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Record W4389486457 · doi:10.1002/esp.5750

The 1870s Saskatchewan River avulsion: Ice jam or open water flood? A probabilistic approach for cold climate river avulsions

2023· article· en· W4389486457 on OpenAlexafffundabout
Cody Kupferschmidt, Emmanuelle Arnaud

Bibliographic record

VenueEarth Surface Processes and Landforms · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversity of Guelph
FundersDalhousie UniversityNatural Sciences and Engineering Research Council of CanadaUniversity of Nebraska-Lincoln
KeywordsAvulsionFlooding (psychology)Flood mythHydrology (agriculture)GeologyEnvironmental sciencePhysical geographyGeographyGeomorphologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Cold climate rivers can experience avulsions due to both open water and ice jam flooding; however, most existing models for evaluating avulsions only consider open water flows. We present a novel approach for determining site‐specific probabilities of avulsion cause (open water vs. ice jam flooding) by combining historical flow data, channel cross‐sections and known avulsion history for the study area. The approach is applied to the Cumberland Marshes region of the Saskatchewan River in Central Canada, which experienced an avulsion in the mid‐1870s that some researchers have suggested may have been triggered by an ice jam. For the study area, overbank flooding was found to occur much more frequently due to ice jams than open‐water floods. Based on an average avulsion return period of 660 years in the study area, the probability of historical avulsions being caused by ice jam flooding was estimated to range from 61% and 80%, using a range of annual ice jam probabilities between 0.1 and 0.5. Results from the study suggest ice jam flooding as the most likely cause of the 1870s avulsion, which is also supported by historical evidence. The developed methodology is relatively simple to apply and could be easily implemented at other cold‐climate sites to evaluate avulsion risks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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 designNot applicable
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

Citations1
Published2023
Admission routes3
Has abstractyes

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