Field comparison of network Sun photometers
Bibliographic record
Abstract
Measurements of aerosol optical depth have become more numerous since the mid‐1990s with the onset of commercially available, high‐quality, low‐maintenance automatic instrumentation. The development of several networks for aerosol measurements, and the next day availability of preliminary data for some, have further enhanced interest in the products this type of measurement can provide. With several networks operating globally and others operating either regionally or continentally within North America the comparability of the data emanating from the various archive centers is an important issue. The Bratt's Lake Observatory operates four separate types of Sun photometers in conjunction with three different networks: Aerosols in Canada, Global Atmosphere Watch, and the U.S. Department of Agriculture UV‐B Monitoring Program. Data collected during the summer of 2001, following the protocols established by the networks and the Meteorological Service of Canada, were analyzed to determine the comparability among these networks. As the instruments and conversion algorithms are similar to other networks from around the globe, it is believed that the results of this comparison can be transferred, at least in part, to other operational networks. The results of the 3‐month study indicate that the data obtained from the networks that operate direct‐pointing instruments are very comparable, being within ±0.01 of an optical depth for instantaneous measurements during cloud‐free line‐of‐sight conditions. Over the length of the comparison the root mean square difference of aerosol optical depth at 500 nm between the direct sun‐pointing instruments was 0.0069. The rotating shadowband instruments did not perform as well. These results indicate that the data from well‐maintained networks of direct sun‐pointing photometers can provide data of the quality necessary to compare stations from across the globe.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".