Increasing trend in ecosystem-scale photosynthetic efficiency in the Yellow River Basin since 2000 caused by afforestation and climate change
Bibliographic record
Abstract
We used the leaf area index (LAI) and solar-induced chlorophyll fluorescence (SIF) to represent vegetation greenness and photosynthetic capacity, respectively, and the ratio of SIF to LAI (SIF/LAI) as an indicator of ecosystem-scale photosynthetic efficiency. We analyzed the spatial-temporal dynamics of SIF/LAI and its driving factors in the Yellow River Basin (YRB) of China from 2000 to 2021. The annual increase rate was 1.89% for SIF and 1.21% for LAI. The greater increase in SIF relative to LAI led to a significant increase in SIF/LAI ratio from 2000 to 2021, with an annual increase of 0.63%. This suggests an enhanced photosynthetic capacity per unit LAI over time. Forest cover explained 48% of the SIF/LAI rising trend, followed by temperature (26%) and the standardized precipitation-evapotranspiration index (SPEI, 16%). Regionally, the SIF/LAI had annual increasing rates of 1.25% and 1.10% in the upper and middle reaches of the YRB, respectively. Meanwhile, the SIF/LAI of the source region and lower reaches did not show a significant trend. This study deepens the understanding of the relationship between vegetation greening and photosynthetic capacity, which has implications for ecosystem management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".