Engineering Perspectives of the Hemp Plant, Harvesting and Processing
Bibliographic record
Abstract
The special characteristics of the hemp (Cannabis sativa L.) plant make it one of the most challenging crops to handle. Several studies, both in the laboratory and the field, have been conducted at the University of Manitoba, Canada, on the engineering perspectives of hemp production, including the physical and mechanical properties of the hemp plant, hemp harvesting and processing. Physical properties of the hemp plant, such as plant height, seed-head length, stem diameter and stem specific mass, vary highly within a field and across fields. The force and energy required for cutting a hemp stem are much greater than those required for cutting maize stalk and forage crops. The two-windrow harvesting concept has been demonstrated to be feasible and can be implemented into a commercial windrower for harvesting dual-purpose hemp. Conditioned hemp dries significantly faster than unconditioned hemp. However, conditioning hemp requires more power than conditioning a forage crop. The basic machine functions required for hemp fibre processing are separating the fibre from the core and cleaning the fibre. A field-going processing unit can be formed by combining a modified forage harvester and a straw walker from a grain combine. However, the effectiveness of such a unit is limited, and the design of new separating and cleaning devices may be required for higher fibre yield and purity.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".