Continuous 1064/532 nm Lidar Measurements (CORALNet-UBC) in Vancouver, British Columbia: Selected Results from a Year of Operation
Bibliographic record
Abstract
CORALNet-UBC was installed in April 2008 as a “proof of concept” and the first facility in a proposed cross-Canada network of similar lidars. Despite its location on the wet West Coast of Canada, data recovery ranged from a low of 45% of the total time in December, when heavy snowfall interrupted operations, to a high of 90% in July. The facility has been a spectacular success in terms of its operational characteristics (as measured by data recovery), the range of phenomena observed and the research that it has spawned. Examples are provided of the types of phenomena observed since its initial deployment. These include observations of boundary layer structure, cloud signatures, forest fire plumes and evidence of singular long-range pollutant and volcanic transport events. Observations from CORALNet-UBC complement data from other aerosol measurement initiatives in the region and provide a rich source of information that is relevant to researchers, operational forecasters and air quality managers. Future research will be directed at elucidating the processes and phenomena revealed by the instrument as well as developing new products that exploit the real-time forecasting and public advisory potential of the facility.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".