Bibliographic record
Abstract
Abstract Biosensors, as defined by Biosensors and Bioelectronics, are analytical devices “incorporating a biological material, a biologically derived material or biomimetic intimately associated with or integrated within a physicochemical transducer or transducing microsystem”. Fluorescence‐based biosensors are those devices that derive an analytical signal from the fluorescence emission process. Such biosensors may be used for a wide variety of tasks, including: detection of compounds of biomedical, 1 environmental 2 or defense interest, 3 on‐line monitoring for process control, 4 monitoring of foodstuffs, 5 selective detection of compounds undergoing a chemical separation, 6 and screening of drug compounds. 7 Advantages of such devices include: 8 high selectivity; rapid response times; reusability; amenability to remote analysis; and immunity from electrical interferences. The selective nature of the complexation between the biomolecule and the analyte, combined with the small size of the device and the advantages of total internal reflection (TIR)‐based spectroscopy, 9 also results in an ability to measure analytes in complex matrixes. Such samples may include highly scattering systems such as milk or whole blood, 10 or relatively inaccessible locations such as groundwater wells, or even intracellular environments. 11 The key limitation of such devices mainly centers around the poor stability of biological compounds, which can lead to a substantial drift in instrument response over time. There is also the potential for interferences related to biological autofluorescence, and the analyte‐dependent sensitivity and limit of detection (LOD) for sensors, which rely on the nature of both the protein and the fluorescent probe utilized. Finally, in cases where immunological reagents are used, the devices can show a lack of reversibility, and may operate only as a “one‐shot” screen, without the potential for continuous, quantitative analysis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".