Comprehensive laboratory study on smoke gases during the thermal oxidative decomposition of forest and vegetation fuels
Bibliographic record
Abstract
Abstract This study investigates the composition of smoke gases in forest and vegetation samples to draw conclusions about the actual smoke gas composition during wildfires. The focus is particularly on regions with extensive pine forests, like in Eastern Germany. The relevance of smoke gases is well illustrated by the example of wildfires in Québec, influencing air quality in New York, in 2023. By employing a modified DIN tube furnace, a bench‐scale test set‐up, the research emphasizes the examination of smoke composition from tree species and ground cover, prioritizing gases while disregarding particles. Key smoke gases are identified as CO, CO 2 , SO 2 , HCN, C 3 H 4 O (acrolein) and CH 2 O (formaldehyde) and their concentrations are compared with Acute Exposure Guideline Levels (AEGL) limits. Acknowledging the limitations of AEGL usage and the problem with direct quantitative comparison of toxicant concentrations (cf. ISO 29903‐1:2020), the study highlights variations in smoke composition across different samples. The results of the studies reveal a significant disparity in CO concentration between dry and fresh pine needles. Frequently, the AEGLs of key gases are exceeded significantly. The elemental analysis of the barks indicates distinct differences in composition, reflecting in the concentrations of smoke gases. The ratio of 1 mole of substance turnover to the identified key components will be used to determine input parameters for the subsequent numerical simulation.
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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) | 0.000 | 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) | 0.000 | 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".