Application of stochastic modelling for simulating hemp fibre peeling behaviour
Bibliographic record
Abstract
The separation of fibre and core components of hemp stem is a fundamental step in hemp decortication, mechanical separation of fibre and core. This research aimed to enhance the understanding of fibre-peeling behaviour of hemp to improve the current decortication technologies. Peel tests were performed on retted and unretted hemp samples for each of two hemp varieties, USO 14 and Alyssa. Results showed that force and work required to peel did not vary with the retting condition, but with the hemp variety. The average peeling force for the Alyssa variety was 0.39 N and that for the USO 14 variety was 0.87 N. Within the Alyssa variety, the work required to peel the fibre from the core was 193 J m-2, and the work required to peel the fibres of the USO 14 variety was 431 J m-2. The Ising model was implemented to produce a stochastic model which simulated the peeling force obtained from the peel tests. The behaviour of the simulated peel test was similar to that observed during the peel test and the process of fibre peeling was successfully simulated through the use of a stochastic algorithm. The stochastic model simulated the average peel force to be 0.86 N for the USO 14 variety and 0.39 N for the Alyssa variety.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".