Bibliographic record
Abstract
A new information system representation, which inherently represents indiscernibility is presented.The basic structure of this representation is a Binary Decision Diagram.We offer testing results for converting large data sets into a Binary Decision Diagram Information System representation, and show how indiscernibility can be efficiently determined.Furthermore, a Binary Decision Diagram is used in place of a relative discernibility matrix to allow for more efficient determination of the discernibility function than previous methods.The current focus is to build an implementation that aids in understanding how binary decision diagrams can improve Rough Set Data Analysis methods. Representaci ón de datos de conjuntos aproximados mediante diagramas de decisi ón binariosResumen.Se expone una nueva representación de sistema de información, que incorpora inherentemente la indiscernibilidad.La estructura básica de esta representación es un diagrama de decisión binario.Se ofrecen los resultados de unas pruebas llevadas a cabo para convertir grandes conjuntos de datos en una representación de sistema de información de diagrama de decisión binario, y se muestra cómo se puede determinar, de forma eficaz, la indiscernibilidad.Además, se utiliza un diagrama de decisión binario en lugar de una matriz de discernibilidad relativa para permitir que la determinación de la función de discernibilidad sea más eficaz que en los métodos anteriores.Actualmente, el interés se centra en la construcción de una implementación que ayude a entender cómo los diagramas de decisión binarios pueden mejorar los métodos de análisis de datos de los conjuntos aproximados.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".