Spectral Grouping of Nominally Aspergillus versicolor Microbial-Collection Deposits by MALDI-TOF MS
Bibliographic record
Abstract
Historical microbial collections often contain samples that have been deposited over extended time periods, during which accepted taxonomic classification (and also available methods for taxonomic assignment) may have changed considerably. Deposited samples can, therefore, have historical taxonomic assignments (HTAs) that may now be in need of revision, and subdivisions of previously-accepted taxa may also be possible with the aid of current methodologies. One such methodology is matrix-assisted laser-desorption and ionization time-of-flight mass spectrometry (MALDI-TOF MS). Motivated by the high discriminating power of MALDI-TOF MS coupled with the speed and low cost of the method, we have investigated the use of MALDI-TOF MS for spectral grouping of past deposits made to the Centre for Agriculture and Bioscience International (CABI) Genetic Resource Collection under the HTA Aspergillus versicolor, a common ascomycete fungus frequently associated with soil and plant material, food spoilage, and damp indoor environments. Despite their common HTA, the 40 deposits analyzed in this study fall into six clear spectral-linkage groups (containing nine, four, four, four, four, and two members, respectively), along with a group of ten spectrally-unique samples. This study demonstrates the clear resolving power of MALDI-TOF MS when applied to samples deposited in historical microbial collections.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".