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Record W3215079237 · doi:10.1080/09715010.2021.1985638

The change process and influencing factors of channel connectivity in Jingjiang River from 1955-2015

2021· article· en· W3215079237 on OpenAlexaff
Yin Chen, Wang Yan-gui, Jian Shen

Bibliographic record

VenueISH Journal of Hydraulic Engineering · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsInternational Development Research Centre
FundersNational Natural Science Foundation of China
KeywordsChannel (broadcasting)Drainage basinSedimentAnalytic hierarchy processHydrology (agriculture)Environmental scienceErosionFluvialStructural basinRiver regimeYangtze riverGeologyGeomorphologyComputer scienceGeographyOperations researchGeotechnical engineeringMathematicsCartographyTelecommunications

Abstract

fetched live from OpenAlex

Human activities have greatly reduced the sediment load in the Yangtze River Basin and have changed the channel connectivity of the Jingjiang River. In this paper, the analytic hierarchy process (AHP) is used to establish the assessment model of channel connectivity based on the river function. Then, combined with the flow, sediment and boundary data of the main hydrological stations, we analyze the variation in processes of the channel connectivity of the Jingjiang River from 1955 to 2015. The results show that the change in process of channel connectivity of the Jingjiang River is basically the same in different reaches. The value of the comprehensive function of connectivity increases gradually with time and is greater than 1.0 after 1990, indicating that the channel connectivity has improved. Spatially, the Shashi-Jianli reach has the best connectivity, while the Zhicheng-Shashi reach has the worst connectivity. In addition, reservoir construction and river straightening are the main factors affecting the channel connectivity of the Jingjiang River. The influence of water and soil conservation on channel connectivity is mainly realized by basin erosion, which is the secondary factor. Some research methods and results can be used as reference for studying channel connectivity in other basins.

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 score0.173
Threshold uncertainty score0.257

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 studies0.0000.000
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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