Molecular identification and DNA barcode screening of acaroid mites in ground flour dust
Bibliographic record
Abstract
Molecular identification of acaroid mites is difficult because of the scarcity of molecular data in GenBank. Here, acaroid mites collected from ground flour dust in Xi’an, China, were preliminarily morphologically classified/grouped. Universal primers were then designed to amplify and screen suitable DNA barcodes for identifying these mites. Sixty mite samples were morphologically classified into six groups. Groups 1–2 were identified to Dermatophagoides farinae and Tyrophagus putrescentiae, while Groups 3–6 were not identified to the species level. ITS2 exhibited higher efficiency in molecular identification in comparison with COI, 12S, and 16S. Groups 1–6 were identified as D. farinae, T. putrescentiae, Suidasia nesbitti, Chortoglyphus arcuatus, Lepidoglyphus destructor, and Gohieria sp., respectively. The phylogenetic results were consistent with the morphological classification. Group 6 was further identified as G. fusca according to the morphology of the reproductive foramen. We conclude that the use of ITS2 and the availability of universal primers provide an ideal DNA barcode for molecular identification of acaroid mites. The use of multiple target genetic markers in conjunction with morphological approaches will improve the accuracy of Acaridida identification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".