NEW GUIDING LIGHT : AN EFFECTIVE AND INEXPENSIVE TECHNOLOGY FOR APRON GUIDANCE AND GATE/DOCKING LIGHTING
Bibliographic record
Abstract
Most airports have a gap between runway surfaces and docking areas where lights give way to pavement markings, supplemented by guide vehicles for traveling on the apron pavement. High costs of lighting systems suitable for these areas and the lack of standards for their operation have stalled adoption. Now, though, complex traffic patterns and more affordable technologies are spurring changes. Vancouver International Airport (YVR) found that visiting flight crews found it difficult to follow apron markings in the dark or under wet conditions, both of which are common. The airport began an experiment with LEDs encapsulated in plastic strips that were embedded in the pavement. Pilots liked it, but maintenance problems plagued the installation. The electrical connections in the LED array kept shorting out. Then airport officials learned about a New Zealand company's technology called smart stud, designed originally for crosswalks. They use a low-profile round plastic shell that houses super bright LED arrays. Power is fed to each array through induction from a nearby buried cable so there are no connections to short out. Installation and maintenance costs are much lower, as are power costs. They're not bright enough yet for taxiways, but YVR officials are pleased with this application.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".