Can plasticity make spatial structure irrelevant in individual-tree models？
Bibliographic record
Abstract
Background:Distance-dependent individual-tree models have commonly been found to add little predictive power to that of distance-independent ones.One possible reason is plasticity,the ability of trees to lean and to alter crown and root development to better occupy available growing space.Being able to redeploy foliage(and roots) into canopy gaps and less contested areas can diminish the importance of stem ground locations.Methods:Plasticity was simulated for 3 intensively measured forest stands,to see to what extent and under what conditions the allocation of resources(e.g.,light) to the individual trees depended on their ground coordinates.The data came from 50 × 60 m stem-mapped plots in natural monospecific stands of jack pine,trembling aspen and black spruce from central Canada.Explicit perfect-plasticity equations were derived for tessellation-type models.Results:Qualitatively similar simulation results were obtained under a variety of modelling assumptions.The effects of plasticity varied somewhat with stand uniformity and with assumed plasticity limits and other factors.Stand-level implications for canopy depth,distribution modelling and total productivity were examined.Conclusions:Generally,under what seem like conservative maximum plasticity constraints,spatial structure accounted for less than 10%of the variance in resource allocation.The perfect-plasticity equations approximated well the simulation results from tessellation models,but not those from models with less extreme competition asymmetry.Whole-stand perfect plasticity approximations seem an attractive alternative to individual-tree models.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".