Afforestation of Black Spruce Lichen Woodlands by Natural Seeding
Bibliographic record
Abstract
Black spruce-lichen woodlands (LW) are naturally occurring unproductive low tree density stands within the eastern North American closed-crown boreal forest. Natural reforestation in LWs is impeded by the lichen mat and ericaceous shrubs that inhibit seedling establishment. Disk scarification is a mechanical site preparation method that creates furrows where lichens and shrubs are removed and mineral soil is exposed, which is the preferred seedbed for black spruce natural regeneration. The objective of this study was to quantify the impact of disk scarification on black spruce establishment in LWs by natural seeding. Disk scarification was performed amid scattered seed trees in six study sites located in the central area of boreal Québec's spruce-moss bioclimatic domain. Newly established black spruce seedlings were significantly more abundant (ca. 81%; χ2 = 28.72, P < 0.001) in the furrows of scarified plots even though the proportion of disturbed soil was small (ca. 20%). Seedling establishment occurred for at least 3 years following scarification, with a peak in the first year. The distribution and density of seed trees (112‐363 stems ha−1) did not limit natural seedling establishment in this study. Five years after scarification, observed densities and stocking levels of newly established black spruce seedlings were sufficient to expect afforestation without planting in scarified LWs.
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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 | 0.001 | 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".