Consequences of Large-scale Production of Marijuana in Residential Buildings
Bibliographic record
Abstract
Based on the data from the breadth of Canada (∼4300 km), one-third of Canadian homes have ventilation rates below the recommended standard of 0.3 air changes per hour and are at risk for moisture problems. For the purposes of this investigation, a literature review was performed on the health risks associated with exposure to living and drying marijuana plants and the fungi associated with large numbers of indoor plantings. Analysis was made of the impact on Canadian homes if used to grow marijuana. These are commonly called “marijuana grow operations” based on measured ventilation rates from homes in Windsor, Ontario and Regina, Saskatchewan (representing diverse climates) and derived moisture loadings from published data. The growing and drying of marijuana plants contributes considerable amounts of water vapour to the indoor environment. Depending on the scale of production, considerable mould damage in the building can result. There are also a number of abiotic hazards resulting from marijuana production including pesticides, carbon monoxide, and products of unvented combustion appliances. Both indirect and direct evidence are described for the health impact of living in these conditions. This has a number of implications in terms of documentation and personal protection for industrial hygienists, home inspectors, and public health officials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".