DESIGN AND OPERATIONAL CONSIDERATIONS TO ACCOMMODATE LONG COMBINATION VEHICLES AND LOG HAUL TRUCKS ON RURAL HIGHWAYS IN ALBERTA, CANADA
Bibliographic record
Abstract
The purpose of this paper is to outline the geometric design features that have been developed to accommodate Long Combination Vehicles (LCVs) and log haul trucks on rural highways in Alberta, Canada. Vehicles longer than 25 m are referred to as LCVs and include the following vehicles: Triple Trailer combinations, 35 m in length; Rocky Mountain Doubles, 31 m in length; log haul trucks which can be up to 30.5 m in length, with a 9 m overhang and Tumpike Doubles, 38 m in length. The paper presents the vehicle dimensions, swept path, and off tracking characteristics of each vehicle type. Geometric design features include intersections, ramps and centre line spacing on two-lane highways. The paper also presents the criteria used to develop the LCV and log haul truck route networks for the province. The movement of over-dimensional equipment, machinery and pre assembled components is accommodated on the high-wide-load (HWL) corridor. This 2100 km HWL corridor allows loads up to 9 m high and weights from 122 tonnes in the summer and 177 tonnes in the winter. Weights as high as 380 tonnes may be conveyed on condition that the size and space requirements of undercarriage wheel assemblies are met for critical bridge structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".