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Record W6950639761 · doi:10.5683/sp3/a7itkl

Replication Data for: Comparison of four competing invasion percolation models for gas flow in porous media

2023· dataset· en· W6950639761 on OpenAlexaff

Bibliographic record

VenueBorealis · 2023
Typedataset
Languageen
FieldComputer Science
TopicTopic Modeling
Canadian institutionsQueen's University
Fundersnot available
KeywordsPercolation (cognitive psychology)Porous mediumReplication (statistics)Flow (mathematics)Vadose zonePercolation theoryPorosity

Abstract

fetched live from OpenAlex

This dataset includes processed gas injection images obtained at the end of nine gas-injection experiments to support the analysis presented in "Comparison of Four Competing Invasion Percolation Models for Gas Flow in Porous Media". Images are presented as processed binary saturations (Matlab file format .mat), following post-processing quality assurance and Light Transmission Method (LTM) conversion. The naming of the images corresponds to the name of the gas-injection experiments described in Van De Ven and Mumford (2019) Characterization of gas injection flow patterns subject to gravity and viscous forces, Vadose Zone Journal, 18, 190014, <a href="https://doi.org/10.2136/vzj2019.02.0014">doi:10.2136/vzj2019.02.0014</a>.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.877
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0020.001
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 head0.191
GPT teacher head0.353
Teacher spread0.162 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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