Bibliographic record
Abstract
A novel approach deemed the Ergonomic Decision System (EDS) was designed to address the physical requirements of modern industry. The EDS, as the name implies, is a system that uses a series of questions and resulting choices to determine the path to the most appropriate ergonomic analysis tool for a given occupational task. The face validity of the EDS has been established through an extensive review of literature. Reliability was evaluated both within and between subjects. In two facilities, 6 Jobs were chosen based upon both injury and illness data and the differing physical requirements of each. These Jobs were video recorded and two Jobs were randomly chosen. Novice subjects (N = 6) were asked to apply the EDS to one of these jobs prior to being provided the basic ergonomic training. Subsequently, all trained subjects (N = 12) applied the EDS to the same 6 recorded Jobs. The results from the EDS applications were then compared to a criterion measure resulting in a total EDS score which was used to determine subject accuracy. A high overall mean accuracy value of 88.4%, was found with experts and novices varying only slightly with mean scores of 92.6% and 84.3%, respectively. Further, a consensus count was taken from each user for each condition to determine consistency. A good overall mean consensus, between subjects, of 76.9% was found with experts scoring 85% and novice subjects 72%. Also, the results of the pre-post training study indicated strong within subject consensus with an average of 88.9% across novice subjects. Finally, after a minimum of two weeks had passed, all subjects applied the EDS to the second randomly chosen Job. Results of the test-retest condition showed good consensus within subjects with a mean of 94.4%, where experts scored 88.9%, and novice subjects showed perfect consensus. The results of the study effectively establish that the EDS provided sufficient subject consistency and accuracy in directing subjects to the most applicable ergonomic resource across Jobs tested.Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .D39. Source: Masters Abstracts International, Volume: 45-01, page: 0435. Thesis (M.A.Sc.)--University of Windsor (Canada), 2006.
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How this classification was reachedexpand
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".