A Consumer Perspective of Service Quality in the Airline Industry
Bibliographic record
Abstract
Heparin is a highly negatively charged sulfated linear polymer glycosaminoglycan that has been widely used as an anticoagulant in medicine. Protamine is a cationic protein rich in arginine that is used to treat the blood-brain barrier during excess heparin surgery. Trypsin is the most important digestive enzyme-encoding generated by the pancreas and can specifically cleave the carboxyl ends of arginine and lysine residues. Heparin, protamine, and trypsin interact and constrain each other, and their fluctuations reflect the body's dysfunction. Therefore, it is necessary to develop a fast, sensitive, and highly selective assay for regularly monitoring the levels of heparin, protamine, and trypsin in serum. Herein, a fluorescent and colorimetric dual-mode upconversion nanoparticle (UCNP) biosensor was used for the determination of heparin, protamine, and trypsin based on the oxidase-mimicking activity of Ce<sup>4+</sup> and electrostatic control. The biosensor exhibited sensitive detection of heparin, protamine, and trypsin with low limits of detection (LODs) of 16 ng/mL, 87 ng/mL and 31 ng/mL, respectively. Furthermore, the designed biosensor could eliminate autofluorescence, which not only effectively increased the accuracy of the sensor but also provided a new sensing pathway for the detection of differently charged biotargets.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".