Pre-shaped fuzzy c-means algorithm (PFCM) for transparent membership function generation
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
The fuzzy c-means algorithm (FCM) is widely used in the generation of membership functions from historical data. However, most of FCM-based membership function generation algorithms consider little on the transparency or the understandability of the resulting membership functions. In other words, there is inconsistency in generating membership functions using traditional FCM algorithm. This paper proposes a pre-shaped fuzzy c-means algorithm (PFCM) to generate more transparent membership functions. PFCM will preserve the predefined transparent shapes of membership functions during the process of the optimization of the clustering algorithm. Numeric experiments based on data collected in a real project demonstrate the feasibility and superiority of the proposed new algorithm.
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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 | 0.001 | 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