Bibliographic record
Abstract
A 36/spl times/24 mm active pixel sensor imaging area device is studied which would be ideal for use with standard 35 mm cameras. By applying multichip methods to active pixel sensors, the 39/spl times/30 mm system contains on board all the control circuitry and A/D converters, so the system outputs digital data. The large area requires a redundancy of design for a high yield. This starts with the active pixel cell, which able to withstand several defects and still be repairable, which CCD cells are not. The whole system is targeted at preventing bad rows or columns. By using spares in the row and column circuitry, as well as spare A/D converters the chip yield is only limited by a relatively small logic and control block. With repairs the yield of this 11.7 sq. cm system goes from almost nil to more than 80%-93% with modest defect densities of 1.5 to 0.5 per sq. cm. By being a retrofit for current 35 mm cameras, and having larger photodiode pixels than current APS's this CMOS device would be nearly as sensitive as CCD's but at much lower production costs and much higher yields.
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 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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".