Role of green alder in boreal conifer growth: competitor or facilitator?
Bibliographic record
Abstract
Sustainable forest management implies successful regeneration after disturbances. Low N availability and competition can, however, limit tree establishment in boreal ecosystems. To develop silviculture strategies that maintain productivity in such context, we established a field trial in northern Québec, Canada. We evaluated if a companion N 2 -fixing species ( Alnus alnobetula) promotes or hinders Picea mariana and Pinus banksiana establishment over six growing seasons. We tested if Alnus has a facilitation effect through nutritional processes and a competition effect through light interception. Foliar stable nitrogen isotope ratio ( δ 15 N = 15 N/ 14 N, ‰) results confirmed that Alnus obtains a substantial part of its N through biological fixation and represents an N source in this system. Although we did not observe increased foliar N concentrations in either conifer species in the presence of Alnus, Pinus growth was nonetheless higher in presence of Alnus, whereas no difference was observed for Picea. In the plots where Alnus cohabited with the conifers, the former had a negative impact on seedling growth, suggesting a significant competition for light. Overall, the net effect of Alnus was positive for Pinus and neutral for Picea. Our results have significant implications for silviculture in N-limited systems, especially in the context of climate change that imposes increased levels of stress on regeneration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".