Implementación de Seguridad Basada en el Comportamiento al Sistema de Gestión de la Seguridad y Salud en el Trabajo en una mina de Tajo abierto en el norte del país
Bibliographic record
Abstract
The purpose of this work is to implement Behavior Based Safety (BBS) as a tool integrated to the OSH Management System in a mine in northern Peru. The objective of BBS is to minimize the occurrence of substandard acts and contribute to the improvement of the Occupational Safety and Health Management System (OSHMS). The research is of a mixed type, where we found that it is analytical and deductive, as well as quantitative, we began with the implementation of the proposal, conducting an analysis of the events that occurred during the first quarter in the U.M. to identify which are the activities that generate more substandard acts and conditions. Subsequently, the SBC program was developed, which began with training on SBC and observer courses for workers in the project area, and at the same time began with the development of the SBC booklet based on the activities where the number of incidents is higher. \nThe analysis of the results before and after the implementation of SBC, showed an increase of more than 26% of safe behaviors in the project area, demonstrating the effectiveness of the program and the management tools implemented in the continuous improvement. Finally, it is concluded that the SBC managed to minimize the risky behaviors that caused incidents and work accidents, therefore, it can be applied in any area of the company.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".