Microfungi associated with <i>Abies</i> needles and <i>Betula</i> leaf litter in a subalpine coniferous forest
Bibliographic record
Abstract
We investigated microfungal assemblages on leaf litter within a subalpine forest in central Japan and their variation with season, litter depth, and litter species. Microfungal assemblages were compared for Abies needles and Betula leaf litter collected from litter and fermentation layers of the forest floor during the growing season in spring, summer, and autumn. A total of 35 and 42 species were isolated from Abies needles and Betula leaf litter, respectively. The observed variation in microfungal assemblages was primarily attributable to seasonal differences. The frequencies of Trichoderma viride, Volutella ciliata, Mucor sp., and Umbelopsis ramanniana increased in summer, leading to a high degree of similarity of microfungal assemblages in different litter depths and litter species. Microfungal assemblages on Abies needles in spring and autumn and those on Betula leaves in spring were characterized by Trichoderma viride, V. ciliata, Thysanophora penicillioides, Trichoderma polysporum, and (or) Mortierella alpina. Microfungal assemblages on Betula leaves in autumn were characterized by the absence of these species and the occurrence of Cladosporium cladosporioides. The results were discussed with an emphasis on the role of microfungi in decomposition processes and the impact on fungi of predicted future increases in global temperature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".