Bibliographic record
Abstract
The Halifax Bank of Scotland (HBOS) is the ultimate legal owner of up to 7% of all new trucks over 7.5 tonnes registered in the UK in 2004. The bank argues that vehicle operators and owners of any size are as entitled to access and use data found in CAN-bus (controller area network) multiplex systems as vehicle manufacturers. Manufacturers accept the argument in principle but highlight the risk of safety-critical systems such as anti-lock braking or engine electronic control units being affected. The bank suggests that it is safe to access useful data using the Society of Automotive Engineers SAE J1939 protocol without affecting safety-critical systems. VOSA (Vehicle and Operator Services Agency) would like to use telematics and data from vehicles to target enforcement more effectively. HBOS has proposed fitting of black boxes to trucks to transmit data to a server that VOSA could access. A 'compliance telematics initiative' trial has been set up involving 1000 HBOS-owned trucks. Each vehicle must have CAN-bus multiplex systems and use the industry-standard FMS (Fleet Management System) telematics interface using the SAE J1939 protocol. Responsibility for project management, telemetry and data processing rests with Recall Support Services (to be reconstructed as Airmax Developments).
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.786 | 0.773 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".