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Record W4409981974 · doi:10.1139/facets-2024-0182

Beneficial microbes for One Health in Canada: a review of evidence and a policy proposal

2025· review· en· W4409981974 on OpenAlexaffvenueabout
Gregor Reid, Emma Allen‐Vercoe, Kait F. Al, Jeremy P. Burton, Brendan A. Daisley, Brian Dixon, Hesam Mousavi, Svein Øivind Sølberg, Raquel S. Peixoto, Michael Silverman, Dragana Skokovic-Sunjic

Bibliographic record

VenueFACETS · 2025
Typereview
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of GuelphUniversity of WaterlooLawson Health Research InstituteWestern University
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

The recognition that microbes are the life support system of the biosphere and play a major role in the health of all life forms provides us with a unique opportunity to channel resources into utilizing them for our benefit. This policy document was prepared by assessing the scientific rationale and evidence for the application of beneficial microbes to One Health. Ten recommendations are made including the establishment of a new national program that creates strategies, provides funding, and partners with industries and end-users to make Canada a self-directed global leader in this transformative field. Issues covered include biotics, fecal microbiota transplant, fundamental and applied research, regulatory, the education system, and applications to pollinators, aquaculture, coral, biofertilizers, livestock, companion animals, horses, and sustainability of agriculture and environmental management. It is hoped that this document will provide politicians, bureaucrats, academics, and representatives of the end users of beneficial microbial products the incentive to appoint an expert panel to develop the means to implement a program of this type. At a time when climate change, biodiversity, long-term health, integration of cultures, and access to home resourced nutritious food are high on government agendas, the proposed program offers a novel means to positively influence residents across the country.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.540
Threshold uncertainty score0.876

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.141
GPT teacher head0.447
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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