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Record W1940770461 · doi:10.1080/07011784.2015.1036123

Background to flood control measures in the Red and Assiniboine River Basins

2015· article· en· W1940770461 on OpenAlexaffvenueabout
Eric-Lorne Blais, Shawn P. Clark, Karen Dow, Bill Rannie, Tricia Stadnyk, Lucas Wazney

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of WinnipegUniversity of ManitobaAmec Foster Wheeler (Canada)
Fundersnot available
KeywordsFlood mythFlooding (psychology)Flood controlContext (archaeology)DamagesFloodplainGeographyArchaeologyCartographyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The City of Winnipeg and southern Manitoba have a long history of flooding, with flood events being recorded soon after the region was settled in the early nineteenth century. A devastating flood on the Red River in 1950 resulted in some of the earliest benefit–cost analyses in Canada with respect to flooding, and justified the construction of major flood mitigation projects on the Red and Assiniboine Rivers in the 1960s and 1970s. These projects were primarily designed to reduce the risk to the City of Winnipeg. Other projects have been constructed outside of the Winnipeg area, which have reduced flood damages to towns, individual farmsteads and rural residences. The level of flood protection has been re-evaluated every time a new flood of record occurs, and this has resulted in significant upgrading of existing works and the addition of more communities with permanent flood protection. As a result of the flood protection system that has been developed in Manitoba over the last 60 years, the damage caused by floods has been significantly reduced over natural conditions. The purpose of this paper is to provide context to flooding in Manitoba with a consideration of how flooding, flood damage and the impact on citizens of Manitoba have been mitigated by permanent flood protection works.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.024
GPT teacher head0.215
Teacher spread0.191 · 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 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

Citations20
Published2015
Admission routes3
Has abstractyes

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