Cannabis as a Feedstock for the Production of Chemicals, Fuels, and Materials: A Review of Relevant Studies To Date
Bibliographic record
Abstract
As societies place greater emphasis on sustainability, there is a move toward creating a circular economy in which renewable resources, such as agriculture and forestry residues, serve as feedstocks in the production of energy and chemicals. One emerging agricultural commodity that may potentially serve as a feedstock for numerous chemicals and materials is cannabis. For most of the last one hundred years the use of cannabis as a biomass feedstock has been all but impossible, due to its legal status. However, over the last 20 years the changing legal status of cannabis has resulted in a large number of studies which have investigated cannabis as a feedstock for diverse bioproducts, including polymers, pulp, and biofuels. Being a relatively new agricultural commodity, the literature on chemicals, fuels, and materials derived from cannabis is spread across numerous disparate disciplines, such as engineering, agriculture, chemistry, and biology. Thus, the purpose of this review is to compile and summarize the relevant studies that illustrate the use of cannabis as a feedstock in the production of chemicals, fuels, and materials as well as to highlight the challenges and possibilities for future research opportunities.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".