Model analysis of the importance of reiteration for branch longevity in<i>Pseudotsuga menziesii</i>compared with<i>Abies grandis</i>
Bibliographic record
Abstract
Reiteration is an important process in the maintenance of tree crowns and in plant longevity. We use a geometric simulation model of branch growth to explore differences in longevity between old-growth Pseudotsuga menziesii (Mirb.) Franco and Abies grandis (D. Don ex Lamb.) Lindl. branches. Reiteration is defined through rules that reflect apical dominance relationships, and these rules are used to define shoot cluster units (SCU) on P. menziesii branches. Reiteration through epicormic production dominates growth in simulated P. menziesii branches and is shown to be a major factor that differentiates growth between P. menziesii and A. grandis. Branch growth is shown to be highly sensitive to rules for bifurcation and capacity for reiteration. The rules employed in the model that define epicormic initiation and SCU independence reveal possible physiological mechanisms through which reiteration occurs in P. menziesii. A simple morphological rule fails to simulate branch growth adequately, whereas a physiological rule through epicormic initiation after release from inhibition of a lateral axis yields realistic simulated branches. Branch growth is best simulated through a combination of physiological controls and morphological rules.Key words: reiteration, old growth, architecture, branch modeling, longevity.
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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 | 0.001 |
| 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 | 0.001 | 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".