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Record W3200515226 · doi:10.1130/g49318.1

Rivers in reverse: Upstream-migrating dechannelization and flooding cause avulsions on fluvial fans

2021· article· en· W3200515226 on OpenAlexaff
Douglas A. Edmonds, Harrison K. Martin, Jeffery M. Valenza, Riley T. Henson, Gary S. Weissmann, Keely Miltenberger, Wade W. Mans, Jason R. Moore, Rudy Slingerland, Martin R. Gibling, A. B. Bryk, Elizabeth Hajek

Bibliographic record

VenueGeology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOverbankGeologyFloodplainFluvialAlluvial fanAvulsionHydrology (agriculture)AlluviumChannel (broadcasting)Flooding (psychology)SinuosityGeomorphologyStructural basinGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract The process of river avulsion builds floodplains and fills alluvial basins. We report on a new style of river avulsion identified in the Landsat satellite record. We found 69 examples of retrogradational avulsions on rivers of densely forested fluvial fans in the Andean and New Guinean alluvial basins. Retrogradational avulsions are initiated by a channel blockage, e.g., a logjam, that fills the channel with sediment and forces water overbank (dechannelization), which creates a chevron-shaped flooding pattern. Dechannelization waves travel upstream at a median rate of 387 m/yr and last on average for 13 yr; many rivers show multiple dechannelizing events on the same reach. Dechannelization ends and the avulsion is complete when the river finds a new flow path. We simulate upstream-migrating dechannelization with a one-dimensional morphodynamic model for open channel flow. Observations are consistent with model results and show that channel blockages can cause dechannelization on steep (10−2 to 10−3), low-discharge (~101 m3 s−1) rivers. This illustrates a new style of floodplain sedimentation that is unaccounted for in ecologic and stratigraphic models.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.999

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)0.0020.000

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 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

Citations20
Published2021
Admission routes1
Has abstractyes

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