Modeling Jack Pine (Pinus banksiana) Foliage Density Distribution
Bibliographic record
Abstract
The vertical distribution of foliage biomass is important because it is associated with photosynthesis and is closely related to some wood quality attributes such as branch diameter and sapwood content. In this article we propose a model to predict foliage biomass distribution within the crown for jack pine trees in Eastern Canada. This model has two parts. The first one distinguishes the proportion of nodal (formed at the end of each yearly shoot) and internodal (formed during the growing season) foliage biomass. The second part of the model predicts the distribution of the biomass depending on the type of foliage (nodal or internodal). This second part is based on a two-parameter beta cumulative distribution function (cdf). The parameterization of this cdf was performed using a mixed-effects nonlinear regression. The proportion of foliage biomass found in the nodal whorls is proportional to dbh and age and inversely proportional to total height. The distribution of the foliage biomass in the nodal whorls is dependent only on tree-level variables whereas the internodal foliage biomass is influenced by both tree- and stand-level variables. The internodal foliage biomass maximum is closer to the crown base than that of nodal foliage biomass. Decomposing the distribution into whorl types leads to a better description of crown characteristics. FOR .S CI. 57(3):180-188.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".