Remote Sensing and Modelling, Ground-based Aerosol Vertical Profile, Oil Sands Region
Bibliographic record
Abstract
LIDAR (LIght Detection And Ranging) is a measurement technique capable of observing the complex vertical structure of the atmosphere. Specifically, information can be provided related to the existence and extent of aerosols and clouds with high spatial (4 m vertical) and temporal (1 min) resolution, making it well-suited for understanding atmospheric dynamics and transport processes. ECCC researchers designed, built and deployed autonomous ground-based aerosol LIDAR systems to three different locations in the oil sands region – AMS13 (November 2012 to September 2013), Oski-ôtin (October 2013 to present) and Mannix (July 2013 to March 2016). Details of the instrument by Strawbridge, 2013 can be found in Atmos. Meas. Tech., 6, 801-816, 2013. The Oski-ôtin LIDAR system was upgraded in November 2016 to include additional aerosol channels, ozone and night time water vapour profiles. Three LIDAR image products are produced daily at each location. The first plot (Figure 1a) is backscatter ratio at 532nm, which is plotted from ground to 13 km using a logarithmic colour bar as a function of the time of the day. The second plot (Figure 1b) is also the backscatter ratio at 532nm, but plotted from ground to 3 km and using a linear colour bar to easily identify relative increases and decreases in particle concentration in the lower atmosphere and boundary layer. The backscatter ratio is an optical quantity that is proportional to particle concentration. The black vertical bands on those images represent regions where LIDAR data are not available due to the presence of optically thick clouds or when the LIDAR system does not operate such as during precipitation events or instrument maintenance. The third plot (Figure 2) is the time dependence of the depolarization ratio at 532nm or 355nm using a linear colour scale. The depolarization ratio provides information about the shape of the particles. Together, the backscatter and depolarization products can provide information on the altitude and extent of particulate matter from biomass burning, mineral dust, industrial plumes, water clouds and ice clouds. The LIDAR data can also be used to identify long-range transport events, dispersion and mixing of plumes and boundary layer height. LIDAR observations also provide the vertical context for other ground and remote sensing systems at the measurement site and vertical profiles for model verification and validation. (See LIDAR Image Description-EngFr.pdf )
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".