Multimedia English for Dentistry
Bibliographic record
Abstract
Los cambios que tienen lugar en la ensenanza superior, en particular en la formacion de los estomatologos y la capacitacion posgraduada de los ya egresados exige una preparacion idiomatica superior, acorde a las exigencias de los tiempos actuales y a los compromisos internacionales del estado cubano. Ello requiere hacerlo en un breve plazo con una tecnologia efectiva y al alcance de estos profesionales. La Multimedia English for Dentistry pretende cubrir estas expectativas, aborda los elementos linguisticos indispensables para cualquier personal vinculado a esta actividad, incluye vocabulario y frases a utilizar durante el interrogatorio, evaluacion, presentacion y discusion del caso, todos con vinculos que permiten escuchar la pronunciacion. Utilizado por estudiantes, profesores y personal de esta especialidad tiene sus antecedentes en un texto de amplio perfil elaborado para el personal de la salud del polo turistico Cayo Largo del Sur. Posteriormente fue incluido en el CD de 4to. ano de medicina. En esta ocasion se presenta ampliado en formato de Multimedia que lo convierte en una poderosa herramienta para el aprendizaje y el estudio independiente, no requiere de instalacion previa. Parte del soporte sonoro fue extraida de las grabaciones realizadas en Canada por la editorial de Ciencias Medicas para el Libro de Texto Inside Dentistry.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.392 | 0.190 |
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".