Evaluation of the effect of particulate matter on construction accidents using relative probability
Bibliographic record
Abstract
Abstract PM10 is known to have a great adverse effect on the human body. However, there is a lack of research analyzing the impact of PM10 on the occurrence of accidents. Accordingly, the purpose of this study is to analyze the correlation between PM10 and accidents in the construction industry and to present a new concentration group to manage accidents caused by PM10 in the construction industry. This study was conducted in the following four stages. (i) collection of data, (ii) classification of data, (iii) relative probability analysis, and (iv) modified PM10 group classification. The main results of this study are as follows. When the frequency analysis of the traditional method was conducted, 3,721 accidents occurred at a PM10 concentration of 32 μg/m3. However, as a result of the relative probability analysis presented in this study, it was confirmed that the relative accident probability increased as the PM10 concentration increased. In addition, the current PM10 concentration is presented by the WHO in six groups from a health perspective. However, in this study, the newly proposed PM10 group was classified into three groups from the perspective of the probability of construction accidents. The group proposed in this study is not from a health perspective but a group for managing construction site accidents. The contribution of this study was to confirm that PM10 also affects accidents occurring at construction sites, and the impact of PM10 on accidents was quantitatively analyzed through the relative probability analysis presented in this study.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".