Changes in spatial pattern of trees and snags during structural development in <i>Picea mariana</i> boreal forests
Bibliographic record
Abstract
Abstract Questions: How do gap abundance and the spatial pattern of trees and snags change throughout stand development in Picea mariana forests? Does spatial pattern differ among site types and structural components of a forest? Location: Boreal forests dominated by Picea mariana, northern Quebec and Ontario, Canada. Methods: Data on the abundance, characteristics and spatial location of trees, snags and gaps were collected along 200 m transects at 91 sites along a chronosequence. Spatial analyses included 3TLQV, NLV and autocorrelation analysis. Non‐parametric analyses were used to analyse trends with time and differences among structural components and site types. Results: Gaps became more abundant, numerous and more evenly distributed with time. At distances of 1–4 m, tree cover, sapling density and snag density became more heterogeneous with time. Tree cover appeared to be more uniform for the 10–33 m interval, although this was not significant. Patch size and variance at 1 m were greater for overstorey than for understorey tree cover. Snags were less spatially variable than trees at 1 m, but more so at intermediate distances (4–8 m). Few significant differences were found among site types. Conclusions: During stand development in P. mariana forest, gaps formed by tree mortality are filled in slowly due to poor regeneration and growth, leading to greater gap abundance and clumping of trees and snags at fine scales. At broader scales, patchy regeneration is followed by homogenization of forest stands as trees become smaller with low productivity due to paludification.
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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 | 0.001 | 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".