Early Realized Gains for Two-cycle Selection for Black Spruce and Their Implications for Testing Effort Allocation
Bibliographic record
Abstract
Abstract Deployment of improved black spruce ( Picea mariana (Mill.) B.S.P.) seedlots in New Brunswick (NB) is a standard silvicultural practice. Most plantations have been established using elite-stand seedlots in the 1980s, first-generation seedling seed orchard seedlots in the 1990s and thereafter second-generation clonal seed orchard seedlots. A large-plot realized gain test was established at six sites in NB to provide estimates of actual gains from planting these improved seedlots. The test compared four improved seedlots, representing seedlots collecting from an elite stand, a first-generation seed orchard, two second-generation orchards and a mix of eight elite full-sib families identified in second-generation testing populations, with one unimproved checklot. This paper presents height measurements taken on trees at age 5. Results indicate that realized genetic gains from two-cycle selection and breeding are reasonably high and, in general, the more improved the seedlot, the higher the gain. Planting the elite-stand or the first-generation orchard seedlot would produce a gain of about 10% (relative to the checklot) in 5-yr height and an additional 5.8% gain could be obtained from planting the second-generation orchard seedlot. Even higher gain could be obtained via the deployment of the elite full-sib families, which was estimated to be about 8.6% more than the deployment of the second-generation orchard seedlot. While the realized gain varied with site, the ranking of gain achieved for the different seedlots was similar between sites. The above information was further used to optimize testing efforts for realized gain tests. To detect a typical gain of 5 to 10% at a significance level of 0.05 with a predetermined power of 0.80, each test should include 5 to 10 blocks per site (the number of sites is fixed at 4) or 4 to 6 sites (the number of blocks per site is fixed at 6) paired with planting 36 to 49 trees per plot. These approximate numbers of sites and blocks per site should be modified, depending on the interactions of seedlot with site and with block within site.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".