Image Analysis of Wildfire Smoke Particles in Kamloops, British Columbia, Summer 2021
Bibliographic record
Abstract
This dataset contains transmission electron microscopy (TEM) images and associated MATLAB files used for image processing of smoke particles collected during forest fires in Kamloops, British Columbia, Canada, in July 2021. Samples were obtained at 1259 Dalhousie Dr, Kamloops, BC V2C 5Z5 (BC Ministry of Environment) using thermophoretic sampling during both daytime (22 Jul. 2021, starting 10:41 am, ~ 1.5 hours duration) and nighttime (21 Jul. 2021, starting 18:48 pm, ~ 9 hours duration) conditions. The TEM images reveal a range of particle types, including soot (both fresh and coated), tarballs, ash-like structures, organics, and newly observed “softball” particles. Processed MATLAB data are included to reproduce the automated image analysis presented at the American Association for Aerosol Research (AAAR) 2022 Conference. Image segmentation was performed using k-means clustering, followed by morphological characterization of particle circularity, optical depth, and boundary sharpness. These metrics support differentiation of particle types and highlight differences in morphology between day and night samples. The particles were also manually classified based on visual assessment of their types. This dataset may be useful for researchers working on biomass burning emissions, aerosol particle morphology, atmospheric aging processes, and automated image analysis methods.
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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 | 0.000 | 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 | 0.000 | 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 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".