MétaCan
Menu
Back to cohort
Record W2255560374 · doi:10.1093/molbev/msv249

Gene Acquisitions from Bacteria at the Origins of Major Archaeal Clades Are Vastly Overestimated

2015· article· en· W2255560374 on OpenAlexaff
Mathieu Groussin, Bastien Boussau, Gergely J. Szöllősi, Laura Eme, Manolo Gouy, Céline Brochier‐Armanet, Vincent Daubin

Bibliographic record

VenueMolecular Biology and Evolution · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsDalhousie University
FundersAgence Nationale de la Recherche
KeywordsBiologyArchaeaCladePhylogenetic treeGeneEvolutionary biologyPhylogeneticsDiversification (marketing strategy)BacteriaGenetics

Abstract

fetched live from OpenAlex

In a recent article, Nelson-Sathi et al. (NS) report that the origins of major archaeal lineages (MAL) correspond to massive group-specific gene acquisitions via HGT from bacteria (Nelson-Sathi et al. 2015. Origins of major archaeal clades correspond to gene acquisitions from bacteria. Nature 517(7532):77-80.). If correct, this would have fundamental implications for the process of diversification in microbes. However, a reexamination of these data and results shows that the methodology used by NS systematically inflates the number of genes acquired at the root of each MAL, and incorrectly assumes bacterial origins for these genes. A reanalysis of their data with appropriate phylogenetic models accounting for the dynamics of gene gain and loss between lineages supports the continuous acquisition of genes over long periods in the evolution of Archaea.

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)0.008
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0010.002
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 head0.014
GPT teacher head0.258
Teacher spread0.245 · 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 designSimulation or modeling
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

Citations34
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMolecular Biology and EvolutionSame topicGenomics and Phylogenetic StudiesFrench-language works237,207