Boreal forest provenance tests used to predict optimal growth and response to climate change. 1. Jack pine
Bibliographic record
Abstract
Height, diameter, and survival data were obtained from 16 rangewide jack pine provenance trials in eastern Canada and the United States. Climate data for each seed source and test location were used to relate height growth to 72 climate variables. Population response functions based on August minimum and January maximum temperatures were developed to predict climate values maximizing height growth for individual seed sources. Site transfer functions based on March precipitation and December minimum temperature were developed to predict climate values maximizing height growth for test locations. Contour lines representing optimal performance were fitted to current (1971–2000) and future (2041–2070) climate grids. Optimal growth is currently achieved between 46°N and 47°N latitude for most seed sources. Future temperature increases are expected to cause a northward shift of the optimal habitat by approximately 2°. Northern sources are growing at temperatures below optimum and would benefit from warmer environments provided other environmental factors do not become limiting. Central sources are growing at close to optimum and will be negatively affected by increased temperatures in the future. Southern sources performed better in cooler environments, and warmer temperatures may cause significant height growth loss and the potential extirpation of these populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".