Solución del problema inverso de la hidrogeología mediante el algoritmo evolución diferencial
Bibliographic record
Abstract
La presente contribución propone un nuevo algoritmo de optimización dentro de la tecnología AQÜIMPE para la calibración automática de los parámetros hidrogeológicos de modelos de acuíferos (conductividad hidráulica y coeficiente de almacenamiento). La propuesta utiliza una técnica metaheurística llamada algoritmo evolución diferencial (differential evolution, DE por sus siglas en inglés) la cual se basa en los principios básicos de los algoritmos genéticos con algunas modificaciones. El acople entre el modelo de flujo AQÜIMPE y DE se implementa en el asistente matemático MATLAB. Se aplica el modelo propuesto en la calibración del modelo del acuífero Cuentas Claras, donde DE demuestra ser una buena opción a la hora de elegir un algoritmo de optimización para la solución del problema inverso de la hidrogeología.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".