Decomposition of <i>Nothofagus</i> wood in vitro and nutrient mobilization by fungi
Bibliographic record
Abstract
The role of fungi in determining rates of wood decomposition and nutrient release in forest ecosystems is poorly understood. The decomposition of wood from three species of Nothofagus by 12 species of widely occurring New Zealand wood-decay fungi was investigated in vitro under standardized conditions. Wood mass loss varied strongly among fungal species and to a lesser extent with the species of wood. The species of fungi in this study were divided into three groups based on the extent of mass loss after 15 weeks: (1) rapid (>65% reduction in mass, Fomes hemitephrus , Pleurotus purpureoolivaceus , Trametes versicolor , and Ganoderma cf. applanatum), (2) intermediate (15%–30%, Phellinus sp., Schizopora radula , Phellinus nothofagi , and Skeletocutis stramenticus ), and (3) slow (<10%, Armillaria novaezelandiae , Postia pelliculosa , Australoporus tasmanicus , and Laetiporus portentosus ). For several fungal species, the final contents of nitrogen, phosphorus, and calcium in the remaining wood exceeded the initial nutrient contents in the wood, indicating that nutrient sequestration from the supporting soil matrix occurred during decomposition. Nutrient dynamics during decomposition varied with wood species, but the variation among different fungal species was much greater, indicating that fungal diversity is an important factor in determining nutrient flux in 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".