Utilization of family genetic variability to improve the rooting ability of white spruce (<i>Picea glauca</i>) cuttings
Bibliographic record
Abstract
Family genetic variability of the rooting characteristics of white spruce ( Picea glauca (Moench) Voss) cuttings harvested from 3-year-old stock plants was evaluated for 75 half-sib families. Growth, root system architecture, and gas exchange of the cuttings during the rooting phase (B+0) and the two subsequent growing seasons (B+1 and B+2) were evaluated. The root initiation phase (B+0) and the root development phases (B+1 and B+2) were found to be under strong genetic control. The weak correlations found between B+0 and the B+1 and B+2 phases may indicate that gene expression during B+0 is not related to root growth and development during B+1 and B+2. Strong positive correlations were observed between plant root and aboveground characteristics at the end of the B+1 and B+2 phases. This suggests that an indirect and efficient selection for white spruce families producing cuttings with heavier root dry masses could be based on the measures of aboveground morphological characteristics. Finally, the strong genetic control of morphological characteristics found in this study indicates that the selection of superior genotypes at a clonal level is possible for intensive forest management.
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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) | 0.001 | 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".