Bowtie analysis for risk assessment of confined space at sewerage construction project
Bibliographic record
Abstract
This paper aims to investigate the issues related to safety in confined space at the sewerage treatment plant construction project in the Hulu Langat district and to provide a solution by proposing a substantial approach to mitigating risk during confined space entry due to an ineffective risk assessment and poor compliance by project management. The methods used were site visit observation and survey, followed by an analysis of the selected risk assessment method. The site visit to the sewerage treatment plant project investigated the compliance of confined space risk assessment documents to established requirements such as OSHA 1994, FMA 1967, ICOP 2010, HIRARC Guidelines 2008, Quebec Regulation 2015, ISO 31010, HSE UK, and BCGA UK. The selected risk assessment method was analyzed with Bowtie Risk Assessment by referring to the preventive approach concept or barrier analysis. Next, additional information relevant to risk assessment from journals was included. Evaluation of Bowtie Risk Assessment was conducted through a focus group discussion (FGD), which plays an essential role in developing the Bowtie risk assessment graphical framework. The proposed Bowtie Risk Assessment graphical framework provides a sewerage treatment plant construction project with a holistic technique for preventing confined space accidents. It also provides a safe work system, manages hazards and risks effectively, promotes good leadership practices, improves company reputation, and significantly reduces accident costs. The framework is also helpful as a reference model for other industry players.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".