How Can Researchers Support Cannabis Sativa L. Home Growers? Investigating Homegrown C. Sativa Horticulture and Defining Best Practices
Bibliographic record
Abstract
The legalization of recreational Cannabis sativa L. in Canada allows citizens to grow four C. sativa plants per household for personal use. Although Cannabis sativa is widely cultivated by home growers, there are gaps in the published literature on what growing practices citizens use and what yields they achieve. The purpose of this thesis is to define the horticultural practices of C. sativa home growers and observe how their methods influence plant outcomes. In my first study, I collaborated with C. sativa home growers to describe their cultivar preferences, horticultural practices, and yields. In my second study, I used meta-analysis to identify the optimal lengths of time to switch between vegetative to flowering lighting to maximize C. sativa yields. This thesis not only provides creative ways to study the homegrown C. sativa culture, but also provides evidence-informed data to lead future decisions on Canadian C. sativa horticulture, research, and policy.
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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 | 0.032 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".