Estimates of mass absorption cross sections of black carbon for filter-basedabsorption photometers in the Arctic
Bibliographic record
Abstract
Abstract. Long-term measurements of black carbon (BC) are warranted for investigating changes in its emission, transport, and deposition. However, depending on instrumentation, parameters related to BC such as aerosol absorption coefficient (babs) have been measured instead. Most ground-based measurements of babs in the Arctic have been made by filter-based absorption photometers, including multi-angle absorption photometers (MAAP), particle soot absorption photometers (PSAP), continuous light absorption photometer (CLAP), and Aethalometers. The measured babs can be converted to atmospheric mass concentrations of BC (MBC) by assuming the value of the mass absorption cross section (MAC = babs/MBC). However, the accuracy of conversion of babs to MBC has not been adequately assessed. Here, we introduce a systematic method for deriving MAC values from babs measured by these instruments and independently measured MBC. In this method, MBC was measured with a filter-based absorption photometer with a heated inlet (COSMOS). COSMOS-derived MBC (MBC (COSMOS)) is traceable to a rigorously calibrated single particle soot photometer (SP2) and the absolute accuracy of MBC (COSMOS) has been demonstrated previously to be about 15 % in Asia and the Arctic. The necessary conditions for application of this method are a high correlation of the measured babs with independently measured MBC, and long-term stability of the correlation slope, which represents the MAC. In general, babs – MBC (COSMOS) correlations were high (r2 = 0.84–0.96 for hourly data) at Fukue in Japan, Barrow in Alaska, Ny-Ålesund in Svalbard, Pallastunturi in Finland, and Alert in Canada, and stable up to for 10 years. We successfully estimated MAC values (11.0–15.2 m2 g−1 at a wavelength of 550 nm) for these instruments and these MAC values can be used to obtain error-constrained estimates of MBC from babs measured at these sites even in the past, when COSMOS measurements were not made. Because the absolute values of MBC in these Arctic sites estimated by this method are consistent with each other, they are applicable to study spatial and temporal variation of MBC and to evaluate performance of numerical model calculations.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".