Lumber grade yield, and value of plantation-grown black spruce from 3 stands in northwestern Ontario
Bibliographic record
Abstract
Three black spruce plantations in northwestern Ontario, with varied stand densities and site indices were studied to determine lumber properties and yield. Based on visual grading rules, each stand produced at least 87% No. 2 and Better grade lumber. Wane and skip were the 2 most prominent defects causing lumber downgrades. The compliance of visually graded No. 2 and Better lumber with bending modulus of elasticity (MOE) design values for visual grades varied among stands, as did the proportion that qualified as machine stress-rated (MSR) lumber. MOE and Modulus of Rupture (MOR) differed among stands, consistent with differences in MSR yield. The stand with the highest density and the lowest site index produced the highest yield of MSR grade lumber, but not the highest compliance with bending MOE design values. Regardless of the grading scenario, the most productive stand with the largest average tree size produced the highest value per hectare ($28K per ha) under an MSR grading scenario. These results suggest that MSR grading can improve revenues, and that it will be needed to maintain market confidence in the quality of lumber from second-growth black spruce stands. Key words: lumber quality, Picea mariana (Mill.) BSP, market value, density regulation, stand-level effects, intensive silviculture
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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 | 0.000 | 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 | 0.001 | 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) | 0.001 | 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".