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
Traditional stop-and-pay toll booths inconvenience drivers and are infeasible for complicated urban areas. As a way to minimize traffic congestion and avoid the inconveniences caused by toll booths, electronic tolling has been suggested. For example, as drivers pass certain locations, a picture of their licence plate may be taken and a bill sent to their home. However, this simplistic method allows the administrator of the system to build a dossier on drivers. While this may be an attractive feature for law enforcement, a society may not wish to trust the tolling agency with such detailed information. We present SPEcTRe, a suite of protocols to maintain driver privacy while ensuring that tolls are accurately collected. Existing protocols for privacy-preserving electronic toll pricing suffer from computational challenges and require an undesirable amount of location data to be collected. We present two schemes: the spot-record scheme, which requires the same amount of location data exposure as prior privacy-preserving schemes, but runs much faster, and the no-record scheme, which collects no location information from honest users and is still able to run efficiently.
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 | 0.000 |
| 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