Desarrollo de una metodología para identificar zonas de potencial acumulación de emisiones en la ciudad de Quito
Bibliographic record
Abstract
En el presente trabajo se desarrolla una metodologia para determinar potenciales zonas de acumulacion de emisiones, en Quito, en base a la caracterizacion de los flujos de viento. Los datos de los parametros medioambientales los genera diariamente el MODEMAT-EPN a traves del Modelo WRF-NMM. La metodologia se aborda delimitando el dominio de estudio al canton Quito, el mismo que se divide en zonas de flujos con caracteristicas similares de viento. En estas zonas se realiza un balance de flujo masico por cada hora dentro de un periodo de cinco meses. Mediante un analisis estadistico se clasifica cada zona segun su potencialidad y se determina posibles tiempos de residencia. Los resultados identifican como zonas de potencial acumulacion de contaminantes: la Ciudad de Quito desde la Terminal del Labrador en el norte hasta Guamani en el sur y parroquias como Pifo, La Merced, Puembo, Tababela, Yaruqui, Checa, El Quinche, Guayllabamba, Mariscal Sucre, Jipijapa, Tumbaco, Cumbaya, entre otras. Tambien resume los periodos o tiempos de residencia en los cuales se tiene bajos flujos de viento. Una limitante puede ser la resolucion del modelo que no garantiza la correcta cobertura y detalles de la complicada geografia, sin embargo, permite realizar esta importante evaluacion preliminar.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".