Modelling specific gravity and diameter inside bark of western hemlock and Sitka spruce growing in southeast Alaska
Bibliographic record
Abstract
Western hemlock (Tsuga heterophylla (Raf.) Sarg.) and Sitka spruce (Picea sitchensis (Bong.)) are two commercially important species in Alaska, with harvests beginning to focus on naturally regenerated young-growth. We developed within-tree models of ring specific gravity (SG) and diameter inside bark (DIB) for young-growth western hemlock and Sitka spruce. Eight even-aged stands (age <75 years) in southeast Alaska were felled and disks collected from multiple height levels; 128 trees and 451 disks were collected for western hemlock, and 217 trees and 952 disks were collected for Sitka spruce. Radial strips were prepared and scanned using X-ray densitometry. We fitted nonlinear mixed-effects models to the data, with cambial age, height within tree, and dominance class used as explanatory variables. The R2 values (fixed effects only) for the SG models were 0.48 and 0.42 for western hemlock and Sitka spruce, respectively. The corresponding fit indices (R2) for the DIB models were 0.86 and 0.85 for western hemlock and Sitka spruce, respectively. Tree maps depicting the within-tree variation in SG showed more variability in Sitka spruce than in western hemlock. The wood and growth properties of young-growth trees in Alaska will continue to become more important as the US Forest Service transitions away from harvesting old-growth trees.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".