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Record W2156672450 · doi:10.1002/eco.1525

Ecohydrological flow networks in the subsurface

2014· article· en· W2156672450 on OpenAlexaff
Lawrence E. Band, Jeffrey J. McDonnell, J. M. Duncan, Ana P. Barros, Adrian Bejan, Tim Burt, W. E. Dietrich, R. E. Emanuel, Taehee Hwang, Gabriel G. Katul, Y. Kim, B. L. McGlynn, Brian Miles, Amilcare Porporato, Charles I. Scaife, P. A. Troch

Bibliographic record

VenueEcohydrology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersUniversity of North Carolina at Chapel HillUniversity of Arizona
KeywordsComputer scienceFlow (mathematics)Set (abstract data type)Subsurface flowFunction (biology)Hydrology (agriculture)Component (thermodynamics)Environmental scienceAggregate (composite)Multidisciplinary approachNatural (archaeology)Range (aeronautics)GeologyGroundwaterGeotechnical engineeringEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Preferential flow in hillslope systems through subsurface networks developed from a range of botanical, faunal and geophysical processes have been observed and inferred for decades and may provide a large component of the bulk transport of water and solutes. However, our dominant paradigm for understanding and modelling hillslope hydrologic processes is still based on the Darcy–Richards matric flow framework, now with a set of additional methods to attempt to reproduce some of the aggregate function of the two‐phase system of network and matrix flow. We call for a community effort to design and implement a set of well planned experiments in different natural and constructed hillslopes, coupled with the development of new theory and methods to explicitly incorporate and couple the co‐evolution of subsurface flow networks as intrinsic components of hydrological, ecological and geomorphic systems. This is a major community challenge that can now benefit from new experimental infrastructure, renewal of older infrastructure and recent advances in sensor systems and computational capacity but will also require a sustained and organized interdisciplinary approach. Copyright © 2014 John Wiley & Sons, Ltd.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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 designSimulation or modeling
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

Citations30
Published2014
Admission routes1
Has abstractyes

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