Mediation and ANCOVA Models to Study the Influence of Solvent Retting Traits and Plant Physique on Bast Fiber Yield and Retting Time
Bibliographic record
Abstract
The study aims in applying two statistical tools to analyze the retting behavior of plant stems for extracting bast fibers for industrial applications. At first, a mediation model is employed to investigate the first hypothesis of this work that involves studying the color response of the retted solvent as a function of retting time on the responsible variable, fiber yield (%). Statistically, there is a significant indirect effect of retting time on fiber yield (%) through retting trait (β = −0.0142, 95% C.I. [−0.0274, −0.0011]) – a statistical inference bolstered by the Sobel test result, confirming the mediation effect (p-value = 0.0329 < 0.05; z-score = −2.1334; bootstrapping of 5000 resamples). Next, the second hypothesis of the current work involves analyzing the impact of stem form-factors on their retting time using the statistical tool, ANCOVA. The partial- η2 indicates that cultivar treatment accounts for 30% variance of the retting time while controlling for the effects of two covariates – diameter and length of the stems, in this case. By controlling the Type-I error, Bonferroni and similar post-hoc tests also confirm the statistical significance of cultivar categories pertaining to their mean retting time. Future work could focus on these underlying hypotheses and study the impact of microorganisms, environmental factors, and cultivar treatment variables on the retting time to optimize the overall fiber yield and production process.
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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 | 0.025 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".