Application of Lidar Data to Assist Airmass Discrimination at the Whistler Mountaintop Observatory
Bibliographic record
Abstract
Abstract A ground-based lidar system that has been deployed in Whistler, British Columbia, Canada, since the spring of 2010 provides a means of evaluating vertical aerosol structure in a mountainous environment. This information is used to help to determine when an air chemistry observatory atop Whistler Mountain (2182 m MSL) is within the free troposphere or is influenced by the valley-based planetary boundary layer (PBL). Three case studies are presented in which 1-day time series images of backscatter data from the lidar are analyzed along with concurrent meteorological and air-chemistry datasets from the mountaintop site. The cases were selected to illustrate different scenarios of diurnal PBL evolution that are expected to be common during their respective seasons. The lidar images corroborate assumptions about PBL influence as derived from analysis of diurnal trends in water vapor, condensation nuclei, and ozone. Use of all of these datasets together bolsters efforts to determine which atmospheric layer the site best represents, which is important when evaluating the provenance of air samples.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".