Using portfolio theory to evaluate the regional deployment of transferring tree seeds under uncertain future climates
Bibliographic record
Abstract
In this study, the problem of deploying seed from multiple tree improvement orchards to multiple sites in an environment of uncertain climate change is addressed. A modeling approach is designed and applied with the objective providing a robust solution, i.e., transferred seed sources perform well across multiple climatic scenarios. The approach involves two steps. First, the focal point seed zone method is employed to predict seed deployment zones under multiple future climate scenarios. Next, a portfolio model is applied to minimize the risk of maladaptation of the transferred seed under multiple climatic scenarios. The method was applied using black spruce (Picea mariana ) field data from 7 sites using 24 seed sources from the Great Lakes area. The focal point seed zone method generated deployment zones for 24 seed sources over three 30-years periods under 12 predicted future climate scenarios. Next, the optimization procedure searched for eligible sites that can receive improved seed sources from 7 provenances considering 12 different climatic scenarios. The portfolio model also produced the optimal composition of candidate seed sources at each eligible site. Sensitivity of the solutions to different emission scenarios is compared. Finally, geographic representations of results were illustrated in Geographic Information System. It was concluded that this modeling framework provides a useful approach for decision-makers to address the problem of deploying seed at regional scale, such that the risk of climatic maladaptation is minimized.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".