Modélisation du vieillissement chimique de panachés de feux de biomasse boreaux
Bibliographic record
Abstract
Biomass burning plays a pivotaI role on atmospheric chemistry by serving as a source of many important trace gases such as reactive compounds (CO, hydrocarbons, organic acids, NH3, NOx) , greenhouse gases (CO2, CH4, N2O), and of course combustion aerosols. This study is principally based on two types of measurements: (1) -Boreal in-plume composition above Canada (ABLE-3B carnpaign: The Arctic Boundary Layer Expedition)-; (2) -Analyses performed along ice cores coming from Greenland which provide a unique record of precipitation chemistry reflecting the atmospheric composition at the time of deposition. They indicate simultaneous enhancement of formic and acetic acids and the formation of ammonium formate . Such sporadic features were attributed to biomass burning events-. A comparison between (1) and (2) indicates a characteristic chemical evolution during the plume ageing. The main goal of this study is to explain the signature of boreal biomass burning in ice cores, and to discern chemical phenomena that govern the plumes ageing. We used a meteorological model, and developed a box model of gas phase and aqueous phase chemistry: we introduce a pH calculation of the clouds droplets, a new solver, and we update, modify, and complete the chemical mechanism. Our results show that air masses are transported from North America to Greenland on a typical time scale of 5 days, with any cloud event, and a chemical equilibrium is reached rapidly. Above Greenland, chemical species are incorporated in the aqueous phase. Aqueous chemistry has a negligible impact on our results, and the gas-water-ice phase transfer phenomena are mostly responsible for the difference in chemical signature between fire plumes and the ice core records. We explain how our model results were extrapolated to concentrations in ice. We determine important physico-chemical parameters related to the incorporation of chemical species in the ice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".