Searching for structure in data with fuzzy clusters of variable dimensionality of feature subspaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Structural relationships in data are revealed by methods of clustering and fuzzy clustering. In essence, clustering leads to the reduction of data. Dimensionality reduction comes as a complementary process in which we eliminate some features (attributes). This study introduces a concept of structure reduction which is guided by a criterion of structure retention. In particular, it is shown that each cluster could be described by a different subset of features so that finally the reduction leads to the local feature subspaces. By analyzing the resulting subspaces, one could gain a better insight into a nature of the contributing features and in this way identify subsets of the most meaningful ones. The reduction problem is formulated and formalized as a certain combinatorial optimization task whose solution is provided by means of particle swarm optimization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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