Relative performance of frequency weighting w h and candidates for alternative frequency weightings when used to predict the occurrence of hand-arm vibration induced injuries
Bibliographic record
Abstract
Different methods of determining cumulative vibration dose using the alternative frequency weightings are investigated and compared to the development sensorineural and vascular hand-arm vibration (HAV) injury. The comparison is based on a large historical database of measured HAV spectra from a wide range industrial machines, and a database of exposure history and injury from subjects attending the Health and Safety Laboratory's (HSL) referral center. Acceleration spectra from the HSL HAV database were analyzed to give weighted values for each of the alternative frequency weightings. HSL's Hand Arm Vibration Syndrome (HAVS) referral center collects data on diagnosis of HAVS and the history of symptoms. Increasing prevalence for any form of HAVS with percentile suggests a useful dose measure. Lower Bayesian Information Criterion (BIC) values suggest stronger dose measures, differences between BIC values of less than two suggest weak evidence for favoring one relationship above another, differences greater than 10 suggest very strong evidence.
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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 | 0.035 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".