Bibliographic record
Abstract
Transport Canada, through its ecoTECHNOLOGY for Vehicles program, retained the services of the National Research Council Canada to undertake a test program to examine the operational and human factors considerations concerning the removal of the side mirrors on a Class 8 tractor equipped with a 53 foot dry van semi-trailer. Full scale aerodynamic testing was performed in a 2 m by 3 m wind tunnel on a system component basis to quantify the possible fuel savings associated with the removal of the side mirrors. The mirrors on a Volvo VN780 tractor were removed and replaced with a prototype camera-based indirect vision system consisting of four cameras mounted in the front fender location; two cameras on either side of the vehicle. Four monitors mounted in the vehicle - two mounted on the right A-pillar and two mounted on the left A-pillar - provided indirect vision information to the vehicle operator. Four commercial drivers were asked to perform a series of tests simulating typical driving scenarios on a closed course test track. The tests included an object identification test, a blind spot comparison test, a coupling and uncoupling test, a quasi-static lane change test, a dynamic lane change test and an evasive manoeuvres test. The tests were performed both with the mirrors and with the camera-based indirect vision system. The results of the study provide an analysis of driver performance while using the mirrors in comparison to driver performance while using the camera-based indirect vision system. Driver acceptance of the camera-based indirect vision system was also analyzed through the use of questionnaires.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.028 |
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".