Biosensors for Monitoring, Detecting, and Tracking Dissemination of Poultry-Borne Bacterial Pathogens Along the Poultry Value Chain: A Review
Bibliographic record
Abstract
The poultry industry plays a crucial role in global food production, with chickens being the most widely consumed as a rich protein source. However, infectious diseases pose significant threats to poultry health, underscoring the need for rapid and accurate detection to enable timely intervention. In recent years, biosensors have emerged as essential tools to facilitate routine surveillance on poultry farms and rapid screening at slaughterhouses. These devices provide producers and veterinarians with timely information, thereby promoting proactive disease management. Biosensors have been miniaturized, and portable platforms allow for on-site testing, thereby enhancing biosecurity measures and bolstering disease surveillance networks throughout the poultry supply chain. Consequently, biosensors represent a transformative advancement in poultry disease management, offering rapid and precise detection capabilities that are vital for safeguarding poultry health and ensuring sustainable production systems. This section offers an overview of biosensors and their applications in detecting poultry diseases, with a particular emphasis on enteric pathogens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".