Robust centroid determination of noisy data using FCM and domain specific partitioning
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
Functional magnetic resonance imaging (FMRI) datasets are composed of spatial and temporal features and contain unique noise degradation. We propose a feature partition along noise-specific domains to fit the fuzzy c-means (FCM) algorithm to this problem. Each domain will consist of unique features and use a domain-specific metric. The distance term in the FCM membership update equation is replaced by a weighted sum of domain distances. Exploiting conceptual cleavage of the sample features invites intuitive remedial action in the form of robust metrics, decreased weighting, or selective enhancement processing. Robust centroids are determined by suppressing the role of feature subsets contaminated by significant noise levels or intractable noise types. This paper examines synthetic datasets of FMRI activations and shows that a specialized FCM algorithm determines higher accuracy centroids in the presence of high noise levels.
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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