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Record W4313839726 · doi:10.1029/2022gl101285

How Is Time Distributed in a River Meander Belt?

2023· article· en· W4313839726 on OpenAlexaff
Alessandro Ielpi, Daniele Pietro Viero, M. G. A. Lapôtre, A. Graham, Massimiliano Ghinassi, Alvise Finotello

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsLaurentian UniversityOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMeander (mathematics)FloodplainGeologyHydrology (agriculture)Channel (broadcasting)GeomorphologyAggradationFluvialGeometryGeotechnical engineeringStructural basinGeography

Abstract

fetched live from OpenAlex

Abstract River meandering controls the age of floodplains through its characteristic paces of growth and eventual cutoff of channel bends, forming oxbows. Hence, floodplain‐age distributions should reflect a river's characteristic size and migration rate. This hypothesis has been previously tested in numerical simulations, yet without systematic comparisons with natural systems. Here we analyze oxbow spacing and timescales of bend evolution and abandonment in natural and numerically simulated meander belts. In both cases, a saturated state is achieved whereby oxbows are spaced ∼1 meander radius apart. At saturation, the distribution of floodplain ages and probability of sediment‐storage time can be constrained from characteristic timescales of bend evolution and abandonment. Owing to the similar relationships between floodplain width and characteristic timescales in natural and simulated rivers, we postulate that this approach should apply to unconfined meandering rivers elsewhere—a hypothesis to be tested with independent geo‐ or dendrochronological data.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
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 head0.026
GPT teacher head0.283
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2023
Admission routes1
Has abstractyes

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