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Record W4385401973 · doi:10.1080/07011784.2023.2234869

The importance of groundwater to the upper Columbia River floodplain wetlands

2023· article· en· W4385401973 on OpenAlexaffvenue
Casey R. Remmer, Rebecca C. Rooney, Suzanne E. Bayley, Catriona Leven

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsUniversity of AlbertaUniversity of Waterloo
Fundersnot available
KeywordsWetlandFloodplainHydrology (agriculture)GroundwaterEnvironmental scienceSpring (device)PrecipitationEcologyGeologyGeography

Abstract

fetched live from OpenAlex

The Columbia Wetland complex is a rare example of a North American river system with relatively little disturbance from human infrastructure and is the only undammed portion of the main 2000 km stretch of the Columbia River. Declining river flows in western North America, including the upper Columbia River, have reduced the area of open water wetlands in the floodplain and raised concern that the Columbia Wetlands will not remain viable under increasing climate change. In this study we use water isotopes (δ18O and δ2H) and electrical conductivity to quantify the proportion of groundwater, river water and precipitation contributing to wetland water balance, as well as the role of evaporation, in the Columbia Wetlands through the spring, summer and fall of 2019. We found strong seasonality of water input sources. Groundwater and precipitation were important in spring and fall, while river water was dominant during the summer. An individual wetlands’ location in the floodplain as well as relative connectivity to the river channels influenced its seasonal pattern of input sources. Quantifying the relative contributions of the main input water sources to wetlands provides important new understanding of hydrologic connectivity in the Columbia Wetlands.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0010.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.204
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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2023
Admission routes2
Has abstractyes

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