CLOUD AND AEROSOL PROPERTIES MEASURED WITH A LIDAR IN THE HIGH ARCTIC AT EUREKA
Bibliographic record
Abstract
The recently commissioned CANDAC Rayleigh?Mie?Raman Lidar (CRL) in Eureka, Nunavut, finished its first winter measurement campaign in 2010, during which over 900 hours of data was collected. A comparison of several inversion techniques are shown to de- termine which one is most appropriate for the CRL aerosol and cloud analyses. Results of a newly implemented automatic beam steering program are shown and discussed. Measure- ments of water vapour and aerosols showed a distinct layering effect in the troposphere. Back trajectories of these layers are compared to the CRL measurements to determine origins of these layers. Measurements of significant aerosol concentrations in the lower stratosphere were seen during the campaign, which were from the Sarychev eruption in June of 2009. The aerosol evolution over Eureka is shown by using different ground-based and satellite-based instruments. Calculations using multi-wavelength aerosol and cloud measurements are used to give insight on aerosol and cloud particle properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".