Effects of fire smoke on soil microorganisms: results of a modelling experiment
Bibliographic record
Abstract
Wildfires are widespread and have major effects on ecosystems. The sensitivity of soil microorganisms to smoke was studied in experimental conditions using smoke generated from burning pine sawdust. High concentrations of toxicants such as NO (40 mg/m3), NO2 (60 mg/m3), CO (3570 mg/m3), C2H4O (241 mg/m3), CH2O (9.5 mg/m3), C6H6O (4.4 mg/m3), and C6H14 (238 mg/m3) were measured in the smoke. Reduced abundance of Azotobacter chroococcum by 9–62% and of microscopic fungi by 25–57% was found when the soil was treated with smoke for 30–120 min. The abundance of soil microorganisms after exposure to smoke depended on time. Several species of soil fungi (Fusarium oxysporum, Venturia inaequalis, Fusarium moniliforme, Fusarium graminearum, Cladosporium cucumerinum, Penicillium chrysogenum, Rhodotorula rubra, Lipomyces starkeyi), and bacteria (Acinetobacter calcoaceticus, Streptomyces violaceus, Kocuria rosea) were studied. The lowest smoke exposure time at which growth inhibition was registered was 1–5 min. The effect of smoke on the enzymatic activity of Haplic Chernozem soils was also evaluated. Catalase activity was found to decrease by 25%, and peroxidase and polyphenol oxidase by 15% and 33%, respectively. High smoke toxicity contributed to changes in microbial abundance and enzymatic activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".