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Inference about Monophyly of the Family Oedipodidae and the Classification of Subfamilies Based on 16S rDNA Sequences

2014· article· en· W1987027250 on OpenAlexvenueno aff
Guo-Fang Jiang, Dianfeng Liu

Bibliographic record

VenueInternational Journal of Biotechnology for Wellness Industries · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrthoptera Research and Taxonomy
Canadian institutionsnot available
FundersPriority Academic Program Development of Jiangsu Higher Education InstitutionsNanjing Normal University
KeywordsMonophylyBiologyInferenceArtificial intelligenceEvolutionary biologyPattern recognition (psychology)Computer sciencePhylogenetic treeGeneticsCladeGene

Abstract

fetched live from OpenAlex

Most of grasshoppers in the family Oedipodidae are the famous agriculture pests in China. However monophyly and the relationships among the subfamilies within this family are unclear up to now. Here the phylogeny of the Oedipodidae was reconstructed based on 16S rDNA sequence fragments by using Mekongiella kingdoni and Atractomorpha sinensis as outgroups under weighted MP, NJ and Bayesian criteria. The 408 bp fragments of mitochondrial 16S rRNA gene were sequenced for 15 species from 4 subfamilies of the family Oedipodidae, and the homologous sequences of other 15 species of grasshoppers were downloaded from the GenBank data library. The numbers of transitions and transversions among pairwise comparisons of the 16S fragments were respectively plotted against percentage sequence differences. Saturation of transitions was discovered, and transversions were not saturated with the increase of percentage sequence difference in the plots. All the individuals of the Oedipodidae excluding Trilophidia annulata were gathered together in the three trees. Our results are very different from the traditionary taxonomy of the Oedipodidae including 4 subfamilies. The Bryodemellinae is not supported as a subfamily, and neither Locustinae nor Oedipodinae are supported as a monophyletic group in this study.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.254
Teacher spread0.228 · 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 designOther design
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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