The risk management for indoor air pollution caused by formaldehyde in housing
Bibliographic record
Abstract
Purpose In order to clarify the determining features of approaches adopted in policies for regulating indoor air pollution, this paper analyzes case studies of the approaches taken, in four countries, to risk management of indoor air pollution caused by formaldehyde in housing. Design/methodology/approach We pursued case studies to provide historical perspectives on early warnings and actions taken in relation to suspected health hazards from exposure to formaldehyde, in Germany, the USA, Canada and Japan. Many investigations of indoor air pollution caused by formaldehyde in housing have been conducted, and regulations established, in these countries. We reviewed the vast quantity of literature and documents relating to governmental and/or industrial actions and of research on indoor air quality produced in the past 40 years, and compared the approaches adopted. Findings The study identified the differing character of the approaches adopted in policies for the regulation of indoor air pollution, in order to clarify the range of actions that may be taken in response to reported risk from indoor air pollutants and describe possible risk management models for indoor air pollution. Practical implications Understanding of the nature of approaches already adopted will help to preserve good indoor air quality and minimize health hazards due to indoor air pollution. Originality/value This paper identifies a range of actions that have been taken in response to suspected risk from indoor air pollutants, through the analysis of its case studies.
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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".