Assessing the Height Gain Trajectory of White Spruce and Hybrid Spruce Provenances in Canadian Boreal and Hemiboreal Forests
Bibliographic record
Abstract
We assessed the impacts of tree improvement programs on the associated gains in yield of white spruce (Picea glauca (Moench) Voss) and hybrid spruce (Picea engelmannii Parry ex Engelmann x Picea glauca (Moench) Voss) over long temporal and large spatial extents. The definition of gain varied in the tree improvement programs. We assessed the definition of gain using a sensitivity analysis, altering the evaluation age with the definitions of the baseline and top performers. We used meta-data from provenance trials extracted from the literature to model the yields of provenances relative to those of standard stocks. Using a previously developed meta-model and a chosen gain definition, a meta-dataset of the gain of plantation ages was developed. Using this gain meta-dataset, a gain trajectory model was fitted for white and hybrid spruce provenances across Canadian boreal and hemiboreal forests. The planting site, mean annual daily temperature, mean annual precipitation, and number of degree days > 5 °C had large impacts on gain. This model can be used to predict gain up to harvest age at any planting site in the boreal and hemiboreal forests of Canada. Further, these gain trajectories could be averaged over a region to indicate the yield potential of tree improvement programs.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".