Derivation of network-wide surface condition corrections for rail noise modelling
Bibliographic record
Abstract
Noise modelling and mapping techniques are increasingly being used to understand noise impacts to communities across large geographic areas. Rail transit noise around a network can vary considerably with factors such as track type, train speed, rail roughness/surface condition, among others. Reference source levels for a particular rolling stock type are normally derived by measurement at a particular location or locations, and applied around the network. When modelling noise network wide, it is important to understand how variable rail roughness can affect predicted noise levels at locations other than those used for measurements. With elevated guideway, it can also be difficult to access appropriate external measurement locations. This study describes a study undertaken for the Vancouver SkyTrain network using the Nord2000 rail noise prediction algorithm. Variations in rolling noise level of the order of 15 dB were identified and attributed to rail surface condition. Noise measurements inside a test train were used to determine frequency dependent train speed coefficients and corrections to apply for rail condition around the network. This paper describes the approach used and the outcomes in relation to this city-wide rail noise mapping project.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".