El impacto del cambio climático en la salud: una aproximación desde el contexto urbano con enfoque One Health
Bibliographic record
Abstract
El cambio climático afecta a la salud ambiental, animal y -directa e indirectamente- a la humana. Los escenarios urbanos, como ámbitos críticos a este respecto, requieren abordar la cuestión de forma urgente. Y, para ello, el concepto One Health ofrece una componente diferenciadora. Aplicado a planificación y diseño urbanos, permite poner sobre la mesa problemáticas y perspectivas en materia de calidad de vida y mejora ambiental que, a la luz de los riesgos derivados del calentamiento global, resulta esencial considerar. El presente artículo constata esa realidad partiendo de una revisión crítica de problemáticas y de aplicabilidad del concepto. Más allá de las barreras y ambigüedades que pueden acompañarlo, el potencial del enfoque reside en la atención prioritaria a las relaciones, y muy especialmente a las interdependencias entre salud humana, animal y del medioambiente, en un contexto de complejidad. Se evidencia también, en este sentido, el valor de huir de perspectivas de autocomplacencia y de apoyarse en la interdisciplinariedad y en visiones amplias de los problemas de salud derivados del cambio climático en el medio y largo plazo.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".