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
This research presents insights into the impact of vibroacoustic factors in conventional and electric buses by investigating seven factors: average speed, bus age, road conditions, road network, bus operating days, times, and occupancy levels. Data on these factors and bus vibroacoustic levels were collected through observations alongside vibroacoustic apps (Sound Meter Pro and iDynamics) from 31 conventional and 12 electric buses in Montreal, Canada. The data was analyzed using Pearson's correlation, and predictive models were generated using multilinear regression to assess 650 sub-scenarios for each bus type. The results indicated that electric buses had better vibroacoustic performance compared to conventional buses under different contexts. Furthermore, the factors of age, road conditions, and average speed had notable but varying effects on both bus noise and vibration levels. Thus, this study suggests that adopting electric buses, reducing the age of existing bus fleet, improving road infrastructure, and lowering operation speeds can effectively minimize bus noise and vibration for better environmental and rider comfort. Future research can be adopted to consider a broader range of variables, including maintenance and traffic density, to further refine the effects of these factors on conventional and electric bus vibroacoustic levels.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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