Genetics of cold hardiness in a cloned full-sib family of coastal Douglas-fir
Bibliographic record
Abstract
Variation in cold-hardiness traits, and their extent of genetic control and interrelationships, were investigated among individuals (clones) within a single large full-sib family of coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco) from Oregon. Cold injury to needle, stem, and bud tissues was evaluated in fall 1996 and spring 1997 following artificial freeze testing of detached shoots collected from 4-year-old ramets (rooted cuttings). Variation among clones in cold-injury scores was significant (p < 0.01) for all shoot tissues in both fall and spring and averaged about three times the magnitude previously observed among open-pollinated families of this species. Thus, improving cold hardiness by within-family selection appears to hold much promise. Striking similarities in relative magnitudes of heritability estimates and genetic correlations in the full-sib family, compared with breeding populations, support the following hypotheses about the quantitative genetics of cold hardiness in this species: (i) heritability of cold hardiness (both broad-and-narrow-sense) is stronger in the spring than in the fall; (ii) cold hardiness of different shoot tissues in the same season is controlled by many of the same genes; and (iii) genetic control of fall cold hardiness is largely independent of cold hardiness in the spring.
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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".