Snow – a photobiochemical exchange platform for volatile and semi-volatile organic compounds with the atmosphere
Bibliographic record
Abstract
Environmental context Recent research has been directed towards the exchange of microorganisms and chemical compounds between snow and air. We investigate how microorganisms and chemical species in snow from the Arctic and temperate regions are transferred to the atmosphere and altered by the sun's energy. Results suggest that snow photo-biochemical reactions, in addition to physical-chemical reactions, should be considered in describing organic matter in air–snow exchanges, and in investigations of climate change. Abstract Field and laboratory studies of organic compounds in snow (12 species; concentrations =17 µg L–1) were conducted and microorganisms in snow and aerosols at urban and Arctic sites were investigated (snow: total bacteria count =40000 colony forming units per millilitre (CFU mL–1), fungi =400 CFU mL–1; air: bacteria =2.2 × 107 CFU m–3, fungi =84 CFU m–3). Bio-organic material is transferred between snow and air and influence on snow-air exchange processes is demonstrated. Volatile organic compounds in snow are released into the air upon melting. In vitro photochemistry indicated an increase of =60 µg L–1 for 1,3- and 1,4-dimethylbenzenes. Bacillus cereus was identified and observed in snow and air with ice-nucleating being P. syringae absent. As a result snow photobiochemical reactions should be considered in describing organic matter air–snow exchanges, and the investigation of climate change.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".