Bibliographic record
Abstract
The omnifocus video camera takes videos, in which objects at different distances are all in focus in a single video display. The omnifocus video camera consists of an array of color video cameras combined with a unique distance mapping camera called the Divcam. The color video cameras are all aimed at the same scene, but each is focused at a different distance. The Divcam provides real-time distance information for every pixel in the scene. A pixel selection utility uses the distance information to select individual pixels from the multiple video outputs focused at different distances, in order to generate the final single video display that is everywhere in focus. This paper presents principle of operation, design consideration, detailed construction, and over all performance of the omnifocus video camera. The major emphasis of the paper is the proof of concept, but the prototype has been developed enough to demonstrate the superiority of this video camera over a conventional video camera. The resolution of the prototype is high, capturing even fine details such as fingerprints in the image. Just as the movie camera was a significant advance over the still camera, the omnifocus video camera represents a significant advance over all-focus cameras for still images.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".