Quantitative uncertainty and post-processing for micro-aethalometers measuring black carbon
Bibliographic record
Abstract
Abstract. Aethalometers measure black carbon mass concentrations by monitoring light attenuation through a particle filter as it becomes laden with aerosols. As the uncertainties in the resulting measurements are not easily quantified via a bottom-up traceable approach, there is a need for inter-device comparisons to provide operationally defined uncertainties. The present work compared five micro-aethalometers to known mass concentrations of laboratory-generated soot, formed using an inverted ethylene flame and a Centrifugal Particle Mass Analyzer-Electrometer Reference Mass Standard (CERMS). Uncertainties were found to scale with mass concentration, with multiplicative errors between devices of approximately 10 % in the best case of long sampling times and/or high mass concentrations. A quantitative expression is provided for the uncertainty in the aethalometer measurements as a function of mass concentration, sampling interval, and flow rate. An open-source algorithm is also provided for the unsupervised reanalysis of aethalometer or other filter photometer data over varying periods to reach a specified target uncertainty.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".