An examination of the feasibility of automated license plate recognition technology for parking lot deployment
Bibliographic record
Abstract
In order to assess the feasibility of ALPR technology in Canada, a pilot project using the\ntechnology was conducted in Surrey, British Columbia, Canada in 2006. As part of this\nlarger pilot project, one unmarked police vehicle equipped with ALPR technology was\ndeployed to a number of major parking lots in Surrey. The purpose of this current study\nwas to examine the data from this one car to analyze the quantity, quality, and location of\n„hits‟ to determine whether it was a viable and useful strategy to deploy ALPR-enabled\npolice vehicles to parking lots. Given the proportion of „hits‟ in parking lots, it is\nrecommended that ALPR not be deployed to parking lots alone. While there may be a\nbenefit to deploying ALPR-enabled vehicles to parking lots between 3 p.m. and 7 p.m., it\nwould appear that the technology might be more useful along high traffic corridors.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".