Differences in fibre properties in cloned Norway spruce (<i>Picea abies</i>)
Bibliographic record
Abstract
In forest breeding programmes, growth has typically been used as a selection trait of prime importance in Norway spruce ( Picea abies (L.) Karst.), whereas less attention has been given to the wood and fibre characteristics. In the above context, we investigated phenotypic relationships between different fibre properties and growth and wood density traits in 20 cloned Norway spruce based on a clonal trial established in the 1970s in southeastern Finland. We found that fibre width showed, on average (2.9%), the lowest phenotypic variation followed by fibre wall thickness (3.4%), coarseness (5.5%), and fibre length (8.1%). All of the phenotypic correlations between the fibre properties were also positive (p < 0.05), ranging from moderate to strong, suggesting that selection for one trait could simultaneously affect the other traits. The phenotypic correlations, on average, were quite weak but positive between growth and fibre properties and slightly negative or weak positive between wood density and different fibre properties (p < 0.05). Individually, some of the clones showed negative correlation between growth traits and fibre length. As a result, selection for fibre properties alone could also reduce overall stem volume (or stem mass) and would not directly indicate wood density traits and vice versa.
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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 | 0.001 | 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".