Microfungus communities of Japanese beech logs at different stages of decay in a cool temperate deciduous forest
Bibliographic record
Abstract
Fallen logs of Japanese beech ( Fagus crenata Blume) at various stages of decomposition were sampled from a cool temperate deciduous forest in Japan and studied for differences in the associated microfungus communities. Wood samples were directly plated onto each of two different media for the identification of fungal species. Approximately 1500 isolations were made, which represent 96 species of filamentous microfungi, consisting of 16 zygomycetes and 80 anamorphic ascomycetes. The number of species per log (α-diversity) increased with log decomposition, while dissimilarity of species composition among logs (β-diversity) showed a unimodal response with the optimum at the intermediate decay stage. The water content and nitrogen concentration of the wood were positively correlated, while the lignocellulose index and relative density were negatively correlated with α-diversity. Stepwise regression models suggested that lignocellulose index was the single most important determinant of variation in α-diversity and explained 31% of variation. Eighteen fungal species frequently isolated from the logs were classified into four groups based on their occurrence patterns. These groups occurred successively during log decomposition, and the succession in early stage of log decomposition was related with relative density, while the succession in late stage was related with water content and nitrogen concentration of wood.
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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".