Supplementary material to "Atmospheric Evolution of Emissions from a Boreal Forest Fire: The Formation of Highly-Functionalized Oxygen-, Nitrogen-, and Sulfur-Containing Compounds"
Bibliographic record
Abstract
S1. Supporting sample collection detailsThe gas-and particle-phase samples discussed here were collected alongside a variety of other measurements including trace gas mixing ratios (e.g.NOx, O3, CO, CO2, CH4, NH3), black carbon concentrations, and gas-and particle-phase chemical characterization via online mass spectrometry.Carbon monoxide mixing ratios, select gas-phase tracer mixing ratios from PTR-ToF-MS, and AMS organic aerosol (OA) concentrations were used as supporting data in this study.Carbon monoxide mixing ratios were measured with a Picarro G2401 analyzer every 2 seconds during the flights.When absolute ion abundances from adsorbent tube or filter data were used to discuss chemical transformations with plume age, abundances were normalized by the total carbon monoxide mass observed during the corresponding adsorbent tube or filter sampling period.A proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS, Ionicon Analytik GmbH, Austria) was installed on the aircraft and collected measurements of volatile organic compounds (VOCs) with a time resolution of 1 second during the flights.The PTR-ToF-MS used a proton transfer reaction with H3O + as the primary reagent ion.VOCs were separated according to their mass to charge (m/z) ratio and detected using a high resolution time-of-flight mass spectrometer.The data were processed using the TOFWARE software (Tofwerk AG).Additional details on these methods can be found in past work (Li et al., 2017).A high resolution aerosol mass spectrometer (AMS, Aerodyne Inc) was used to measure mass concentrations of organics, NO3, SO4 and NH4 (only organics are discussed here).Using an aerodynamic lens, particles were sampled into a region of low vacuum where they impacted a heated surface (600°C), were vaporized, and then ionized by 70 eV impaction.Ions were then
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.733 | 0.191 |
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".