Methodology for Assessing the Impact of Workplace Ergonomic Factors on Airport Security Screener´s Reliability and Performance
Bibliographic record
Abstract
The article deals with the questions of the connection of the working environment ergonomics to the effectiveness of security screening in air transport. The main objective of the research is to improve aviation safety by optimizing the working conditions for operators screening. Therefore the authors recommend methodological procedures for assessing the influence of ergonomic parameters of the working environment on the screening performance and reliability. In order to meet the stated goal, HODERG method and expert analysis were the most important managerial or scientific methods that have been used. The result is the proposed methodology that should serve as a managerial tool for assessing ergonomic risks in relation to the protection of air traffic against unlawful acts. A necessary prerequisite without which the main aim could not be fulfilled was the fulfilment of the objectives of the partial ones, which at the same time aimed at enriching the scientific knowledge in the related scientific disciplines such as an identification of the set of ninety measurable parameters of working environment that could potentially affect the performance or reliability of the security screener and their analysis. The possibilities of application of the methodology were experimentally verified at the Václav Havel Airport Prague.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".