Effects of <i>Corylus cornuta</i> stem density on root suckering and rooting depth of <i>Populus tremuloides</i>This article is one of a selection of papers published in the Special Issue on Poplar Research in Canada.
Bibliographic record
Abstract
Trembling aspen ( Populus tremuloides Michx.) is capable of regenerating vegetatively through the formation of adventitious shoots (suckers) from roots. This field study investigated how sucker regeneration following harvest of aspen is affected by understories of beaked hazel ( Corylus cornuta Marsh.). Aspen stands with a high density of understory hazel (>45 000 stems per hectare (sph)) or a low density (<5000 sph) were cut in the fall. After one growing season, aspen sucker density and growth were assessed. Soil trenches were excavated to examine the root density and rooting depth of both aspen and hazel. Aspen sucker regeneration was 68 200 sph in areas with low hazel density and 43 600 sph in areas with high hazel density; the cross-sectional surface area of aspen roots in shallow soil layers (0–10 cm) was significantly lower under high densities of hazel. As aspen usually produces most of its root suckers from shallow roots, the reduction of roots in the upper 10 cm of the soil was the likely cause of lower sucker densities. Height growth of the suckers was not influenced by pre-harvest hazel density, possibly owing to high light transmission as a result of the reduced leaf area of the hazel after the harvest.
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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 | 0.000 |
| 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".