Granular Encoding-Decoding for the Design of Granular Architectures
Bibliographic record
Abstract
Fuzzy constructs (models) interact with numeric entities. This communication is realized through mechanisms of encoding and decoding. Encoding realizes a representation of input data through a collection of information granules and, as such, can be viewed as a nonlinear transformation of the original numeric entity to some internal (granular) format. The decoding is carried out in the opposite direction: the result at the level of information granules is brought back to the numeric entity. This study provides a unified view of the functionalities and design of these mechanisms by studying their components information granules. The optimization concerns a minimization of loss functions guided by criteria of minimal reconstruction error and a retention of semantics of the codebooks (landmarks) encountered in the encoding and decoding procedures. The role of triangular fuzzy sets is discussed along with associated learning mechanisms. Illustrative applications to hierarchical models and fuzzy cognitive maps are covered.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".