International harmonized research activities report of working group on intelligent transport systems (ITS)
Bibliographic record
Abstract
The International Harmonized Research Activities Working Group on Intelligent Transport Systems was established to coordinate government research aimed at developing harmonized procedures for the evaluation of safety of in-vehicle information, control and communication systems with respect to human performance and behaviour. It deals with human-machine interactions in the broadest sense of that phrase. However, it is acknowledged that even in its broadest sense, human-machine interactions is only a part of ITS safety. This report describes the activities completed in the past five years, including the formulation of an overall framework for ITS safety assurance indicating the role of the ITS WG within this framework, a series of workshops on the safety test and evaluation of ITS, and definition of priority research problem statements. Recommendations are provided to address the formidable challenges facing the WG. It is anticipated that increased public and government concerns about ITS safety in the future will stimulate increased interest, expectation and funding of harmonized research. For the covering abstract see ITRD E111577.
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 | 0.075 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.013 |
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".