A Gamma-Poisson model for vertical location and frequency of buds on lodgepole pine (Pinus contorta) leaders
Bibliographic record
Abstract
The aim of this work was to model the vertical location and number of branch primordia (buds) on the leader of lodgepole pine ( Pinus contorta Doug. ex Loud.) trees in central British Columbia. For species such as lodgepole pine, where branches occur in clusters rather than individually, the Gamma-Poisson model provides a natural framework for describing and simulating the distribution of buds on the annual shoot. Parameters in the model are identifiable with measurable attributes, that is, the average number of clusters per unit length of the annual shoot and the average number of buds per cluster, and can be related to explanatory variables via a log link. Applicability of the Gamma-Poisson model was demonstrated for a sample of 58 lodgepole pine trees ranging in age from 29 to 103 years old. The agreement between observed and expected cluster counts and spacing, cluster sizes, and total number of branches was good. Height to crown base and length of the annual shoot were selected as the best predictors of the number of clusters and number of buds per cluster, respectively, although other single variables were also identified as having significant predictive value.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".