A new method for the characterisation and quantitative speciation of base metal smelter stack particulates
Bibliographic record
Abstract
Base metal smelters may be a source of particulates containing metals of environmental concern released to the atmosphere. Knowledge of the quantitative chemical speciation of particulate releases from base metal smelters will be of value in smelter emission fingerprinting, site-specific risk assessments, predictions of the behaviour of smelter stack particulates released to the environment and in resolving liability issues related to current and historic releases. Accordingly, we have developed an innovative approach comprising bulk chemical analysis, a leaching procedure, X-ray diffraction analysis and scanning electron microscopy/electron probe microanalysis characterisation in a step-wise apportioning procedure to derive the quantitative speciation of particulate samples from the stacks of three copper smelters designated as A, B and C. For the A smelter stack particulates, the major calculated percentages were 29 CuSO(4), 20 ZnSO(4).H(2)O, 13 (Cu(0.94)Zn(0.06))(2)(AsO(4))(OH), 11 PbSO(4) and four As(2)O(3). For the B smelter stack particulates, the primary calculated percentages were 20 ZnSO(4).H(2)O, 20 PbSO(4), 12 CuSO(4) and nine As(2)O(3). Finally, we calculated that the C smelter stack particulates mostly comprised 34 ZnSO(4).H(2)O, 19 (Cu(0.84)Zn(0.16))(AsO(3)OH), 11 PbSO(4), 10 As(2)O(3) and nine Zn(3)(AsO(4))(2). Between 56% and 67% by weight of the smelter stack particulates, including the As, was soluble in water. For these and other operations, the data and approach may be useful in estimating metals partitioning among water, soil and sediment, as well as predictions of the effects of the stack particulates released to the environment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".