Residual-tree growth responses to partial stand harvest in the black spruce (<i>Picea mariana</i>) boreal forestThis article is one of a selection of papers published in the Special Forum IUFRO 1.05 Uneven-Aged Silvicultural Research Group Conference on Natural Disturbance-Based Silviculture: Managing for Complexity.
Bibliographic record
Abstract
Variants of partial harvesting are gaining favour as means to balance ecosystem management and timber production objectives on managed boreal forest landscapes. Understanding how residual trees respond to these alternative silvicultural treatments is a critical step towards evaluating their potential from either a conservation or a wood supply perspective. We used dendroecological techniques combined with a chronosequence approach to quantify the temporal radial growth response pattern of residual black spruce ( Picea mariana (Mill.) BSP) trees to partial harvest in northeastern Ontario. At its peak, 8–9 years after harvest, radial growth of residual trees had doubled. The growth pattern was characterized by a 2-year phase of no response, a subsequent period of increase 3–9 years after harvest, and a stage of declining rates 10–12 years after harvest. The magnitude of tree growth response depended strongly on tree age: peak postharvest growth was substantially higher for young trees, while old trees displayed only modest growth increases. Both the large magnitude and the time delay in postharvest growth responses have important implications for the development of more accurate quantitative tools to project future yields and, more generally, for determining whether partial harvesting is a viable management option for 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 | 0.000 | 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 | 0.000 | 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".