Bibliographic record
Abstract
Human exposure (o vibration, and mcclianical shock, is commonly experienced in daily life, for example, in trains, cars, ships and buildings.All of these situations involve motion of the whole body transmitted through a seat, or from the floor in (he case of a standing person, where (he human response is commonly related to She sensation of motion or the relative motion of body parts.Exposures also occur in occupations involving operation of hand-held power tools, primarily to the hand and arms.The vibration and shocks, become of consequence when comfort or activities are influenced (e.g., motion sickness), or health is threatened (e.g., back injury, or damage to the blood vessels and nerves of the hand).Exposures are quantified by measurement procedures described in standards prepared by the International Organization for Standardization (ISO).The metrics to employ in some situations remain the subject of research, and form the subject o f a companion paper.*The CSA Subcommittee on Human Response to Vibration has participated in, and its members have made important contributions to, the development of international standards in this field Tor over twenty years.The concepts underlying much of this work arc summarized in this paper, and follow the description in a recent publication.2Many of these ISO standards arc now being proposed Tor Canadian standards, a process similar to that already underway in the U.S.A.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".