{"id":"W2004365119","doi":"10.1504/ijmme.2015.067951","title":"Jet grouting: using artificial neural networks to predict soilcrete column diameter - part II","year":2015,"lang":"en","type":"article","venue":"International Journal of Mining and Mineral Engineering","topic":"Grouting, Rheology, and Soil Mechanics","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial neural network; Range (aeronautics); Jet (fluid); Column (typography); Grout; Field (mathematics); Data mining; Engineering; Artificial intelligence; Computer science; Machine learning; Geotechnical engineering; Mathematics; Structural engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004295455,0.0001906339,0.0002655812,0.0002275452,0.00004161107,0.0000769047,0.00021204,0.00009559395,0.000005078003],"category_scores_gemma":[0.0002783892,0.0001896441,0.00008274548,0.0000982362,0.00001703956,0.0001895025,0.00009162458,0.0002419422,5.911489e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040143,"about_ca_system_score_gemma":0.00002413634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001232314,"about_ca_topic_score_gemma":0.000009600954,"domain_scores_codex":[0.998776,0.00001755196,0.0005024155,0.0001252103,0.0002862557,0.0002925243],"domain_scores_gemma":[0.9992977,0.00006754825,0.0001027031,0.00007448981,0.0001931326,0.0002643627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004080728,0.00001219788,0.001027868,0.00000821661,0.0001412549,0.0001049295,0.001040654,0.9909856,0.002227446,0.0003691521,0.0007991954,0.003242632],"study_design_scores_gemma":[0.0004291715,0.0001780364,0.0001938477,0.000125336,0.00003388564,0.0004581223,0.0002671109,0.9969848,0.0002441712,0.00003651919,0.0008531888,0.0001957528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249507,0.0002810059,0.06936226,0.00007857844,0.005082008,0.00004288581,0.000005182846,0.00006652743,0.0001308861],"genre_scores_gemma":[0.989451,0.0000124632,0.00850161,0.00007475322,0.00188866,0.000002099049,0.000003825909,0.00003754598,0.00002806239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06450031,"threshold_uncertainty_score":0.7733461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02809443848013872,"score_gpt":0.2370631197773609,"score_spread":0.2089686812972222,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}