Bibliographic record
Abstract
Reported animal-vehicle crashes (AVCs) and deer carcass removal have been used to define the deer-vehicle collision problem, identify its locations of concern, and evaluate its countermeasures. However, it has been shown that AVC magnitudes can be dramatically different. This research quantifies and compares the magnitude and patterns of AVC and deer carcass removal data from Iowa. Differences in these characteristics can affect the results produced by these activities. The difference in the magnitude of these two databases is confirmed, and some of the factors that may affect the size of this difference are discussed. Visual and quantitative comparisons are completed by using summary measures, geographic information system plots, and generalized linear regression models with a negative binomial error structure. This modeling approach has not been applied in the past to AVC or deer carcass removal data. AVC and deer carcass removal prediction (not causal) models for rural two-lane and multilane roadways were developed. The similarities and differences in the AVC and deer carcass removal models are discussed and the implications of these differences described. The differences found make the choice of database used critical to AVC-related roadway development decisions and policies, countermeasure location identification, and interpretation of research results. The recommendations provided focus on how AVC or carcass removal databases, as they typically exist, might be used and what improvements might be made for a more well-defined collection and application of these data.
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 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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".