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Record W3005586864 · doi:10.1002/esp.4841

Channel mobility drives a diverse stratigraphic architecture in the dryland Mojave River (California, USA)

2020· article· en· W3005586864 on OpenAlexafffund
Alessandro Ielpi, M. G. A. Lapôtre, Alvise Finotello, Massimiliano Ghinassi, Andrea D’Alpaos

Bibliographic record

VenueEarth Surface Processes and Landforms · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluvialHydrology (agriculture)Ephemeral keyGeologyWatershedChannel (broadcasting)Drainage basinSTREAMSSurface runoffAggradationFlood mythStructural basinGeomorphologyArchaeologyEcologyGeography

Abstract

fetched live from OpenAlex

Abstract The links between flood frequency and rates of channel migration are poorly defined in the ephemeral rivers typical of arid regions. Exploring these links in desert fluvial landscapes would augment our understanding of watershed biogeochemistry and river morphogenesis on early Earth (i.e. prior to the greening of landmasses). Accordingly, we analyse the Mojave River (California), one of the largest watercourses in the Great Basin of the western United States. We integrate discharge records with channel‐migration rates derived from dynamic time‐warping analysis and chronologically calibrated subsidence rates, thereby constraining the river's formative conditions. Our results reveal a slight downstream decrease in bankfull discharge on the Mojave River, rather than the downstream increase typically exhibited by perennial streams. Yet, the number of days per year during which the channel experiences bankfull or higher stages is roughly maintained along the river's length. Analysis of historical peak flood records suggests that the incidence of channel‐formative events responds to modulation in watershed runoff due to the precipitation in the river's headwaters over decades to centuries. Our integrated analysis finally suggests that, while maintaining hydraulic geometries that are fully comparable with many other rivers worldwide, ephemeral desert rivers akin to the Mojave are capable of generating a surprisingly wide range of depositional geometries in the stratigraphic record. © 2020 John Wiley & Sons, Ltd.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.678

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)0.0010.000

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 head0.012
GPT teacher head0.203
Teacher spread0.192 · 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

Citations25
Published2020
Admission routes2
Has abstractyes

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