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Record W1969595164 · doi:10.1089/cmb.2007.a005

Common Intervals and Symmetric Difference in a Model-Free Phylogenomics, with an Application to Streptophyte Evolution

2007· article· en· W1969595164 on OpenAlexafffund
Zaky Adam, Monique Turmel, Claude Lemieux, David Sankoff

Bibliographic record

VenueJournal of Computational Biology · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenome Rearrangement Algorithms
Canadian institutionsUniversité LavalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanadian Institute for Advanced Research
KeywordsPhylogenomicsBiologyEvolutionary biologyBotanyPhylogeneticsGenetics

Abstract

fetched live from OpenAlex

The common intervals of two permutations on n elements are the subsets of terms contiguous in both permutations. They constitute the most basic representation of conserved local order. We use d, the size of the symmetric difference (the complement of the common intervals) of the two subsets of 2({1,n}) thus determined by two permutations, as an evolutionary distance between the gene orders represented by the permutations. We consider the Steiner Tree problem in the space (2({1,n}), d) as the basis for constructing phylogenetic trees, including ancestral gene orders. We extend this to genomes with unequal gene content and to genomes containing gene families. Applied to streptophyte phylogeny, our method does not support the positioning of the complex algae Charales as a sister group to the land plants. Simulations show that the method, though unmotivated by any specific model of genome rearrangement, accurately reconstructs a tree from artificial genome data generated by random inversions deriving each genome from its ancestor on this tree.

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)0.003
metaresearch head score (Gemma)0.014
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.003
Research integrity0.0010.002
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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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

Citations35
Published2007
Admission routes2
Has abstractyes

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