Speciation/fractionation of nickel in airborne particulate matter: Improvements in the Zatka sequential leaching procedure
Bibliographic record
Abstract
Modifications are reported to the sequential leaching analytical method for nickel speciation/fractionation specified by Zatka so that larger sample masses can be analyzed. Improvements have been made in the completeness of the sulfide/metallic separation during the peroxide-citrate leach step by use of a larger volume of leachant, a longer leach duration and an orbital shaker. Minimal extraction of metallic nickel in this prolonged sulfidic nickel extraction has been confirmed. An increase in the number of samples analyzed simultaneously using these modifications has resulted in substantial productivity improvements and concomitant lower costs. It is critical for practitioners of sequential leaching techniques to recognize potential limitations and to use professional judgment when interpreting results. For example, results obtained may not be biologically relevant in assessing health risks; the acts of sampling and storage may result in changes in fractionation with time; surface coatings/films may alter the ability of a leachant to react with the target compound; and leaching behaviours may be different for samples differing only in particle size distributions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".