Determining Diversity of Freshwater Fungi on Decaying Leaves: Comparison of Traditional and Molecular Approaches
Bibliographic record
Abstract
Traditional microscope-based estimates of species richness of aquatic hyphomycetes depend upon the ability of the species in the community to sporulate. Molecular techniques which detect DNA from all stages of the life cycle could potentially circumvent the problems associated with traditional methods. Leaf disks from red maple, alder, linden, beech, and oak as well as birch wood sticks were submerged in a stream in southeastern Canada for 7, 14, and 28 days. Fungal biomass, estimated by the amount of ergosterol present, increased with time on all substrates. Alder, linden, and maple leaves were colonized earlier and accumulated the highest fungal biomass. Counts and identifications of released conidia suggested that fungal species richness increased, while community evenness decreased, with time (up to 11 species on day 28). Conidia of Articulospora tetracladia dominated. Modifications of two molecular methods-denaturing gradient gel electrophoresis (DGGE) and terminal restriction fragment length polymorphism (T-RFLP) analysis-suggested that both species richness and community evenness decreased with time. The dominant ribotype matched that of A. tetracladia. Species richness estimates based on DGGE were consistently higher than those based on T-RFLP analysis and exceeded those based on spore identification on days 7 and 14. Since traditional and molecular techniques assess different aspects of the fungal organism, both are essential for a balanced view of fungal succession on leaves decaying in streams.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".