d) with an average of 1.7 [95% CI 1.6-1.7] meals cooked per day. Homes consistently used the stoves over 5 months (63% and 62% of s-d in months 1 and 5, respectively), while use of the traditional stove decreased from baseline (90% of s-d) to post-intervention (45% and 23% in months 1 and 4, respectively) (p<0.01). Concurrent use of the traditional and new stove occurred on 21% of the stove-days monitored. Early results indicate that the novel energy package was highly adopted and used consistently over 5 months, coinciding with a significant reduction in use of the traditional stove. Real-time stove use and air pollution data will be presented along with results from the full study.
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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 | 0.001 |
| 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 it