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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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 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".