Modelling of wood density and fibre dimensions in mature Norway spruce
Bibliographic record
Abstract
Basic density (BD), fibre length (FL), fibre width (FW), and fibre wall thickness (FWT) were investigated in 46 Norway spruce ( Picea abies (L.) Karst.) trees from five different stands in eastern Norway. From each tree, wood samples were collected in different radial and longitudinal positions. Random coefficient mixed models were used to investigate variation within as well as among trees, both within and among stands. The R2 with random effects included, describing the best possible (individual) fit of the observed data to the models, were 0.90 for BD, 0.99 for FL, 0.88 for FW, and 0.91 for FWT. With only fixed effects, the best model explained 56% of the total variation for BD, 94.5% for FL, 61% for FW, and 63% for FWT. A common model for all trees, without tree and site information, predicted FL well but BD, FW, and FWT poorly. Adding site index, breast height diameter, and tree height to the models reduced the residual variance considerably for FW, FWT, and particularly BD, whereas only a minor improvement was gained for FL. The latter type of models might be easier to use for industrial purposes. Although information about ring width gave further improvements, ring width measurements are time consuming and difficult to perform in the forest and in industrial environments.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".