Ice nuclei emissions from biomass burning
Bibliographic record
Abstract
Biomass burning is a significant source of carbonaceous aerosol in many regions of the world. When present, biomass burning particles may affect the microphysical properties of clouds through their ability to function as cloud condensation nuclei or ice nuclei. We report on measurements of the ice nucleation ability of biomass burning particles performed on laboratory‐generated aerosols at the second Fire Lab at Missoula Experiment. During the experiment we generated smoke through controlled burns of 21 biomass fuels from the United States and Asia. Using a Colorado State University continuous flow diffusion chamber, we measured the condensation/immersion freezing potential at temperatures relevant to cold cumulus clouds (−30°C). Smokes from 9 of the 21 fuels acted as ice nuclei at fractions of 1:10,000 to 1:100 particles in at least one burn of each fuel; emissions from the remaining fuels were below the ice nuclei detection limit for all burns of each fuel. Using a bottom‐up emission model, we estimate that smokes that emit ice nuclei fractions exceeding 1:10,000 particles can perturb ice nuclei concentrations on a regional scale.
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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) | 0.001 | 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".