Molecular approaches promise a deeper and broader understanding of the evolutionary ecology of aquatic hyphomycetes
Bibliographic record
Abstract
Research on aquatic hyphomycetes has been dominated by process-oriented approaches. The main objectives have been accurate estimates of fungal biomass and production and measuring fungal impact on plant litter decomposition. In some cases, these estimates have been complemented by community assessments based on spore counts. Many other ecological and evolutionary topics, commonly studied in macroorganisms, were largely inaccessible, in part because of the low morphological complexity of fungal structures and the near impossibility of identifying them in situ unless attached to propagules. Molecular methods rely on extraction, amplification (polymerase chain reaction) and characterization (denaturing gradient gel electrophoresis, digitized fluorescent restriction-fragment length polymorphism, sequencing) of deoxyribonucleic acid (DNA), which occurs in all cells regardless of their reproductive status. Molecular methods allow more comprehensive characterizations of fungal diversity and evolution. Enzymatic activities can be explored at the level of gene presence (DNA amplification and sequencing), gene transcription (reverse transcription of messenger ribonucleic acid [mRNA]), by quantifying the total amount of specified enzymes in a sample via global antibodies, or by estimating their effect on model compounds. Selected actual and potential applications of these techniques are reviewed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".