Bibliographic record
Abstract
Abstract A photodiode is a solid‐state sensor that responds to light by producing a measurable electronic current. Operating as a quantum threshold detector, a photodiode will only detect photons that exceed a certain energy level (the bandgap energy of the photodiode material) while not detecting photons below this level. This article covers the basics of pn‐junction, PIN, and avalanche photodiode operation, as well as the use of photodiodes in several example applications in biology and medicine. Photodiodes are attractive for biomedical applications because they are inexpensive compared with many other detection mechanisms, easy to use, small scale, readily available, and capable of operation in an array for parallel sensing. They are ideal for applications involving bed‐side monitoring such as pulse oximetry, rapid diagnosis including point‐of‐care instrumentation based on lab‐on‐a‐chip technology, and other instances where equipment benefits from greater miniaturization or portability. Semiconductor photodiodes are fabricated using similar methods used to produce transistors and integrated circuits and continue to evolve together with integrated circuits to be efficient and inexpensive photodetectors while maintaining their basic simplicity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.089 | 0.074 |
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".