Light absorption and thermal measurements of black carbon in different regions of Canada
Bibliographic record
Abstract
A study was conducted to determine the relationship between light absorption measurements from an aethalometer and a particle soot absorption photometer (PSAP) and black carbon mass concentrations across Canadian environments ranging from urban to remote areas. To achieve this, filter samples were collected for thermal analysis of black carbon simultaneously with light absorption measurements at several locations in Canada. Previously, it has been shown that the k relating particle light absorption to black carbon measured with an aethalometer changes with age, type, and composition of the aerosol, which varies with time and by space [ Liousse et al., 1993 ; Petzold and Niessner, 1995 ; Lavanchy et al., 1999 ; Ruellan and Cachier, 2000 ]. Consequently, to convert light absorption measurements to the black carbon mass, the standard values used for k of 19 m2 g−1 for aethalometer and 10 m2 g−1 for PSAP, respectively, need to be fine‐tuned to fit the conditions at the site of interest to obtain the site‐specific attenuation, referred to as k(S.S.). Our results also indicate that k(S.S.) varies; on a broad scale, from remote continental to rural, suburban, and urban locations and from winter to summer and, on a small scale, within an urban area and from measurement to measurement at a single location. The range of median k(S.S) values observed was from 6.4 m2 g−1 to 20.1 m2 g−1. We suggest that the variability is connected to the distribution of sources and processes contributing to the carbonaceous aerosols at the sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".