Microchip capillary electrophoresis dairy device using fluorescence spectroscopy for detection of ciprofloxacin in milk samples
Bibliographic record
Abstract
Detecting antibiotics in the milk supply chain is crucial to protect humans from allergic reactions, as well as preventing the build-up of antibiotic resistance. The dairy industry has controls in place at processing facilities, but controls on dairy farms are limited to manual devices. Errors in the use of these manual devices can result in severe financial harm to the farms. This illustrates an urgent need for automated methods of detecting antibiotics on a dairy farm, to prevent the shipment of milk containing antibiotics. This work introduces the microchip capillary electrophoresis dairy device, a low-cost system that utilizes microchip capillary electrophoresis as well as fluorescence spectroscopy for the detection of ciprofloxacin contained in milk. The microchip capillary electrophoresis dairy device is operated under antibiotic-absent conditions, with ciprofloxacin not present in a milk sample, and antibiotic-present conditions, with ciprofloxacin present in a milk sample. The response curve for the microchip capillary electrophoresis dairy device is found through experimental operation with varied concentrations of ciprofloxacin. The sensitivity and limit of detection are quantified for the microchip capillary electrophoresis dairy device.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".