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Record W4220822419 · doi:10.1029/2021jf006367

Floods on Alluvial Fans: Implications for Reworking Rates, Morphology and Fan Hazards

2022· article· en· W4220822419 on OpenAlexafffund
Anya Leenman, Brett Eaton, Lucy MacKenzie

Bibliographic record

VenueJournal of Geophysical Research Earth Surface · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsAlluvial fanHydrographFlood mythHydrology (agriculture)AlluviumGeologySedimentErosionFlow (mathematics)FloodplainDeposition (geology)GeomorphologyStructural basinEnvironmental scienceGeographyGeotechnical engineeringGeometryCartographyMathematicsArchaeology

Abstract

fetched live from OpenAlex

Abstract Flood events are the agents of change on alluvial fans. However, most alluvial fan experiments have used constant flows to model fans and the channels upon them. Here, we present results from a series of alluvial fan experiments with different patterns of flow variation (i.e., different hydrograph shapes). We conducted experiments with (a) constant flow, (b) alternating high and low flows, (c) a moderate flood peak that decayed slowly, alternating with a constant low flow, and (d) a high flood peak that decayed rapidly, alternating with a constant low flow. We found that different hydrographs generated fans with different slopes, even though all experiments had the same mean flow and sediment supply. In addition, higher peak flows led to increased lateral migration rates and increased erosion and deposition. These results challenge the notion that a single representative flow can be used to approximate the geomorphic effects of a range of flows in a natural stream. Moreover, our findings indicate that hydrograph shape can govern the geomorphic impact of a flood event. This means that altered basin hydrology (for instance, through land cover change) likely exerts an important impact on geomorphic change and natural hazards on alluvial fans.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.353
Teacher spread0.304 · 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

Citations21
Published2022
Admission routes2
Has abstractyes

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