Multiplex Dipstick Technologies for Rapid and Simultaneous Screening of Analytes of Importance in Agri-Food-Nutrition and Health Care: A Review
Bibliographic record
Abstract
Dipstick test kits are being widely used for the rapid screening of a range of antigens or toxins in food, agriculture, and health care. They provide specific results on-site within 10 min with suitable accuracy and are, therefore, cost-effective. Multiplex dipsticks also provide the opportunity for simultaneous detection of multiple antigens in the target sample without using expensive instrumentation, minimizing the cost of analysis as well as the duration of assay. Because of these benefits, dipstick kits are widely being used in the simultaneous detection of several antigens/toxins in large number of samples and in high-throughput manner. This review focuses on the current status of developed multiplex strips and its working principles and future direction of the technology in the agriculture, food, nutrition, and health care sectors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".