Developing Environmentally Friendly Products from Rice Stumps for Community Economy
Bibliographic record
Abstract
This research is a study and development of environmentally friendly products from rice stumps for community economy. The study was done by testing the coatings for heavy metals and volatile organic compounds to match with material choices with low environmental impacts. The author therefore chose 9 types of popular coatings which the community can easily obtain from the market. Testing was done in two parts: the first part was to find 7 heavy metals by using Thailand’s green label standard and standard criteria set by the European Union while the second part was to test for volatile organic compounds (VOCs) and evaluate the environmental impacts in order to list materials and energy used by the products for their entire lifetime. Lastly, a survey was conducted using environmentally friendly products from rice stumps as models in order to investigate the perceptions in relation to manufacturing factors consistent with the manufacturers, the designers, as well as the perceptions of consumers. The study has found that rice stump coatings that passed the standard criteria are white shellac, gloss lacquer, wood preservatives, varnish and polyurethane respectively. It was found that manufacturers and designers had differing opinions in using low-impact materials and avoiding harmful materials while manufacturers, designers and consumers had statistically significant differing opinions in terms of the appropriate sizes and colors of products. In terms of product aesthetics, convenience of use, promotion of environmental friendliness, indication of natural manufacturing process and ease of elimination after the end of product lifetime, there were no differing opinions which were at a good level.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".