Differences in growth and wood property traits in cloned Norway spruce (<i>Picea abies</i>)
Bibliographic record
Abstract
Volume growth has typically been used as a selection trait of prime importance in forest tree breeding. Less attention has been given to the genetic or phenotypic relationships between the growth or yield and wood density traits. In the above context, we aimed to investigate the phenotypic relationships among different growth, yield, and wood density traits of 20 Norway spruce ( Picea abies (L.) Karst.) clones grown in southeastern Finland, in order to identify whether a high growth rate was associated with low wood density in any of the clones. Compared with growth or yield traits, the wood density traits showed lower phenotypic variations. The phenotypic correlations between growth, yield, and wood density traits were, on average, from moderate to high, suggesting that selection for one trait would simultaneously affect the other traits. Compared with volume production, selection based on stem mass could be more profitable if a clonal stand is managed for pulpwood rather than mechanical wood processing and vice versa; whereas selection for overall wood density alone would reduce both the stem volume and stem mass. However, by compromising the gain or loss in wood density and stem volume, clones with high stem volume and a relatively high wood density could be found.
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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 | 0.000 | 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) | 0.000 | 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".