A Practical Tool for Ambient Illumination Comparisons at Dusk/Dawn
Bibliographic record
Abstract
Nighttime visibility is an important consideration in collision reconstruction and personal injury investigation. Decreased contrast in low ambient lighting conditions can greatly affect human perception and response. Because ambient lighting levels change rapidly at dawn and dusk, forensic investigators must have an accurate knowledge of the time of day and the cloud conditions at the time of the incident before initiating a nighttime visibility assessment. Previously, human factors experts attempting re-enactments at dawn or dusk have had to wait for sky conditions that match those at the time of the incident, making the investigation of those cases extremely difficult, if not unfeasible. In this study, an ambient illumination equivalency tool has been developed based on a database of time-lapse light meter readings collected by the authors. This new tool can be used to facilitate nighttime visibility assessments on any day by providing a time adjustment factor to account for the changes in ambient illuminance due to differences in the cloud conditions between the day of the incident and the day of the re-enactment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".