Wounding of aspen roots promotes suckering
Bibliographic record
Abstract
In early May, 1-m sections of trembling aspen (Populus tremuloides Michx.) roots in a forest cutblock were carefully exposed and examined for damage. Undamaged roots were subjected to one of three wounding treatments (scrape, sever, or uninjured control) and were then reburied to either the full normal organic layer depth or to one third of the normal depth. Following one growing season, the roots were reexposed and assessed for aspen sucker numbers and growth rates. Results indicate that injured roots produced suckers nearly twice as often as uninjured roots. Further, injured roots produced more suckers per root, and these suckers were taller and had greater leaf area. Roots buried under shallow organic layers also generated more suckers, regardless of injury type. The side of injury (distal or proximal) did not affect any of the measured variables. The present study suggests that moderate wounding of aspen roots increases initial sucker numbers and growth rates.Key words: trembling aspen, root sucker, root injury, 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 | 0.000 | 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 | 0.001 |
| 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".