Using portfolio theory to improve yield and reduce risk in black spruce family reforestation
Bibliographic record
Abstract
Abstract Family forestry, defined as the deployment of families in mixture into plantations, is becoming an attractive option for black spruce (Picea mariana (Mill.) BSP) in New Brunswick, Canada. With many elite families of black spruce being available, there is a knowledge gap regarding how to compose a mixture of families that optimally balances the objectives of increased yield and reduced risk. This study, based on real field test data, investigates the application of a model based on the modern portfolio theory to optimally balance yield and risk when selecting a portfolio (mixture) of black spruce families to deploy in reforestation. The risk was expressed as the variance of the family portfolio, an effective indicator of yield stability. This is an innovative approach in forestry and it is compared to the currently used method, truncation-deployment, defined as the equal deployment of seed of selected families. Results show that the portfolio theory searched for the combination of yield and stability and produced family portfolios maximizing yield at a given stability or minimizing yield instability at a given yield. The portfolio theory was never inferior in maximizing yield to the truncation- deployment approach when yield stability is a concern. We recommend using portfolio theory to determine family portfolios for family forestry. While this study targets to family forestry, the results may be relevant to other deployment strategies where stability is a concern, such as clonal forestry.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".