{"id":"W4407995955","doi":"10.1061/9780784486016.006","title":"Simulation of CPT Points and Empirical Settlement Using Simulated CPT Points with Kronecker-Product Gaussian Process Regression Approach","year":2025,"lang":"en","type":"article","venue":"","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Rocscience (Canada)","funders":"","keywords":"Gaussian process; Kronecker product; Computer science; Process (computing); Regression; Product (mathematics); Regression analysis; Kronecker delta; Algorithm; Statistics; Econometrics; Mathematics; Gaussian; Machine learning; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006976401,0.0003987665,0.0003720742,0.0005126936,0.000258155,0.0005757001,0.0008313602,0.0009308503,0.001545299],"category_scores_gemma":[0.002967856,0.0003029974,0.0003994752,0.000494019,0.0005212451,0.0005457394,0.000500829,0.0005772403,0.0002009482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000578485,"about_ca_system_score_gemma":0.0006793684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01434229,"about_ca_topic_score_gemma":0.01144142,"domain_scores_codex":[0.9997668,0.0000759824,0.00001432651,0.00004086445,0.00006843678,0.00003369843],"domain_scores_gemma":[0.9985073,0.0008999734,0.0001320631,0.0001240176,0.0002683316,0.00006839281],"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":[0.00001492867,0.00001348829,0.00106073,0.000005566207,0.000003266688,0.00002743111,0.00001651622,0.9951644,0.000323071,0.0008797814,0.00006054216,0.002430242],"study_design_scores_gemma":[0.000001490727,0.000003492802,0.00007434087,6.235194e-7,3.908812e-7,0.000003776407,0.000002699811,0.9995116,0.0001612038,0.0002057125,0.00003336905,0.000001439899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3383722,0.00004527944,0.6567445,0.0001331131,0.00002964524,0.00007389907,0.0003160755,0.0008803062,0.003404982],"genre_scores_gemma":[0.9256608,0.00002693955,0.07312485,0.00002459728,0.000004847812,0.00007190198,0.0002268259,0.0000476642,0.0008114688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01434229,"threshold_uncertainty_score":0.02851766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02823148479450237,"score_gpt":0.3204075707673106,"score_spread":0.2921760859728083,"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."}}