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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".