PTR‐MS observations of photo‐enhanced VOC release from Arctic and midlatitude snow
Bibliographic record
Abstract
To evaluate the release mechanism of volatile organic species (VOCs) from solar illuminated snowpacks, this study used proton transfer reaction–mass spectrometry (PTR‐MS) for the first time to study VOCs within Arctic snow and to compare these results to VOC release from illuminated snow in the laboratory. The field measurements were conducted in April during the 2009 Ocean–Atmosphere–Sea Ice–Snowpack (OASIS) campaign in Barrow, Alaska, whereas in the laboratory four natural snow samples, from the Arctic (Alert, Nunavut), a rural site (Egbert, Ontario), and an urban area (Toronto, Ontario), were exposed to a Xe arc lamp with a 295 nm longpass filter. Similar VOCs were observed in both the field and laboratory experiments suggesting that these light‐driven processes occur not only in polar regions but in midlatitude snows as well. Also, because the laboratory samples were temperature controlled, we conclude that the release mechanism is primarily photochemical and not temperature mediated. The snow composition may have influenced VOC production because aged Toronto snow samples, with both the highest total organic carbon content and concentration of oxidant precursors (i.e., NO 3 − ), exhibited the largest production of VOCs upon irradiation.
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 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 | 0.000 | 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".