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
Effective transportation planning must take into account urban factors and public space in order to achieve sustainable and efficient solutions to transportation problems in urban areas. Public space includes not only parks, squares, and markets, but also sidewalks and roads. Properly managed transportation in public space can help minimize traffic congestion, ensure efficient and safe movement of vehicles, pedestrians, and cyclists, and overall improve its quality. The specific example which is frequently used in urban areas is adaptive traffic control system. It is able to automatically modify the length and frequency of green signals for individual directions depending on traffic situation. Adaptive traffic control uses sensors and camera systems that can detect traffic flow. Based on this data, they can modify traffic signal timing in real-time. It is important to consider urban factors and public area when we are using adaptive traffic control, For example, traffic signals control system may prioritize the traffic flow of pedestrians, cyclists or public transport. This prioritization can contribute to a more balanced use of the public area, thereby improving its overall quality. They can also help improve smoother traffic flow, reducing traffic congestion and its impact on air quality and noise pollution in urban areas.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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