Replication Data for: Summer 2023 Canadian Boreal Forest Wildfires: An Analysis of CO, HCHO, and NO2 Downwind Enhancement Rates
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 Western Canadian boreal forest wildfires and the 2019 FIREX-AQ wildfires analyzed in the paper "Summer 2023 Canadian Boreal Forest Wildfires: An Analysis of CO, HCHO, and NO2 Downwind Enhancement Rates" by Hearne et al. (submitted, 2025). <br><br> The CSV files have been named as follows "NameOfFire_Species_Date_oOrbit.0.csv": <br>(1) NameOfFire is the naming convention found in Hearne et al. (2025), <br>(2) Species is either CO, HCHO, or NO2, <br>(3) Date is the date of the fire in YYYYMMDD format, and <br>(4) oOrbit.0 is the orbit number for TROPOMI overpass led by an "o" and followed by a ".0". <br><br> <br>Inside each CSV file is the following information by column: <br>(A) "loc" is the location, in 4 km increments, of the start of each integration box, in the downwind direction, <br>(B) "windspeed" is the average wind speed, in km/hr, of the average wind speed within each 4 km box for 150 km downwind. <br>(C) "windspeedy" is the wind speed at averaged within each 4 km box, <br>(D-G) Numbered columns (0,1,...,etc.) are described in detail, below. <br>(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), <br>(I) "Line" is the line density (molec/cm), <br>(J) "STD" is the standard deviation from the centre of the Gaussian fit profile across the ΔVCDs (molec/cm^2), <br>(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". <br><br><br> 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: <br><br> <b><u>CO:</u></b> <br>'0' ---> <b>τ</b> = 336 hours<br><br> <b><u>HCHO:</b></u> <br>'0' ---> <b>τ</b> = 1.5 hours <br>'1' ---> <b>τ</b> = 3.0 hours <br>'2' ---> <b>τ</b> = 6.0 hours <br><br> <b><u>NO2:</u></b> <br>'0' ---> <b>τ</b> = 1.0 hours <br>'1' ---> <b>τ</b> = 2.0 hours <br>'2' ---> <b>τ</b> = 4.0 hours <br>'3' ---> <b>τ</b> = 6.0 hours
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".