Evaluating the Influence of Stem Form and Vigor on Product Potential, Growth, and Survival for Northern Commercial Hardwood Species
Bibliographic record
Abstract
Northern hardwood and mixed-wood forest types occupy a considerable percentage of the forest landscape across the Northeastern United States and portions of eastern Canada. While capable of producing valuable saw timber and veneer products, hardwood species demonstrate a wide range of stem quality resulting from the large variety of stem forms and defects that these species can manifest. The effect of different stem forms and damage has largely not been accounted for in predictions of volume, growth, and mortality. In addition to potential bias in growth and yield applications, the lack of quantification of these features has left the efficacy of silvicultural tools such as tree classification guides untested. Using a tree classification system developed by the Northern Hardwood Research Institute (NHRI), form and risk classifications were assigned to several commercial hardwood species across sites in Maine, New Hampshire, and New Brunswick. Regression analyses were used to accomplish the following objectives; 1) quantify sawlog recovery as a function of a trees size, form, and risk; 2) determine the occurrence of stem form and risk among species; 3) and evaluate the influence of stem form and risk on individual tree diameter growth and survival. For the first chapter, a linear mixed effects model was used to quantify the proportion of sawlog material in individual trees. Results indicated three form classifications and a binary classification of risk were sufficient to account for variation in sawlog recovery. The average proportion of sawlog was largest for trees with single straight stems and smallest for those displaying a large significant fork on the first 5 m of their stem. Stem damage also had substantial implications on product recovery where trees considered to be high-risk had overall lower proportions of sawlog volume. Using the simplified form and risk classes, a series of logistic regression models were developed to predict the occurrence of risk and form across hardwood species. Among the species in the analysis, yellow birch and red maple had the highest probability of being high-risk. Sugar maple had the highest probability of demonstrating good form while red maple and red oak were the most likely to have poor form. In the second chapter continuous forest inventory data from five locations in Maine and New Hampshire were used to evaluate the influence of form and risk on tree growth and survival for hardwood species. The influence of form and risk on growth were analyzed by assessing bias in the regional diameter increment equation used in the Acadian Variant of the Forest Vegetation Simulator FVS-ACD and through development of a periodic annual increment model (PAI). The regional FVS-ACD equation tended to over predict for species and risk class while binary form and risk classifications were significant variables in the PAI model, although their effect was relatively small. A nonlinear model was used to quantify annualized individual tree survival. Trees with single straight stems had statistically higher survival probabilities compared to all other stem forms, however the magnitude of the difference in survival was not substantial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".