Provenances and families show different patterns of relationship between bud set and frost hardiness in <i>Picea abies</i>
Bibliographic record
Abstract
We have compared bud set and frost hardiness among Norway spruce (Piceas abies (L.) Karst.) provenances and families in two cold-acclimation regimes in a phytotron; low light intensity and high night temperatures (LL-HNT), and high light intensity and low night temperatures (HL-LNT) under shortening day lengths. Nine provenances from 59-66°N and altitude 100-700 m within Norway, and nine open-pollinated families from a single stand (61°N, 270 m elevation) were used. Both provenances and families started bud set and frost hardening earlier in LL-HNT than in HL-LNT. Correlations between the same trait expressed in two regimes were high for both bud set and hardiness at the provenance level and slightly lower at the family level. The variation among family means in bud set and hardiness was large. The differences found between the family extremes were up to 75% of those found between provenance extremes. The relationship between bud set and frost hardiness was strong among the provenance means within both environments (r = 0.92) but weak for the families (r = 0.22-0.44). Causal factors influencing phenotypic variation within traits and covariation among traits may differ for provenances and families within stands. The strong relationships among traits that are found at the provenance level cannot be generalized to the levels of families or 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".