Diversity of indoor fungi as revealed by DNA metabarcoding
Bibliographic record
Abstract
In the present study, we conducted DNA metabarcoding (the nuclear ITS2 region) for indoor fungal samples originating from two nursery schools with a suspected mould problem (sampling before and after renovation), from two university buildings, and from an old farmhouse. Good-quality sequences were obtained, and the results showed that DNA metabarcoding provides high resolution in fungal identification. The pooled proportions of sequences representing filamentous ascomycetes, filamentous basidiomycetes, yeasts, and other fungi equalled 62.3%, 8.0%, 28.3%, and 1.4%, respectively, and the total number of fungal genera found during the study was 585. When comparing fungal diversities and taxonomic composition between different types of buildings, no obvious pattern was detected. The average pairwise values of SørensenChao indices that were used to compare similarities for taxon composition between samples among the samples from the two university buildings, two nurseries, and farmhouse equaled 0.693, 0.736, 0.852, 0.928, and 0.981, respectively, while the mean similarity index for all samples was 0.864. We discovered that making explicit conclusions on the relationship between the indoor air quality and mycoflora is complicated by the lack of appropriate indicators for air quality and by the occurrence of wide spatial and temporal changes in diversity and compositions among samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".