Quantification of atmospheric visibility with dual digital cameras during daytime and nighttime
Bibliographic record
Abstract
Abstract. A digital optical method "DOM-Vis" was developed to measure atmospheric visibility. In this method, two digital pictures were taken of the same target at two different distances along the same straight line. The pictures were analyzed to determine the optical contrasts between the target and its sky background, and subsequently, visibility is calculated. A light transfer scheme for DOM-Vis was delineated, based upon which, algorithms were developed for both daytime and nighttime scenarios. A series of field tests were carried out under different weather and meteorological conditions to study the impacts of such operational parameters as exposure, optical zoom, distance between the two camera locations, and distance of the target. This method was validated by comparing the DOM-Vis results with those measured using a co-located Vaisala® visibility meter. The visibility under which this study was carried out ranged from to 1 km to 20 km. This digital photography based method possesses a number of advantages compared with traditional methods. Pre-calibration of the detector with a visibility meter is not required. In addition, the application of DOM-Vis is independent of several factors like the exact distance of the target and several camera setting parameters. These features make DOM-Vis more adaptive under a variety of field conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".