{"id":"W2771386828","doi":"10.1139/cgj-2017-0219","title":"Interpolating spatially varying soil property values from sparse data for facilitating characteristic value selection","year":2017,"lang":"en","type":"article","venue":"Canadian Geotechnical Journal","topic":"Geotechnical Engineering and Analysis","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"City University of Hong Kong","keywords":"Property (philosophy); Confidence interval; Selection (genetic algorithm); Bayesian probability; Statistics; Sampling (signal processing); Geotechnical engineering; Data mining; Mathematics; Set (abstract data type); Computer science; Engineering; Artificial intelligence","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":[0.0007045819,0.000270956,0.0003882509,0.0001975999,0.0009872031,0.000623499,0.001261639,0.0002405451,0.00006493686],"category_scores_gemma":[0.002312663,0.0002318143,0.000154502,0.00009484618,0.00006759828,0.0005108457,0.0001296296,0.0009292876,0.00001910097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003333981,"about_ca_system_score_gemma":0.0002102503,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02352441,"about_ca_topic_score_gemma":0.003226545,"domain_scores_codex":[0.9981652,0.00003360216,0.0005553622,0.0003729926,0.0002153103,0.0006574581],"domain_scores_gemma":[0.9980744,0.0001578529,0.0001574188,0.0008715367,0.0001109252,0.0006278328],"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":[0.00001127983,0.00001185778,0.0001709535,0.00005872251,0.0002060412,0.00003945606,0.00006975513,0.9026077,0.01075108,0.00002613355,0.0009758198,0.08507121],"study_design_scores_gemma":[0.0002598008,0.00003960321,0.0008509218,0.0002395882,0.0001038798,0.00004988596,0.00001634454,0.9912552,0.0001801492,0.0005011142,0.006172161,0.0003313849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05418245,0.0001786618,0.9423832,0.0008827194,0.0008683935,0.0002855684,0.0005865404,0.0004393646,0.0001930686],"genre_scores_gemma":[0.9865636,0.00003528894,0.01228743,0.00006724003,0.0007705398,0.00001582038,0.0001137186,0.00006347421,0.00008294665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9323811,"threshold_uncertainty_score":0.982978,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105675957406373,"score_gpt":0.2435699859578785,"score_spread":0.2125132263838148,"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."}}