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Record W4311736849 · doi:10.1080/07011784.2022.2154170

Comparing paleo reconstructions of warm and cool season streamflow (1400–2018) for the North and South Saskatchewan River sub-basins, Western Canada

2022· article· en· W4311736849 on OpenAlexaffvenueabout
Samantha A. Kerr, Yuliya Andreichuk, David Sauchyn

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsStreamflowDrainage basinPrecipitationStructural basinWater balanceClimatologyClimate changeEnvironmental scienceGeologyHydrology (agriculture)Physical geographyGeographyOceanography

Abstract

fetched live from OpenAlex

The North and South Saskatchewan River sub-basins comprise the Saskatchewan River Basin (SRB), which originates in the eastern slopes of the Rocky Mountains of Alberta (Canada) and Montana (USA), extending across the vast landscape of three Canadian Provinces. The SRB is the most populated region of the Northern Great Plains, where water demands from agriculture, industry, and municipalities can be a substantial proportion of supply during periods of low flow and hydrological drought. Changing climatic conditions and shifts between periods of extreme wet and dry weather emphasize the need for a better understanding of past and future seasonal variability of the surface water balance within and between the sub-basins. Climate extremes, such as hydrological drought and excessive moisture conditions occurring simultaneously can create detrimental effects. Using a multi-species network of over 80 tree-ring chronologies, warm (May through August) and cool (December through April) season streamflow (1400–2018) were independently reconstructed for the North and South Saskatchewan River sub-basins. Departures from seasonal flow and spectral and wavelet analyses of the reconstructions indicate significant variability at inter-annual and multi-decadal scales.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.176
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.

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

Citations6
Published2022
Admission routes3
Has abstractyes

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