Uncertainty Assessment of Carbon Monoxide in Exhaust Gas Based on Fixed Potential Electrolysis
Bibliographic record
Abstract
In order to ensure the accuracy and reliability of the test results, the carbon monoxide content in the waste gas of fixed pollution sources was used on the field data. By explaining the steps and principles of the field operation, Establishing a mathematical model for the uncertainty of the carbon monoxide content in the exhaust gas, the main sources of the uncertainty components are analyzed, and the uncertainty assessment of class A and class B is passed. The results show that the main sources of uncertainty components include repetitive measurement, instrument value error and CO standard gas, which are 4.15%, 0.982% and 1.15% respectively; the extended uncertainty is ± 10 (k=2). The results show that under the condition of ensuring the stability of the process emission, increasing the number of field measurement is the key to reduce the uncertainty, which is conducive to improving the quality level of the fixed source waste gas monitoring data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".