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Record W2786640838 · doi:10.1073/pnas.1722325115

Metaproteomics method to determine carbon sources and assimilation pathways of species in microbial communities

2018· article· en· W2786640838 on OpenAlexaff
Manuel Kleiner, Xiaoli Dong, Tjorven Hinzke, Juliane Wippler, Erin Thorson, Bernhard Mayer, Marc Strous

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMetaproteomicsMetagenomicsMicroorganismAutotrophBiologyCarbon fixationIsotopes of carbonMicrobial ecologyMicrobial population biologyHeterotrophComputational biologyEcologyEnvironmental chemistryBacteriaChemistryBiochemistryGeneGeneticsTotal organic carbonCarbon dioxide

Abstract

fetched live from OpenAlex

Significance To understand the roles that microorganisms play in diverse environments such as the open ocean or the human intestinal tract, we need an understanding of their metabolism and physiology. A variety of methods such as metagenomics and metaproteomics exist to assess the metabolism of environmental microorganisms based on gene content and gene expression. These methods often only provide indirect evidence for which substrates are used by a microorganism in a community. The direct protein stable isotope fingerprint (SIF) method that we developed allows linking microbial species in communities to the environmental carbon sources they consume by determining their stable carbon isotope signature. Direct protein-SIF also allows assessing which carbon fixation pathway is used by autotrophic microorganisms that directly assimilate CO 2 .

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.063
GPT teacher head0.291
Teacher spread0.228 · 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 designBench or experimental
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

Citations77
Published2018
Admission routes1
Has abstractyes

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