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Record W4407298697 · doi:10.15576/repourk/2025.1.3

Determination of soil particle size distribution using computer analysis of microscopic images

2025· report· en· W4407298697 on OpenAlexaboutno aff
Tomasz Głąb, Jarosław Knaga, Tomasz Zaleski, Paweł Dziwisz, J. Gluza, Dariusz Glanas

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEngineering
TopicSoil Mechanics and Vehicle Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsParticle-size distributionDistribution (mathematics)Particle (ecology)Computer scienceStatistical physicsBiological systemComputer graphics (images)MineralogyParticle sizeGeologyMathematicsPhysicsBiologyMathematical analysisPaleontology

Abstract

fetched live from OpenAlex

The project aims to develop a prototype device for determining the texture of soils and mineral deposits. The innovation of the designed solution consists in a significant reduction in the time of composition analysis with the possibility of any division into granulometric groups and the complete automation of the measurement from the moment the sample is introduced into the apparatus until the result is obtained. As part of the project, industrial research and experimental development are planned to be divided into the following stages: 1. Development of the measuring system. 2. Development of the structure and construction of device prototypes. 3. Development of the construction of the measuring system 4. Development of a mathematical model for processing data from the measuring system. 5. Preparation of software for device control and data recording. 6. Making the final prototype of the device. 7. Test tests of the final version of the device. The research will be conducted by a research consortium consisting of the project leader, i.e. Aumatic sp. z o.o. and the University of Agriculture in Krakow. The developed product will be intended for sale both on the domestic market and for export. Due to the number of entities potentially interested in the apparatus and the financial possibilities of potential recipients, the largest market should be developed countries (e.g. EU countries, USA, Canada, etc.). The main target groups of clients were: 1. Scientific institutions (universities, research institutes). 2. Institutions and enterprises performing analyzes for the needs of precision farming. 3. Chemical and Agricultural Stations. 4. Ceramic clay mining plants. 5. Manufacturers of ceramic products. 6. Laboratories conducting geotechnical tests for the needs of construction. 7. Laboratories carrying out environmental tests in the field of soil quality. 8. Provincial Inspectorates for Environmental Protection, Regional Directorates for Environmental Protection

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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