MétaCan
Menu
Back to cohort
Record W2026091332 · doi:10.2166/nh.2008.102

Analysis of cold season streamflow response to variability of climate in north-western North America

2008· article· en· W2026091332 on OpenAlexaffabout
Ming‐ko Woo, Robin Thorne

Bibliographic record

VenueHydrology research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStreamflowPacific decadal oscillationClimatologyEnvironmental scienceForcing (mathematics)Climate changeDrainage basinStructural basinNorth Atlantic oscillationSpring (device)OceanographyEl Niño Southern OscillationGeologyGeography

Abstract

fetched live from OpenAlex

Previous studies have correlated interannual streamflow fluctuations with changes in the climate. We note that decadal shifts in climate forcing can impart a stronger signal on streamflow than does the long-term climatic trend. In north-western North America, the Pacific Decadal Oscillation (PDO), which is strong in the cold season, may exert influence on interannual variations in spring high flows. In the 20th century, several major shifts in the PDO have been recognized. However, the rivers of Alaska, Yukon, Northwest Territories, British Columbia and Alberta have variable response to such climate signals. An analysis of the flow of rivers in this region indicates that a number of rivers draining the Pacific coast are positively correlated with PDO and some rivers in the interior correlate negatively. Not all river flows correlate with the PDO because factors such as location, topography and storage can overwhelm the climatic influence. Given these considerations, the interpretation of long-term trends in streamflow should take account of the interdecadal climatic shifts and basin characteristics that affect flow generation.

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 score0.925
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.308
Teacher spread0.278 · 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

Citations19
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueHydrology researchSame topicHydrology and Watershed Management StudiesFrench-language works237,207