The Field as a Lab: Design and Development of Household Energy Interventions
Bibliographic record
Abstract
The Field as a Lab: Design and Development of Household Energy InterventionsAbstract Number:2852 Ellison Carter*, Ming Shan, and Jill Baumgartner Ellison Carter* The University of Minnesota Institute on the Environment, United States, E-mail Address: [email protected] , Ming Shan Tsinghua University, China, E-mail Address: [email protected] , and Jill Baumgartner McGill University Institute for Health and Social Policy and Dept of Epidemiology, Biostatistics & Occupational Health, Canada, E-mail Address: [email protected] AbstractNearly 3 billion people worldwide use solid fuels to meet their domestic energy needs, including cooking and heating. The smoke produced from these activities negatively impacts human health and the environment, and interventions are needed to reduce people's exposure to harmful byproducts of solid fuel combustion. Typically, stove and fuel intervention programs heavily target the replacement of cookstoves and cooking fuels, while innovation in space heating receives far less attention or none at all. The relative contribution that solid fuel use for space heating might make to overall personal exposures and total household emissions, however, is expected to be quite large (Baumgartner et al., 2011) in colder regions of the world, such as China, where solid fuel is the primary energy source and space heating is necessary for comfort and survival throughout a significant portion of the year. Interventions that can be scaled up to the population level are needed to comprehensively address this global environmental health issue. Research in the field should be carefully conducted to shed light on potential problems for scaling up that might not have been identified in the lab. The work presented outlines the design and implementation of energy interventions and highlights several key findings from preliminary studies of this program. The comprehensive intervention program consists of low-polluting cooking and heating stoves, low-polluting solid fuels, coupled heating and cooking systems, and passive energy savings techniques. Development of the cooking and heating stoves took place over the course of several years, and lessons learned in the design process include: the value of user feedback throughout the technology development process and a balanced approach to testing technology performance in the field as well as in the lab. The approach taken for this intervention program serves as a model for future programs of this kind.
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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 | 0.033 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".