Remote keyless entry honking, convenience horn honking, and audible car alarms: redundancies and quieter options
Bibliographic record
Abstract
Vehicle sound emissions, car alarms, and horn honking are the subject of many noise complaints. Auto manufacturers spent years engineering quieter vehicles, and have created cars whose approach is so subtle that they pose a danger to blind pedestrians. But while engine noise has decreased and car alarms are less reactive, horn honking that is linked with remote keyless entry (RKE) technology increasingly contributes to community noise. RKE horn noise has never been the subject of public health inquiry. In scientific literature, discussion of road noise and health does not distinguish noise among separate sources, and tends to measure aggregate ambient noise levels rather than impulsive noise. RKE horn noise violates state traffic laws and some local noise ordinances regarding horn use, but there have been no legislative attempts to address the technology. This raises questions about whether political leaders and policy setters are not exposed to RKE noise or do not discern RKE sounds from traffic noise, and are therefore unaware of it. Using available auto industry data and case studies, this paper will introduce key facts about RKE horn use in the United States and Canada, reviewing new technologies that render noisy counterparts still in use as redundant.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".