Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This synthesis will be of interest to state departments of transportation (DOTs) and departments of natural resources (DNRs), as well as to others who work with them in the area of animal–vehicle collision (AVC) data collection. It examines the extent to which data from AVC accident reports and animal carcass (AC) counts are collected, analyzed, and used throughout the United States and Canada. Most survey respondents reported collecting AVC data; fewer reported collecting AC data. The primary obstacles to improving AVC and AC data collection and analysis were determined to be a lack of a demonstrated need, underreporting, poor data quality, and delays in data entry. The use of more rigid and standardized procedures were specifically mentioned to address problems and improve procedures, as well as to improve the coordination between DOTs and DNRs that share a vested interest in the data. Surveys were distributed to DOTs and DNRs in the United States and Canada. In addition, a literature review of AVC data collection was undertaken.
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.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it