Harnessing agri-food system microbiomes for sustainability and human health
Bibliographic record
Abstract
Food system microbiomes include complex microbial networks that range from soil and marine environments to primary agriculture, farming, food processing, and distribution, and which influence human and environmental health. Advances in “omics” technologies, such as metagenomics, metatranscriptomics, metaproteomics, metabolomics, and culturomics, and their integration have deepened our understanding of microbiome dynamics and interactions. This growing knowledge is being leveraged to develop microbiome-based solutions enabling more sustainable food systems. This review explores microbiome interconnections along the food system and how this and other knowledge relating to microbiomes can be harnessed to, among other things, enhance crop resilience and productivity, improve animal health and performance, refine management practices in fishing and aquaculture, or prolong shelf life and reduce food spoilage during distribution. The often-overlooked role of bacteriophages on shaping microbiomes is discussed, as is the impact of diet on the human gut microbiota and, in turn, health. Despite advances, knowledge remains incomplete in particular areas and targeted experimental approaches are necessary to fill these gaps—going beyond merely predicting microbiome functionality. Ultimately, the ideal development of microbiome-based innovations in food systems will require collaboration between stakeholders and regulators to ensure safety, efficacy, and widespread adoption, unlocking its full potential to improve the health of animals, humans and the environment globally.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".