Preliminary Analysis of Night‐time Aerosol Optical Depth Retrievals at a Rural, Near‐urban Site in Southern Canada
Bibliographic record
Abstract
In the summer of 2007, a SPSTAR03 starphotometer was installed at Egbert, Canada (44°13′ N, 79°45′ W, alt 264 m) and a continuous series of initial measurements was performed between August 26 and September 19. Several sunphotometry parameters such as the aerosol optical depth (AOD) and the “fine” and “coarse” optical depths were extracted from the SPSTAR03 extinction spectra. The SPSTAR03 data was analyzed in conjunction with sunphotometry and zenith‐pointing lidar data acquired during the same time period. Preliminary results show coarse continuity between the day‐ and night time AOD values (with the mean difference between the measured and the interpolated values being 0.05) as well as a qualitative correlation between the “fine” and “coarse” optical depths and the normalized lidar backscatter coefficient profiles. It was also found that the spectra produced with the differential two‐star measurement method were sensitive to non‐horizontally homogeneous differences in the line‐of‐sight conditions of both stars. The one‐star method helps to reduce the uncertainties but requires the determination of a calibration constant.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".