Composition and ecology of macrofungal and myxomycete communities on oak woody debris in a mixed-oak forest of Ohio
Bibliographic record
Abstract
Woody debris is recognized as an important structural component in forests, but little is known about the epixylic communities that it supports in many forest types. The goal of this study was to identify the macrofungal (asco mycetes and basidiomycetes) and myxomycete communities found on woody debris in the topographically dissected mixed-oak forests of southern Ohio and identify environmental parameters that influence species richness and species distributions. Fifty oak (Quercus spp. L.) logs were selected across slope aspects and slope positions throughout the landscape to maximize microsite variability. Over a 2-year period, 130 epixylic species were collected (28 ascomy cetes, 72 basidiomycetes, and 30 myxomycetes). Log surface area explained a significant amount of variation in species richness (R2 = 0.51, P < 0.001). Richness was significantly (P < 0.05) correlated with volume of woody debris in the plot (+) and with study log volume (+), lichen cover (), and surface structural characteristics (amount of bark (+), solid wood (), and fragmented wood (+)). Canonical correspondence analysis revealed that slope aspect, bark cover, percent slope, and woody stem density influenced individual epixylic species distributions. Because of their influence on epixylic communities, various environmental parameters must be accounted for in regional epixylic studies.
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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 | 0.001 | 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".