COMBINED NONPARAMETRIC CHI-SQUARED AND BINOMIAL STATISTICAL TEST ON TRUCK TRAFFIC VOLUME CHANGES IN CANADIAN PROVINCIAL HIGHWAY NETWORK
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the effect of weather conditions on truck type distribution using combined nonparametric chi-squared and binomial probability statistical tests. Influence of the winter conditions on truck type distribution is investigated in this paper by classifying trucks into single-unit trucks, single-trailer, and multi-trailer units. The investigation is based on 5 years Weigh-In-Motion (WIM) traffic data collected from Alberta provincial highway network in Canada. The WIM data is collected from six WIM sites located on Highway 2, Highway 2A, Highway 3, Highway 16 and Highway 44. The objective of this study is to investigate the association of three truck type distribution with month and season depending on weather conditions by means of nonparametric statistical test. The statistical results indicate that the variation of truck type distribution is influenced by type of highway facility, such as regional commuter roads and rural long distance highways. The season of the year (winter and non-winter) may also affect the truck type distribution on some types of roads. Findings of this study can benefit highway agencies in developing programs and policies related to efficient monitoring of truck traffic and maintaining highway network throughout the year.
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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 | 0.000 | 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 | 0.000 | 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 it