Cellulose Textiles from Hemp Biomass: Opportunities and Challenges
Bibliographic record
Abstract
Worldwide demand for man-made cellulosic fibres (MMCF) are increasing as availability of cotton fibre declines due to climate change. Feedstock for MMCF include virgin wood, agricultural residues (e.g., straw), and pre- and post-consumer cellulosic materials high in alpha-cellulose content. Lyocell MMCF (L-MMCF) offer large advantages over other MMCF processes in terms of both environmental and social impacts: the solvent for cellulosic dissolution, n-methyl-morpholine-n-oxide, can be recycled, and the process utilizes non-toxic chemicals and low amounts of water. Hemp can be a preferential cellulosic feedstock for L-MMCF as hemp cultivation results in carbon dioxide sequestration, and it requires less water, fertilizers, pesticides, and herbicides than other L-MMCF feedstock crops. These factors contribute to hemp being an environmentally conscious crop. The increased legalization of industrial hemp cultivation, as well as recent lifts on cannabis restrictions worldwide, allows accessibility to local sources of cellulose for the L-MMCF process. In addition, hemp biomass can offer a much larger feedstock for L-MMCF production per annum than other cellulosic sources, such as eucalyptus trees and bamboo. This paper offers perspectives on the agricultural, manufacturing, and economic opportunities and challenges of utilizing hemp biomass for the manufacturing of L-MMCF.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".