Soil moisture gradients and climate change: predicting growth of a critical boreal tree species
Bibliographic record
Abstract
Understanding the complex relationships among climate, tree growth, and water availability is key for predicting the performance of environmentally and economically salient tree species like Pinus banksiana Lamb. Ecologically, P. banksiana occupies the extreme habitats of soil moisture gradient, from very sandy dry soils to waterlogged bogs. However, little is known about how its growth may be affected by future climate in these two habitats. We assessed the effect of climatic variability on the growth of this species under different moisture conditions (sandy dry soils and bogs). Trees in the bog site had the highest growth rates. Individuals in all sites responded positively to increased spring temperature, whereas those in the bog site showed the highest response to increased summer moisture. However, in dry years, growth response in the bog site declined by 17%, whereas in the two drier sites, growth declined between 1% and 9.6%, equalizing growth rates across sites. Further, the decline in growth associated with drier summers eliminated the benefits of warmer springs at the bog site. Sites near bogs are currently associated with high growth performance and are commonly planted with this species. Yet, under the projected climate for the region, trees growing in these sites will likely lose their advantage.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".