On Aethalometer measurement uncertainties and multiple scatteringenhancement in the Arctic
Bibliographic record
Abstract
Abstract. Several types of filter-based instruments are used to estimate aerosol light absorption coefficients.Two significant results are presented based on Aethalometer measurements at six Arctic station from 2012–2014. First, an alternative method of post-processing the Aethalometer data is presented which reduces measurement noise and lowers the detection limit of the instrument more effectively than boxcar averaging. The biggest benefit of this approach can be achieved if instrument drift is minimized. Moreover, by using an attenuation threshold criterion for data post-processing, the relative uncertainty from the electronic noise the instrument is kept constant. This approach results in a time series with a variable collection time (Δt), but with a constant relative uncertainty with regard to electronic noise in the instrument. An additional advantage of this method is that the detection limit of the instrument will be lowered at small aerosol concentrations at the expense of temporal resolution, whereas there is little to no loss in temporal resolution at high aerosol concentrations (>2.1–6.7 Mm−1 as measured by the Aethalometers). At high aerosol concentrations, minimizing the detection limit of the instrument is less critical. Second, utilizing co-located reference methods of aerosol absorption, a multiple cattering enhancement factor (Cref) of 3.10 specific to low elevation Arctic stations is found. Cref is a fundamental part of most of the Aethalometer corrections available in literature, and this is the first time a Cref value has been obtained for the Arctic.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".