Development of differential absorption lidar (DIAL) for detection of CO<sub>2</sub>, CH<sub>4</sub>and PM in Alberta
Bibliographic record
Abstract
Rapid expansion of the oil and gas industry in Alberta, including the oil sands, has challenged the Alberta Government to keep pace in its efforts to monitor and mitigate the environmental impacts of development. The limitations of current monitoring systems has pushed the provincial government to seek out advanced sensing technologies such as satellite imagery and laser based sensors. The Space Dynamics Laboratory (SDL) of Utah State University, in cooperation with Alberta Environmental Monitoring, Evaluation and Reporting Agency (AEMERA), has developed North America’s first mobile differential absorption lidar (DIAL) system designed specifically for emissions measurement. This instrument is housed inside a 36’ trailer which allows for mobility to travel across Alberta to characterize source emissions and to locate fugitive leaks. DIAL is capable of measuring concentrations for carbon dioxide (CO2) and methane (CH4) at ranges of up to 3 km with a spatial resolution of 10 meters. DIAL can map both CO2 and CH4, as well as particulate matter (PM) in a linear fashion; by scanning the laser beam in both azimuth and elevation DIAL can create images of emissions in two dimensions. DIAL imagery may be used to understand and control production practices, characterize source emissions, determine emission factors, locate fugitive leaks, assess plume dispersion, and confirm air dispersion modeling. A system overview of the DIAL instrument and some representative results will be discussed.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".