Use of a rapid-scanning backscatter LIDAR to validate dispersion models
Bibliographic record
Abstract
We review the history and capabilities of UMIST's Rapid- scanning Lidar (RASCAL). This is a backscatter Lidar designed to study aerosol dispersion from industrial plant. The system is fully computer-controlled and is based around a frequency- doubled Nd-YAG laser having a pulse repetition rate of 30 Hz. The signal is measured with a 10-bit, 60 MHz digitizer. Overall, a plume cross-section can be obtained in < 2 s and repeated every approximately 4 s. Such scanning can continue for several hours. Range resolution is typically 5 m with sensitivity down two a few (mu) g m-3 of aerosol. Over 10 years we have developed software to analyze the returns to estimate plume height, spread and intermittency; wind speed at plume height; and mixing layer depth. The backscatter from combustion plant plumes appears to be well enough conserved to allow point measurements within the plume to be interpreted as concentration/flux ratios, (c/Q) for comparison with dispersion models. This technique has recently been successfully tested using a chemical tracer. A substantial dataset acquired with the system has been used to test the predictions of various regulatory models. We present recent comparisons of modelled and measured c/Q at a small power station: the ensemble values show impressive agreement.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".