COMPARISON AND VALIDATION OF OHM AND SCEM MEASUREMENTS FOR A FULL-SCALE COAL-FIRED POWER PLANT
Bibliographic record
Abstract
Mercury emission measurements were performed at a 250 MW coal-fired power plant using the Ontario Hydro method (OHM) and semi-continuous emission monitors (SCEM). Flue gas sampling was performed at the inlet of the air preheater and at the outlet of the electrostatic precipitator. The results indicated that there is some agreement between the OHM and SCEM measurements on the total mercury species. However, the SCEM results were not always in good agreement with the OHM measurements on the elemental mercury species. These discrepancies in elemental mercury concentrations are probably the result of the differences in the location of the SCEM and OHM probes, the temperature difference between the SCEM sampling probe and the flue gas, and the nonuniformities in mercury concentration over the flue gas duct cross section. The other factor that contributed to the deviation between the SCEM and OHM measurement results is the sampling method: the SCEM measurements were performed at a single point while the OHM probe was traversed over multiple points over the duct cross section and the results were averaged. The effect of the SCEM sampling probe temperature was investigated by designing a sampling probe that could be heated to the sampled flue gas temperatures. This resulted in improvements in the accuracy of the elemental mercury measurements by the SCEM system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".