Determination of Nickel Species in Stack Emissions from Eight Residual Oil-Fired Utility Steam-Generating Units
Bibliographic record
Abstract
XAFS spectroscopy has been used to determine the Ni species in particulate matter collected on quartz thimble filters in the stacks of eight residual (No. 6 fuel) oil-burning electric utility steam-generating units. Proper speciation of nickel in emitted particulate matter is necessary to correctly anticipate potential health risks. Analysis of the spectroscopic data using least-squares linear combination methods and a newly developed method specific for small quantities of Ni sulfide compounds in such emissions show that potentially carcinogenic Ni sulfide compounds are absent within the detection limits of the method (≤ 3% of the total Ni) in the particulate matter samples investigated. In addition to the major nickel sulfate phase (NiSO(4)·6H(2)O), lesser amounts of (Ni,Mg)O and/or NiFe(2)O(4) were also identified in most emission samples. On the basis of the results from these emission characterization studies, the appropriateness of the U.S. Environmental Protection Agency's assumption that the Ni compound mixture emitted from residual oil-fired power plants is 50% as carcinogenic as nickel subsulfide (Ni(3)S(2)) should be re-evaluated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".