Developing breeding objectives for radiata pine structural wood production. I. Bioeconomic model and economic weights
Bibliographic record
Abstract
Economic breeding objectives were developed for production of radiata pine (Pinus radiata D. Don) structural timber in Australia. Production systems of eight companies, including plantation growers, sawmills, and integrated-system companies, were examined. A bioeconomic model linking the breeding-objective traits mean annual increment (MAI), stem sweep, average branch size, and modulus of elasticity (MoE) with production-system components was constructed using data obtained from industry and published sources. For a plantation grower the most important trait for improvement was MAI (31% improvement of net present value after a 10% trait improvement). For a sawmill the most important trait was MoE (29% improvement of profit after a 10% trait improvement). For an integrated-system company the two most important traits were MoE and MAI (24% and 21% improvement of net present value after a 10% trait improvement, respectively). There was a high correlation between breeding objectives of plantation growers within a region (r G > 0.99), but a negative correlation between breeding objectives of plantation growers and sawmills (r GS = –0.32) and only an intermediate correlation (r GI < 0.65) between those of growers and integrated-system companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".