Precision and Accuracy of Asbestos Fiber Counting by Phase Contrast Microscopy
Bibliographic record
Abstract
A new method was developed to prepare permanent asbestos slides with relocatable fields of view by imprinting these fields directly on the cleared wedges of filters containing asbestos fibers. The image quality of the fibers is comparable with that of the fibers prepared by the acetone/triacetin and the dimethyl formamide/Euparal method. The slides are suitable for evaluating the intercounter precision and accuracy of fiber counts by phase contrast microscopy. Seventeen chrysotile and 16 amosite slides, prepared from American Industrial Hygiene Association/National Institute for Occupational Safety and Health Proficiency Analytical Testing program samples, were evaluated by 58 analysts of 38 government and private laboratories in Canada. By asking the analysts to examine the same fields of view of the slides, the present study found that when examined at 400x, an average of 59.2 fiber counting errors were made for every 100 chrysotile fibers reported and 24.4 errors for 100 amosite fibers reported. The chrysotile fibers were underestimated by 25.0%, but there was no bias in counting amosite fibers. Remedial steps have been proposed to control the major source of errors, which is the subjective ability of the analyst to observe and size fibers. The slides may also be used to harmonize various phase contrast optical microscopy methods and proficiency testing programs.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".