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Record W2133999100 · doi:10.1177/1420326x11411954

Consequences of Large-scale Production of Marijuana in Residential Buildings

2011· article· en· W2133999100 on OpenAlexafffundabout
Luke Johnson, J. David Miller

Bibliographic record

VenueIndoor and Built Environment · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicIndoor Air Quality and Microbial Exposure
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaHealth CanadaNational Academy of Sciences
KeywordsEnvironmental scienceEnvironmental healthStoveScale (ratio)ToxicologyEnvironmental protectionEnvironmental engineeringWaste managementGeographyEngineeringMedicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.226
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2011
Admission routes3
Has abstractyes

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