CSC smudging toxicity: testing
Bibliographic record
Abstract
National Research Council (NRC) was approached by Correctional Services Canada (CSC) to evaluate the potential health hazard induced by inmates’ smudging activities to inmates as well as correctional facility staff. The study included three tasks. Task 1 encompasses a literature review to investigate the typical composition of smudging materials, probable combustion effluents and their yields. Task 2 assesses potential impacts from exposure to smudging effluents. The findings from Tasks 1 and 2 were reported to CSC in a previous report¹. These findings demonstrated that an insufficient amount of information exists particularly on yields of effluents produced during smudging. Therefore, Task 3 was planned to obtain the chemical effluent and smoke particulate matter data from smudging. This report presents the findings from Task 3, where comprehensive evaluation of the hazards associated with smudging was conducted by testing four common smudging materials; black sage, juniper, tobacco, and white sage; in two testing facilities: 1) Tube furnace connected to a Fourier-transform infrared spectroscopy (FTIR) to detect and quantify the chemical combustion effluents 2) Testing room equipped with a Nanozen particle monitor to measure the particulate matter (PM) resulting from smudging. The combustion effluents and PM data from the testing were analyzed to estimate concentrations likely to be present in an inmate cell and their health effects. Finally, combustion effluent hazard assessment and recommendations were provided.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".