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METHODS FOR DETERMINING THE HYDRAULIC CONDUCTIVITY OF SHALLOW DRAINAGE SYSTEMS DURING OPERATION

2025· article· W4416049434 on OpenAlex
Oleksa Tyshchenko

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAutomobile Roads and Road Construction · 2025
Typearticle
Language
FieldEnvironmental Science
TopicAdvanced Scientific Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsDrainageHydraulic conductivityRange (aeronautics)GroundwaterDrainage system (geomorphology)Drainage networkNatural (archaeology)Sample (material)

Abstract

fetched live from OpenAlex

Summary. The article examines modern laboratory, field, geophysical, and numerical methods for determining the hydraulic conductivity of shallow drainage systems. A comparative analysis of the methods is conducted, taking into account accuracy, cost, ease of implementation, and application range [1,2]. Foreign experience in the use of these methods in the USA, Canada, and Europe is presented [1,2]. Examples of integrated applications to improve the accuracy of hydraulic conductivity assessment are provided [3,4]. The results of the study can be used for the design, monitoring, and optimization of drainage systems in construction and engineering practice [5,10]. The study shows that laboratory methods provide high measurement accuracy but are limited by sample size and testing conditions [3–5]. Field methods allow consideration of natural soil heterogeneity and seasonal fluctuations in groundwater levels [2,9]. Geophysical methods make it possible to assess large areas and deep soil layers without disturbing the environment [2]. Numerical modeling integrates data from various sources and allows prediction of drainage system performance under different scenarios [10]. Examples of integrated method application are provided to reduce maintenance costs and improve water drainage efficiency [1,2]. The article emphasizes the importance of a comprehensive approach to ensure long-term stable operation of drainage systems [1,2,10]. Additionally, the cost and efficiency of each method are evaluated, enabling engineers to make economically sound decisions [2,9]. Examples of method application in different soil types and climatic conditions are presented [1,3,5]. The combination of laboratory, field, and numerical methods is discussed to enhance prediction accuracy [4,10]. The article also considers the prospects for implementing modern technologies and automation in measurements to optimize the process of determining hydraulic conductivity in shallow drainage systems [10]. Keywords: Hydraulic conductivity, shallow drainage systems, laboratory methods, field methods, geophysical methods, numerical modeling, FEM, COMSOL Multiphysics, integrated approach.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.933

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 studies0.0010.001
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 head0.011
GPT teacher head0.326
Teacher spread0.315 · 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