A framework for modeling the dynamics of first-order branches and spatial distribution of knots in loblolly pine trees
Bibliographic record
Abstract
A stochastic model to simulate the processes of initiation, diameter growth, death, and self-pruning of branches in loblolly pine ( Pinus taeda L.) trees is presented. Information on whorl formation and branch growth was obtained from destructive sampling of whorl sections from 34 trees growing under 10 different initial spacings. Three different components were modeled and hierarchically connected: whorl, branches, and knots. For each new growing season, whorls and branches are assigned stochastically along and around the stem. Thereafter, branch diameter growth is predicted as a function of relative location within the live crown and stem growth. The branch model was linked to an individual-tree growth model, PTAEDA3.1, to simulate the dynamics of first-order branches arising from the main stem. Information on (i) vertical trend of branch diameter along and around the stem, (ii) volume of knots (live and dead portions), and (iii) spatial location, size, and type (live and dead) of knots can be obtained. In its current stage, the framework allows evaluation of the quality of trees and sawlogs produced, inclusion of additional wood properties, and linkage with industrial conversion processes (e.g., sawing simulation). However, further research is needed to obtain data on branch dynamics to validate the overall performance of the model and improve developed submodels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".