Spatial and Temporal Properties of Stereoscopic Surface Interpolation
Bibliographic record
Abstract
It is well established that under a wide range of conditions when a sparse collection of texture elements varies smoothly in depth, the spaces between the elements are assigned depth values. This disparity interpolation process has been studied in an effort to define some of its fundamental spatial and temporal constraints. To assess disparity interpolation we employed two tasks: a novel task that relies on the bisection of illusory boundaries created when subjective stereoscopic surfaces intersect, and one that relies on a 3-D shape discrimination. The results of both experiments show that there is no improvement in performance when texture density is increased from near 0.20 to 0.85 or when exposure duration is increased from 50-100 to 1000 ms. This lack of dependence on the addition of features that define the interpolated surface, along with the abrupt decline in performance below a critical value, is consistent with the view that surface interpolation is an important function of human stereoscopic vision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".