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Record W2936934709 · doi:10.1139/gen-2018-0168

LSU rDNA D5 region: the DNA barcode for molecular classification and identification of <i>Demodex</i>

2019· article· en· W2936934709 on OpenAlexvenueno aff
Li Hu, Yae Zhao, Yuanjun Yang, DongLing Niu, Rui Yang

Bibliographic record

VenueGenome · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicStudy of Mite Species
Canadian institutionsnot available
FundersChina Postdoctoral Science FoundationNational Natural Science Foundation of China
KeywordsBiologyIntraspecific competitionDemodexRibosomal DNADNA barcodingInterspecific competitionInternal transcribed spacerEvolutionary biologyPhylogenetic treeRibosomal RNAAcariformesGeneticsZoologyGeneBotanyAcariMite

Abstract

fetched live from OpenAlex

Whether ribosomal genes can be used as DNA barcodes for molecular identification of Demodex (Acariformes: Demodicidae) is unclear. To examine this, Demodex folliculorum, D. brevis, D. canis, and D. caprae were collected for DNA extraction, rDNA fragments amplification, sequencing, and analysis. The V2 and V4 regions of SSU rDNA; D5, D6, and D8 regions of LSU rDNA; and ITS region were obtained from the four morphospecies. BLAST analysis showed that the obtained sequences matched those of Demodex or Aplonobia (Acariformes: Tetranychidae) in Raphignathae. Phylogenetic trees derived from V2, V4, D5, D6, and D8 regions, but not from ITS region, showed that the four species of Demodex clustered independently. Sequence divergence analysis further demonstrated that D5, D6, and D8 regions had obvious barcoding gap between intraspecific and interspecific divergences, with the gap of D5 (16.91%) larger than that of D6 (11.82%) and D8 (4.66%). The V2 and V4 regions did not have a barcoding gap, as the intraspecific and interspecific divergences partially overlapped. For the ITS region, intraspecific and interspecific divergences completely overlapped. These results suggest that the D5, D6, and D8 regions of LSU rDNA, especially D5, are suitable DNA barcodes for Demodex.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.089

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.213
Teacher spread0.188 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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