Water-soluble organic carbon in snow and ice deposited at Alpine, Greenland, and Antarctic sites: a critical review of available data and their atmospheric relevance
Bibliographic record
Abstract
Abstract. While it is now recognized that organic matter dominates the present-day atmospheric aerosol load over continents, its sources remain poorly known. The studies of organic species or organic fractions trapped in ice cores may help to overcome this lack of knowledge. Available data on the dissolved (or total) organic carbon (DOC or TOC) content of snow and ice often appear largely inconsistent, and, until now, no critical review has been conducted to understand the causes of these inconsistencies. To draw a more consistent picture of the organic carbon amount present in solid precipitation that accumulates on cold glaciers, we here review available data and, when needed, complete the data set with analyses of selected samples. The different data sets are then discussed by considering the age (modern versus pre-industrial, Holocene versus Last glacial Maximum) and type (surface snow, firn, or ice) of investigated samples, the deployed method, and the applied contamination control. Finally, the OC (DOC or TOC) levels of Antarctic, Greenland, and Alpine ice cores are compared and discussed with respect to natural (biomass burning, vegetation emissions) and anthropogenic sources (fossil fuel combustion) contributing to atmospheric OC aerosol.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".