Physical and Chemical Characterization of Beryllium Particles from Several Workplaces in Québec, Canada—Part B: Time-of-flight Secondary-ion Mass Spectroscopy
Bibliographic record
Abstract
The problems associated with detecting and characterizing beryllium (Be) particles in industrial samples from Québec were addressed in the companion article (Rouleau et al., 2005 Rouleau, M., Dion, C., Plamondon, P., Kennedy, G., L'Espérance, G. and Zayed, J. 2005. Physical and chemical characterization of beryllium particles from several workplaces in Québec—Part A: Determining methods for the analysis of low levels of beryllium. J. Toxicol. Environ, Health A, [CSA] [Google Scholar]). The present study is a continuation of the work aimed at redefining the current occupational exposure level for beryllium. The goals were to determine the principal chemical forms and the principal physical characteristics of Be particles sampled in four Québec industries. Bulk particle chemistry was determined using inductively coupled plasma–mass spectroscopy (ICP-MS) and flame atomic absorption spectrophotometry (FAAS). Time-of-flight secondary-ion mass spectroscopy (TOF-SIMS) was used to characterize particle surface chemistry and physical particle size. The dust samples collected had Be concentrations varying from 58 to 146 µg/g. Results showed that numerous fine Be particles or aggregates were evenly dispersed throughout the samples. Thus, Be does not appear to be concentrated in large particles. However, it was not possible to confirm if these fine particles were combined to specific compounds, chemically or physically, or independent Be particles. Most of the particles containing Be were fine, with diameters less than 10 µm, which is important from an occupational health and safety standpoint. TOF-SIMS should be considered as an appropriate technique for qualitative characterization of Be particles, and a valuable complement to the recognized quantitative methods ICP-MS and FAAS.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".