Growing Mediums for Medical Cannabis Production in North America
Bibliographic record
Abstract
The production and use of cannabis for medical purposes has been legalized in Canada and several states in the USA. Due to the historically illegal nature of cannabis, there is very little information available in academic publications about appropriate growing media for growing cannabis. The purpose of this review is to provide an overview of the most commonly used growing media for the production of medical cannabis and to discuss their advantages and disadvantages. Based on current knowledge, there is a general agreement on the properties of a suitable growing medium within the cannabis industry. However, there is little consensus among growers on the best growing medium to grow cannabis. Different categories of growing media are widely used in North America. In this review, we classified them into several main categories principally based on the type of material used in their composition and the growth stages of the plant. The main categories include: coir-based, peat-based, rockwool, phenolic foam, and living soil. It is not easy to suggest the best growing medium for cannabis production. Each category of growing medium has its strengths and weaknesses. Overall, it seems that coir-based products are the intermediate substrates showing more advantages and less weakness; however, choosing any of these categories depends a lot on the growing technique and production system. Future research should focus on determining the optimal level of growing media properties to produce high yielding medical cannabis with the desired quality.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".