Bibliographic record
Abstract
Abstract Biophotoreceptor arrays are hybrid electronic devices that utilize spectrally sensitive neuronal cells and biomolecules as the primary photon‐detecting element in a distributed network of light sensors. The patterned microelectrode arrays that interface with the biological material are constructed from readily available silicon‐based circuitry or, more recently, electrodes imprinted on flexible polymer substrates. From an engineer's perspective, these biological materials present new design opportunities for developing imaging systems that more closely emulate the signal detection and information processing structures found in nature. Several important attributes of natural vision include highly sensitive light receptors based on molecular phototransduction, a nonregular distribution of sensing elements on the imaging surface, and nonplanar image formation in the eye. The primate retina is one illustrative example of how phototransduction and neuronal interactions can lead to efficient imaging of dynamic three‐dimensional environments. The extraction and deposition of nerve cells and molecular proteins on microelectrode array platforms provide researchers with a means to observe the behavior of these distributed sensing systems. Organic and flexible polymer electronics represent a new technology that permit the creation of nonrigid photoreceptor arrays which replicate the curved imaging surfaces of single aperture and apposition compound eyes. A simple motion detection system is presented as an illustration of how photosensitive biomaterials and flexible printed electronics provide an alternative paradigm for developing new types of imaging systems. An important area where biomedical researchers can exploit engineered biophotoreceptor arrays is the fabrication of biocompatible artificial retinas and prosthetic devices for patients with severe visual impairment.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".