Frequency of root grafting in naturally and artificially regenerated stands of Pinus banksiana: influence of site characteristics
Bibliographic record
Abstract
We investigated the frequency of root grafting in naturally and artificially regenerated stands of jack pine ( Pinus banksiana Lamb.) in the western boreal forest of Quebec, Canada. Twelve 30–60 m2 plots were hydraulically excavated to determine effects of site characteristics on frequency and timing of root grafting. Naturally regenerated stands had grafted tree percentages similar to artificially regenerated stands (21%–71% across plots) but greater numbers of root grafts per tree (naturally regenerated, 0.73 graft·tree–1; artificially regenerated, 0.52 graft·tree–1). Mean percentages of grafted trees, number of grafts per tree, and the speed of graft formation were greater in sandy soils (61%, 0.71 graft·tree–1 and 2.43 years, respectively) compared with clay soils (44%, 0.54 graft·tree–1 and 2.97 years, respectively). Proximity of trees was a better predictor of root grafting than stand density, despite many root grafts being found with distant trees (>2 m) in artificially regenerated stands. Our results suggested that root grafts form early in stand development. Even if trees are initially separate entities, this relatively high level of root grafting produces stands where trees are extensively interconnected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".