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Record W2806630789 · doi:10.1038/s41598-018-24057-z

Trends in the Timing and Magnitude of Ice-Jam Floods in Canada

2018· article· en· W2806630789 on OpenAlexafffundabout
Prabin Rokaya, Sujata Budhathoki, Karl‐Erich Lindenschmidt

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersGlobal Institute for Water Security, University of Saskatchewan
KeywordsMagnitude (astronomy)Climate changeNorthern HemisphereClimatologyEnvironmental scienceGeographyEnvironmental resource managementPhysical geographyGeologyOceanography

Abstract

fetched live from OpenAlex

Ice-jam floods (IJFs) are important hydrological and hydraulic events in the northern hemisphere that are of major concern for citizens, authorities, insurance companies and government agencies. In recent years, there have been advances in assessing and quantifying climate change impacts on river ice processes, however, an understanding of climate change and regulation impacts on the timing and magnitude of IJFs remains limited. This study presents a global overview of IJF case studies and discusses IJF risks in North America, one of the most IJF prone regions according to literature. Then an assessment of shifts in the timing and magnitude of IJFs in Canada is presented analyzing flow data from 1107 hydrometric stations across Canada for the period from 1903 to 2015. The analyses show clear signals of climate change and regulation impacts in the timing and magnitude of IJFs, particularly in small basins.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), 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

Citations95
Published2018
Admission routes3
Has abstractyes

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