Bibliographic record
Abstract
The British Columbia Ministry of Transportation (BCMoT) has been operation its Wildlife Accident Reporting System (WARS) for over 20 years. Through BCMoT’s network of private maintenance contractors, detailed species and location data on wildlife accident is systematically collected on a daily basis on major highways in British Columbia. Information contained in the WARS database provides a unique opportunity to examine the highway/wildlife habitat interface. The database provides a rare and invaluable collocation of information for species of both large and small wild animals that cannot be assembled from any other information sources. The WARS system enables highway planners to reduce the fragmenting effect of highway corridors on wildlife habitats by ensuring wildlife migration routes which cross highway alignments are identified and protected. Efforts are made to protect critical populations of rare or endangered species by providing structures for the animals to cross highways safely. Over time, the WARS system has become a critical component in BCMoT’s continuing efforts to ensure the safety of the motoring public by reducing wildlife mortality on existing highways and the potential for wildlife mortality on new highways. Given its fundamental simplicity, ease of implementation and low operational cost, the WARS system can provide a model is suitable for any transportation agency with the need to document wildlife mortality on roads, highways and railways. The model can be implemented by most transportation agencies within their existing organizational maintenance reporting structures.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".