Incorporating economic weights into radiata pine breeding selection decisions
Bibliographic record
Abstract
This article introduces the concept of “robust selection”, which proposes tree selection based on the stochastic simulation of economic values to account for the inherent uncertainty of economic weights used in tree selection for breeding programs. The proposed method uses both median ranking and ranking variability as criteria for breeding selection. Using consensus genetic and economic parameters from the New Zealand Radiata Pine Breeding Company program, we compare three selection strategies: deterministic application of economic weights from a vertically integrated bioeconomic model, an equal-weight index often used in operations, and robust selection. All strategies aim to increase value for a breeding objective that includes four traits, i.e., volume, stem sweep, branch size, and wood stiffness (measured as modulus of elasticity), based on a selection index that considers five criteria, i.e., stem diameter at breast height (1.3 m), straightness, branching score, wood density, and modulus of elasticity. Two-thirds of the selected trees were unique for each of the selection strategies. Robust selection achieved the best realised gain for three of the four selection criteria and was the middle performer in the last selection criteria. Considering the high intrinsic uncertainty of economic weights, we suggest that the relevant criterion for the selection of individuals is the maximum median ranking, subject to an acceptable level of variation in that ranking, rather than their narrow performance under a single economic scenario. This will lead to tree selections that perform well under a wide range of economic circumstances.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".