FTIR spectroscopic discrimination of<i>Saccharomyces cerevisiae</i>and<i>Saccharomyces bayanus</i>strains
Bibliographic record
Abstract
In this study, we tested the potential of Fourier-transform infrared absorption spectroscopy to screen, on the one hand, Saccharomyces cerevisiae and non-S. cerevisiae strains and, on the other hand, to discriminate between S. cerevisiae and Saccharomyces bayanus strains. Principal components analysis (PCA), used to compare 20 S. cerevisiae and 21 non-Saccharomyces strains, showed only 2 misclassifications. The PCA model was then used to classify spectra from 14 Samos strains. All 14 Samos strains clustered together with the S. cerevisiae group. This result was confirmed by a routinely used electrophoretic pattern obtained by pulsed-field gel electrophoresis. The method was then tested to compare S. cerevisiae and S. bayanus strains. Our results indicate that identification at the strain level is possible. This first result shows that yeast classification and S. bayanus identification can be feasible in a single measurement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".