Effects of interspecific competition on early growth of genetically improved white spruce in mixedwood stands in northeastern Alberta
Bibliographic record
Abstract
While deployment of white spruce (Picea glauca (Moench) Voss) from seed orchards is increasing in Alberta, genetic gain is only considered in pure spruce stands due to uncertainties in measuring yields in mixedwood stands. To better understand the performance of improved spruce in mixedwood stands, we compared the effects of interspecific competition on growth of improved (1.9% height gain at a 100-year rotation) and unimproved spruce in northeastern Alberta. By age 8 years the improved spruce showed no advantage over the unimproved spruce in either height (1.36 ± 0.36 m vs. 1.42 ± 0.38 m) or diameter (23.68 ± 7.33 mm vs. 25.65 ± 6.85 mm), and the largest diameter trees were found in a nutrient-poor subxeric site. A distance-independent Lorimer’s index including tree size ratio, combined with a power function, accounted for most of the growth variation in diameter and height from 2016 to 2017. The unimproved and improved spruce had different growth-competition curves across ecosites, and their height growth was less sensitive to competition than diameter growth. These results highlight several considerations for managing improved spruce, including (i) deploying higher genetic worth seedlots, (ii) developing realized gain trials with a good statistical design, and (iii) developing growth and yield functions for improved spruce in mixedwood stands.
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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 | 0.000 |
| 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".