Characteristics of Solar-Induced Chlorophyll Fluorescence in the Three River Headwaters Region, Qinghai-Tibetan Plateau during 2001 to 2020
Bibliographic record
Abstract
The ecology of the Three River Headwaters Region (TRHR) is related to the long-term sustainable development of Qinghai Province and the whole of China. The change in chlorophyll fluorescence is an important index to measure the ecological environment. Therefore, it is of great significance to study the spatial and temporal distributions of Solar-induced chlorophyll fluorescence (SIF)and the related influencing factors in the TRHR. In this study, a high-resolution SIF dataset (2001–2020) was selected to be averaged on a time scale of years and months to investigate the annual and seasonal SIF characteristics, and the influencing climate factors were analyzed in combination with meteorological data by statistical method. The results showed that the SIF values ranged from 0.05 to 0.073 during 2001–2020, with a peak value of 0.073 in 2005 and 2009 and a minimum value of 0.05 in 2002. The averages of SIF values were higher in the source regions of the Yellow River source region (YR) and Langcang (Mekong) River source region (LCR) than in the source region of the Yangtze River source region (YZR). The SIF values of the TRHR in July, August and September were significantly higher than those in other months. The maximum value occurred in August at 0.11, and the minimum value was 0.008 in December. The precipitation had greater effect on the inter-annual variations in SIF. The monthly variation of SIF is influenced by precipitation, temperature and relative humidity. In addition, the influence of human activities and altitude on SIF should not be ignored. The results have certain reference value for protecting vegetation in the TRHR, and provide a reference for other regions to analyze the spatiotemporal changes and influencing factors by using SIF data.
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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) | 0.001 | 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".