and Applied Chemistry University of Toronto
Bibliographic record
Abstract
The hcture toughness of wood fibre reinforced polyethylene has been studied and several methods of improving this important property have been developed. A mechanistic hctun: model which incorporates the features of wood fibres and the fibrdmatrix interface was also developed. The modeling revealed that the key to enhancing the fncture toughness is to promote fibre pull-out during composiu: hcture. Bascd on the modeling, three novel techniques-proper fibre selection, fibrehnatrix interface modiication, and fibre alignment- wen: used to enhance the hcture toughness. The experimental alul~s confvmed the etrectiveness of thw three techniques. The fracture toughness of the composites was enhanced without sacrifcing other mechanical properties such as the tensile strength and modulus. Because the modifications suggested are less expensive than traditional methods, the modified WFRP producls an? commercially significant. Acknowledgments I would like to thank my supervisor. Prof. M. T. Kortschot, for hiis encouragement and guidance during the course of my studies. The insights and enthusiasm provided by Prof. J. J. Balatinecz have also contributed immeasurably to my understanding of wood fibre properties. Special thanks are extended to Prof. M. R. Piggotr for allowing the use of his laboratory facilities, and for the valuable discussions and suggestions about the fracture behavior of fibre composites. I would also like to thank Prof. S. Balke for his advice and suggestions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.377 | 0.172 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".