Relationship between wood-inhabiting fungi determined by molecular analysis (denaturing gradient gel electrophoresis) and quality of decaying logs
Bibliographic record
Abstract
We investigated the fungal communities inhabiting decaying logs in a seminatural boreal forest stand in relation to host tree species, stage of decay, density, diameter, moisture, C to N ratio, Klason lignin content, and water- and ethanol-soluble extractives. Communities were profiled using denaturing gradient gel electrophoresis fingerprinting of the rDNA ITS1 region coupled with sequencing of fungal DNA extracted directly from the wood. In addition, polypore fruit bodies were inventoried. Logs from different tree species had different fungal communities and different physicochemical properties (e.g., C to N ratio, density, ethanol extractives, and diameter). Ascomycetes comprised a larger portion of communities inhabiting deciduous birch ( Betula spp.) and European aspen ( Populus tremula L.) logs compared with those living on coniferous Norway spruce ( Picea abies (L.) Karst.) and Scots pine ( Pinus sylvestris L.). A relationship between mycelial community structure and density of decaying spruce logs suggested a succession of fungi with mass loss of wood. The fruit body inventory underestimated fungal diversity in comparison with the culture-free denaturing gradient gel electrophoresis analysis that also detected inconspicuous but important species inhabiting decaying 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 | 0.001 |
| 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 | 0.001 | 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".