Route tracking of border crossing vehicles using inductance signatures of loop detectors
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
Monitoring border-crossing vehicles has been one of the focus areas of the Department of Homeland Security since its inception. The USA has a total of 7,514 land miles of borders with Canada and Mexico, and more than 140 million vehicles enter the US border every year. The vehicles then travel using a part of 4 million miles of available US public roads. Monitoring and tracking so many vehicles is a huge challenge and requires automated non-intrusive computerized technology. The paper proposes a new way of tracking vehicle routes through vehicle sensors that exist in the US transportation infrastructure. In most US highways and local roads, inductive loop detectors (ILDs) are embedded in the pavement to monitor traffic conditions, and the number of installations is constantly increasing. The paper introduces a method that utilizes the inductive signatures of vehicles generated by ILDs for vehicle identification and tracking. Signal processing techniques of inductance signatures and the experimental results on a highway data are presented.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it