Modelling the relationship of tree-level product value with tree characteristics in black spruce
Bibliographic record
Abstract
The establishment of the relationship between tree-level product value and tree characteristics will allow for predicting the potential value of individual trees and a stand directly using tree characteristics. Using statistical and elasticity analysis methods this study examined the relationship of tree-level product value with selected tree characteristics in black spruce (Picea mariana). The study was based a sample of 139 trees from 48-year-old black spruce plantations grown in Ontario, Canada. The sample trees showed large variation in tree characteristics and tree-level product value. Models were developed and compared on the basis of statistics of the estimated and predicted criteria. Results show that the model, including only tree DBH, tree height and stem taper, is the best in describing the relationship of the tree-level product value with tree characteristics. Furthermore, relationships including input-output and interaction factors in the model were analyzed by calculating the elasticity of production and scale and the cross partial derivative of output with respect to the inputs. The analyses indicate that tree DBH has the largest and positive influence on tree-level product value, followed by tree height; however, stem taper has a negative effect on tree-level product value. When tree DBH, tree height and stem taper each increase by 1%, the quantities of output elasticity show 2.53%, 0.64% and -0.37% changes in the product value, respectively; while the scale elasticity shows a 2.81% increase in tree-level product value with a simultaneous 1% change in tree DBH, tree height and stem taper. Results indicate that the model is suitable for predicting tree-level product value using those tree characteristics from forest inventory and also reflects biological behaviour.Key words: black spruce, regression models, elasticity analysis, product value, tree characteristics
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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 | 0.001 |
| 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 | 0.000 | 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".