Bibliographic record
Abstract
Purpose To describe the light detection and ranging (LIDAR) technique and to discuss recent surveys by Environment Canada which have employed a novel scanning LIDAR system, the “RASCAL”, to study air pollution in British Columbia. Design/methodology/approach The RASCAL LIDAR system is based on a Nd:YAG laser which emits 0.5 J pulses of IR light at 1,064 nm and green light at 532 nm into the atmosphere at a rate of 20 Hz. The beam is steered by two 24‐inch mirrors and the backscattered signal is detected by a system based on high sensitivity avalanche photodiodes and photomultiplier tubes. The unit is mobile and housed in a small van. Findings Surveys with the mobile LIDAR identified and located sources of airborne particulate pollution at various locations in British Columbia. A series of high‐resolution elevation scans revealed the complex vertical structure of aerosol layers above the town of Golden. An outcome of this survey was the introduction of local laws banning wood‐burning stoves in new homes in the region. Further, Saharan sand was identified in the atmosphere in the region for the first time. Originality/value The surveys showed that mobile LIDAR can detect and characterise airborne particulate pollution and contribute to an understanding of its dispersion and motion in the atmosphere. It will also aid in assessing the associated risks to human health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".