Bibliographic record
Abstract
The first CSA hearing protectors’ standard was published back in 1965. Since then, there have been 6 editions, the last issued in 2002. There is a need to update the standard because manufactures improve their products and introduce new types, also because of advances in methods for testing protectors and in analysis oft the results. The big problem has been (and is still) how to equate the lab testing results with real life situations. The present edition of the standard appears under the name of Z94.2-14 Hearing Protection Devices – Performance, selection, care and use. It has just gone through a mandatory Public Review and is being prepared for publication, probably before the year end. This edition expands on performance requirements and rating schemes to help the user to select the device most appropriate for a given work situation. It now includes the widely used Noise Reduction Rating (NRR) and a derating scheme to obtain more reliable noise level estimates. Potential use of Field Attenuation Estimation Systems (FAES) is also included. This Standard should be used in conjunction with CSA Z1007, Management of occupational hearing conservationprograms , that deals with all aspects of the creation and management of hearing conservation programs.
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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.060 | 0.040 |
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".