MétaCan
Menu
Back to cohort

Simultaneous Estimation of Soil Moisture and Hydraulic Parameters for Precision Agriculture. Part A: Methodology

2022· article· en· W4296912146 on OpenAlexafffund
Erfan Orouskhani, Bernard T. Agyeman, Jinfeng Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsWater contentSoil waterInterpolation (computer graphics)Soil scienceSensitivity (control systems)Computer scienceWork (physics)Field (mathematics)MoistureEnvironmental scienceVariable (mathematics)Pedotransfer functionState variableEstimation theoryGeotechnical engineeringMathematicsHydraulic conductivityAlgorithmEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we propose a systematic procedure to simultaneously estimate the soil moisture and soil hydraulic parameters for large-scale three-dimensional agro-hydrological systems with heterogeneous soils. The key steps of the proposed procedure include augmented system construction, sensitivity analysis, variable selection, simultaneous state and parameter estimation, and parameter interpolation. The proposed method is applied to a simulated three-dimensional field with heterogeneous soils, and the simulation results of the considered agro-hydrological system illustrate the applicability and effectiveness of the proposed method on the performance of soil moisture estimation. In the second part of this work [1], the application of the proposed procedure to a real agriculture field is presented.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.264

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 head0.024
GPT teacher head0.259
Teacher spread0.235 · 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 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

Citations2
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicSoil Moisture and Remote SensingFrench-language works237,207