A statistical approach to engineer a biocomposite formulation from biofuel coproduct with balanced properties
Bibliographic record
Abstract
ABSTRACT A 32‐full factorial design of experiment (DOE) and regression modeling were implemented together as a practical approach to attain a distillers' grains‐filled biocomposite with balanced mechanical and physical properties. The effects of compatibilizer and lubricant on tensile strength, flexural modulus, impact strength and melt flow index of the biocomposites were studied. Analysis of variance (ANOVA) was implemented to develop least square regression models containing statistically significant main effects (linear and quadratic) and interaction effect. The developed models showed good predictability for the new measurements. The statistical approach adopted in this work including overlaying contour plots of the response surfaces in the studied level domain was effective in highlighting an optimized region that leads to balanced mechanical and physical properties. © 2014 The Authors Journal of Applied Polymer Science Published by Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014, 131, 40443.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".