Ozone Production and Photochemistry of Biomass Burning Plumes in Rural Idaho
Bibliographic record
Abstract
Biomass burning events influence photochemistry and ozone production due to substantial emissions of particulate matter, ROx (OH, HO₂, and RO₂) precursors, VOCs, and NOx. Dependent on these emissions are the key ozone precursor of peroxy radicals (HO₂ and RO₂). These peroxy radicals are essential in determining tropospheric ozone formation due to their reaction with nitric oxide (NO) to produce nitrogen dioxide (NO₂). However, there exists a large knowledge gap in the area of radical photochemistry as measurements of any ROx species are extremely limited in regard to biomass burning plumes. We present results from a field study in the rural low-NOx environment of McCall, Idaho in 2018 which experienced smoke from several aged biomass burning plumes. Measurements of peroxy radicals, which were made using an ethane-based chemical amplification instrument known as ECHAMP, along with a variety of other photochemically relevant compounds have been used here to evaluate photochemical parameters including instantaneous ozone production (P(O₃)) during times of biomass burning influence. While ozone and peroxy radical enhancements from biomass burning were observed, background NOx concentrations were low (typically ~0.1 ppbv NO) with minimal enhancements from biomass burning. This indicates increases in ozone production during times of biomass burning influence with P(O₃) rates being very small overall (<4 ppb hr⁻¹).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".