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Record W4398771640 · doi:10.1002/eco.2664

Enhancing ecological success in Yangtze River channel construction: A numerical modelling approach

2024· article· en· W4398771640 on OpenAlexaff
Pengcheng Li, Yike Li, Yang Yang, Qianqian Wang, Weiwei Yao

Bibliographic record

VenueEcohydrology · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of China
KeywordsYangtze riverChannel (broadcasting)Environmental scienceHydrology (agriculture)Environmental resource managementWater resource managementEcologyComputer scienceGeographyGeologyChinaGeotechnical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract Channel constructions significantly impact river hydrodynamics, subsequently influencing river ecosystems. To mitigate the negative influence of channel construction and protect fish habitat, it is essential to evaluate fish habitat suitability through the integration of hydrodynamic and habitat models. This study models channel constructions on both the left and right riverbanks to evaluate habitat suitability for Four Major Chinese Carps and the Chinese sturgeon. Initially, flow velocity, water depth and grain size are simulated, followed by an assessment of habitat conditions using the Habitat Suitability Index (HSI) for three distinct construction strategies. Results reveal that constructing channels on the left bank mitigates adverse effects on fish habitat, while constructions on the right bank and both riverbanks lead to habitat degradation. Long‐term effects on riverbed elevation and fish habitat suitability post‐construction were also investigated. Notably, the Four Major Chinese Carps consistently demonstrate greater suitability for the studied river, regardless of flow rate or construction strategies, in comparison to the Chinese sturgeon. These findings underscore diverse responses to channel construction, providing valuable insights for identifying critical habitat areas for endemic fish conservation. This research presents a robust framework for assessing fish ecology in river systems, offering support for conservation decision‐makers.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.997

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)0.0040.001

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 head0.012
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.

Study designSimulation or modeling
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

Citations3
Published2024
Admission routes1
Has abstractyes

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