Bivariate distribution functions for predicting twig leaf area within hybrid spruce crowns
Bibliographic record
Abstract
The vertical and horizontal distribution of leaf area per centimetre of twig (APCM) for hybrid spruce (Picea engelmannii Parry ex Engelm. × Picea glauca (Moench) Voss × Picea sitchensis (Bong.) Carrière) tree crowns was modelled using bivariate Weibull and beta distribution functions. Horizontal position was represented by relative position on the first-order branch. Vertical position was based on branch position from the tree apex relative to tree height, since the base of the live crown is often difficult to locate. Sample APCM measures were obtained using systematic sampling of 12 tree crowns taken from stands at three developmental stages (20, 60, and 140 years of age). For comparison, univariate Weibull and beta distribution functions using only vertical distribution were also fitted. Generally, APCM decreased from the tree apex downward and from the branch tips toward the stem, although variation in the values was quite high. Trees from the middle stand age (60 years) had the highest average APCM values, followed by the smallest, youngest trees (stand age 20), and the lowest values were found for the largest trees in the oldest stand (140 years). As anticipated, the bivariate Weibull and beta distribution functions resulted in more precise representations of APCM within tree crowns than the univariate Weibull and beta distribution functions, although the improvements were minimal. Results were generally poorer for trees from the oldest stand. These functions could be used to evaluate other variables distributed over the tree crown, such as specific leaf area. The resulting models from this study were used to reconstruct the entire crown of every sample tree for conducting sampling simulations.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".