Cartographie et atténuation du bruit urbain : une étude de cas du paysage sonore de Loja, Équateur
Bibliographic record
Abstract
Noise pollution in Ecuador and other Latin American countries is an environmental problem that has been scientifically assessed but ignored when implementing internal policies. Up to 2022, Loja was listed as the ninth most populated city in the country and the tenth, with exponential growth, in its automotive sector over the last decade. In 2006, Loja's municipal ordinance aimed to address noise pollution. Since then, the need for well-planned assessments has limited the effectiveness of implementing internal policies. This study evaluates the levels of sound pressure in the urban area of Loja in Ecuador to assess the current noise pollution and develop the first noise map to derive internal policies. Seventy-six sampling points were distributed around residential, commercial, industrial, and service areas which were monitored using type 1 integrating sound level meters. Results revealed that 94% of the points exceeded the maximum allowed limits, with an average of 67 dBA, ranging between 55 and 72.9 dBA. The noisiest places are associated with major traffic routes, primarily avenues with a constant flow of vehicles. To mitigate noise pollution, traffic flow management during peak hours, awareness campaigns promoting quieter transportation modes, and biannual controls are suggested.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".