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Record W2274912893 · doi:10.3389/fmicb.2016.00073

Back to the Future of Soil Metagenomics

2016· article· en· W2274912893 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Microbiology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsAgriculture and Agri-Food CanadaWestern UniversityUniversity of Waterloo
FundersNatural Environment Research CouncilInstitut National de la Recherche AgronomiqueCentre National de la Recherche ScientifiqueMinistère de l'Education Nationale, de l'Enseignement Supérieur et de la RechercheMinistère de l'Education Nationale, de l'Enseignement Superieur et de la RechercheSight Research UKAgence Nationale de la RechercheNational Science Foundation
KeywordsMetagenomicsBiologyFront (military)EcologyComputational biologyGeologyOceanographyGenetics

Abstract

fetched live from OpenAlex

Direct extraction and characterization of microbial community DNA through PCR amplicon surveys and metagenomics has revolutionized the study of environmental microbiology and microbial ecology. In particular, metagenomic analysis of nucleic acids provides direct access to the genomes of the “uncultivated majority.” Accelerated by advances in sequencing technology, microbiologists have discovered more novel phyla, classes, genera, and genes from microorganisms in the first decade and a half of the twenty-first century than since these “many very little living animalcules” were first discovered by van Leeuwenhoek. The unsurpassed diversity of soils promises continued exploration of a range of industrial, agricultural, and environmental functions. The ability to explore soil microbial communities with increasing capacity offers the highest promise for answering many outstanding who, what, where, when, why, and with whom questions such as: Which microorganisms are linked to which soil habitats? How do microbial abundances change with changing edaphic conditions? How do microbial assemblages interact and influence one another synergistically or antagonistically? What is the full extent of soil microbial diversity, both functionally and phylogenetically? What are the dynamics of microbial communities in space and time? How sensitive are microbial communities to a changing climate? What is the role of horizontal gene transfer in the stability of microbial communities? Do highly diverse microbial communities confer resistance and resilience in soils?

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.622
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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