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Record W2951398685 · doi:10.1038/s42003-018-0086-7

Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria

2018· article· en· W2951398685 on OpenAlexafffundabout
David Colatriano, Patricia Q. Tran, Céline Guéguen, William J. Williams, Connie Lovejoy, David A. Walsh

Bibliographic record

VenueCommunications Biology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversité LavalFisheries and Oceans CanadaTrent UniversityConcordia University
FundersOffice of ScienceDivision of Ocean SciencesJoint Genome InstituteFisheries and Oceans CanadaDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsPelagic zoneEnvironmental scienceArcticCarbon cycleOrganic matterOceanographyTotal organic carbonDissolved organic carbonEcosystemEcologyGeologyBiology

Abstract

fetched live from OpenAlex

The Arctic Ocean currently receives a large supply of global river discharge and terrestrial dissolved organic matter. Moreover, an increase in freshwater runoff and riverine transport of organic matter to the Arctic Ocean is a predicted consequence of thawing permafrost and increased precipitation. The fate of the terrestrial humic-rich organic material and its impact on the marine carbon cycle are largely unknown. Here, a metagenomic survey of the Canada Basin in the Western Arctic Ocean showed that pelagic Chloroflexi from the Arctic Ocean are replete with aromatic compound degradation genes, acquired in part by lateral transfer from terrestrial bacteria. Our results imply marine Chloroflexi have the capacity to use terrestrial organic matter and that their role in the carbon cycle may increase with the changing hydrological cycle.

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)0.000
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

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

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 head0.063
GPT teacher head0.314
Teacher spread0.251 · 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

Citations110
Published2018
Admission routes3
Has abstractyes

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