Observation of a Chemical Softener’s Effects on Stem-Specific Lignocellulosic <i>Brassica napus</i> (Type: Canola) (Cultivar: HYHEAR 3) Fiber Quality
Bibliographic record
Abstract
Chemical softener (Cepreton UN) is used to soften the cellulosic fiber (cotton) in the textile industries to make clothes better to touch. Therefore, this study investigated the effects of Cepreton UN on both physical (length, aspect ratio, contact angle, and moisture regain) and mechanical (load at break, elongation at break, tensile stress, young’s modulus, and tenacity) properties of the lignocellulosic canola (HYREAR 3) fibers extracted from narrow, medium, and wide stems. ANOVA showed that fiber diameter had strong effects on elongation at break, load at break, tensile stress, young’s modulus, and aspect ratio for all fibers. Corrgram values showed that tensile stress, young’s modulus, and aspect ratio were negatively correlated to fiber diameter whereas load at break and tenacity were mostly positively correlated to fiber diameter. The fibers were treated with 2% and 10% Cepreton UN and compared with control fibers. In most cases, the fiber diameter was decreased in both 2% and 10% treated medium stem fibers. The mean values of elongation at break, load at break, tenacity, and contact angle were decreased for 10% and increased for 2% and the mean values of tensile stress, young’s modulus, and aspect ratio were decreased for 2% and increased for 10% treated medium stem fibers. Moisture regain (%) mostly decreased for 2%, and increased for 10% treated fibers. Low pH (4.5) had an almost similar effect on fibers as 2% Cepreton UN. Overall, 2% Cepreton UN treatment is found to be better than 10% to make canola fibers less stiff and low pH was found to be an alternative softener treatment strategy.
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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 | 0.000 | 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".