Performance and emission characteristics of a micro-gasifier-based cook stove using solid biomass, <i>Melia dubia</i> and <i>Casuarina</i>
Bibliographic record
Abstract
Many large-scale improved cooking stove systems have been introduced in various developing countries to replace existing obsolete conventional biomass cooking stoves. Improved cooker stoves exhibit higher performance and lower emissions. In this work, two solid biomass fuels, Melia dubia and Casuarina, were investigated on a dry basis in a forced micro-gasifier stove. The effect of the M. dubia and Casuarina fuels on the thermal efficiency and emission reduction of the forced micro-gasifier stove was analysed using the water boiling test protocol version 4.2.3. The experimental results revealed that the thermal efficiency of the micro-gasifier stove for the fuels M. dubia and Casuarina are 42% and 40%, respectively. The fuel consumption of the micro-gasifier stove was estimated to be 86 g/L for M. dubia and 90 g/L for Casuarina. The carbon monoxide and particulate matter emissions of M. dubia were slightly lower than those of Casuarina. Emissions of carbon monoxide and particulate matter were detected for M. dubia at 22 ppm and 0.05 mg/m3, respectively, and for Casuarina at 25 ppm and 0.07 mg/m3, respectively. The efficiency and emission values for the selected fuels, M. dubia and Casuarina, have shown promising results for the selected micro-gasifier stove.
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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 | 0.000 | 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 | 0.000 |
| 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".