Modelling number, vertical distribution, and size of live branches on coniferous tree species in British Columbia
Bibliographic record
Abstract
A compound, nonhomogeneous Poisson process was used to model the number, vertical distribution, and size of branches on four coniferous tree species: 134 western hemlock ( Tsuga heterophylla (Raf.) Sarg.) on six sites, 45 amabilis fir ( Abies amabilis Douglas ex J. Forbes) (three sites), 60 lodgepole pine ( Pinus contorta var. latifolia Engelm. ex S. Watson) (six sites), and 60 white spruce ( Picea glauca (Moench) Voss) trees (five sites) and two varieties: 66 coastal Douglas-fir ( Pseudotsuga menziesii var. menziesii (Mirb.) Franco) (five sites) and 50 interior Douglas-fir ( Pseudotsuga menziesii var. glauca (Mayr) Franco) (four sites). Branches of these species are typically more or less clustered and have a characteristic, nonuniform vertical distribution along annual shoots. Total number and relative positions of clusters varied with shoot age. Clustering patterns in three of four species and two varieties appeared to scale proportionally with shoot length. However, in lodgepole pine, which has fewer clusters per metre and more branches per cluster, the vertical distribution of clusters along shoots ≤5 years old was consistent with a gamma-Poisson model but converged to a nonhomogeneous Poisson process model in shoots >5 years old. Separate mixed-effect regression models were developed for each species relating length and diameter of live branches to tree (crown), shoot, and branch (cluster) predictor variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".