Developing breeding objectives for radiata pine structural wood production. II. Sensitivity analyses
Bibliographic record
Abstract
In a companion paper we describe a bioeconomic model of a production system for radiata pine (Pinus radiata D. Don) structural timber and estimated economic weights for four breeding-objective traits: mean annual increment, stem sweep, average branch size, and modulus of elasticity. In this paper we show estimation errors and the sensitivity of the economic weights to the input parameters of the production-system model. We also illustrate the linearity and independence of the economic weights and the sensitivity of selection indices to variation in economic weights and genetic parameters. Estimated standard errors of economic weights were generally low. Among the production-system parameters studied, discount rate and roundwood and sawn-timber prices had the largest impact on the estimates of economic weights. The relationship between the change in values of breeding-objective traits and the corresponding change in profit was nonlinear. The profit change due to change in one objective trait was not independent of the values of the other traits. The most important factors affecting the sensitivity of the selection index were the phenotypic variances of early selection traits such as tree diameter and wood basic density and the economic weights of mean annual increment and modulus of elasticity.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".