Bibliographic record
Abstract
Viruses and pathogenic bacteria spread rapidly through the population via air, contaminated water and food, body fluids, or close contact with infected individuals. They cause millions of deaths worldwide; a notable recent example is the COVID-19 pandemic. Medical considerations are different for viral and bacterial infections, and it is vital to distinguish them before starting any treatment plan, but viruses and bacteria alike require rapid detection and quantification methods. The early detection of viruses and bacteria can minimize human health issues associated with infections and reduce their environmental, social, and economic impacts. Quantum dots have recently attracted researchers’ attention as a type of fluorescent dye/tag and signal amplifier for biosensing applications due to their outstanding optical and physicochemical properties. Quantum dot-based biosensors have proven to be reliable and fast methods for detecting bacteria and viruses. They have mainly been utilized in optical and electrochemical biosensor design and pathogen imaging. Herein, we summarize recent developments in quantum dot-based biosensors for bacteria and viruses. The most commonly used transducers in current biosensor designs involve fluorescence microscopy, fluorescence spectroscopy, and electrochemistry.
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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".