Growth, phenology, and cold hardiness of 32 Douglas-fir full-sib families
Bibliographic record
Abstract
Thirty-two full-sib families of coastal Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco var. menziesii) with a range of predicted breeding values were monitored for growth rate, phenology, and cold hardiness over 2 years on two sites to investigate if other traits are being selected when family selection is based on height. Significant differences among families existed in most phenological, growth, and cold-hardiness traits. On average, taller families burst bud later but did not have significantly different growth rates or length of growing period than other families. We found no significant correlations between family date of bud burst and cold hardiness in late spring or between duration of shoot growth or height and autumn freezing damage. Family differences in freezing tolerance were greatest in September and October. In these months, family current-year leaf nitrogen was positively correlated with cold hardiness. Families that were most hardy in the autumn were not the most hardy families in spring. We conclude that, for the studied breeding series, selection based on height does not have a significant impact on cold hardiness. We found no consistent relationships between phenological, growth, or cold-hardiness parameters and final height that could explain family ranking by height. Relationships between grandparent elevation and dates of bud burst and cold hardiness were observed.
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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 | 0.001 | 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".