Climatic and anthropogenic impacts on water and sediment generation in the middle reach of the Jinsha River Basin
Bibliographic record
Abstract
Abstract Response of water and sediment generation to climate change and anthropogenic activities is becoming a hot topic in the middle reach of the Jinsha River Basin. In this research, coefficients of variation and concentration degree (i.e., Cv and Cd) and double mass curve (DDC) were adopted to examine the changes in water and sediment discharge and their relationships with precipitation. The contribution rates of climatic and anthropogenic factors to water and sediment discharge were assessed through comparisons between the measured and predicted values in the baseline and postbaseline periods in the basin. The main results were: (a) the water and sediment discharges of the basin showed a decreasing trend from 2006 to 2014, with peak points of Cv and Cd of sediment discharge of the basin in 2009 and 2010, respectively; (b) the precipitation had a major influence on water discharge variations, and the peak point of the DDC of the cumulative precipitation and sediment discharge was consistent with the cumulative water and sediment discharge; and (c) under annual and flooding season scales, the contribution rates of anthropogenic factors to water discharge were 63.060% and 70.457%, respectively. The contribution rates of anthropogenic factors on sediment discharge were 84.790% and 85.541%, respectively. The impacts of anthropogenic factors on water and sediment discharge were more significant than the impacts of precipitation, in which the construction and operation of cascade hydropower stations (CHS) were believed to play a crucial role.
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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 | 0.001 | 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".