SIBILA Guía simple de citación y referencia
Bibliographic record
Abstract
SIBILA es una plataforma en línea especializada en citación y referencia, diseñada para optimizar los procesos de creación de citas y bibliografías y de gestión de formato en los 9 estilos de citación más utilizados: APA7, APA6, ICONTEC, Chicago Notas y Bibliografía, Chicago Autor-Año, Harvard, IEEE, MLA8 y Vancouver. Con una interfaz intuitiva y un amplio número de ejemplos en español e inglés, SIBILA facilita la enseñanza y aplicación de estos estilos, optimizando el tiempo de profesores, estudiantes e investigadores. Además, SIBILA incluye a PitIA, una asistente de inteligencia artificial que brinda soporte y asistencia en tiempo real, respondiendo a las preguntas y necesidades de los usuarios de manera eficiente. Esta combinación de recursos asegura la correcta atribución de fuentes, previene el plagio y refuerza la integridad académica, ofreciendo una solución integral y rentable para la gestión bibliográfica en comunidades académicas. (Tomado de la fuente).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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; both teacher heads agree on what is shown here.
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".