Study of Local West Sumatera Stove Performances in Boiling Gambir (Uncaria Gambir Roxb.)
Bibliographic record
Abstract
Gambir is one of traditional export commodity of West Sumatera Province mainly used as raw material of pharmaceutical industry, batik coloring, leather thinner and clarifier in beer refineries. This paper describes how the LWS Stove like in Siguntur village works to boil gambir leaves and branches. There are 400 “rumah kempa” home industries operates every day in Pesisir Selatan district to produce gambir using old model LWS Stove made of mixture of clay and cement. Gambir leaves and branches are harvested twice a year and one “rumah kempa” operates one month each harvesting period to produce around one tone of dried extract gambir and consumed 1.5 cubic meters of fire-wood as the main fuel. The dried solid waste of leaves and branches after being pressed, called “katapang”, is used as additional fuel. Five aspects were considered in LWS Stoves performances evaluation, namely: heat efficiency, service life and simplicity in operation, health and safety, economics, and local environmental impact. The methods used in this study were identification and evaluation. Data was collected by surveying and interview, analyzing, and calculation. The results show that heat transfer efficiency of LWS Stove is lower than twelve percents; short service life but very simple operation; indoor pollution due to smoke and burnt risk are high because of no chimney and hot flue gas temperature is still higher than 200 0C; economically, the stove is very cheap; while environmentally, it caused seriously impact on local deforestation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".