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Record W3000633094 · doi:10.1002/rra.3572

Climatic and anthropogenic impacts on water and sediment generation in the middle reach of the Jinsha River Basin

2020· article· en· W3000633094 on OpenAlexaff
Yanpeng Cai, Wei Yang, Yujun Yi, Zhifeng Yang

Bibliographic record

VenueRiver Research and Applications · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Regina
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsEnvironmental scienceSedimentPrecipitationHydrology (agriculture)Structural basinHydropowerDrainage basinDischargeWater dischargeGeologyGeographyEcologyGeomorphology

Abstract

fetched live from OpenAlex

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., C v and C d ) 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 C v and C d 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.311
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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