Tracking moose- and deer-vehicle collisions using GPS and landmark inventory systems in British Columbia.
Bibliographic record
Abstract
Vehicle collisions with moose ( Alces alces ) and deer ( Odocoileus spp.) pose a serious threat to all motorists travelling highways traversing habitats of these two ungulates. In British Columbia, mitigation measures to reduce such collisions are based on spatially-accurate records of collisions involving moose and deer that are collected by the province’s highway maintenance contractors. To date, the British Columbia Ministry of Transportation and Infrastructure (BC MOTI) uses the paper-based Wildlife Accident Reporting System (WARS) established in 1978 to maintain carcass records. We compared carcass location data collected in 2010 to 2014 by BC MOTI using WARS to that collected by Northern Health Connections bus drivers using a newly developed GPS-based system (Otto® Wildlife device). In total, 6,929 carcasses (1,231 moose, 5,698 deer) were recorded using WARS and 474 (167 moose, 410 deer) using the Otto® Wildlife device. We compared data collected along 2,800 km on the same highways in the same seasons of the same years. We found more carcass locations were identified with the WARS method, but that in certain geographic regions, the Otto® Wildlife system identified several unique locations. We contend that more complete and finer-scale carcass location data is possible using a GPS-based system such as Otto® Wildlife, than currently collected solely with the paper-based WARS method.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".