Consequences of climate change for biogeochemical cycling in forests of northeastern North AmericaThis 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
A critical component of assessing the impacts of climate change on forest ecosystems involves understanding associated changes in the biogeochemical cycling of elements. Evidence from research on northeastern North American forests shows that direct effects of climate change will evoke changes in biogeochemical cycling by altering plant physiology, forest productivity, and soil physical, chemical, and biological processes. Indirect effects, largely mediated by changes in species composition, length of growing season, and hydrology, will also be important. The case study presented here uses the quantitative biogeochemical model PnET-BGC to test assumptions about the direct and indirect effects of climate change on a northern hardwood forest ecosystem. Modeling results indicate an overall increase in net primary production due to a longer growing season, an increase in NO3–leaching due to large increases in net mineralization and nitrification, and slight declines in mineral weathering due to a reduction in soil moisture. Future research should focus on uncertainties, including the effects of (1) multiple simultaneous interactions of stressors (e.g., climate change, ozone, acidic deposition); (2) long-term atmospheric CO2enrichment on vegetation; (3) changes in forest species composition; (4) extreme climatic events and other disturbances (e.g., ice storms, fire, invasive species); and (5) feedback mechanisms that increase or decrease change.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".