Analysis on Cognitive Behaviors and Prevention of Human Errors of Coalmine Hoist Drivers
Bibliographic record
Abstract
Human errors are commonplace among hoist drivers in the hoisting task of coalmines. To reduce these errors and prevent accidents, it is necessary to identify the features of cognitive behaviors and main cognitive errors of the hoist drivers. This paper analyzes the accident cases and operating flow of coalmine hoist, and establishes a cognitive process model of coalmine hoist drivers. Further, the cognitive behaviors and functions of the drivers were analyzed stage by stage, revealing the distributions of their main cognitive behaviors and functions. It is learned that most coalmine hoist accidents concentrate in two stages: lifting, and operation monitoring. The operating processes in the two stages were further deliberated. The specific operations were extracted as the influencing factors of human errors, and the importance of each index was calculated through analytic hierarchy process (AHP). The research results provide a theoretical reference for identifying the key factors affecting the human errors of the operations by coalmine hoist drivers, and shed new light on how to prevent such errors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".