The Adrian/CIE Visibility Model: A Visibility Level Calculator & Future Research
Bibliographic record
Abstract
Diminished visibility in dim light degrades performance and safety on real-world tasks that depend on the timely detection of visual targets.The goals of this paper are to: 1. review factors that affect nighttime visibility, with an emphasis on driving, 2. provide the reader with online access to an automated modified Adrian/CIE visibility level (VL) calculator (VLC), and 3. suggest future research for enhancing the objective measurement of visibility.Recognizing that luminance contrast is the primary sensory determinant of nighttime visibility, several contrast-detection models have been proposed to quantify visibility in dim lighting.Of these, the Adrian (1989) model accounts for comparatively more of the important variables and has been the most widely accepted.The mathematical steps for calculating target VL in the modified Adrian/CIE model are presented and the VLC user interface is explained in step-by-step order.The VLC provides an easy-to-use tool for calculating target VL.Several additional factors that affect VL that are not currently included in the model provide important research opportunities for enhancing the measurement of target visibility in nighttime conditions.The VLC is an open-access application intended to foster the measurement of VL in professional practice and to foster research to advance the utility of the Adrian/CIE model.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".