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 reports on the state of the practice in reducing roadside litter as it involves state departments of transportation (DOTs). The report provides information concerning the prevention and removal of roadside litter, unfulfilled needs, knowledge gaps, and under-performing activities. It covers enforcement, education, awareness, and engineering methods for both litter prevention and collection. The synthesis focuses on state DOT personnel involved in roadside litter prevention and their contractors who conduct litter prevention and removal programs. Also, as roadside litter prevention appears to be a multiple stakeholder activity, policy makers and practitioners from other government agencies and environmental organizations, as well as groups and volunteers may be interested in this synthesis. A 46-question survey was distributed to maintenance personnel in all 50 U.S. states, Puerto Rico, and 10 Canadian provinces. A literature search was also undertaken. Together the North American survey and the literature review provide a comprehensive snapshot of the state of the practice in roadside litter abatement. Four case studies were undertaken highlighting DOT litter prevention programs considered leaders in the field.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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