Quantifying the adoption, usage patterns, and air pollution concentrations from a novel household energy package in the Tibetan Plateau
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".