Response and adaptation of terrestrial ecosystem processes to climate warming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Terrestrial ecosystems are characterized by a series of spatiotemporally continuous, multiple scaled, and mutually connected processes. Since most of these ecological processes are regulated by temperature, climate warming will profoundly impact terrestrial ecosystems at global scale. Recently, how key processes in terrestrial ecosystems respond and/or adapt to climate warming has become a fundamental question in global change ecology. Here, we reviewed the recent research progress related to such question. This review focuses on key ecosystem processes, such as plant ecophysiological processes, phenology, community dynamics, productivity and carbon allocation, decomposition of litter and soil organic carbon, nutrient cycling, and carbon-nitrogen coupling. Based on a literature review, we propose perspectives for future research to tackle fundamental questions, such as the predictability of plant traits on ecosystem processes, coupling between biogeochemical cycles, mechanisms driving ecosystem responses to extreme climate and asymmetric warming, and ecological forecasting with models. We finally suggest more research efforts on warming adaptation rather than response on China's specific ecosystems, and on the integration of experiments, observations, and models for coordinating studies across scales.
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.
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 | 0.001 | 0.002 |
| 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 it