How do advance regeneration and planted seedlings of <i>Thuja occidentalis</i> and <i>Picea rubens</i> acclimate under a first irregular shelterwood cut?
Bibliographic record
Abstract
Eastern white cedar ( Thuja occidentalis) and red spruce ( Picea rubens) contribute to the biodiversity and resilience of mixedwood forests. However, cuts that remove most or all the forest cover can cause the decline of these species. Among partial cutting options, the irregular shelterwood system (ISS) can create successful conditions for the development of advance regeneration and enrichment planting. We studied 6 years of ecophysiology of lower advance regeneration and planted seedlings of eastern white cedar and red spruce growing under a first ISS cut according to three cutting intensities: light cut (35% removal, 20 m 2 ·ha −1 residual basal area), moderate cut (42%, 18 m 2 ·ha −1 ), and heavy cut (52%, 15 m 2 ·ha −1 ). Light-saturated photosynthesis and height growth of planted cedar and both spruce types decreased with increasing cutting intensity, in response to an increase in competing vegetation. Therefore, to limit the negative impact of competing species on cedar and spruce regeneration, we recommend protecting tall advance regeneration (2 m+) during partial cut operations and using large-sized containerized seedlings (40–50 cm height) for enrichment planting. Results also suggest that a mechanical release could help optimize the physio-morphology and growth of both regeneration types of cedar and red spruce.
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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".