Harnessing agri-food system microbiomes for sustainability and human health
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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