Analyzing the existing hazards in structuring the metal frame of the building with PHA method
Bibliographic record
Abstract
Every day in workplaces, continues events occur that cause death and injury.These accidents usually happen because of lack of exploring the potential hazards and lack of training of employees.Hence, with exploring and evaluating the hazards of workplace and utilizing the suitable procedures, it is possible to prevent from many of these hazardous incidents.Exploring, evaluating and controlling the potential hazards have been the initial stages of scientific safety assurance in every system.Preliminary hazard analyzing is the first effort in analyzing hazards.In this method, usual hazards in sighted job are explored, using the usual hazards table for developing the basis of PHA, the PHA checklist is prepared and at last the PHA table completed and the appropriate suggestions are given.In this paper, we present an implementation of PHA method in one of industries located in city of Tehran, Iran.The proposed study uses 15 explored hazards, where 2 are unacceptable, 9 are undesirable and 4 are acceptable with need of revisal.By eliminating and reducing each hazards risk, some controlling solutions are suggested.The most important of these solutions are utilizing and using the regulations of the welding with electrical archer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".