Opportunities for Standardization of Beryllium Sampling and Analysis
Bibliographic record
Abstract
Abstract Since the U. S. Department of Energy (DOE) published the DOE Beryllium Rule (10 CFR 850) in 1999, DOE sites have been required to measure beryllium in air filter and surface wipe samples for purposes of worker protection and for release of materials from beryllium-controlled areas. Measurements in the nanogram range on a filter or wipe are typically required. Industrial hygiene laboratories have applied methods from various analytical compendia, and a number of issues have emerged concerning sampling and analysis practices. As a result, a committee of analytical chemists, industrial hygienists, and laboratory managers was formed in November 2003 to address the issues. The committee developed a baseline questionnaire and distributed it to DOE sites and other agencies in the U.S., Canada, and the U.K. The results of the questionnaire are presented in this paper. These results confirmed that a wide variety of practices was in use in the areas of sampling, sample preparation, and analysis. Additionally, although these laboratories are generally accredited by the American Industrial Hygiene Association (AIHA), there are inconsistencies in execution among accredited laboratories. As a result, there are significant opportunities for development of standard methods that could improve consistency. The current availabilities and needs for standard methods are further discussed in a companion paper.
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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 | 0.319 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".