Bibliographic record
Abstract
Alberta Transportation (AT) is enhancing roadway safety by twinning Highway 63 from Atmore to Fort McMurray (240 km study area), one of Alberta’s most important transportation corridors. As part of this commitment to safety, AT recognizes the opportunity to better understand wildlife-vehicle collisions to help address the resultant property damage and risk to human life. The media have coined Highway 63 as “Alberta’s Deadliest Highway”, with wildlife-vehicle collisions representing about 45% of all reported vehicle accidents, and the frequency of wildlife-vehicle collisions are expected to increase with rising traffic volumes, white-tailed deer range expansions, and abundant deer and moose populations. Alberta Transportation retained Tetra Tech EBA Inc. (Tetra Tech EBA) to carry out long-term construction monitoring covering a wide range of physical and biological attributes associated with the twinning works, including studies to gain an understanding of wildlife movement zones, locations prone to wildlife-vehicle collisions, and the magnitude of the issue along this route. This led to the development of the Alberta Wildlife Watch (AWW) smartphone application (app) by the project team to address AT’s challenging commitment, and is incorporated into the long-term monitoring plan for highway mitigations. In 2015, AT estimated wildlife-vehicle collisions in Alberta cost society approximately $280 million (both direct and indirect costs) a year. As a demonstration, $2.8 million would be saved each year, as well as possibly a life (human and wildlife), if mitigation were to effectively reduce wildlife-vehicle collisions by only 1% across the province. This alone is a significant savings, but AT strives to do much better
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 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.250 | 0.075 |
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".