Influence of various retting methods on properties of kenaf fiber
Bibliographic record
Abstract
Gum, as the important noncellulosic tissue present in kenaf fiber, has a close relation with downstream processing and product properties, so the predominant task in pretreatment of kenaf fiber for textile application, retting, is to remove gum including pectin, hemicellulose, lignin, and other impurities without damage to cellulose fiber. The traditional retting method is water retting; that is, the harvested kenaf bast is soaked in natural water (rivers or tanks) in which indigenous bacteria attack the gum in an anaerobic process, yielding much water pollution. Currently, much interest has been focused on various retting methods in order to seek one environmentally-friendly method. Therefore, microbe, chemical, water, and microbe–chemical rettings are performed in this experiment. Retted kenaf fibers at optimal conditions of various retting methods are then characterized and compared by light microscopy and indices consisting of residual gum content, fineness, tenacity, elongation, and softness. In addition, chemical oxygen demand (COD) is also tested. The results indicate that microbe retting induces higher residual gum content and lower elongation but better tenacity and softness and finer fiber; chemical retting gives lower tenacity and thicker fiber; water retting produces weak, poor quality fiber; and microbe–chemical retting produces moderate indices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".