Determining and projecting realised genetic gains: Results from early-stage spruce improvement programmes in New Brunswick, Canada
Bibliographic record
Abstract
Two series of realised genetic gain tests of large plots, one for black spruce (Picea mariana (Mill.) B.S.P.) and one for white spruce (Picea glauca [Moench] Voss), were established in the early 1990s in New Brunswick, Canada, to investigate realised gains from planting improved seedlots representing early-stage tree improvement activities. Individual-tree growth was recorded up to age 15 (one-quarter of their rotation age). Four improved seedlots were included in the black spruce gain test. Planting the superior stand seedlot (CAN101) could obtain moderate gain (7.0 % in volume/tree and 3.5 % in volume/ha at age 15). Growth improvement for the seedlot (UNROG) collected from a seedling seed orchard (FRA_SSO) established using phenotypically selected plus trees was negligible but genetic roguing improved the FRA_SSO seedlot’s growth, resulting in 3.3 and 2.1 % increase in 15-yr volume per tree and per hectare, respectively. The highest gain was observed by deploying the elite half-sib family (01-15), which resulted in a gain of 27.6 % in 15-yr volume/ha. Three improved seedlots were included in the white spruce realised-gain test. The seedlot (OVSSO) collected from a provenance seedling seed orchard had 9.2 % more volume/ha at age 15 years. Much higher gains were observed in the seedlots collected from an unrogued clonal seed orchard (DNR_CSO). Mixed cone collections from the DNR_CSO achieved 25.6% more volume/ha at age 15 than the unimproved seedlot. Practicing supplemental mass pollination with unimproved pollen in the DNR_CSO greatly reduced its seedlot gains, i.e. 12.9 % in 15-yr volume/ha. In both tests, the gain varied with age or
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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 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".