{"id":"W2509500451","doi":"10.1139/cgj-2016-0189","title":"Integrated framework for characterization of spatial variability of geological profiles","year":2016,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hong Kong Government","keywords":"Autocovariance; Borehole; Outlier; Spatial variability; Variance (accounting); Geology; Spatial correlation; Spatial analysis; Restricted maximum likelihood; Residual; Statistics; Soil science; Algorithm; Computer science; Mathematics; Maximum likelihood; Geotechnical engineering","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004981147,0.00007069696,0.0001529146,0.00004122169,0.00005999467,0.000007308754,0.0001823983,0.0001473366,0.001341187],"category_scores_gemma":[0.001323219,0.00004602524,0.0000553457,0.00009677941,0.0002373198,0.00005060052,0.00003291154,0.0001336005,0.000006241355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001594959,"about_ca_system_score_gemma":0.0001182312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005269028,"about_ca_topic_score_gemma":0.0009215478,"domain_scores_codex":[0.9991528,0.00005083545,0.0003195288,0.00012965,0.0001279791,0.0002191709],"domain_scores_gemma":[0.9992114,0.0002041675,0.0001703601,0.0001251885,0.00005136698,0.0002375328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001847692,0.0001600957,0.1913958,0.00004591633,0.00004803203,0.00001938606,0.0001020201,0.0003002115,0.3082262,0.02727408,0.0006179222,0.4716255],"study_design_scores_gemma":[0.0005572454,0.0004213125,0.8604268,0.0002397305,0.00003242784,0.00004507447,0.00001665127,0.004600516,0.007133097,0.1105151,0.01575799,0.0002540369],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3719001,0.000001093624,0.6271169,0.0005340742,0.00008939009,0.0001341792,0.0001549724,0.000005164476,0.00006414864],"genre_scores_gemma":[0.9883291,0.00000870461,0.01148519,0.00007926512,0.00004924207,0.000007566218,0.0000101067,0.000005462997,0.000025395],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.669031,"threshold_uncertainty_score":0.9995717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01317248871347116,"score_gpt":0.2247451374655517,"score_spread":0.2115726487520805,"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."}}