Differences in growth and wood density in clones and provenance hybrid clones of Norway spruce
Bibliographic record
Abstract
The growing forest bioeconomy calls for enhancing wood production in Finland. Accordingly, we studied phenotypic differences and correlations for growth and wood density traits in 25 Norway spruce (Picea abies (L.) Karst.) genotypes grown in a field trial established in the 1970s in southeastern Finland. We also studied the effect of the geographical origin of parent trees. The clones represented six southern Finnish and two southwestern Russian clones and three Finnish–Swiss, eight Finnish–German, three Finnish–Latvian, and three Finnish–Estonian hybrid clones. Some local Finnish clones (e.g., V43) and provenance hybrid clones (e.g., Finnish–German V449 and V381) clearly displayed higher stem volume than the average over all of the clones and relatively high overall wood density (and wood biomass yield). The increase in latitudinal transfer distance of parent trees compared with the latitude of the trial seemed to decrease the height, diameter at breast height, and stem volume, but the effect was not significant (p > 0.05). The overall wood density was affected significantly only by the latitude of the father parent trees (p < 0.05). Wood density traits showed clearly lower phenotypic variation compared with other traits. Contrary to our hypothesis, none of the studied hybrids showed superior properties compared with the local Finnish clones.
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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 | 0.001 |
| 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) | 0.001 | 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".