The use of anonymous DNA markers in assessing worldwide relatedness in the yeast species<i>Pichia kluyveri</i>Bedford and Kudrjavzev
Bibliographic record
Abstract
Pichia kluyveri, a sexual ascomycetous yeast from cactus necroses and acidic fruit, is divided into three varieties. We used physiological, RAPD, and AFLP data to compare 46 P. kluyveri strains collected worldwide to investigate relationships among varieties. Physiology did not place all strains into described varieties. Although the combined AFLP and RAPD data produced a single most parsimonious tree, separate analysis of AFLP and RAPD data resulted in significantly different trees (by the partition homogeneity test). We then compared the distribution of strains per band to an expected distribution. This suggested we could separate both the AFLP and RAPD datasets into bands from rapidly and slowly changing DNA regions. When only bands from slowly changing regions (from each dataset) were included in the analysis, both the RAPD and AFLP datasets supported a single tree. This second tree did not differ significantly from the cladogram based on all of the DNA data, which we accepted as the best estimate of the phylogeny of these yeast strains. Based on this phylogeny, we were able to demonstrate the strong influence of geography on the population structure of this yeast, confirm the monophyly of one variety, question the utility of maintaining another variety, and demonstrate that the physiological differences used to separate the varieties did not do so in all cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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".