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Record W2949064127 · doi:10.1007/s11104-019-04068-z

An efficient three-dimensional rhizosphere modeling capability to study the effect of root system architecture on soil water and reactive transport

2019· article· en· W2949064127 on OpenAlexfundno aff
Yilin Fang, Steven B. Yabusaki, Amir Ahkami, Xingyuan Chen, Tim Scheibe

Bibliographic record

VenuePlant and Soil · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant nutrient uptake and metabolism
Canadian institutionsnot available
FundersPacific Northwest National LaboratoryBiological and Environmental ResearchOffice of ScienceWestern Sydney UniversityOntario Ministry of Agriculture, Food and Rural AffairsBattelleU.S. Department of Energy
KeywordsRhizosphereTranspirationEnvironmental scienceBulk soilChemistryBiogeochemical cycleSoil scienceBrachypodium distachyonNutrientWater flowSoil waterEnvironmental chemistrySoil organic matterBiologyPhotosynthesis

Abstract

fetched live from OpenAlex

The objective of this research was to develop a three-dimensional (3D) rhizosphere modeling capability for plant-soil interactions by integrating plant biophysics, water and ion uptake and release from individual roots, variably saturated flow, and multicomponent reactive transport in soil. We combined open source software for simulating plant and soil interactions with parallel computing technology to address highly-resolved root system architecture (RSA) and coupled hydrobiogeochemical processes in soil. The new simulation capability was demonstrated on a model grass, Brachypodium distachyon . In our simulation, the availability of water and nutrients for root uptake is controlled by the interplay between 1) transpiration-driven cycles of water uptake, root zone saturation and desaturation; 2) hydraulic redistribution; 3) multicomponent competitive ion exchange; 4) buildup of ions not taken up during kinetic nutrient uptake; and 5) advection, dispersion, and diffusion of ions in the soil. The uptake of water and ions by individual roots leads to dynamic, local gradients in ion concentrations. By integrating the processes that control the fluxes of water and nutrients in the rhizosphere, the modeling capability we developed will enable exploration of alternative RSAs and function to advance the understanding of the coupled hydro-biogeochemical processes within the rhizosphere.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.151

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 head0.009
GPT teacher head0.192
Teacher spread0.182 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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