Environmental Impact of Outdoor Cannabis Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Environmental impacts of cannabis production are of increasing concern because it is a newly legal and growing industry. Although a handful of studies have quantified the impacts of indoor production, very little is known about the impact of outdoor cannabis agriculture. Outdoor production typically uses little direct energy but can require significant fertilizer and other inputs due to dissipative losses via runoff and mineralization. Conversely, fertilizer high in nitrogen can be counterproductive, as it produces flowers with decreased cannabinoid content. This study has two aims: (1) To identify reduced-fertilizer regimes that provide optimal cannabis flower yields with reduced inputs and (2) to quantify how this shifts greenhouse gas emissions, resource depletion (fossil and metal), terrestrial acidification, and the eutrophication potential of outdoor cannabis production. Primary data from a fertilizer response trial are incorporated into a life-cycle assessment model. Results show that outdoor cannabis agriculture can be 50 times less carbon-emitting than indoor production. Dissemination of this knowledge is of utmost importance for producers, consumers, and government officials in nations that have either legalized or will legalize cannabis production.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it