Canadian Biomass Burning Aerosol Properties Modification during a Long-Ranged Event on August 2018
Bibliographic record
Abstract
The aim of this paper is to study the spatio-temporal evolution of a long-lasting Canadian biomass burning event that affected Europe in August 2018. The event produced biomass burning aerosol layers which were observed during their transport from Canada to Europe from the 16 to the 26 August 2018 using active remote sensing data from the space-borne system Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). The total number of aerosol layers detected was 745 of which 42% were identified as pure biomass burning. The remaining 58% were attributed to smoke mixed with: polluted dust (34%), clean continental (10%), polluted continental (5%), desert dust (6%) or marine aerosols (3%). In this study, smoke layers, pure and mixed ones, were observed by the CALIPSO satellite from 0.8 and up to 9.6 km height above mean sea level (amsl.). The mean altitude of these layers was found between 2.1 and 5.2 km amsl. The Ångström exponent, relevant to the aerosol backscatter coefficient (532/1064 nm), ranged between 0.9 and 1.5, indicating aerosols of different sizes. The mean linear particle depolarization ratio at 532 nm for pure biomass burning aerosols was found equal to 0.05 ± 0.04, indicating near spherical aerosols. We also observed that, in case of no aerosol mixing, the sphericity of pure smoke aerosols does not change during the air mass transportation (0.05-0.06). On the contrary, when the smoke is mixed with dessert dust the mean linear particle depolarization ratio may reach values up to 0.20 ± 0.04, especially close to the African continent (Region 4).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".