Profiling pollen and biomass burning particles over Payerne, Switzerland using laser-induced fluorescence lidar and in situ techniques during the 2023 PERICLES campaign
Bibliographic record
Abstract
Abstract. Vertical profiles of pollen and biomass burning particles were obtained at a semi-rural site at the MeteoSwiss station near Payerne (Switzerland) using a novel multi-channel elastic-fluorescence lidar combined with in situ measurements during the spring 2023 wildfires and pollination season during the PERICLES (PayernE lidaR and Insitu detection of fluorescent bioaerosol and dust partiCLES and their cloud impacts) campaign. Pollen particles were detected near ground (up to 2 km height), showing strong fluorescence backscatter coefficients bF at 355 nm (bF ~2 x 10-4 Mm-1sr-1 to 8 x 10-4 Mm-1sr-1). Smoke plumes from Canada and Germany were detected at higher altitudes (3–5 km) and showed lower bF values compared to those from pollen particles near ground. In situ measurements and in vivo fluorescence spectra were used to classify pollen particles near ground. Ice nucleating particle (INP) concentrations relevant for mixed-phase clouds showed high concentrations at warm temperatures, characteristic of the contribution of biological particles to the INP population. This was further supported by the correlation of INPs at –14 °C with WIBSABC particles, indicating a contribution from fluorescent biological aerosol particles, while INPs at –20 °C were more strongly linked to coarse-mode dust. The analysis of bF values across two European LIF lidar stations revealed that aged air masses containing smoke particles can show a ~50 % reduction of these values during their transport in the free troposphere (3–5 km) possibly due to photochemical aging and mixing with other non-fluorescent particles.
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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".