MétaCan
Menu
Back to cohort

Correction of Hydraulic Conductivity Deviations in the Medium Moisture Range Using the PDI Model

2025· article· en· W4414108364 on OpenAlexaff
David R. Collins, Yuxin Chen, Laura M Fraser, André Gagnon, Helen J. Mitchell

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsHydraulic conductivityMoistureEvaporationRange (aeronautics)Conductivity

Abstract

fetched live from OpenAlex

Systematic underestimation of hydraulic conductivity in medium moisture ranges (θ = 0.15-0.30 cm³•cm⁻³) was addressed by incorporating film flow dynamics into the PDI framework. Laboratory evaporation experiments were performed under controlled conditions (20°C, suction range 0-1500 kPa). The PDI model, based on a simplified Langmuir isotherm, reduced deviations by 28% compared to van Genuchten functions. Bayesian posterior sampling (n = 10,000 iterations) revealed that τ and ω parameters showed strong identifiability, with posterior standard deviations < 5% of mean values. This improvement is crucial for modeling hydrological processes in peatland and wetland soils, where accurate representation of medium-range flow dominates seasonal 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.250
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicFlow Measurement and AnalysisFrench-language works237,207