Mapping road traffic noise descriptors in a sub-Saharan city: An extensivae mobile data collection in Abidjan (Ivory Coast)
Bibliographic record
Abstract
Road traffic noise is an issue which can impact the well-being and health of urban populations. Mapping this pollution at a fine scale is essential for urban planning and public health. Most of urban road noise mapping studies have been carried out in northern cities. However, the issue of road noise is particularly compelling in cities of the Global South. Conducted in Abidjan (Ivory Coast), this study has three goals: 1) to collect data on several road traffic noise descriptors ( L Aeq,30s , L Amin , L Amax , L A10 , L A50 , and L A90 ); 2) to identify the road environmental characteristics significantly affecting noise levels; and 3) to produce noise exposure maps displaying these various noise descriptors for the entire city. An analysis conducted using Bayesian generalized additive models with an autoregressive term (GAMAR) reveals that the most relevant noise predictors are the type of route taken; the presence of traffic signals; the distance to the nearest major road; and the geographical location (the spline with geographic coordinates). The road noise potential exposure maps obtained with these models show extremely significant differences in noise between residential streets and major roads, but also between some regions of the city, despite all else being equal.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".