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<i>Fibrobacteria class. nov.</i>

2015· other· en· W2217359604 on OpenAlexaff
Anne M. Spain, Cecil W. Forsberg, Lee R. Krumholz

Bibliographic record

VenueBergey's Manual of Systematics of Archaea and Bacteria · 2015
Typeother
Languageen
FieldEnvironmental Science
TopicMicrobial Community Ecology and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsClass (philosophy)CombinatoricsType (biology)GenusMathematicsType genusBiologyComputer scienceType speciesZoologyArtificial intelligencePaleontology

Abstract

fetched live from OpenAlex

Abstract Fi.bro.bac.te'ri.a. N.L. masc. n. Fibrobacter type genus of the type order of the class; suff. ‐ ia ending proposed by Gibbons and Murray and by Stackebrandt et al. to denote a class; N.L. neut. pl. n. Fibrobacteria the Fibrobacter class. Taxonomic and Nomenclature Notes According to the List of Prokaryotic names with Standing in Nomenclature (LPSN), the taxonomic status of the class Fibrobacteria is: correct name (last update, February 2025) * . LPSN classification: Bacteria / Pseudomonadati / Fibrobacterota / Fibrobacteria The class Fibrobacteria can also be recovered in the Genome Taxonomy Database (GTDB) as c__Fibrobacteria (version v220) ** . GTDB classification: d__Bacteria / p__Fibrobacterota / c__Fibrobacteria * Meier‐Kolthoff et al. ( 2022 ). Nucleic Acids Res , 50 , D801 – D807 ; DOI: 10.1093/nar/gkab902 ** Parks et al. ( 2022 ). Nucleic Acids Res , 50 , D785 – D794 ; DOI: 10.1093/nar/gkab776

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), 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: Other · Consensus signal: none
Teacher disagreement score0.124
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.233
Teacher spread0.221 · 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
GenreOther

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
Published2015
Admission routes1
Has abstractyes

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