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Record W2076861628 · doi:10.1139/cjce-2014-0540

Hydrotechnical advances in Canadian river ice science and engineering during the past 35 years

2015· article· en· W2076861628 on OpenAlexafffundvenueabout
Spyros Beltaos, Brian C. Burrell

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsAmec Foster Wheeler (Canada)Environment and Climate Change Canada
FundersNational Research Council CanadaUniversity of Alberta
KeywordsIce formationEnvironmental scienceEngineeringCivil engineeringHydrology (agriculture)GeologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Greater opportunity now exists compared to 35 years ago for civil engineers to apply river ice knowledge to practical problems of planning, designing, and operating hydro-power facilities, water intakes, bridges, and other infrastructure along ice-covered rivers. This is due to major advancements made during this period in understanding the physical processes of river ice formation, growth and breakup, in developing instrumentation for acquisition of information on winter environments, and in developing numerical modelling tools. An increasing number of journal articles, as well as papers presented at the river ice workshops of the CGU Committee on River Ice Processes and the Environment (CRIPE) and the ice symposia of the International Association for Hydro-Environment Engineering and Research (IAHR) attest to the advancement in river ice science and engineering knowledge that has occurred during the past 35 years. This paper reviews the developments in river ice science and engineering from a Canadian perspective and briefly discusses future directions.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.753
Threshold uncertainty score0.824

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 communication0.0000.001
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 head0.004
GPT teacher head0.163
Teacher spread0.159 · 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

Citations38
Published2015
Admission routes4
Has abstractyes

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