Biotoxin sensing in food and environment via microchip
Bibliographic record
Abstract
Biotoxin contamination in food and environmental samples has threatened health or life of human and animals. Thus, a rapid lab-independent sensing method for biotoxin determination is urgently required. Microchip sensing system allows a promising rapid and low-cost detection strategy. Herein, the recent development of various microchips, including microfluidic chip and microarray, has been discussed to sense various biotoxins in food and environmental samples (i.e. phytotoxin, animal toxin, marine toxin, and mycotoxin). Microchip can be served as both analyte transportation and sensing platform, via either labeling or labeling-free sensing strategy. Because of its fast sensing time, low sample consumption, ready portability, and high compatibility, it has been extensively employed in biotoxin determination in both academic and industrial circle. With the advances of fabrication strategies and sensing modes, the microchip performance has been dramatically improved, including sensitivity, efficiency, reliability, stability, cost saving, portability. The potential applications can be found wide spread in biotoxin sensing in the near future, while their practical application in real sample need to be addressed.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.002 |
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".