Evaluation of Future Impacts of Climate Change, CO2, and Land Use Cover Change on Global Net Primary Productivity Using a Processed Model
Bibliographic record
Abstract
Few studies have focused on the combined impact of climate change, CO2, and land-use cover change (LUCC), especially the evaluation of the impact of LUCC on net primary productivity (NPP) in the future. In this study, we simulated the overall NPP change trend from 2010 to 2100 and its response to climatic factors, CO2 concentration, and LUCC conditions under three typical emission scenarios (Representative Concentration Pathway RCP2.6, RCP4.5, and RCP8.5). (1) Under the predicted global pattern, NPP showed an increasing trend, with the most prominent variation at the end of the century. The increasing trend is mainly caused by the positive effect of CO2 on NPP. However, the increasing trend of LUCC has only a small positive effect. (2) Under the RCP 8.5 scenario, from 2090 to 2100, CO2 has the most significant positive impact on tropical areas, reaching 8.328 Pg C Yr−1. Under the same conditions, climate change has the greatest positive impact on the northern high latitudes (1.175 Pg C Yr−1), but it has the greatest negative impact on tropical areas, reaching −4.842 Pg C Yr−1. (3) The average contribution rate of LUCC to NPP was 6.14%. Under the RCP8.5 scenario, LUCC made the largest positive contribution on NPP (0.542 Pg C Yr−1) globally from 2010 to 2020.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".