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Record W2789711576 · doi:10.1080/15715124.2018.1439496

Lessons learned from past ice-jam floods concerning the challenges of flood mapping

2018· article· en· W2789711576 on OpenAlexaff
Karl‐Erich Lindenschmidt, Mikko Huokuna, Brian C. Burrell, Spyros Beltaos

Bibliographic record

VenueInternational Journal of River Basin Management · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsEnvironment and Climate Change CanadaGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsFlood mythFlooding (psychology)DamagesHazardNatural hazard100-year floodEnvironmental scienceHydrology (agriculture)Flood stageWater resource managementGeographyGeologyMeteorologyEcologyArchaeology

Abstract

fetched live from OpenAlex

Delineation of flood hazard and risk on maps is useful as a means of public education and as a basis for measures aimed at lessening future flood damages. In many northern countries, rivers and streams are prone to ice-related flooding that often results in higher water levels and more extensive damages than open-water events. Procedures and standards for analysing ice-related flooding, however, are much less common than well-established standardized approaches for the open-water events. Nonetheless, the inherent flood hazard along many northern and mid-latitude rivers is not fully represented on flood-plain, flood-hazard, and flood-risk mapping if the possibility of ice-jam floods is ignored. Fortunately, the biophysical, past-flood, and flood-envelope approaches for flood hazard can be readily applied to ice-related floods, and hydrotechnical approaches based on an improved understanding of river-ice processes have been developed. In this paper, the nature and severity of ice-jam flooding, the present status of delineating ice-related flood events, and challenges to delineating ice-related floods are discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.055
GPT teacher head0.299
Teacher spread0.243 · 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 designOther design
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

Citations34
Published2018
Admission routes1
Has abstractyes

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