Bibliographic record
Abstract
In 1996, the provincial government of Alberta, Canada, outsourced highway maintenance for the provincial highway network. Private contractors were hired to perform maintenance activities under a 5-year, geographically based unit-price contract. The 1996 contracts specified the minimum number of trucks for each area, and the old Alberta Transportation (AT) shops were leased to the successful contractors. Starting in 1998, the government began selling AT maintenance shops, and by 2000 most properties were no longer under government control. Then, in the fall of 2000, the government began to transfer road authority for secondary highways from the municipal governments (i.e., counties) and more than doubled the length of the network under provincial jurisdiction. Prospective contractors for contracts tendered after 2001 were required to propose new shop locations and the shop size and number of trucks to be provided in their new contract area. AT's tasks were to benchmark the existing (2000) winter maintenance service on the existing network; predict the requirements for the number of trucks needed to meet provincial standards on the new (expanded) network and provide the same level of performance; and evaluate contract proposals when shop locations and number of plow trucks were not specified. The department's solution was a spreadsheet model of plowing and sanding-salting times with the total calculated time to complete one pass of the entire network as the benchmark. The model was used to determine how many trucks to add within each district as the secondary highways were transferred to provincial control. Contract proposals from prospective contractors were evaluated on whether their proposals showed equal to or slightly better than benchmark parameters. In broad terms, the benchmark model was developed by breaking the highway network into areas with similar traffic volumes, calculating the paved area (2-lane equivalent km) per plow truck, adding the newly transferred highways to the network, and determining the number of new trucks needed to complete work on the whole network within allowable times. This paper gives details of the benchmarking process, including assumptions used, how highway topography and geometric characteristics were used to affect the length of highway each plow truck can be assigned before it was fully allocated, the business rules chosen to model actual work habits, calculations used to determine the time required to plow and spread sand or salt over each segment, and the improvements made over three successive rounds of tendering.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".