Cloud partitioning of isocyanic acid (HNCO) and evidence of secondary source of HNCO in ambient air
Bibliographic record
Abstract
Abstract Although isocyanic acid (HNCO) may cause a variety of health issues via protein carbamylation and has been proposed as a key compound in smoke‐related health issues, our understanding of the atmospheric sources and fate of this toxic compound is currently incomplete. To address these issues, a field study was conducted at Mount Soledad, La Jolla, CA, to investigate partitioning of HNCO to clouds and fogs using an Acetate Chemical Ionization Mass Spectrometer coupled to a ground‐based counterflow virtual impactor. The first field evidence of cloud partitioning of HNCO is presented, demonstrating that HNCO is dissolved in cloudwater more efficiently than expected based on the effective Henry's law solubility. The measurements also indicate evidence for a secondary, photochemical source of HNCO in ambient air at this site.
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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 | 0.001 | 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".