Developing a portable, autonomous aerosol backscatter lidar for network or remote operations
Bibliographic record
Abstract
Abstract. Lidar has the ability to detect the complex vertical structure of the atmosphere and can therefore identify the existence and extent of aerosols with high spatial and temporal resolution, making it well suited for understanding atmospheric dynamics and transport processes. Environment Canada has developed a portable, autonomous lidar system that can be monitored remotely and operated continuously except during precipitation events. The lidar, housed in a small trailer, simultaneously emits two wavelengths of laser light (1064 nm and 532 nm) at energies of approximately 150 mJ/pulse/wavelength and detects the backscatter signal at 1064 nm and both polarizations at 532 nm. For laser energies of this magnitude, the challenge resides in designing a system that meets the airspace safety requirements for autonomous operations. Through the combination of radar technology, beam divergence, laser cavity interlocks and using computer log files, this risk was mitigated. A Continuum Inlite small footprint laser is the backbone of the system because of three design criteria: requiring infrequent flash lamp changes compared to previous Nd : YAG Q-switch lasers, complete software control capability and a built-in laser energy monitoring system. A computer-controlled interface was designed to monitor the health of the system, adjust operational parameters and maintain a climate-controlled environment. Through an Internet connection, it also transmitted the vital performance indicators and data stream to allow the lidar profile data for multiple instruments from near ground to 15 km, every 10 s, to be viewed, in near real-time via a website. The details of the system design and calibration will be discussed and the success of the instrument as tested within the framework of a national lidar network dubbed CORALNet (Canadian Operational Research Aerosol Lidar Network). In addition, the transport of a forest fire plume across the country will be shown as evidenced by the lidar network, HYSPLIT back trajectories, MODIS imagery and CALIPSO overpasses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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 teacher head, 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".