External knot size and frequency in black spruce trees from an initial spacing trial in Thunder Bay, Ontario
Bibliographic record
Abstract
As growing space available for a given tree increases, crown size increases and branch size (and thus knot size) is generally greater. Increased tree spacing may also result in a higher knot frequency. Using a combination of nonlinear, multilevel mixed effects and generalized nonlinear modeling techniques, a series of equations were developed to predict size and number of knots with respect to vertical location in black spruce (Picea mariana [Mill.] BSP) trees from one of the oldest initial spacing trial in Thunder Bay, Ontario. The models developed in this paper focus only on live whorls and are intended to be linked with a growth and yield model. The maximum knot size model accounted for 74% of the total variation and had a root MSE of 2.03. Random effects terms accounted for an additional 3% of the total variation. The relative knot size model accounted for 32% of the total variation and no significant random effects were found. The knot frequency model accounted for 45% of the total variation and the root MSE was 2.11 and no significant autocorrelation or random effects were observed. The results of this study indicated that black spruce knot properties were relatively insensitive to tree spacing given that they were largely accounted for by bole and crown size covariates. Key words: maximum knot size, relative knot size, number of knots per whorl, plantation black spruce, nonlinear regression, nonlinear mixed effects
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".