VISAT™: The Avenue to Highway Data Bank
Bibliographic record
Abstract
The creation of a Geographic Information System (GIS) for road networks requires large amounts of data which currently are obtained by manually digitizing existing maps with some update from conventional photogrammetric and surveying techniques. In order to collect digital data faster, the use and integration of digital sensors is required which work in kinematic mode, i.e., from a moving vehicle. Absolute Mapping Solutions Inc., Canada, has developed a precise mobile survey system – VISAT™ – for road inventory and general GIS applications. The system integrates a cluster of CCD cameras, a Global Positioning System (GPS) receiver, and an Inertial Navigation System (INS), to automatically collect data in a road corridor at velocities of up to 120 km/h and to store the data in a GIS system format. The updated GPS/INS information is used to geometrically correct the images collected by the CCD cameras which record all details along the highway within a corridor of about 50 m. The shutters of the cameras and the output of the INS system are synchronized by the clock of the GPS receiver. This paper introduces the VISAT™ system, its components, and its application in building highway infrastructure data banks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.065 | 0.076 |
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".