Variation in stem form and risk of four commercially important hardwood species in the Acadian Forest: implications for potential sawlog volume and tree classification systems
Bibliographic record
Abstract
Northern hardwood trees display a wide variety of stem forms and defects, which can substantially reduce their financial value and also complicate their silviculture. While attributes of stem form and defect have been incorporated into tree classification systems, their ability to assess product value and recovery in standing trees has not been adequately tested. To address this issue, we classified stem form and risk using a system developed by the Northern Hardwoods Research Institute (NHRI) for four species across several locations in Maine, New Hampshire, and New Brunswick: sugar maple (Acer saccharum Marsh.), red maple (Acer rubrum L.), yellow birch (Betula alleghaniensis Britton), and northern red oak (Quercus rubra L.). Using these data, we (i) quantified interspecific and regional variation in stem form and damage, (ii) related potential sawlog recovery to tree size, form, and risk, and (iii) compared the efficacy of the NHRI system with a commonly used classification system and a continuous measure of stem quality. High variation in both stem form and damage among the species was found, with red maple showing the largest range. A simplified NHRI system including three form classes proved to be sufficient in differentiating sawlog potential in individual trees, while a model using a continuous measure of stem quality (estimated merchantable sawlog height) performed best.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".