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Record W3199887469 · doi:10.3390/w13182541

Effects of River-Ice Breakup on Sediment Transport and Implications to Stream Environments: A Review

2021· review· en· W3199887469 on OpenAlexaff
Spyros Beltaos, Brian C. Burrell

Bibliographic record

VenueWater · 2021
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBreakupSedimentHydrology (agriculture)Environmental scienceErosionSediment transportDeposition (geology)GeologyRiver morphologyGeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

During the breakup of river ice covers, a greater potential for erosion occurs due to rising discharge and moving ice and the highly dynamic waves that form upon ice-jam release. Consequently, suspended-sediment concentrations can increase sharply and peak before the arrival of the peak flow. Large spikes in sediment concentrations occasionally occur during the passage of sharp waves resulting from releases of upstream ice jams and the ensuing ice runs. This is important, as river form and function (both geomorphologic and ecological) depend upon sediment erosion and deposition. Yet, sediment monitoring programs often overlook the higher suspended-sediment concentrations and loads that occur during the breakup period owing to data-collection difficulties in the presence of moving ice and ice jams. In this review paper, we introduce basics of river sediment erosion and transport and of relevant phenomena that occur during the breakup of river ice. Datasets of varying volume and detail on measured and inferred suspended-sediment concentrations during the breakup period on different rivers are reviewed and compared. Possible effects of river characteristics on seasonal sediment supply are discussed, and the implications of increased sediment supply are reviewed based on seasonal comparisons. The paper also reviews the environmental significance of increased sediment supply both on water quality and ecosystem functionality.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.238
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations34
Published2021
Admission routes1
Has abstractyes

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