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Record W2064044227 · doi:10.1021/ie0499658

Properties Required to Determine Moisture Transport by Capillarity, Gravity, and Diffusion in Potash Beds

2004· article· en· W2064044227 on OpenAlex
Ru Gang Chen, Hong Chen, Robert W. Besant, Richard W. Evitts

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIrrigation Practices and Water Management
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPotashMoistureParticle sizePorositySaturation (graph theory)Permeability (electromagnetism)Materials scienceDiffusionParticle (ecology)MineralogyMechanicsRange (aeronautics)ChemistryThermodynamicsComposite materialGeologyMetallurgyPhysicsPotassiumMathematicsPhysical chemistry

Abstract

fetched live from OpenAlex

In this paper, the important properties required to model moisture transfer in granular porous potash fertilizer by capillarity, gravity, and diffusion within a particle bed (i.e., porosity, permeability, specific surface area, and irreducible saturation) are investigated experimentally and theoretically for narrow ranges of particle size. Special attention is directed toward minimizing the uncertainty of each measurement and calculation. The irreducible saturation level (or moisture content) was deduced by using experimental data and theoretical/numerical simulations of moisture movement by capillarity, gravity, and diffusion within a granular potash bed. It is shown that, for a mixture with a wide range of particle sizes, the potash bed properties can be predicted from the properties for each narrow range of particle size in the mixture.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.306

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 science0.0000.000
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 head0.085
GPT teacher head0.275
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