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Record W4402028525 · doi:10.1002/vzj2.20373

Modeling soil water dynamics of an intensively cultivated histosol

2024· article· en· W4402028525 on OpenAlexafffundabout
Farhan Ahmad, Chandra A. Madramootoo

Bibliographic record

VenueVadose Zone Journal · 2024
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsMcGill UniversitySte. Anne's Hospital
FundersRoyal SocietyNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsHistosolEnvironmental scienceSoil waterPermanent wilting pointField capacityWater contentHydrology (agriculture)Hydric soilSoil scienceSoil organic matterSoil biodiversityGeology

Abstract

fetched live from OpenAlex

Abstract Understanding soil–water dynamics in cultivated organic soils (histosols) is crucial for sustainable agriculture, ecosystem preservation, and climate change mitigation. Data on the soil water retention curves (SWRCs) of these histosols in Canada are not readily available in the literature. The Hydrus‐1D model was used to predict SWRCs for a cultivated organic soil in Quebec. The model was validated with matric potential measured in a soil column at 10, 26, and 48 cm. The optimized hydraulic parameters in the Hydrus model resulted in field capacity moisture contents of 0.6, 0.696, and 0.49 cm 3 cm − 3 at the three depths, respectively. Wilting point moisture contents of 0.165, 0.107, and 0.14 cm 3 cm − 3 were obtained at the same depths. Model accuracy was confirmed with a Nash–Sutcliffe efficiency of 0.76, 0.91, and 0.78 for the three depths, respectively. These findings can inform decisions regarding water management practices such as irrigation and drainage requirements for intensively cultivated organic soils.

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 score0.092
Threshold uncertainty score0.433

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations3
Published2024
Admission routes3
Has abstractyes

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