Decision making models and human factors: TOPSIS and Ergonomic Behaviors (TOPSIS-EB)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An effective safety management requires attention to human factors as well as system components which make risky or safe situations at technical components. This study evaluates and analyze ergonomic behaviors in order to select the best work shift group in an Iranian process industry, in 2010.The methodology was based on the Ergonomic Behavior Sampling (EBS), and TOPSIS method. After specifying the unergonomic behaviors and with reference to the results of a pilot study, a sample of 1755 was determined, with a sampling accuracy of 5% and confidence level of 95%. However, in order to gain more confidence, 2631 observations were collected. The results indicate that 43.6% of workers' behaviors were unergonomic. The most frequent unergonomic behavior was amusing of legs while load lifting with 83.01% of total unergonomic behaviors observations. Using TOPSIS method, the most effective shift group and the least attractive alternatives for intervention were selected in this company. Findings declare high number of unergonomic behaviors. Catastrophic consequences of accidents in petrochemical industry necessitate attention to workers' ergonomic behaviors in the workplace and promotion of them.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it