Snow – a photobiochemical exchange platform for volatile and semi-volatile organic compounds with the atmosphere
Classification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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 it