On-line single particle analysis of ice particle residuals from mountain-top mixed-phase clouds using laboratory derived particle type assignment
Bibliographic record
Abstract
Abstract. In-situ single particle analysis of ice particle residuals (IPR) and out-of-cloud aerosol particles was conducted by means of laser ablation mass spectrometry during the intensive INUIT-JFJ/CLACE campaign at the high alpine research station Jungfraujoch (3580 m a.s.l.) in January/February 2013. During the four week campaign more than 70000 out-of-cloud aerosol particles and 595 IPR were analyzed covering a particle size diameter range from 100 nm to 3 µm. The IPR were sampled during 273 hours while the station was covered by mixed-phase clouds at ambient temperatures between −27 °C and −6 °C. The identification of particle types is based on laboratory studies of different types of biological, mineral and anthropogenic aerosol particles. As outcome instrument specific marker peaks for the different investigated particle types were obtained and applied to the field data. The results show that the sampled IPR contain a larger relative amount of natural, primary aerosol, like soil dust (13 %) and minerals (11 %), in comparison to out-of-cloud aerosol particles (2 % and < 1 %, respectively). Additionally, anthropogenic aerosol particles, like particles from industrial emissions and lead-containing particles, were found to be more abundant in the IPR than in the out-of-cloud aerosol. The out-of-cloud aerosol contained a large fraction of aged particles (30 %, including organic material and secondary inorganics), whereas this particle type was much less abundant (3 %) in the IPR. In a selected subset of the data where a direct comparison between out-of-cloud aerosol particles and IPR in air masses with similar origin was possible, a pronounced enhancement of biological particles was found in the IPR.
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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 | 0.001 | 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".