Exploring Metric Dimensions for Dimensionality Reduction and Navigation in Rough Graphs
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we propose to calculate multiple metric dimensions using different distances.This approach can lead to the implementation of dimensionality reduction techniques for a specific information system.By combining traditional graph theory with rough set theory, which involves using uncertain or ambiguous data, we can construct a rough graph to depict the relationships between attributes.The rough graph is constructed based on the rough membership function, which defines the link between the conditional and decision features.By utilizing degree-based metric dimensions, we can identify and remove inconsistent features from the information system.If each vertex's vector of distances from the other vertices in the set is unique, it means that the set of vertices can fully determine the graph.The metric dimension, which represents the smallest cardinality of a resolving set, plays a role in facilitating navigation and aiding in location determination within the graph.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it