Replication Data for: Canadian 2023 and 2025 Boreal Forest Wildfires: Downwind Formaldehyde to Nitrogen Dioxide Ratios and Implications for Surface Ozone Production
Bibliographic record
Abstract
This dataset contains TXT files (comma-delimited) for the background-corrected enhancement rates and ΔVCDs calculated from TROPOMI satellite measurements using the Gaussian-flux method for the 2023 and 2025 Canadian boreal forest wildfires analyzed in the paper "Canadian 2023 and 2025 Boreal Forest Wildfires: Downwind Formaldehyde to Nitrogen Dioxide Ratios and Implications for Surface Ozone Production" by Hearne et al. (submitted, 2026). The CSV files have been named as follows "NameOfFire_Species_Date_oOrbit.0.csv": (1) NameOfFire is the naming convention found in Hearne et al. (2026), (2) Species is either CO, HCHO, or NO2, (3) Date is the date of the fire in YYYYMMDD format, and (4) oOrbit.0 is the orbit number for TROPOMI overpass led by an "o" and followed by a ".0". Inside each CSV file is the following information by column: (A) "loc" is the location, in 4 km increments, of the start of each integration box, in the downwind direction, (B) "windspeed" is the average wind speed, in km/hr, of the average wind speed within each 4 km box for 150 km downwind. (C) "windspeedy" is the wind speed at averaged within each 4 km box, (D-G) Numbered columns (0,1,...,etc.) are described in detail, below. (H) "VCD" is the vertical column density enhancement (ΔVCD) in molec/cm^2 (background-corrected, i.e. with the background VCD between 20 to 50 km upwind subtracted), (I) "Line" is the line density (molec/cm), (J) "STD" is the standard deviation from the centre of the Gaussian fit profile across the ΔVCDs (molec/cm^2), (K-N) The last four columns include META DATA for the fire and include the fire name "name", trace gas "species", the data of the fire in YYYYMMDD "date", and TROPOMI overpass "orbit". Numbered columns (0, 1, ..., etc.) in the CSV file are background-corrected enhancement rates in tonnes/hour for each species. Each number corresponds to a lifetime (τ) used to calculate the enhancement rates, following the following key: CO: '0' ---> τ = 336 hours HCHO: '0' ---> τ = 1.5 hours '1' ---> τ = 3.0 hours '2' ---> τ = 6.0 hours NO2: '0' ---> τ = 1.0 hours '1' ---> τ = 2.0 hours '2' ---> τ = 4.0 hours '3' ---> τ = 6.0 hours
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.173 | 0.069 |
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".