Integrating standing value estimations into tree marking guidelines to meet wood supply objectives
Bibliographic record
Abstract
The identification of low-vigor trees with potential for sawlog production is a key objective of tree marking guidelines used for partial cuts in northern hardwoods. The aim of this study was to measure the impact of various vigor-related defects on the monetary value of hardwoods. To achieve this, we sampled 64 sugar maple (Acer saccharum Marshall) and 32 yellow birch (Betula alleghaniensis Britton) trees from two locations in southern Quebec, Canada. We identified over 420 defects, which were grouped into 8 categories. The trees were then harvested and processed into lumber, and the value per unit volume of each stem was calculated from the value of the product assortment (lumber, chips, and sawdust). We found that visible evidence of fungal infections (sporocarps and (or) stroma) and cracks had the largest negative influence on value in both species. A model that included these defects was almost as good at predicting value as one that included a specifically designed quality classification. A more accurate assessment of value could be achieved using wood decay assessment tools and (or) by considering site-specific variables. Results from this study showed that visual identification of fungal infections and cracks could be used to enhance tree marking guidelines for hardwoods. This would meet both the silvicultural objective of selection cuts, by removing low-vigor trees, and the wood supply objective, by improving stem quality assessment prior to harvest.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".