Genetic parameters and clonal variation in growth and nutritional traits of containerized white spruce somatic seedlings
Bibliographic record
Abstract
Clonal forestry can significantly increase forest productivity and its establishment requires a high level of clonal variation to maximize genetic gain and diversity. An evaluation of the genetic parameters of clones at a juvenile stage is necessary to better understand the amplitude of clonal variability and the degree of genetic control. The analysis of variance of white spruce clones showed a highly significant clonal effect for the majority of the growth characters at the end of both growing seasons and for the mineral status at the 2+0 stage. Our results reveal that height exhibits a high clonal heritability value which remained stable over the two growing seasons (H2 c= 0.60). Strong genotypic and phenotypic correlations were observed between height and diameter at the end of the first growing season and between height and the rest of the growth characters at the end of the second growing season. The strong clonal variation, genetic control and genetic correlation particularly of height found in this study indicate that the selection ability of the best performing clones is possible for intensive forest management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".