Biosensors for the Detection of Antibiotics in Poultry Industry—A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antibiotic resistance is emerging as a potential threat in the next decades. This is a global phenomenon whereby globalization is acting as a catalyst. Presently, the most common techniques used for the detection of antibiotics are biosensors, ELISA and liquid chromatography-mass spectrometry. Each of these techniques has its benefits as well as drawbacks. This review aims to evaluate different biosensing techniques and their working principles in order to accurately, quickly and practically detect antibiotics in chicken muscle and blood serum. The review is divided into three main sections, namely: a biosensors overview, a section on biosensor recognition and a section on biosensor transducing elements. The first segment provides a detailed overview on the different techniques available and their respective advantages and disadvantages. The second section consists of an evaluation of several analyte systems and their mechanisms. The last section of this review studies the working principles of biosensing transducing elements, focusing mainly on surface plasmon resonance (SPR) technology and its applications in industries.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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 it