Beneficial microbes for One Health in Canada: a review of evidence and a policy proposal
Bibliographic record
Abstract
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.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".