Bibliographic record
Abstract
Introduction In 1997, we were designing experiments to assess the stability of correspondence between points in the two retinas and the phenomenon of stereoscopic hysteresis (Diner and Fender, 1987; Fender and Julesz, 1967). As part of these experiments, we presented binocularly uncorrelated random-dot images to the two eyes in a stereoscope. Binocularly uncorrelated images produce a percept of noisy, incoherent depth, since there is no consistent disparity signal. However, when we moved the images in the two eyes laterally in opposing directions we obtained a compelling sense of coherent motion in depth. When the display was stopped, the stimulus again appeared as noisy depth. We quickly realized that the motion-in-depth percept was consistent with dichoptic motion cues in the stimulus. Thus, a compelling sense of changing depth can be supported by a stimulus that produces no coherent static depth. This was quite surprising, since experiments several years earlier had suggested that stereoscopic motion-in-depth perception could be fully explained by changes in disparity between correlated images. Unknown to us, Shioiri and colleagues had made similar findings, which they reported at the same Association for Research in Vision and Opthalmology (ARVO) meeting where we first presented our findings (Shioiri et al ., 1998, 2000), although we found out they had also presented them earlier at a meeting in Japan. We performed a number of experiments on this phenomenon, reported as conference abstracts (Allison et al ., 1998; Howard et al ., 1998) that were subsequently cited.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".