Natural dynamics-based silviculture for maintaining plant biodiversity in <i>Populus tremuloides</i>-dominated boreal forests of eastern CanadaThis article is one of a selection of papers published in the Special Issue on Poplar Research in Canada.
Bibliographic record
Abstract
Southern boreal forests dominated by trembling aspen ( Populus tremuloides Michx.) are notable for the biological richness of their plant communities. We used 12 plant community and plant functional group indicators to test the hypothesis that natural dynamics-based silvicultural systems better maintain biodiversity in aspen plant communities than conventional clear-cutting. Using CA ordination, box-and-whisker diagrams, and ANOVA, we compared the range of variability of our 12 bioindicators among five experimental stand types of the sylviculture et aménagement forestier écosystémiques (SAFE) project: mature (78 years) uncut; mature 1/3 partial-cut; mature 2/3 partial-cut; young (3 years) unburned clear-cut; young burned clear-cut; and three closely matched aspen stand types of northwest Quebec and northeast Ontario: old (105 years) uncut; young unburned clear-cut; young wildfire. Burned clearcuts partially emulated wildfires by reducing tall shrub abundance and regenerating post-fire specialists, but snags were lacking. The dual disturbance also retarded aspen regrowth and caused a 7-fold increase in non-native plants. Partial-cuts retained most attributes of mature uncut stands, but after 3 years showed little evidence of accelerating development of old stand characteristics. We concluded that SAFE natural dynamics-based silviculture better recreated the range of variability of naturally disturbed aspen plant communities than conventional clear-cutting. Improvements, including alternative burn prescriptions and snag or green tree retention in clearcuts, are nontheless warranted.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".