Early silvicultural guidelines for intensive management of hybrid larch plantations on fertile sub-boreal sites
Bibliographic record
Abstract
Use of fast-growing tree plantations on dedicated areas is proposed as a means of reconciling fibre production with conservation objectives. Success of this approach however requires fine-tuning silvicultural scenarios so that survival and growth are optimized while management and environmental costs are minimized. This is particularly challenging for hybrid larch ( Ã Coaz), a shade-intolerant species planted on fertile sites in Quebec (Canada) where legislation prevents the use of chemical herbicides. In this context, multiple motor-manual release treatments are often required, with high impacts on costs and social issues related to the scarcity of a qualified workforce. We established a split-split-plot design on a recently harvested site to assess the main and interaction effects of mechanical site preparation (MSP) intensity (five modalities of trenching or mounding), motor-manual release scenario (one or two treatments) and planting depth (0â3 cm or 3â10 cm) on hybrid larch seedling growth and survival six years after planting. Mechanical site preparation intensity and planting depth did not influence seedling growth after 6 years. The lack of significant interaction between MSP and release scenarios indicates that these operations should be planned independently. A more intensive MSP treatment cannot replace a second motor-manual release on fertile sites, as proposed to reduce costs. Our results also show the significant advantage of performing two motor-manual release treatments two years apart (the first one early in the scenario), over performing a single treatment. Our study provides silvicultural guidelines for the establishment of high-yield exotic larch plantations.Larixmarschlinsii
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".