Evaluation of Wildlife Reflectors in Reducing Vehicle Deer Collisions on Indiana Interstate 80/90
Bibliographic record
Abstract
The Indiana Department of Transportation is committed to reducing 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 did not reduce vehicle-deer collisions. However, some other states (British Columbia-Canada, Iowa, Minnesota, Oregon, Washington State and Wisconsin) found that the use of wildlife reflectors did reduce vehicle-deer collisions. The main objective of this experimental study is to evaluate the effectiveness of the Reflectors in reducing vehicledeer collisions. The experimental design uses one-mile long road sections 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). In this design there are sixteen treatment combinations. A complete set of treatment combinations is called a replicate and the design had two replicates. Two one-mile control sections were placed at each end of each replicate. Data for the peak months of April, May, October and November was used in the data analyses. Poisson Regression models were used to analyze the data. No statistically significant differences among reflectors combinations or between reflectors and controls were found. When comparing all combined reflector sites with all combined control sites, the Poisson Regression Analyses indicate that the difference between the Poisson Mean (μ) of the all reflectors sections and all the control sections is statistically significant. The use of reflectors provides an expected reduction in deer-vehicle collisions of 19% with 95% confidence limits of 5% to 30%. Maximum reduction is associated with 100 ft spacing regardless of the reflector color, median with or without reflectors, single or double reflectors. The cost effectiveness of this reduction will be behind any decision to use reflectors to reduce vehicle-deer collisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".