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Phylogenetic relationships and systematics of the genus Tetranychus: A morphological and molecular approach

2024· article· en· W4408808303 on OpenAlexaff
Lokesh Ravilla, Shobhit Goyal, Aseem Aneja, Abhinav Mishra, Jayshree Nellore, Swoyam Singh

Bibliographic record

VenueJournal of Entomological Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsImpact
Fundersnot available
KeywordsSystematicsPhylogenetic treeGenusBiologyZoologyMolecular phylogeneticsEvolutionary biologyPhylogeneticsTaxonomy (biology)GeneticsGene

Abstract

fetched live from OpenAlex

AbstractThe genus Tetranychus (Acari: Tetranychidae), commonly known as spider mites, includes many agriculturally significant species. Understanding the phylogenetic relationships within Tetranychus is critical for improving pest management strategies. This research work explores the evolutionary relationships of the genus using both morphological and molecular approaches. The introduction highlights the significance of Tetranychus as pests and the need for precise taxonomic frameworks. We also discuss the limitations of using either morphological or molecular data in isolation. Through the results, we reveal phylogenetic relationships based on analyses of mitochondrial genes like cytochrome oxidase I (COI) and ribosomal sequences, correlating these findings with morphological traits such as body setation and sensory structures. Our findings indicate that while morphological characteristics offer key diagnostic features, molecular data provide more robust support for phylogenetic groupings within Tetranychus. This integrated approach not only improves species delimitation but also helps resolve taxonomic ambiguities. Combining morphological and molecular methods enhances our understanding of Tetranychus systematics. This dual approach provides insights into the evolutionary history of the genus, enabling more accurate classifications and contributing to effective pest control strategies.

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)0.002
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
Threshold uncertainty score0.176

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.078
GPT teacher head0.334
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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