Scanning lidar: a means of characterizing the Noranda-Hornesmelter plume
Bibliographic record
Abstract
The Meteorological Service of Canada has recently developed a mobile scanning lidar facility (RASCAL: Rapid Acquisition SCanning Aerosol Lidar) capable of fast azimuth and elevation scanning profiles of the lower troposphere. Lidar is a remote sensing technique that provides high temporal and spatial information on atmospheric particulates and was ideally suited for characterizing stack plume dynamics. RASCAL was located predominantly downwind of the Noranda-Horne smelter stack source located in northern Quebec. Two three-week periods during February (winter) and July/August (summer) of 2000 were chosen to examine the behaviour of the plume because of the differences in humidity and boundary layer dynamics. The scanning speeds were adjusted to allow a complete scanning profile to be collected within 30–60 seconds. Along-plume-axis and cross-sectional scans provided an opportunity to directly measure plume dynamics and interaction with the planetary boundary layer, including the ability to detect fumigation events. Several algorithms have been developed to quantify the area, shape, horizontal and vertical extents of the plume as a function of the distance from source. Examples are shown where the cross-sectional area of the plume remained constant at a given distance from source even though its shape was highly variable. Also, boundary layer height, wind speed and direction of the plume can be extracted from the RASCAL data under certain conditions. These data are valuable for comparison with model predictions as well as providing initialization input for long range dispersion models.
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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 | 0.000 | 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".