Preliminary measurements of tropospheric water vapor using Raman lidar system in the Great Lakes area
Bibliographic record
Abstract
Obtaining high resolution vertical profiles of water vapor is crucially important to understand short and long term global climate changes. Raman lidar technique is widely recognized as the most effective tool to study water vapor and aerosols profiles in the lower atmosphere. The Great lakes area is one of the ideal areas to study the environmental impact of water vapor and aerosols profiles on air quality due to its dynamic ecological system, and proximity to most North American industrial centers. Latest results of a newly developed water vapor Raman lidar instrument at the Environment Canada's Centre for Atmospheric Research Experiments (CARE) (44°14'02" North, 79°45'40" West) will be presented. In this study, the instrument is described and its capabilities are illustrated along with preliminary measurements. The CARE Raman lidar setup utilizes third harmonic (355 nm) output of employed YAG laser to probe aerosols, water vapor, and nitrogen profiles. By manipulating inelastic backscattering lidar signals of the Raman nitrogen channel (386.7 nm) and Raman water vapor channel (407.5 nm), a vertical profile of water vapor mixing ratio from the near ground up to 12 km geometrical altitude is deduced. Vertical profile of the backscattering ratio obtained at 1064 nm using another elastic lidar will be shown and related to the Raman lidar results.
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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) | 0.000 | 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) | 0.000 | 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".