Influence of Leaf Litter and Humus Composition on the Development of Black Spruce Seedlings: A Greenhouse Experimentation
Bibliographic record
Abstract
In boreal forests in Canada, broadleaf stands are characterized by generally well-drained soils and a humus-rich layer. In contrast, spruce-moss stands are often characterized by more poorly drained soils and acidic humus layer. However, presence of these two forest types in various degrees of mixture in stands can be beneficial to spruce seedlings productivity. It was hypothesized that leaf litter and humus from pure spruces-moss stands, pure broadleaf stands, and mixed stand may influence Black spruce (Picea mariana (Mill.) BSP) seedling growth and development differently. A greenhouse experiment was carried out to evaluate the effect of different leaf litter and different humus on spruces seedlings. Our results suggest better development for seedlings grown in humus from mixed stands and pure broadleaf stands compared to humus from pure B. spruce or standard forest nursery substrate. Furthermore, leaf litter from broadleaf trees species, such as species Speckle alder (Alnus rugosa (Du Roi) R.T. Clausen), T. aspen (Populus tremuloides Michx), Willows (Salix spp.) and Paper birch (Betula papyrifera Marsh.), has shown distinct results in the growth and development of B. spruce seedlings in greenhouse. Furthermore, promotion of mixed stand can increase B. spruce productivity by improving the physicochemical composition of the forest floor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".