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Record W1981061126 · doi:10.1080/07011784.2014.942575

Detection and attribution of variability and trends in streamflow records from the Canadian Prairie Provinces

2014· article· en· W1981061126 on OpenAlexafffundvenueabout
Jeannine‐Marie St. Jacques, Yuhui Huang, Yang Zhao, Suzan Lapp, David Sauchyn

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Regina
FundersNatural Resources CanadaManitoba Hydro
KeywordsStreamflowAttributionEnvironmental scienceGeographyClimatologyHydrology (agriculture)Physical geographyPsychologyGeologyCartographyDrainage basinSocial psychology

Abstract

fetched live from OpenAlex

Emerging period of record trends and low frequency (i.e. centennial-scale) variability were examined in streamflow records over the entire Canadian Prairie Provinces. The average record length was 52 years, with the longest record spanning 1911–2010. A modified Mann-Kendall trend analysis showed decreasing flows in Alberta and in southwestern Saskatchewan, no significant trends in the central Prairies and increased flows in Manitoba. Using composite analysis, this study also detected the impacts of the Pacific Decadal Oscillation (PDO), the North Pacific Index (NPI), the El Nino-Southern Oscillation (ENSO) and the Pacific North American mode (PNA) on mean daily discharge. There are increased flows during the negative phases of the PDO and PNA, La Nina events and weak Aleutian lows, and decreased flows during the positive phases of the PDO, El Nino and strong Aleutian lows. A much weaker effect of the Arctic Oscillation (AO) was detected. The ~60-year cycle of the PDO has important implications for the rec...

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)0.001
metaresearch head score (Gemma)0.004
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 score0.013
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.195
Teacher spread0.183 · 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

Citations27
Published2014
Admission routes4
Has abstractyes

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