Changes in fungal communities in evergreen broad-leaved forests across a gradient of urban to rural areas in JapanThis article is one of a selection of papers published in the Special Forum on Towards Sustainable Forestry — The Living Soil: Soil Biodiversity and Ecosystem Function.
Bibliographic record
Abstract
We investigated the structure of the fungal community of evergreen broad-leaved forests dominated by evergreen oak ( Castanopsis sieboldii or Quercus myrsinaefolia ) through surveying sporocarps in urban, suburban, and rural areas of the Kanto District, Japan. In a 4 year census, 132 species of fungi were recorded and classified into five groups on the basis of growth substrate: 22 litter decomposers, 39 wood rotters, 10 rotted-wood decomposers, 23 humus decomposers, and 38 ectomycorrhizal species. A long-term survey of fungi revealed lower species richness and diversity of ectomycorrhizal fungi in the urban and suburban forest than in the rural forest. The low species diversity of ectomycorrhizal fungi in the urban forest was related to low species richness of Amanitaceae and a high frequency of some Russulaceae species such as Russula japonica . In contrast, species richness and abundance of litter decomposers and wood rotters were higher in the urban forest than in the rural forest. The uneven litter distribution on soil surfaces in the mountainous rural forest may have caused the lower species richness of litter decomposers. Rotted-wood decomposers and humus decomposers showed no significant differences among the three types of forest.
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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 | 0.000 | 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) | 0.001 | 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".