Use of response functions in selecting lodgepole pine populations for future climates
Bibliographic record
Abstract
Abstract Although growth response functions have previously been developed for lodgepole pine ( Pinus contorta Dougl. ex Loud.) populations in British Columbia, new analyses were conducted: (1) to demonstrate the merit of a new local climate model in genecological analysis; (2) to highlight new methods for deriving response functions; and (3) to evaluate the impacts of management options for existing geographically defined seed planning units (SPUs) for reforestation. Results of this study suggest that new methods for anchoring population response functions, and a multivariate approach for incorporating climate variables into a single model, considerably improve the reliability of these functions. These functions identified a small number of populations in central areas of the species distribution with greater growth potential over a wide range of mean annual temperature (MAT). Average productivity of lodgepole pine is predicted to increase (up to 7%) if moderate warming (∼2°C MAT) occurs in the next few decades as predicted, although productivity would substantially decline in some SPUs in southern BC. Severe global warming (>3°C MAT) would result in either a drastic decline in productivity or local populations being extirpated in southern SPUs. New deployment strategies using the best seed sources for future reforestation may not only be able to mitigate the negative impact of global warming, but may even be able to increase productivity in some areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".