Bleaching of leaf litter and associated microfungi in subboreal and subalpine forests
Bibliographic record
Abstract
Fungal decomposition of lignin leads to the whitening, or bleaching, of leaf litter, especially in temperate and tropical forests, but less is known about such bleaching in forests of cooler regions, such as boreal and subalpine forests. The purposes of the present study were to examine the extent of bleached area on the surface of leaf litter and its variation with environmental conditions in subboreal and subalpine forests in Japan and to examine the microfungi associated with the bleaching of leaf litter by isolating fungi from the bleached portions of the litter. Bleached area accounted for 21.7%-32.7% and 2.0%-10.0% of total leaf area of Quercus crispula and Betula ermanii, respectively, in subboreal forests, and for 6.3% and 18.6% of total leaf area of B. ermanii and Picea jezoensis var. hondoensis, respectively, in a subalpine forest. In subboreal forests, elevation, C/N ratio and pH of the FH layer, and slope aspect were selected as predictor variables for the bleached leaf area. Leaf mass per area and lignin content were consistently lower in the bleached area than in the nonbleached area of the same leaves, indicating that the selective decomposition of acid unhydrolyzable residue (recalcitrant compounds such as lignin, tannins, and cutins) enhanced the mass loss of leaf tissues in the bleached portions. Isolates of a total of 11 fungal species (6 species of Ascomycota and 5 of Basidiomycota) exhibited leaf-litter-bleaching activity under pure culture conditions. Two fungal species (Coccomyces sp. and Mycena sp.) occurred in both subboreal and subalpine forests, which were separated from each other by approximately 1100 km.
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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 | 0.000 |
| 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 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".