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Record W2089290964 · doi:10.4236/jwarp.2012.41001

Ice Jam Modelling of the Lower Red River

2012· article· en· W2089290964 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Water Resource and Protection · 2012
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsEnvironment and Climate Change CanadaKGS Group (Canada)Manitoba Hydro
Fundersnot available
KeywordsSpillageHydrology (agriculture)DeltaFloodplainChannel (broadcasting)River deltaEnvironmental scienceFlooding (psychology)Lead (geology)GeologyGeomorphologyGeographyComputer scienceGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

The lower reach of the Red River between Winnipeg and Lake Winnipeg is very prone to ice jam flooding. The one- dimensional ice jam model RIVICE was implemented for this reach to better understand the processes leading to such events and to provide a tool to evaluate strategies for ice jam mitigation. The most downstream portion of this river stretch flows through a delta and marsh system which poses challenges in modelling ice jams in such an area of low-lying topography and river banks. Solutions to overcome these challenges are discussed in this paper and results of one such solution using water abstractions from the main channel are also presented. Abstractions are inserted in the model to represent under-ice leakage from the main channel to side channel storage and diversions (up to 65% in the Red River delta) and spillage into the delta floodplain.

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.

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 score0.305
Threshold uncertainty score0.112

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)0.0000.000

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 head0.020
GPT teacher head0.183
Teacher spread0.163 · 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