Identification and Typing of <i>Malassezia</i> Species by Amplified Fragment Length Polymorphism and Sequence Analyses of the Internal Transcribed Spacer and Large-Subunit Regions of Ribosomal DNA
Bibliographic record
Abstract
Malassezia yeasts are associated with several dermatological disorders. The conventional identification of Malassezia species by phenotypic methods is complicated and time-consuming, and the results based on culture methods are difficult to interpret. A comparative molecular approach based on the use of three molecular techniques, namely, amplified fragment length polymorphism (AFLP) analysis, sequencing of the internal transcribed spacer, and sequencing of the D1 and D2 domains of the large-subunit ribosomal DNA region, was applied for the identification of Malassezia species. All species could be correctly identified by means of these methods. The results of AFLP analysis and sequencing were in complete agreement with each other. However, some discrepancies were noted when the molecular methods were compared with the phenotypic method of identification. Specific genotypes were distinguished within a collection of Malassezia furfur isolates from Canadian sources. AFLP analysis revealed significant geographical differences between the North American and European M. furfur strains.
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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 | 0.001 | 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".