Height Growth Models for High-Elevation Subalpine Fir, Engelmann Spruce, and Lodgepole Pine in British Columbia
Bibliographic record
Abstract
Abstract To estimate potential productivity of the high-elevation Engelmann Spruce and Subalpine Fir (ESSF) zone of British Columbia forests, the height growth models developed from low-elevation forests are currently used to estimate site indices of subalpine fir (Abies lasiocarpa), Engelmann spruce (Picea engelmannii), and lodgepole pine (Pinus contorta). Whether these models are adequate to describe height growth of high-elevation forests is of concern. We sampled a total of 319 naturally established, even-aged, and undamaged stands with breast height age ≥50 yr (165 for subalpine fir, 87 for Engelmann spruce, and 67 for lodgepole pine) ranging widely in climate and available soil moisture and nutrients. In each sampled stand, three dominant trees were destructively sampled for stem analysis. Height growth models developed from fitting data to a conditioned logistic function explained > 97% variation in height for all three study species. Examined by residual analysis, no models showed lack of fit. These models provided more accurate estimates of site index than the currently used models developed from low-elevation stands or different species. It is recommended that the models developed in this study be applied to estimate site index of the three species in the ESSF zone in British Columbia. West. J. Appl. For. 15(2):62-69.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".