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<i>Thermotogales</i>

2025· other· en· W4411815026 on OpenAlexaff
Anne A. Farrell, Olga Zhaxybayeva, Camilla Nesbø, Stéphane L’Haridon

Bibliographic record

VenueBergey's Manual of Systematics of Archaea and Bacteria · 2025
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicLight effects on plants
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsBusiness

Abstract

fetched live from OpenAlex

Abstract Ther.mo.to.ga'les. N.L. fem. n. Thermotoga type genus of the order; ‐ ales ending to denote an order; N.L. fem. pl. n. Thermotogales , the order of Thermotoga. The members of the order Thermotogales are sheathed, thermophilic or hyperthermophilic, strictly anaerobic, fermentative, and hydrogen‐producing bacteria. This is the only order of the Thermotogota phylum that contains hyperthermophilic members. The separation of Thermotogales from other Thermotogota orders, and the division of Thermotogales into two families is supported by phylogenetic analysis of 50 concatenated ribosomal proteins and the presence of shared conserved signature indels. The Thermotogales encompasses two families: Thermotogaceae, which includes the genera Thermotoga and Pseudothermotoga, and Fervidobacteriaceae, which consists of the genera Thermosipho and Fervidobacterium. Type genus : Thermotoga Huber et al. 1986, VL22, emend. Bhandari and Gupta 2014. Taxonomic and Nomenclature Notes According to the List of Prokaryotic names with Standing in Nomenclature (LPSN), the taxonomic status of the order Thermotogales is: correct name (last update, February 2025) * . LPSN classification: Bacteria / Thermotogati / Thermotogota / Thermotogae / Thermotogales The order Thermotogales can also be recovered in the Genome Taxonomy Database (GTDB) as o__Thermotogales (version v220) ** . GTDB classification: d__Bacteria / p__Thermotogota / c__Thermotogae / o__Thermotogales * 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.805

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 studies0.0000.000
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 head0.007
GPT teacher head0.211
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2025
Admission routes1
Has abstractyes

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