Bibliographic record
Abstract
In recent studies, we showed how pupil size variations had assisted us to distinguished between two distinct human visual awareness that are produced by the retinal centralis versus the retinal peripheries. In the present proposal, however, we would like to discuss how pupil shape variations could affect the mammalians’ visual awareness. It had been suggested that, animals with vertical pupils such as cats are hunters; but those with horizontal ones such as horses are emotional animals, see reference 1. Humans however have rounded pupils, meaning, they had been standing in the gray region. Few humans, however, had been suffering from cat-eye syndrome, a syndrome that can negatively affects not only their visual awareness, but also their cardiac health and their respiration, see reference 2. We therefore suggested that the aforementioned syndrome should be simulatingly investigated in healthy human subjects and factually in affected human subjects. Namely, healthy human subjects should wear a specialized bio-compatible lens contact that should be convey the geometry of cat’s pupils, namely, it will restrict significant number of light rays to interact with the retinal peripheries. Both healthy and affected human subjects are assumed to perform several visual tasks simultaneous with brain imaging, ECG and eye tracking data acquisition. If the extracted features of these affected human subjects’ data are sufficiently matched with healthy subjects’ data; corrective pupil surgeries might be recommended. We understand that such surgeries will require lots of work on bio-materials complex designs & manipulations, in addition to, surgical precision so that the new implementations will be mechanically cooperating synchronically with the mechanics of the iris, ciliary muscles & fibers in an optimal way. It is a complex operation; however, we think that these corrections might not only avoid further deteriorations against the patients’ visual awareness but it may improve their cardiac and respiration well-being.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".