Evaluation of Wildlife Reflectors for Reducing Vehicle-Deer Collisions on Indiana Interstates I-80 and I-90
Bibliographic record
Abstract
Indiana Department of Transportation is increasingly committed to reduce vehicle-deer collision incidents on the Indiana Interstate I-80/90 as well as on the other roads. Very few of the studies to reduce vehicle-deer collisions incorporated any sound and complete statistical design. Some states (California, Colorado, Maine, Ontario-Canada, Washington State and Wyoming) have found that the use of wildlife reflectors was not effective to reduce the number of vehicle-deer collisions. However, some other states (British Columbia-Canada, Iowa, Minnesota, Oregon, Washington State and Wisconsin) found that the use of wildlife reflectors were effective to reduce the number of vehicle-deer collisions. The main objective of this experimental study is to evaluate the effectiveness of the Reflectors in reducing vehicle-deer collisions. In order to address the major variables (factors), the design of this experiment was prepared to have a minimum of one road section, one-mile long, for each combination of reflector colors (red and blue/green), reflector spacing ( 30 m and 45 m), reflector design (single and dual reflectors), and median (one with and one without reflectors). The above design yields sixteen treatment combinations, which is called a replicate. This replicate was repeated two times and four miles long control sections were maintained in between and two miles at both ends of the replicates. The data for the peak months of April, May, October and November from 1999 to 2005 were used in the data analyses. Poisson Regression Analyses indicated that the reflectors have not significantly reduced vehicle-deer collisions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".