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Record W4200273052 · doi:10.1177/09596836211060542

The need for paleoflood investigations on the American reach of the Red River of the North

2021· article· en· W4200273052 on OpenAlexaboutno aff
Scott St. George, Joseph Zeleznik, Judith Avila, Matthew Schlauderaff

Bibliographic record

VenueThe Holocene · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsFlood mythFlooding (psychology)FloodplainContext (archaeology)Channel (broadcasting)GeographyHydrology (agriculture)GeologyArchaeologyCartography

Abstract

fetched live from OpenAlex

Over the past century, the Red River of the North has been the least stationary river in the continental United States. In Canada, historical and paleoenvironmental evidence indicates severe floods were common during the early 1800s, with the record ce 1826 flood having an estimated peak discharge 50% higher than the second-most severe flood ever observed. Unfortunately, the recorded history of flooding upstream in the United States does not begin until seven decades after this event. If 1826 was an equally exceptional flood on American reach of the river, then current flood-frequency curves for the river underestimate significantly the risks posed by future flooding. Alternatively, if the American stretch did not produce a major flood in 1826, then the recent spate of flooding that has occurred over the past two decades is exceptional within the context of the past 200 years. Communities in the Fargo-Moorhead metropolitan area are building a 58-km long, $2.75 billion (USD) diversion channel that would redirect floodwaters westward around the two cities before returning it to the main channel. Because this and other infrastructure in North Dakota and Minnesota is intended to provide protection against low-probability, high-magnitude floods, new paleoflood investigations in the region would help local, state, and federal policy-makers better understand the true flood threats posed by the Red River of the North.

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 score0.065
Threshold uncertainty score0.789

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 studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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