Implementation of Appropriate Technology for Durian Peel Waste Extractor in Rowosari Village to Realise An Independent Village for Green Economy
Bibliographic record
Abstract
The University of Jember's Student Village Program (Promahadesa) was implemented in Pringpadhu Hamlet, Rowosari Village, Sumberjambe District, Jember Regency, with the aim of overcoming the problem of durian peel waste that has not been utilized and causes environmental pollution. This activity aims to process the waste into products of economic value through the application of appropriate technology tools (TTG) in the form of extractors. The method used in this activity is Participatory Action Research (PAR), which involves the community actively in all stages of the activity, from problem identification, planning, implementation, to evaluation. The implementation of activities includes: design and assembly of household scale extractor tools, pretreatment of durian skin waste, the process of making dish soap and liquid organic fertilizer (POC), as well as socialization and demonstration of tools to the community. The results of this activity are the creation of TTG extractor tools and two main processed products, namely dish soap and POC, which are produced from durian skin distillate. The conclusion of this activity shows that the application of technology can increase community knowledge, minimize environmental pollution, and potentially add local economic value through the utilization of waste into village superior products.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".