Effects of stratospheric wildfire smoke on ozone depleting substances and ozone levels in the northern midlatitudes
Bibliographic record
Abstract
The goal of this project was to examine whether there are correlations between recent wildfires in the northern hemisphere and stratospheric ozone anomalies in the midlatitudes following the creation of ozone depleting substances. To achieve this goal, data from the Aura satellite's Microwave Limb Sounder instrument was used and processed with self-written Python scripts. Firstly, in order to analyze the data appropriately and to set it into context, results of previously published papers regarding the 2019-20 Australian wildfire were verified with the data. As a result, perturbations in hydrogen chloride, chloromethane, and ozone following the fires were found. The underlying chemical mechanism can possibly be explained by the hydrogenation of smoke particles and subsequent reactions on their surfaces. As an example for the northern hemisphere, the 2017 wildfire in British Columbia was analyzed in accordance with these findings. The process resulted in no connections between the Canadian wildfire and ozone destruction produced by ozone depleting substances being found in the data. Therefore, in conclusion, there might be a certain threshold in injected smoke particle mass into the stratosphere. The injection of the Canadian wildfire was then potentially not enough in order to invoke sufficient production of ozone depleting substances compared to the initial production of ozone by smoke particles to deplete the midlatitude ozone layer measurably.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".