Effects of climate on growth of lodgepole pine and white spruce following site preparation and its implications in a changing climate
Bibliographic record
Abstract
Site preparation and vegetation control can be used to mitigate climate change effects on early plantation growth in boreal forests. In this study, we explored growth of lodgepole pine ( Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) and white spruce ( Picea glauca (Moench) Voss) in relation to climate and site preparation using 20 years of data collected from studies in British Columbia. Results indicate that up to 45% of the variation in spruce growth and up to 37% of the variation in pine growth over this 20-year period can be explained by selected climatic variables. Monthly climate variables showed a stronger relationship to conifer growth than seasonal and annual variables. Climate variables related to the preceding year accounted for more than half of the variables in the final equations, indicating a lagged response in conifer growth. Future projections indicated that height growth of young lodgepole pine plantations in the sub-boreal zone could benefit (in the short term) from longer growing seasons by up to 12% on untreated stands. Untreated young white spruce plantations in the boreal zone may suffer height growth decreases of up to 10% due to increased drought stress. Vegetation control and mechanical site preparation treatments appear to mitigate effects of climate change to some extent.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".