Bibliographic record
Abstract
Alberta Transportation (AT) has partnered with the province’s private highway maintenance contractors (HMC) on an innovative project to equip all snowplows with an Automated Vehicle Location System (AVLS). The primary objectives are to monitor and audit the work being done by the HMC and to increase productivity and efficiency through a newly-developed automated billing system. The entire AVLS consists of two basic components – hardware and software. The truck-mounted hardware consists of a Global Positioning System (GPS) unit, a wireless communications device, and sensors that provide real time data input on the use of the plow equipment (plow blade actions, spreader controls, and pre-wetting actions). The software program developed specifically for this project will collect snowplow data such as location, speed, truck identification, and actions, and automatically generate a billing record for AT to review and approve for payment. This will be the first deployment of such an automated billing system based on GPS tracking by a transportation agency anywhere in North America. In addition to the main benefits, other potential uses of this system are: the ability of the department and HMC to monitor the amount and location of salt and sand being placed so as to mitigate environmental impacts; in conjunction with other intelligent transportation systems (ITS) technologies such as the Road Weather Information System (RWIS), to optimize equipment resources during storm events; in conjunction with other department computer programs as a post-storm analysis tool based on the information gathered; and to assist with inquiries from the public and possible litigation matters.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".