EXPERIMENTAL APPROACH TO CURVE RECONSTRUCTION BASED ON HUMAN VISUAL PERCEPTION
Why this work is in the frame
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Bibliographic record
Abstract
Curve reconstruction is the problem of constructing polygonal curve(s) from a set of sample points. Among all the research to solve this problem, visual perception based algorithms, DISCUR and VICUR, come up in recent years as intuitive methods. DISCUR and VICUR connect points into patterns that agree with human visual perception by applying two major Gestalt laws: nearness and smoothness. In this paper, the work in DISCUR and VICUR is extended by conducting an experiment to quantify how these two laws underlie human vision. DOE and ANOVA are used to test the hypotheses about how a connection may be influenced by its neighboring points whereas the multivariable nonlinear regression model is adopted to formulate the influence of Gestalt laws on point connectivity. The experimental results show that the proposed approach is effective.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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