Basidiome production of ectomycorrhizal and saprophytic agaricoid fungi respond differently to forest management
Bibliographic record
Abstract
Forest management generates border effects in mature dense forests. How agaricoid fungi species react to this disturbance depends on climatic and site conditions, as well as forest management system used and its intensity. We compared abundance and richness of ectomycorrhizal and saprophytic species in managed and unmanaged stands in Nothofagus pumilio (Poepp. & Endl.) Krasser forests of Patagonia, Argentina. We found that basidiome abundance and richness of ectomycorrhizal and saprophytic species were favoured by different forest structure and climatic factors. Ectomycorrhizal species basidiome production was significantly correlated to mean relative humidity of the 15 days prior to sampling and tree density (number of trees per hectare) existing prior to management activities. The latter implies that the tree density an ecosystem is capable of sustaining is crucial to the establishment of ectomycorrhizal species. Saprophytic species were favoured by the increased amount of woody material generated by logging together with maximum temperature in the 15 days prior to sampling and mean annual precipitation. Our results indicate that agaricoid fungi are not affected by low- to medium-intensity forest management, establishing the forestry level that fungal community can tolerate without loss of species in Patagonia.
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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 | 0.000 | 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) | 0.001 | 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".