Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate
Bibliographic record
Abstract
These datafiles are used in analyses contained in the manuscript Tanner, A.L., Leroux, S.J., and Saunders, P.W. [submitted]. Road characteristics best predict the probability of vehicle collisions with a non-native, hyperabundant ungulate. These data include information on environmental variables at 600 moose-vehicle collision locations, and 3296 randomly selected locations in Newfoundland, Canada. There are three buffer sizes (500m, 2736m, and 5471m) used for all variables that require buffered areas. This file also contains the R code that we used to conduct the analyses. We have included comments throughout the code in order to understand the operations being performed. When importing a new dataset (either: spatialall2.csv or spatialallcorr2.csv) while running the R code, be sure to set the Please read Metadata file carefully before using any of this data.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.018 |
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".