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Record W4402326932 · doi:10.1016/j.geodrs.2024.e00862

Groundwater table prediction and seasonal variation influenced by short rotation willow plantation on marginal riparian lands of the Prairie potholes in Canada

2024· article· en· W4402326932 on OpenAlex
Shayeb Shahariar, Raju Soolanayakanahally, Angela Bedard‐Haughn

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGeoderma Regional · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsWillowRiparian zoneWater tableHydrology (agriculture)Environmental scienceTable (database)GroundwaterForestryGeologyGeographyEcologyBiology

Abstract

fetched live from OpenAlex

Shallow groundwater consumption via phreatophytic transpiration and resulting vegetation-linked groundwater table (GWT) fluctuation is a typical soil hydrological process in wetland riparian areas. However, upland and riparian land use alterations may further influence the shallow GWT fluctuation, temporally and spatially. In this multi-year field study, we investigated whether introducing short rotation willow (SRW) positively or negatively affects the shallow GWT, soil water availability, and soil health on marginal riparian lands of the Prairie Pothole Region (PPR). We compared the impact of SRW on these parameters to two common land uses: annual crop (AC) and pasture (PA). Depth to GWT was monitored via data loggers from 28 wells in two semi-arid PPR sites. The GWT depth varied by land use practices only in site B ( p < 0.001; PA > SRW = AC) but not significantly in site A ( p = 0.325), and the patterns were inconsistent between sites. In GWT depth prediction, the performance of Artificial Neural Network (ANN) was better than Autoregressive Integrated Moving Average (ARIMA) models but was inconsistent alike with field observations. The GWT depth responded to seasonal precipitation and potential evapotranspiration (ET) patterns. The monthly GWT fluctuations peaked between June and August due to increased precipitation, while they were lower during May and September with reduced precipitation; however, these variations were not significant ( p > 0.05). Higher precipitation and lower potential ET throughout the wet year (i.e., in 2014) significantly ( p < 0.05) raised GWT (i.e., decreased depth to GWT) under all land uses, and vice versa. Our study indicated that planting SRW in marginal riparian land of the PPR would not negatively impact shallow GWT or soil water availability. Moreover, the SRW plantation could also help manage soil salinity without severely depleting the soil's nutrient pools or diminishing soil quality and health indicator parameters measured during the first rotation.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.111
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.185
Teacher spread0.180 · 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