Mercury speciation in the flue gas of a small-scale coal-fired boiler\nin Guiyang, PR China
Bibliographic record
Abstract
\nChemical speciation of mercury in the flue gas of a boiler with wet flue gas precipitator and\ndesulfurization (WFGPD) system were studied by using Ontario Hydro Mercury Speciation Method. The average\nconcentrations of Hg$^{\\rm p}$, Hg$^{2+}$, Hg$^{0}$, and Hg$^{\\rm t}$ in flue gas before the WFGPD system, were 0.29 $\\mu$g/m$^3$, 0.64 $\\mu$g/m$^3$,\n0.79 $\\mu$g/m$^3$ and 1.71 $\\mu$g/m$^3$, respectively, and the percentage of Hg$^{\\rm p}$, Hg$^{2+}$ and Hg$^0$ were 22.8%, 32.8% and 44.4%\nwith regard to Hg$^{\\rm t}$, respectively. However, in the flue gas after thé WFGPD system, the average concentrations of\nHg$^{\\rm p}$, Hg$^{\\rm 2+}$, Hg$^0$, and Hg$^{\\rm t}$ were only 0. 07 $\\mu$g/m$^3$, 0.04 $\\mu$g/m$^3$, 0.58 $\\mu$g/m$^3$ and 0.69 $\\mu$g/m$^3$, respectively, and the\npercentage of Hg$^{\\rm p}$, Hg$^{\\rm 2+}$ and Hg$^0$ with regard to Hg$^{\\rm t}$ were 14.3%, 8.8% and 76.9%, respectively. The mean\nremoval percentage of Hg$^{\\rm 2+}$ and Hg$^{\\rm p}$ by the WFGPD system, conceming Hg$^{\\rm t}$, was 93.8% and 74.2%. However, Hg$^0$\nremoval efficiency was only 25.8%. In the aggregate, the Hg removal efficiency was 57.2%.\n
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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) | 0.000 | 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 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".