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Record W2011580438 · doi:10.2166/nh.2010.055

Relationship between the Atlantic Multidecadal Oscillation index and variability of mean annual flows for catchments in the St. Lawrence watershed (Quebec, Canada) during the past century

2010· article· en· W2011580438 on OpenAlexaffabout
Ali A. Assani, David Landais, Mhamed Mesfioui, Martin Matteau

Bibliographic record

VenueHydrology research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsAtlantic multidecadal oscillationStreamflowShoreNorth Atlantic oscillationWatershedClimatologyEnvironmental sciencePeriod (music)OceanographyGeographyGeologyDrainage basin

Abstract

fetched live from OpenAlex

This paper, based on an analysis of the mean annual flow (MAF) of 16 natural rivers over the period 1934–2000, has the following goals: (1) to determine the different temporal variability modes of the MAF in the St. Lawrence watershed, (2) to describe the temporal variability of the streamflow in each mode and (3) to analyze the influence of the AMO (Atlantic Multidecadal Oscillation) on the interannual and interdecadal variability of mean annual flows in this Quebec watershed. This paper shows that the interannual variability of mean annual flow was not synchronous on both sides of the river. During the period analyzed, MAF variability was characterized by a tendency to decrease on the south shore (right bank) but to increase on the north shore (left bank). A correlation analysis reveals that the influence of the AMO was limited exclusively to the north shore of the river, which is characterized by a continental climate. This correlation is negative. On the south shore, streamflow is correlated negatively to the AO (Arctic Oscillation).

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 score0.039
Threshold uncertainty score0.078

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 studies0.0010.000
Scholarly communication0.0010.000
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 head0.035
GPT teacher head0.298
Teacher spread0.263 · 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

Citations20
Published2010
Admission routes2
Has abstractyes

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