Does the Presence of Asbestos-Containing Materials in Buildings Post-construction and Before Demolition Have an Impact on the Exposure to Occupants in Non-occupational Settings?
Bibliographic record
Abstract
This narrative review aims to determine if asbestos-containing materials in buildings pose a hazard to building occupants in non-occupational settings. This paper is limited to the post-construction and pre-demolition stages of a building. The researchers selected 19 studies from the 126 studies screened, concerning exposure to asbestos fibers in non-occupational building settings, with a focus on post-construction and pre-demolition phases. The literature review found that certain conditions, such as the measurement techniques, standards, and previous data availability, prevent a conclusive answer to the research question. Some studies have pointed towards an effect of asbestos-containing materials on health of occupants in non-occupational settings. But, there are some that do not suggest a positive relationship between non-occupational exposure and the presence of asbestos-containing materials, and therefore these provide scope for further research, as these studies also do not rule out the relationship completely. The present study highlights the gaps in current knowledge and indicates areas for further research. Until conclusive evidence based on revised threshold standards and accurate measurement techniques is available, asbestos-containing materials may be considered unsafe for use in non-occupational settings, especially ones that young people and children occupy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".