New perspectives on metagenomic analysis for pathogen monitoring in sustainable freshwater aquaculture production: a systematic review
Bibliographic record
Abstract
The freshwater and saltwater aquatic food sector has experienced the most significant growth in recent years and is increasingly recognized as a sustainable alternative for fostering prosperous societies self-sufficiently and ecologically. One primary economic and health risk factor in aquaculture production is health control, with potentially more severe impacts observed in tropical and developing countries. While metagenomics holds great promise for application in agro-industrial fields like aquaculture, its adoption remains limited. Consequently, this study aimed to assess the prospects for developing and applying metagenomics in identifying pathogens in freshwater aquaculture. The WIPO database was used to search for patents developed using metagenomics to monitoring pathogens in freshwater aquaculture. Metagenomics methods have been extensively employed in different fields, such as, medicine, veterinary, biotechnology, agriculture, particularly in studies focusing on microbial communities in different ecosystems. In aquaculture, the utilization of metagenomics has predominantly revolved around investigating antibiotic resistance genes, primarily in saltwater farms. Despite this, freshwater aquaculture, particularly in fish and crustacean farming, aligns closely with sustainable development goals, notably (SDGs) 2, 3, 6, and 13. Countries such as the United States of America, South Korea, and Canada stand at the forefront of utilizing metagenomics for disease monitoring in freshwater aquaculture, evidenced by their active patent developments. The metagenomic analysis, coupled with bioinformatics tools and databases, represents a rapid, secure, and non-invasive approach to environmental monitoring for preventive purposes. Systematic review registration https://osf.io/srpyz/ , identifier 10.17605/OSF.IO/SRPYZ .
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".