Image processing methods for morphological characterization of biomass smoke particles
Bibliographic record
Abstract
Image processing methods have found wide use in interpretation of microscopic micro/nanoscale data. For the special case of soot, they are typically used along with Transmission Electron Microscopy (TEM) to study different morphological properties such as aggregate size, primary particle size, number of primaries, and fractal properties. Recently, fast automated image segmentation and primary particle detection algorithms have mostly replaced the time and effort taking user-adjusted analyses of soot image data. The automated approaches include both the more classic methods that involve filtering, transformation, and morphological operations calibrated for certain applications, and the newer unsupervised and supervised machine learning methods that self-tune the operations discussed. While the previous studies showed that these methods are more-or-less robust for fresh soot, they have not been explored so much for the scenarios where the emissions age post-combustion and produce quite diverse particle populations. Here, we investigate an example of such cases where the brown haze images from a forest fire near Kamloops, BC in July 2021 were examined using a machine learning method that implements k-means clustering to decide on the segmentation of particles from their backgrounds and prepares them for further higher-level analyses. The particles segmented from the images are evaluated by their size and three other less-studied parameters, i.e., circularity, optical depth, and acutance- that account for the shape and radiative properties. Manual classification of particles is also performed to find possible relations between the quantities studied and the real structure of the particles. It is shown that k-means interestingly performs well enough to capture the particle boundaries. Morphological distributions are also fairly distinguished using the introduced morphological quantities. The acutance particularly shows promising performance in separation of day-time and night-time particles.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".