A user-centered, iterative engineering approach for advanced biomass cookstove design and development
Bibliographic record
Abstract
Unclean combustion of solid fuel for cooking and other household energy needs leads to severe household air pollution (HAP) and adverse health impacts in adults and children. Replacing traditional solid fuel stoves with high efficiency, low-polluting semi-gasifier stoves can potentially contribute to addressing this global problem. The success of semi-gasifier cookstove implementation initiatives depends not only on the technical performance and safety of the stove, but also the compatibility of the stove design with local cooking practices, the needs and preferences of stove users, and community economic structures. Many past stove design initiatives have failed to address one or more of these dimensions during the design process, resulting in failure of stoves to achieve long-term, exclusive use and market penetration. This study presents a user-centered, iterative engineering design approach to developing a semi-gasifier biomass cookstove for rural Chinese homes. Our approach places equal emphasis on stove performance and meeting the preferences of individuals most likely to adopt the clean stove technology. Five stove prototypes were iteratively developed following energy market and policy evaluation, laboratory and field evaluations of stove performance and user experience, and direct incorporation of stove user input. The most current stove prototype achieved high performance in the field on thermal efficiency (ISO Tier 3) and pollutant emissions (ISO Tier 4), and was received favorably by rural households in Sichuan province of Southwest China. Among household cooks receiving the final prototype of the intervention stove, 88% reported lighting and using it at least once. At five-months post-intervention, the semi-gasifier stoves were used at least once on an average of 68% [95% CI: 43, 93] of days. Our proposed design strategy can be applied to other stove development initiatives in China and other countries.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".