Relative contributions of site conditions and interspecific competition to post-harvest regeneration in boreal mixedwoods
Bibliographic record
Abstract
In Canadian boreal forests, clearcutting has traditionally been the main harvest treatment, resulting in relatively uniform, even-aged stand structures. With the shift to ecosystem-based management, partial harvest is proposed as an alternative that helps to maintain complex stand structures. Few studies have assessed the combined effects of edaphic conditions and competition on regeneration survival and growth. Our objective was to assess how different harvest intensities influence the role of edaphic conditions and interspecific competition in the height growth of white spruce ( Picea glauca [Moench] Voss) and balsam fir ( Abies balsamea (L.) Mill) seedlings. Edaphic condition parameters included nitrogen (N) and phosphorus (P) concentrations, pH and cation exchange capacity (CEC). Competition indices were angular height (AH) and vegetation cover. Our results showed that in the clearcut treatment, P concentration explained 23.4% of balsam fir and 23.9% of white spruce seedling growth, while AH accounted for 27.9% of balsam fir growth. In the partial cut treatment, the studied parameters explained poorly the variability in seedling growth, likely due to a limited number of measured variables. In the unharvested stands, both pH and AH explained most of balsam fir and white spruce seedlings growth. Overall, growth requirements varied more between harvest treatments than between species.
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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 | 0.000 |
| 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) | 0.001 | 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".