Long-term growth response of black spruce advance regeneration (layers), natural seedlings and planted seedlings to scarification: 25th year update
Bibliographic record
Abstract
Careful logging around advance growth and tree planting following site preparation or not (fill planting) have been widely used to regenerate black spruce (Picea mariana) stands in the boreal forest of Canada. An experiment was established in 1990 to compare these regeneration modes and natural seeding over a 25-year period following two types of scarification (cone- or disk-trenching) that were applied at two intensities (single- or double-pass). Without scarification, the three types of regeneration had slow height growth during the first 10 years (3.4–5.8 cm/year). Thereafter, planted seedlings (6.1–9.8 cm/year) and pre-established layers (5.8–8.5 cm/year) had 2–3 times higher growth than natural seedlings (2.6–3.1 cm/year). Scarification improved height growth of the three types of regeneration, but planted seedlings responded much earlier, and more strongly over the 25 years (+9.4 cm/year) than layers (+2.8 cm/year) and natural seedlings (+2.7 cm/year). A shift in the proportion of layers and natural seedlings was observed following treatment. Long-term monitoring revealed that double-pass scarification was beneficial to layer growth from years 15 to 20, while no positive effect was observed on natural or planted seedlings. Our results suggest that decades may be required for natural regeneration to respond to scarification in cold soils of the boreal forest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".