Intracone variation explains most of the variance in <i>Picea abies</i> seed weight: implications for seed sorting
Bibliographic record
Abstract
Norway spruce (Picea abies (L.) Karst.) seed is collected from both forest stands after final felling and from seed orchards. To produce high-germinability seed lots that are easy to use in nursery sowing machines, empty, insect-damaged, and other poor-quality seeds are culled. Sorting is done typically by weight or size. Previous studies of conifer seed have indicated wide variation in seed weight between individual trees or clones. However, the intratree or intraclone variations have rarely been taken into account, and intracone variation in seed weight has not been examined. We collected cones from a forest stand and from a clonal seed orchard in central Finland. Each seed from each cone was extracted, weighed, and x-rayed to assess their quality. Trees and clones differed in terms of the proportions of different quality seed. Variance component analysis showed that the intracone variation explained a larger proportion of the total variation in seed weight than did the intercone/intertree or interclone variations. Thus weight-based seed sorting has less effect on the genetic diversity of a seed lot than previously believed. We also conclude that the large differences in proportion of full seed among trees and clones impact the contribution of genotypes in seed and, eventually, in seedling lots.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".