Comparison of fixed and focal point seed transfer systems for reforestation and assisted migration: a case study for interior spruce in British Columbia
Bibliographic record
Abstract
In forestry, science-based seed transfer systems, the foundation of effective reforestation programs, will likely be used in some form to mitigate the negative effects of climate change. In this study, we developed fixed and focal point seed transfer systems for interior spruce ( Picea glauca (Moench) Voss, Picea engelmannii Parry ex Engelm., and their hybrids) in British Columbia, Canada, and compared the effectiveness of both systems. Growth, phenology, and physiology traits were measured for 112 populations, and population means were transformed to principal components that were modeled using climate variables and multiple regression analysis. Compared with the fixed seed zone system, the focal point system had a greater area of seed use for a given risk of maladaptation. The relationship between growth and adaptive distance (i.e., adaptive similarity between test populations and populations local to test sites) was used to calculate critical seed transfer distances for focal point seed zones, which were defined according to expected forfeiture of growth. Changes in climate observed over the past 100 years and predicted in the next one third of a rotation were used to calculate appropriate assisted migration distances and develop methods for incorporating assisted migration into a focal point seed transfer system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".