Bibliographic record
Abstract
407 ETR, also known as Highway 407, is the world's first fully electronic highway to serve only an urban area; it operates in the Greater Toronto Area (GTA) of Canada. It is a toll road in the GTA, built to relieve Highway 401, the busiest road in North America; about 67km of its eventual 154km is currently open. This paper describes the model development and calibration processes used to estimate the daily usage of the 407 ETR. Forecasting was required for the period 15 June 1998 to 31 December 1998, using data from 14 October 1997 to 14 June 1998. The calibration and forecasting were especially difficult, because major increases in user numbers were being obtained through progressive ramp-up. The data used here came from the electronic tolling system, which is an excellent system for highway monitoring and planning, which contains data about every vehicle using the system. The paper first develops and explains the four mathematical models used, then gives calibration results to find which model is best. After that, it gives validation results, comparing the predicted numbers of highway users with the observed numbers during a five-month period; the two sets of numbers agreed reasonably well. Statistical methods used included significance tests, regression analysis, and time series analysis.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".