Lidar and sunphotometry observations on the long-range transport of smoke and dust events
Bibliographic record
Abstract
The remote sensing techniques of Lidar and Sunphotometry are well suited for understanding the optical characteristics of aerosol layers aloft. Lidar has the ability to detect the complex vertical structure of the atmosphere and can therefore identify the existence and extent of aerosols that have undergone long-range transport. Inversion techniques applied to Sunphotometry data can extract information about the aerosol fine and coarse modes. As part of the REALM network (Regional East Atmospheric Lidar Mesonet), routine measurements are made with a vertically-pointing lidar at the Centre For Atmospheric Research Experiments (CARE). In addition, a CIMEL sunphotometer resides at CARE (part of AERONET) yielding an opportunity to achieve an optical climatology of aerosol activity over the site. Environment Canada's mobile lidar facility called RASCAL (Rapid Acqusition SCanning Aerosol Lidar), operating in zenith mode only, was also deployed to Western Canada during the months of March and April, 2005 to provide an opportunity to measure the long-range transport of trans-Pacific pollutants that impact the coastal areas of British Columbia frequently. During that time a long-range transport event was observed on 13-14 of March 2005. Further analysis has shown the event originated from North African dust storms during the period 28 February to 3 March. The optical coherency of these active and passive remote sensors will be presented, along with other supporting observations, for forest fire smoke plumes transported over CARE (in 2003) and the first documented case of Saharan dust to impact Western North America.
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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".