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Record W2895141889 · doi:10.1111/1752-1688.12690

Groundwaters at Risk: Wetland Loss Changes Sources, Lengthens Pathways, and Decelerates Rejuvenation of Groundwater Resources

2018· article· en· W2895141889 on OpenAlexafffund
Ali Ameli, Irena F. Creed

Bibliographic record

VenueJAWRA Journal of the American Water Resources Association · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBaseflowWetlandEnvironmental scienceGroundwaterHydrology (agriculture)Surface waterSubsurface flowAquiferWatershedDrainage basinGeologyStreamflowEcologyEnvironmental engineeringGeography

Abstract

fetched live from OpenAlex

Abstract Wetland loss alters the hydrology of wetlandscapes in poorly understood ways. To quantify the effects of wetland loss on subsurface hydrology, a physically based hydrologic model that simulates the timing and pathways of subsurface hydrologic connections was coupled with wetland inventories over a 50‐year period during which substantial wetland loss occurred. The model revealed, based on vertical variations in saturated hydraulic conductivities, wetland loss of different degrees led to a contraction of catchment contributing areas to local surface waters but an expansion of contributing areas to the regional surface water body. This shift in groundwater contributing areas reflected (1) a decrease in baseflow contribution to the local surface water bodies, and (2) an increase in the transit time and length of subsurface hydrologic connections with an associated increase in the magnitude and age of baseflow discharging to the regional surface water body. The model also showed regions with thick permeable aquifers were particularly sensitive to the loss of wetlands. Our ability to predict these changes in hydrology of the watershed provides important support for designing science‐based policies to promote sustainable water resource management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.450

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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.

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

Citations47
Published2018
Admission routes2
Has abstractyes

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