Partial cutting as an analogue to stem exclusion and dieback in trembling aspen (<i>Populus tremuloides</i>) dominated boreal mixedwoods: implications for deadwood dynamicsThis 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
In the winter of 1998–1999, two partial harvesting treatments that removed 33% (1/3) and 61% (2/3) of stand basal area were applied to even-aged trembling aspen ( Populus tremuloides Michx.) stands and compared with unharvested control stands. Stands in the 1/3 treatment were low thinned, while stands in the 2/3 removal were crown thinned. Coarse woody debris dynamics were assessed during the following 6 years by means of permanent sampling plots and downed wood inventories. Between 1999 and 2004, tree mortality was, respectively, 18%, 17%, and 32% in control stands and 1/3 and 2/3 harvesting treatments. Although total snag density was similar between controls and partial cutting treatments, total snag basal area was significantly higher in controls in 2004. Between 1999 and 2004, net change in aspen snag density was positive for controls and negative for both partial cutting treatments. Partial cutting also exacerbated mortality of small-diameter white birch ( Betula papyrifera Marsh.). Downed wood volume increased by 35 m3·ha–1 in controls and by 25 m3·ha–1 in the 2/3 harvesting treatment, while it decreased by 7 m3·ha–1 in the 1/3 harvesting treatment. Coarse woody debris goals can be established in silviculture prescriptions; type, timing, and intensity of partial cutting are crucial to the outcome.
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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".