Effects of multiaged silvicultural systems on reserve tree growth 19 years after establishment across multiple species in the Acadian forest in Maine, USA
Bibliographic record
Abstract
This study investigated the growth response of mature, isolated reserve trees (n = 528) in two multiaged silvicultural systems in the Acadian Forest Ecosystem Research Project (AFERP). Absolute and percent increases in basal area increment (BAI; cm2·year−1) were assessed for the five predominant reserve tree species in AFERP: Acer rubrum L., Picea rubens Sarg., Pinus strobus L., Thuja occidentalis L., and Tsuga canadensis (L.) Carrière. Absolute growth was significantly greater in the large-gap treatment (23.7 ± 1.1 cm2·year−1; mean ± SE) than in the small-gap treatment (16.3 ± 0.9 cm2·year−1). Percent growth increase was greater in the small-gap treatment (187.6% ± 15.8%) than in the large-gap treatment (143.4% ± 19.3%), and both treatments had greater percent increases than the unharvested control (9.6% ± 5.2%). Species differed in their response to treatment. Pinus strobus had the greatest absolute increase (large-gap, 52.5 cm2·year−1), while Tsuga canadensis (large-gap, 270% ± 71.6%) and Acer rubrum (small-gap, 262% ± 42.4%) had the greatest percent increases. Growth responses typically diminished with increasing tree size and pretreatment growth rate; however, reserve trees showed greater growth responses than their paired analogues in the control across all initial tree sizes and prior growth rates. The results suggest that these silivicultural systems accelerate the development of large trees.
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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) | 0.000 | 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".