Análisis de aguas residuales con fines epidemiológicos: aplicaciones a la estimación del consumo de sustancias de abuso y en salud pública en general. Red española ESAR-Net
Bibliographic record
Abstract
En este artículo se presenta la metodología de análisis de aguas residuales con fines epidemiológicos (wastewater-based epidemiology, WBE) y su potencial para abordar diversos aspectos relacionados con la salud pública. Esta metodología permite obtener datos a una escala temporal y espacial relativamente pequeña (típicamente datos diarios-semanales sobre un municipio) de hábitos de consumo de sustancias de abuso, ilegales (como la cocaína o el cannabis) o legales (como el alcohol) a través de la determinación de biomarcadores de consumo (el compuesto original no metabolizado o alguno de sus metabolitos) en el agua residual. Aparte de discutir los fundamentos, ventajas y limitaciones de WBE, se comentan los precedentes más relevantes a nivel internacional, y las actividades más destacables en España en este ámbito. Finalmente, se exponen, los objetivos de la Red Española de Análisis de Aguas Residuales con Fines Epidemiológicos (ESAR-Net), una “Red de Excelencia” que agrupa a investigadores españoles con amplia experiencia en el área de WBE, así como las perspectivas de futuro de esta metodología puede tener para mejorar las competencias de la Salud Pública en España
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".