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Record W2883352049 · doi:10.1128/msystems.00115-18

Announcement of 2019 Keystone Symposia Conference: “Microbiome: Chemical Mechanisms and Biological Consequences”

2018· editorial· en· W2883352049 on OpenAlexaboutno aff
Emily P. Balskus, Peter J. Turnbaugh, Dennis W. Wolan

Bibliographic record

VenuemSystems · 2018
Typeeditorial
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobiomeMultidisciplinary approachEnvironmental ethicsIntersection (aeronautics)Engineering ethicsBiologySociologyEngineeringGeographyBioinformaticsPhilosophySocial scienceCartography

Abstract

fetched live from OpenAlex

The Keystone Symposia will be hosting a conference organized by Emily Balskus, Peter Turnbaugh, and Dennis Wolan entitled "Microbiome: Chemical Mechanisms and Biological Consequences" 10 to 14 March 2019 in Montreal, Québec, Canada. Our goal for this meeting is to focus attention on the intersection of chemistry and biology by bringing together scientists in these two disciplines, while also including talks about other hosts, environmental microbiomes, and multidisciplinary research platforms. The focus of this conference is to emphasize our community's need to continue adopting other scientific disciplines to ultimately generate a broad understanding of microbiomes and the cross talk microbes have with their environment. We are inviting speakers from across the globe that interrogate fundamental chemical processes of microbiomes, including small-molecule and xenobiotic metabolism, natural product synthesis, and the many microbial enzymes responsible for the production of these biologically relevant metabolites. The ability to link the chemical foundations of microbes with biological outcomes would provide tremendous contributions to this emerging field of study.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.238
Teacher spread0.226 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2018
Admission routes1
Has abstractyes

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