White spruce enrichment planting in boreal mixedwoods as influenced by localized site preparation: 11-year update
Bibliographic record
Abstract
Ecosystem-based management aims to maintain the natural proportion of native species over a given landscape. White spruce (Picea glauca (Moench) Voss) is a species sensitive to environmental conditions; it is especially demanding in terms of nutrients and its regeneration is negatively affected by clearcut harvesting. Its proportion is now significantly lower than what it was in the preindustrial forests of Québec (Canada). As a native species in boreal Québec, efforts to maintain its proportion in the landscape are undertaken for white spruce, but little is known about the best practices to maximize establishment success of seedlings planted in the balsam fir (Abies balsamea)–white birch (Betula papyrifera) bioclimatic domain. Our general objective was to identify planting practices as related to microsite treatment that favour white spruce sapling survival and size after 11 growing seasons following enrichment planting of sites harvested by mechanized careful logging in an ecosystem-based management context. We also aimed at comparing white spruce performance with that of black spruce (Picea mariana (Mill.) BSP), a native species that is less sensitive to abiotic stress. Finally, we wanted to assess stand composition at this juvenile stage, as a function of microsite treatment and planted species. Localized site preparation did not significantly affect growth or survival for white spruce compared to control conditions. Furthermore, localized site preparation did not increase the proportion of white and black spruce, as evaluated by basal area. Our results suggest that white spruce can be successfully established in enrichment planting in fir-dominated boreal forests, without site preparation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".