Effects of leaf litter on the growth of boreal feather mosses: Implication for forest floor development
Bibliographic record
Abstract
Abstract Question: What is the effect of leaf litter on growth and mortality of feather mosses. Three experiments were conducted to isolate the shading effect from the effect of leaching of soluble compounds from the leaf litter effect on feather moss mortality and growth. Location: Edmonton, Alberta and the northern boreal coniferous forest, west‐central Alberta, Canada. Methods: In a field experiment Populus tremuloides (aspen) leaf litter was applied to beds of feather mosses dominated by Hylocomium splendens . Treatments were one layer of leaves held in place with netting; one layer of leaves coarsely ground, sprinkled over the moss layer; a shade cloth equivalent to one layer of leaves; and a control. In two growth chamber experiments, the application of aqueous extracts of P. tremuloides and Pinus contorta leaf litter and the effect of shade and soluble carbohydrates were tested on the growth and mortality of Ptilium crista‐castrensis . Results: Mortality of Hylocomium was greatest under the intact leaves, followed by the treatments using shade cloth and ground leaves and finally the control. The application of aqueous extracts of aspen leaf litter resulted in senescence or death of nearly all Ptilium shoots compared to no effects for the control or for similar extracts of pine needle litter. Extracts from aspen litter had greater concentrations of phenolic compounds and soluble sugars than pine extracts. Addition of sugars to Ptilium allowed it to grow and accumulate carbohydrates, even in low light conditions. Conclusions: Results suggest that broad‐leaved deciduous overstory species can limit the growth of feather mosses through their leaf litter and by implication affect the humus form, nutrient cycling, and understory composition of these 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) | 0.000 | 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".