Stabilité mécanique et caractéristiques racinaires de plants de fortes dimensions de Picea mariana produits en récipients ou à racines nues
Bibliographic record
Abstract
In Québec (Canada), large planting stock are used for reforestation of high-competition sites, sometimes in combination with soil scarification. Large conifer seedlings are typically produced in containers >300 cm3, or as bare-root stock. Nursery practices are expected to influence seedling mechanical stability. We measured large containerized and bare-root black spruce (Picea mariana) seedling stability (resistance to winching), and characterized their root architecture, during their seventh growing season since planting in scarified or non-scarified plots devoid of any competing vegetation. We detected no significant stock type or scarification effect on seedling height, diameter, height/diameter ratio, stability, total number of roots and adventive roots. Occurrences of root deformations, as well as vertical and horizontal root distributions, were not influenced significantly by the treatments. The height/diameter ratio was the sole significant predictor of the resistance to winching. Our results indicate that the use of either large containerized or large bare-root stock has limited silvicultural consequences. In this context, the choice of large stock type should be based on other factors, such as handling constraints.Key words: black spruce, reforestation, stock type, scarification, root system
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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".