Información Investigador: Labrador Pérez, Rafael Orlando
Bibliographic record
Abstract
Resumen Curricular \n\n\n\n \n \tRafael Labrador es Medico Cirujano (ULA), con Maestría en Neurociencias y Doctorado en Medicina y Cirugía, en el Programa de Neurociencias (Universitat Autónoma de Barcelona, España), donde además fue docente de prácticas de Fisiología en el pregrado de Medicina y de Neuromicrocirugía en el Doctorado de Neurociencias. Actualmente es profesor Agregado de Neurofisiología y coordina el grupo de Reparación y Plasticidad Neural, en el Laboratorio de Investigaciones Biomédicas (LIB) de la Extensión San Cristóbal de la Facultad de Medicina. Además es Investigador Invitado del Instituto de Estudios Avanzados (IDEA), trabajando en el Centro de Biociencias y Medicina Molecular de dicho instituto. Actualmente trabaja en proyectos que buscan la Reparación de Lesiones de Tejido Nervioso, combinando técnicas de Neuromicrocirugía y de Biología Celular y Molecular, y cuyos resultados se han comunicado y publicado en diversos eventos y publicaciones internacionales.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.032 |
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".