Composition and carbon dynamics of forests in northeastern North America in a future, warmer worldThis article is one of a selection of papers from NE Forests 2100: A Synthesis of Climate Change Impacts on Forests of the Northeastern US and Eastern Canada.
Bibliographic record
Abstract
Increasing temperatures, precipitation extremes, and other anthropogenic influences (pollutant deposition, increasing carbon dioxide) will influence future forest composition and productivity in the northeastern United States and eastern Canada. This synthesis of empirical and modeling studies includes tree DNA evidence suggesting tree migrations since the last glaciation were much slower, at least under postglacial conditions, than is needed to keep up with current and future climate warming. Exceedances of US and Canadian ozone air quality standards are apparent and offset CO 2 -induced gains in biomass and predispose trees to other stresses. The deposition of nitrogen and sulfate in the northeastern United States changes forest nutrient availability and retention, reduces reproductive success and frost hardiness, causes physical damage to leaf surfaces, and alters performance of forest pests and diseases. These interacting stresses may increase future tree declines and ecosystem disturbances during transition to a warmer climate. Recent modeling work predicts warmer climates will increase suitable habitat (not necessarily actual distribution) for most tree species in the northeastern United States. Species whose habitat is declining in the northeastern United States currently occur in Canadian forests and may expand northward with warming. Paleoecological studies suggest local factors may interact with, even overwhelm, climatic effects, causing lags and thresholds leading to sudden large shifts in vegetation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".