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
The proliferation of vehicles and subsequent increase in traffic accidents has led to a heightened focus on driving safety. As a result, various researchers have been examining ways to enhance driving safety in daily life by implementing smart car technology. In-vehicle sensing utilizing radar technology has emerged as a leading method for monitoring the driver’s health, emotions, and attention, owing to its numerous advantages over traditional sensors, including the ability to detect subjects through non-metallic surfaces and the inherent privacy-preserving mechanisms. In recent years, in-vehicle sensing through radar has undergone significant advancements. This paper aims to provide a comprehensive survey of the applications, system level design, and signal processing of in-vehicle sensing through radar. The published works in this field are categorized into three main groups: occupancy detection, gesture recognition and occupant status monitoring. The paper will discuss the highlighted works and their respective advantages and limitations in terms of applications.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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