Bibliographic record
Abstract
The thesis researches truck weights in Manitoba. Its purpose is to develop axle load spectra that accurately represent static and dynamic axle load distributions for trucks operating on Manitoba highways. Monitoring and understanding truck weights has become a principal focus for traffic monitoring activity in North America, especially with increased levels of awareness of the impacts truck traffic have on transportation systems. This focus on trucking activity and truck weights is reflected in traffic data collection guidelines provided in the most recent U.S. FHWA Traffic Monitoring Guide and Long Term Pavement Performance Program (LTPP) publications. The need for axle load spectra is further magnified with the shift to a mechanistic-based design procedure for pavements by AASHTO. The imminent introduction of a new (2002) AASHTO Pavement Design Guide will require axle load spectra as traffic load inputs for the pavement design software included. The thesis provides a comprehensive analysis of static truck weights accumulated using a truck data collection system created during the course of the research, and also dynamic truck weights from Weigh-In-Motion (WIM) devices. The analysis uses Manitoba weight data available in 2001. The research provides new insights into the spatial and temporal characteristics of static and dynamic truck weights. It develops a detailed understanding of the entire population of 17,264 trucks sampled using the static truck weight data collection program at the Headingley, Westhawk and Emerson weigh scales in 2001, as well as over 600,000 trucks sampled by various WIM devices during the same time frame. The research proposes a methodology and the related criteria for accepting or rejecting the massive amounts of WIM data collected on the basis of the results obtained from the analysis of static truck weights. Finally, the thesis formulates a methodology to generate representative static and dynamic axle load spectra for roadways with readily available weight information, and a procedure concept to determine truck load distributions for roadways without available weight data but having volume and classification counts...
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".