Local dust plume analysis and classification using ground-based remote sensing and microphysical measurement acquired at Lhù’ààn Mân’ (Kluane Lake), Yukon
Bibliographic record
Abstract
The sub-Arctic Lhù’ààn Mân’ (Kluane Lake) region in the Canadian Yukon is subject to regular drainage wind-induced dust plumes emanating from the Slims River basin. This dust emissions site is just one of many current and potential future proglacial dust sources in the Canadian North. We employed ground-based passive and active remote sensing (RS) techniques to analyze the complementarity and redundancy of such RS retrievals relative to springtime (May 2019) Kluane Lake microphysical measurements. This included correlation analyses between ground-based coarse mode (CM) aerosol optical depth (AOD) retrievals from AERONET AOD spectra, CM AODs derived from co-located Doppler lidar profiles and OPS (Optical Particle Sizer) surface measurements of CM particle-volume concentration ( ). An automated dust classification scheme tied to intercorrelations between lidar-derived CM AOD, AERONET-derived CM AODs and variations was developed to objectively identify local dust events. Lidar ratios derived from a priori refractive indices and OPS-derived effective radius statistics were also validated using AERONET-derived CM AODs. Bi-modal CM PSDs from AERONET inversions showed CM peaks at ~ 1.3 µm and 5 – 6.6 µm radius: we argued that this was associated with springtime Asian dust and Lhù’ààn Mân’ dust, respectively. Correlations between the CIMEL-derived fine-mode (FM) AOD and FM OPS-derived particle-volume concentration suggest that remote sensing techniques can be employed to monitor FM dust (which is arguably a better indicator of the long-distance transport of HLD).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".