Production of Active Packaging from Debris of Heliconia and Thai Herbs for Reducing Anthracnose Disease of Mango and Papaya
Bibliographic record
Abstract
A study of production of active packaging from debris of Heliconia and Thai herbs for reducing anthracnose disease of mango and papaya comprises four steps: 1) create mold for producing packaging 2) prepare pulp from agricultural waste, 3) produce active packaging, and 4) test of packaging efficiency to reduce anthracnose disease in mango and papaya. The study revealed that creating mold from white cement was easy and quick, and the mold was strong. Regarding the method to produce packaging, pouring of pulp onto muslin cloth in the mold did not give uniform thickness, whereas hand tapping of pulp into the mold offered uniform thickness but it took more time to do. However, the latter took only one day to dry while the first took 2-3 days. The packaging was less shrink by tapping than pouring method. The shape of packaging can well support mango and papaya fruits, and its strength and thickness was adjustable by the amount of pulp used. Heliconia pulp can be used to produce packaging and provided natural color as well as the identity of hand-made products which is free from harmful chemicals in the pulping process. The active packaging produced from Heliconia pulp and herbal extracts from galangal stem, kaffir lime leaves, cassia leaves and Plai essential oil was found to be effective in controlling anthracnose disease. The anthracnose lesions on mango fruits were smaller than those on papaya fruits.
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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 | 0.001 |
| 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".