Selection Approaches in High-Elevation Coastal Douglas-fir in The Presence of GxE Interactions
Bibliographic record
Abstract
Abstract Regeneration obligations in British Columbia for high-elevation coastal sites requires a secure seed supply of quality seed in coastal Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco). Consequently, a seed orchard is under development to supply seed after genetic testing and selection. For this purpose, 55 coastal Douglas-fir families were field-tested for 11 years on two contrasting high-elevation sites to examine differential growth performance and tolerance to cold conditions. Although heritabilities for growth on both sites were moderate at age 11, the higher elevation colder site had substantially slower growth and over 90% of the trees exhibited some form of cold damage to foliage, branches and stems; however, variation in this damage was not significant at the family level. Combined site analysis revealed a highly significant genotype by environment (GxE) component in height that could not be removed or reduced by using site-specific error variances or spatial analysis (i.e., GxE was primarily due to rank changes of families across the two sites). This was also reflected by a drop in heritability estimates obtained from the combined site analyses. In the presence of this type of GxE, independent culling, considering height a separate trait on each site, was employed to identify parents that were at a threshold breeding value of 5% or greater in growth superiority on both sites. Average breeding values for the selected parents, based on a combined site analysis, were around 5% above the trial mean for height at age 11. The use of independent culling, for situations where accurate genetic parameters are difficult to obtain, should be considered a practical alternative to more complex and error prone methods of selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".