An economic analysis of seed source options under a changing climate for black spruce and white pine in Ontario, Canada
Bibliographic record
Abstract
We present a model that maps the net present value (NPV) associated with planting black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) and white pine (Pinus strobus L.) seed sources across a study area centred on Ontario, Canada. The model accounts for climate change through the use of universal response functions, which (in principle) predict the growth of any seed source under any climatic conditions. We demonstrated the use of the model for two locations in northern Ontario; both species exhibited significant variation in NPV across the study area and significant gains associated with climate-smart seed movements. For example, the NPV associated with potential white pine seed sources varied by more than $1500·ha−1 for a planting site at North Bay, Ontario. We also compared the NPV maps with climate similarity maps to examine the degree to which simple climate matching can act as a proxy for the detailed genecology relationships contained in the universal response functions. Overall, the climate similarity maps were well-correlated with the NPV maps; however, there was poor agreement regarding white pine seed deployment from North Bay, for which the two approaches identified opposite seed transfer directions. We propose that this situation can arise when species show strong adaptation to a central climatic optimum.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".