On Aethalometer measurement uncertainties and an instrument correction factor for the Arctic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 stations from 2012 to 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 minimised. Moreover, by using an attenuation threshold criterion for data post-processing, the relative uncertainty from the electronic noise of 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, minimising the detection limit of the instrument is less critical. Additionally, utilising co-located filter-based absorption photometers, a correction factor is presented for the Arctic that can be used in Aethalometer corrections available in literature. The correction factor of 3.45 was calculated for low-elevation Arctic stations. This correction factor harmonises Aethalometer attenuation coefficients with light absorption coefficients as measured by the co-located light absorption photometers. Using one correction factor for Arctic Aethalometers has the advantage that measurements between stations become more inter-comparable.
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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 | 0.001 | 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 | 0.001 | 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 it