{"id":"W4366495908","doi":"10.1139/cgj-2022-0372","title":"Spatial prediction of rockhead profile using the Gaussian process regression method","year":2023,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Jiangxi Province; Education Department of Jiangxi Province; National Natural Science Foundation of China","keywords":"Borehole; Ground-penetrating radar; Kriging; Geology; Gaussian process; Covariance function; Covariance; Gaussian; Geotechnical engineering; Engineering; Statistics; Radar; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003754041,0.0001145971,0.000143513,0.0002221447,0.0002334241,0.00003389266,0.0002167507,0.0001467344,0.00004535024],"category_scores_gemma":[0.00007699383,0.0000768686,0.00006690129,0.000397565,0.00003564462,0.00009882091,0.00001591317,0.0006037413,0.000004248396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002140045,"about_ca_system_score_gemma":0.000230015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0019874,"about_ca_topic_score_gemma":0.0006489425,"domain_scores_codex":[0.9989972,0.00003315993,0.0002721719,0.00009657184,0.000205068,0.0003958965],"domain_scores_gemma":[0.999445,0.00002412002,0.00006079588,0.0001530032,0.00008186451,0.0002351823],"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":[0.00002063841,0.000005465472,0.001460403,0.0001363062,0.00007773172,0.000168526,0.0007073545,0.8530182,0.03332864,0.0001183893,0.01037475,0.1005836],"study_design_scores_gemma":[0.0003059598,0.00006111369,0.01147717,0.0005174025,0.00004827047,0.0006740129,0.0005656389,0.9561208,0.02030473,0.001410044,0.008300068,0.0002147788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3998064,0.0003564114,0.5894661,0.001247918,0.00594052,0.0007577268,0.0001399491,0.000653623,0.001631344],"genre_scores_gemma":[0.997674,0.00002302755,0.001441718,0.00002281278,0.0007704853,0.000006496626,0.000004987422,0.00002766709,0.00002881358],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5978676,"threshold_uncertainty_score":0.3134611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01634790449958741,"score_gpt":0.2713124277463782,"score_spread":0.2549645232467908,"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."}}