Croissance et statut nutritif de marcottes, de semis naturels et de plants d'épinette noire à la suite du scarifiage : résultats de 10 ans
Bibliographic record
Abstract
Careful logging around advance growth (CLAAG) and tree planting following site preparation or not (fill planting) are widely used to regenerate black spruce (Picea mariana (Mill.) BSP) stands in the boreal forest of Quebec, Canada. However, few mid-term studies have compared these different regeneration modes. In this study, we examined height growth and nutrient status of black spruce layers, natural seedlings, and planted seedlings over a 10-year period, in an experimental design combining CLAAG, natural seeding, planting, and two types of scarification (cones and disks). Without scarification, growth of planted seedlings (5.8 cm·year 1 ) was slightly greater than that of layers (4.4 cm·year 1 ) and natural seedlings (4.1 cm·year 1 ). Scarification improved growth of the three types of regeneration, but the treatment was more beneficial for planted seedlings (+7.1 cm·year 1 ) than for natural seedlings (+1.6 cm·year 1 ) and layers (+1,0 cm·year 1 ). Five years after treatment, scarification had increased the current-year needle N concentration of the three types of regeneration, but this beneficial effect on N was still detectable only in foliage of layers after 10 years. The effect of the treatment was variable for P and K contents, for which natural regeneration seems to have taken advantage more than plants. Our results indicate that scarification can improve the initial growth and nutrient status of both natural and artificial regeneration of black spruce and thus accelerate site recovery after cutting in the boreal forest. Furthermore, fill planting without site preparation appeared to be clearly less efficient than planting combined with scarification.
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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 | 0.006 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".