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Record W2127350583 · doi:10.1111/aji.12319

Microbes Central to Human Reproduction

2014· review· en· W2127350583 on OpenAlexaff
Gregor Reid, Patrizia Brigidi, Jeremy P. Burton, Nikhat Contractor, Sylvia H. Duncan, Emilie Fargier, Colin Hill, Sarah Lebeer, Rocı́o Martı́n, Andrew J. McBain, Gil Mor, Catherine O’Neill, Juan M. Rodrı́guez, Jonathan R. Swann, Saskia van Hemert, Juliett Ansell

Bibliographic record

VenueAmerican Journal of Reproductive Immunology · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsHuman reproductionLongevityReproductionBiologyEnvironmental ethicsDiseaseMedicineEcologyPathology

Abstract

fetched live from OpenAlex

As studies uncover the breadth of microbes associated with human life, opportunities will emerge to manipulate and augment their functions in ways that improve health and longevity. From involvement in the complexities of reproduction and fetal/infant development, to delaying the onset of disease, and indeed countering many maladies, microbes offer hope for human well-being. Evidence is emerging to suggest that microbes may play a beneficial role in body sites traditionally viewed as being sterile. Although further evidence is required, we propose that much of medical dogma is about to change significantly through recognition and understanding of these hitherto unrecognized microbe-host interactions. A meeting of the International Scientific Association for Probiotics and Prebiotics held in Aberdeen, Scotland (June 2014), presented new views and challenged established concepts on the role of microbes in reproduction and health of the mother and infant. This article summarizes some of the main aspects of these discussions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.015
GPT teacher head0.331
Teacher spread0.317 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations55
Published2014
Admission routes1
Has abstractyes

Explore more

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