Decomposition and nutrient release from four epiphytic lichen litters in sub-boreal spruce forests
Bibliographic record
Abstract
Epiphytic lichens are highly abundant in many sub-boreal forests and may be important components of nutrient cycling. Decomposition of, and nutrient release from, two cyanolichens (with N2-fixing cyanobacterial partners) and two chlorolichens (with green-algal partners) were quantified to estimate N inputs from epiphytic lichen litter in late-seral forests. Initial decay rates were strongly correlated with initial %N; the high-N cyanolichen litters ( Nephroma helveticum Ach. and Lobaria pulmonaria (L.) Hoffm.) lost 26% more mass than the lower-N chlorolichen litters ( Alectoria sarmentosa (Ach.) Ach. and Platismatia glauca (L.) W.L. Culb. & C.F. Culb.) over the first 4 months. Morphological characteristics also influenced decay, as decomposition of the hair chlorolichen (A. sarmentosa) was similar to that of the foliose cyanolichens, despite an N concentration that was 87% lower. N was immediately released from cyanolichen litters and retained in chlorolichen litters. After 24 months of decay, N concentrations remained highly divergent with 22–27 and 7–8 mg N·g–1in cyanolichen and chlorolichen litter, respectively. Cyanolichen litter represents 0.1%–2.3% of the total aboveground litter biomass and 0.5%–11.5% of the total N input from aboveground litterfall. Decomposition of cyanolichen litter is estimated to release up to 2.1 kg N·ha–1·year–1of newly fixed N that would otherwise be unavailable in mature sub-boreal forests.
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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 | 0.001 | 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) | 0.000 | 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".