Volatile organic compounds in snow in the Quebec‐Windsor Corridor
Bibliographic record
Abstract
Volatile organic compounds (VOC) were determined in snow to investigate the role of the snowpack as an exchange medium for atmospherically active compounds of anthropogenic and biogenic origin. The major question was which VOC species occur in snow and how the species identity and selected concentrations are related to the sampling area and environmental conditions. Samples were collected using a standardized sampling protocol in two distinct areas in six locations (near Mont‐Saint‐Hilaire, 45°33′06″N, 73°03′03″W (semirural) and Mont Tremblant, 46°20′11″N, 74°30′36″W (rural) in the heavily populated Quebec‐Windsor Corridor, Canada, with different characteristics regarding location and proximity to urban centers. A solid‐phase microextraction (SPME) procedure was employed for analysis, and VOC were identified using a gas chromatography method with mass spectrometric detection (GC/MS). Results revealed a broad spectrum of VOC in snow samples, including ethers, aldehydes, and aromatic and halogenated compounds, all of them active precursors for atmospheric reactions. Quantification was carried out for 11 aromatic and/or oxygenated compounds. Concentrations were found to be between 1.0 ± 0.2 ng/L (ethylbenzene) and 2.67 ± 0.06 μg/L (acetophenone), and limits of detection varied between 0.30 ng/L (benzene) and 78.2 ng/L (benzaldehyde). Principal component analysis was carried out to assess similarities between the sampling locations based on the types of species identified and concentration profiles. We discuss the implication of our results for atmosphere‐snowpack interactions of VOC including back trajectory calculations for the sampling dates.
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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 | 0.002 | 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".