Relationships of wood anatomy with growth and wood density in three Norway spruce clones of Finnish origin
Bibliographic record
Abstract
The relationships between anatomical characteristics of wood, growth, and wood density were studied in three Finnish Norway spruce (Picea abies (L.) Karst.) clones, which had differences in average stem volume and wood density. This was done to determine which anatomical characteristics are affected by growth and which affect wood density and to determine if clones of different geographical origins (Southeastern, C43; Southern, C308; Southwestern, C332) differ from each other in these respects. In this study, tracheid double wall thickness, lumen diameter, and wall to lumen ratio, numbers, sizes, and percentages of resin canals, and numbers of rays were correlated with ring, earlywood, and latewood widths and densities. The wood density correlated positively with the wall to lumen diameter ratio. Rapid growth decreased the number of rays independently of the clone. Furthermore, the effects of growth on the number and size of resin canals depended strongly on the clone. C332 had very thin tracheid walls in latewood, which decreased wood density. However, the high number of rays and resin canals increased it. Growth significantly influences wood anatomy and, consequently, wood density. Hence, wood anatomy should be considered in the selection of proper genotypes for forest cultivation in a changing growing environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".