Meta-Modelling to Quantify Yields of White Spruce and Hybrid Spruce Provenances in the Canadian Boreal Forest
Bibliographic record
Abstract
Tree improvement programs can improve forest management by increasing timber yields in some areas, thereby facilitating conservation of other forest lands. In this study, we used a meta-analytic approach to quantify yields of alternative white (Picea glauca (Moench) Voss) and hybrid spruce (Picea engelmannii Parry ex Engelmann x Picea glauca (Moench) Voss) stocks across planting sites in the boreal and hemiboreal forests of Canada. We extracted meta-data from published tree improvement program results for five Canadian provinces covering 38 planting sites and 330 white and hybrid spruce provenances. Using these meta-data and a random-coefficients nonlinear mixed-effects modelling approach, we modelled average height over time trajectories for varying planting site characteristics, as well as climate transfer distances between planting sites and provenances. Climatic transfer distances had strong effects on the height trajectory parameters. In particular, the asymptote parameter had a nonlinear increasing trend with planting site versus provenance mean annual temperature differences. We incorporated the height trajectory meta-analysis model into an existing growth and yield model to predict volume yields. Overall, this research provides a mechanism to quantify yields of alternative provenances at a particular planting site, as a component of decision support models for evaluating evaluate forest management investment into improved planting stocks alternatives under current and possible future climates.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.015 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".