Modelo de evaluación del servicio de agua y drenaje en el Distrito Federal
Bibliographic record
Abstract
Durante las ultimas decadas, la escasez de agua, el deterioro de su calidad y su desigual distribucion han incrementado los riesgos que enfrentan los habitantes del D.F., convirtiendo su prevencion y mitigacion en un desafio que exige el diseno y ejecucion de perspectivas innovadoras en materia de gestion, donde la evaluacion del desempeno de las instancias responsables en la prestacion de dichos servicios es un requisito indispensable. Esta investigacion propone un metodo innovador para evaluar la efi cacia de la gestion del agua en el D.F. a fi n de suministrar a la poblacion con un volumen de agua sufi ciente que cumpla con los estandares de calidad, asi como disponer las aguas residuales de manera rapida e higienica. Asimismo, este metodo identifi ca los grupos mas vulnerables para enfrentar las amenazas potenciales generadas por el insufi ciente abastecimiento de agua o su defi ciente calidad, por la inadecuada extraccion de las aguas residuales y por las zonas y los niveles de riesgo
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".