Effect of variable retention cutting on the relationship between growth of coarse roots and stem of <i>Picea mariana</i>
Bibliographic record
Abstract
Silviculture heading for structural heterogeneity creates many single trees standing at stand margins, inner edges or in remnant tree groups. As they played just a minor role in the age class forest, the growth behaviour of strongly released trees is rather unexplored. Here we show how retention cutting, presently spreading in the boreal of Québec province, affects stem and coarse root growth of remained single black spruce [<i>Picea mariana</i> (Mill.) Britton]. Increment cores from roots and stems of 125 trees show that retention cutting triggers coarse root growth of the remaining trees. Compared with reference trees retention trees accelerate root in relation to stem growth. Mean and variability of the root–stem allometry significantly rise after retention cutting. The found acceleration of root in relation to stem growth means mechanical stabilisation of the retention trees and corroborates the retention cutting method. Evaluation of silvicultural treatments can be incomplete and misleading as long as they are just based on aboveground reactions and neglect root growth.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".