Spatial partitioning of competitive effects from neighbouring herbaceous vegetation on establishing hybrid poplars in plantations
Bibliographic record
Abstract
The spatial effects of vegetation control on early tree growth were investigated in central Alberta, Canada, for four years after the establishment of hybrid poplar plantations including the two clones Walker (Populus deltoides × (P. laurifolia × P. nigra)) and its progeny Okanese (Walker × (P. laurifolia × P. nigra)). Tree survival and growth, herbaceous vegetation cover, soil nutrient availability, moisture, temperature, and light availability were assessed. Tree growth in the first two years after establishment was improved through selective in-row vegetation control close (within 50 cm) to trees for both aboveground (mechanical) and above- and below-ground (chemical) control. This was associated with increased light availability for trees. In contrast, growth in the third and fourth years benefitted from control of aboveground vegetation within 140 cm of the stem, and this was associated with increased nutrient availability. These findings suggest that the effects of neighbouring vegetation on trees shift from aboveground competition near the tree stem to belowground competition further (>50 cm) away; thus between-row vegetation control is more important starting in the third year after establishment. Okanese outperformed Walker poplar across all treatments and was more responsive to vegetation control, reflecting its superior performance, higher plasticity, and greater potential for short-rotation intensive-culture plantations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".