Effects of height-growth selection on wood density in black spruce in New Brunswick, Canada
Bibliographic record
Abstract
Height growth was the main selection criterion for the early-stage black spruce (Picea mariana [Mill.] BSP) breeding programmein New Brunswick, which has produced significant increases in volume growth. In this study we investigate howthe height-growth selection influences growth traits and wood density. Two genetic tests, a realized gain test of large plotsand a progeny test of small plots, were used for this purpose. Wood density was measured using the Resistograph methodon the standing trees. Growth and wood density of the improved seedlots were compared with those of an unimprovedstand checklot. In the progeny test, height-growth selection not only made the improved seedlots taller but also produceda proportional increase in DBH growth relative to the checklot. In the realized gain test, height-growth selection didincrease height, but did not produce a corresponding increase in DBH growth. Effects of height-growth selection on wooddensity varied with tests: the improved seedlot produced a greater although statistically non-significant decline in wooddensity in the progeny test; this reduction was at a much lesser extent or even non-existent in the realized gain test. Overall,results suggest that the improved growth from early stage improvement activities might not substantially and negativelyaffect wood density in plantation forestry and the predicted reduction in wood density in genetic tests of small plotsmight be inflated. Key words: Resistograph, realized gain test, progeny test, tree improvement
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 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) | 0.002 | 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".