Black carbon and other light‐absorbing particles in snow of central North America
Bibliographic record
Abstract
Abstract Vertical profiles of light‐absorbing particles in seasonal snow were sampled from 67 North American sites. Over 500 snow samples and 55 soil samples from these sites were optically analyzed for spectrally resolved visible light absorption. The optical measurements were used to estimate black carbon (BC) mixing ratios in snow ( ), contributions to absorption by BC and non‐BC particles, and the absorption Ångström exponent of particles in snow and local soil. Sites in Canada tended to have the lowest BC mixing ratios (typically ~5–35 ng g−1), with somewhat higher in the Pacific Northwest (typically ~5–40 ng g−1) and Intramountain Northwest (typically 10–50 ng g−1). The Northern U.S. Plains sites were the dirtiest, with typically ~15–70 ng g−1 and multiple sample layers with >100 ng g−1 BC in snow. Snow water samples were also chemically analyzed for standard anions, selected carbohydrates, and various elements. The chemical and optical data were input to a Positive Matrix Factorization analysis of the sources of particulate light absorption. These were soil, biomass/biofuel burning, and fossil fuel pollution. Comparable analyses have been conducted for the Arctic and North China, providing a broad, internally consistent data set. As in North China, soil is a significant contributor to snow particulate light absorption in the Great Plains. We also examine the concentrations and sources of snow particulate light absorption across a latitudinal transect from the northern U.S. Great Plains to Arctic Canada by combining the current data with our earlier Arctic survey.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".