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Soil Moisture Profile Model for Two-Layered Soil Based on Sharp Wetting Front Approach

2001· article· en· W2041887051 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Hydrologic Engineering · 2001
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsRichards equationInfiltration (HVAC)Water contentSoil scienceMoistureWettingSoil waterEnvironmental scienceHydrology (agriculture)GeologyGeotechnical engineeringThermodynamicsMeteorologyPhysics

Abstract

fetched live from OpenAlex

A two-layer model for 1D vertical unsaturated flow based on the hypothesis of a sharp wetting front is presented. The model further assumes a uniform pore pressure throughout the wetting zone at any given time during the infiltration and redistribution processes. These assumptions allow to reduce the theoretical Richards equation to an ordinary differential equation that can track average soil moisture in two soil layers behind the wetting front as the front moves downward. The model has been tested on many soil types by comparing results against numerical solutions of the Richards equation. It was shown that the conceptual model was able to represent local infiltration and average upper soil moisture accurately for complex rainfall sequences. Errors in soil moisture estimates never exceeded 0.04 cm3/cm3, which is the range of field measurement accuracies. Furthermore, as the conceptual model is numerically faster by an order of magnitude over the solution of the Richards equation, its use makes it possible to model spatially distributed infiltration and soil moisture at the watershed scale where soil layering is known to influence the water balance.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.014
GPT teacher head0.206
Teacher spread0.192 · 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