Biotic and abiotic responses of the boreal forests carbon cycles to climate change and management
Bibliographic record
Abstract
Boreal forests are a large carbon sink and are as important as the tropical forests due to huge carbon stock in both plants and soils. However, the boreal forests carbon sink is affected by climate change on one hand and by management on the hand in last several decades and the need for better understanding of how boreal forests respond to climate and management in a long term is still urgent. In this study we used the process-based CoupModel combining the long-term in-situ measurements to successfully constrain the energy, water and carbon fluxes modeling in a boreal coniferous forest. We noticed that during the extreme drought years, there were large impacts from temperature on boreal forests growth, but not from water and radiation. The harvest of plants has made the boreal forests exposed to lower thresholds of environmental factors, but the impacts of harvest on net carbon fluxes was found just for short period due to the higher ecosystem respiration after harvest. The calibrated model generally depicted good performance for water, energy and carbon fluxes at hourly, monthly, yearly and multi-year scales, but the systematic biases indicated that considering the elevated atmospheric CO2 and nutrients dynamics, the climate variations as well as the more detailed management impacts on boreal ecosystems is of importance. Our study provided new insights into the boreal forests responses to climate change and management over a long period and contributed to better understanding of boreal forests for both the modeling and observation communities.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".