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Record W2802474559 · doi:10.1002/hyp.11625

Effect of hydrogeomorphic setting on calcareous fen hydrology

2018· article· en· W2802474559 on OpenAlexafffund
Tim P. Duval, J. M. Waddington

Bibliographic record

VenueHydrological Processes · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcMaster UniversityGeneral Electric (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHydrology (agriculture)Groundwater rechargeEnvironmental scienceWetlandRiparian zoneWater balanceGroundwaterSubsurface flowGeologyAquiferEcologyHabitat

Abstract

fetched live from OpenAlex

Abstract Calcareous fens are species‐rich peatlands that are dependent on minerotrophic water sources for wetland functioning, with current conceptual models suggesting the water source is ubiquitously groundwater upwelling. By quantifying the water balance and subsurface water flow paths and fluxes over 3 growing seasons for calcareous fens in 3 different hydrogeomorphic settings (Riparian, Trough, and Basin), we show evidence that challenges this conceptual model. The Riparian Fen received an order of magnitude more water inputs than the Trough or Basin Fens and was dominated by stream recharge inputs and groundwater outputs. Precipitation and evaporation dominated the water balance of the Trough Fen whereas only the Basin Fen received sizeable groundwater inputs. Indeed, subsurface water fluxes were low at all fens due to weak hydraulic gradients and low saturated hydraulic conductivity in some areas of each wetland, though variations in growing season precipitation led to subsurface flow reversals in all 3 fens. Our results demonstrate the importance of understanding landscape position, or hydrogeomorphic setting, on calcareous fen hydrology for improving conservation, management, and restoration efforts of these important ecosystems.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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

Citations9
Published2018
Admission routes2
Has abstractyes

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