Virtual Reality Spaces for Visual Data Mining with Multiobjective Evolutionary Optimization: Implicit and Explicit Function Representations Mixing Unsupervised and Supervised Properties
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
Multi-objective optimization is used for the computation of virtual reality spaces for visual data mining and knowledge discovery. Two methods for computing new spaces are discussed: implicit and explicit function representations. In the first, the images of the objects are computed directly, and in the second, universal function approximators (neural networks) are obtained. The pros and cons of each approach are discussed, as well as their complementary character. The NSGA-II algorithm is used for computing spaces requested to minimize two objectives: a similarity structure loss measure (Sammon's error) and classification error (mean cross-validation error on a k-nn classifier). Two examples using solutions along approximations to the Pareto front are presented: Alzheimer's disease gene expressions and geophysical fields for prospecting underground caves. This approach is a general non-linear feature generation and can be used in problems not necessarily oriented to the construction of visual data representations.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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