Describing Functionalities and Reactions of Cars and Managing Their Feature Interactions
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
We develop an Automotive Reaction System (ARS) framework to support cars by capabilities to react to various situations. With ARS, the states and actions of a car are designed as objects of a high level object-oriented language, called ARS-language. ARS permits also to design the reactions of a car to various situations by an ARS-specification consisting of rules “condition→action”. The ARS-objects and ARS-specification are implemented in a car to provide her with capabilities to function and react online. ARS permits also to model certain actions of a car at a high abstraction level by an ARS-model consisting of rules “condition→operation”. With ARS, we are confronted to conflicts (or feature interactions) which denote situations where an ARS-specification implies simultaneous executions of incompatible actions. We propose an approach to detect and resolve feature interactions.
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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