{"id":"W1999351221","doi":"10.1139/l07-049","title":"Hierarchical Bayes methods for systems with spatially varying condition states","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Bayes' theorem; Context (archaeology); Bayesian inference; Statistical model; Data mining; Conditional independence; Inference; Spatial analysis; Statistical inference; Bayesian probability; Machine learning; Artificial intelligence; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01035909,0.001230024,0.002013481,0.002192373,0.00110325,0.001945918,0.003220261,0.001851416,0.006035533],"category_scores_gemma":[0.03383746,0.001374669,0.001812542,0.002010164,0.002283525,0.002978395,0.002076322,0.003253021,0.001014776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002862465,"about_ca_system_score_gemma":0.003280465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0247146,"about_ca_topic_score_gemma":0.0281801,"domain_scores_codex":[0.9953721,0.002760888,0.0001842996,0.0006506593,0.0008213456,0.0002107078],"domain_scores_gemma":[0.9774213,0.0195044,0.001128089,0.0008073879,0.0009691495,0.0001695685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000679821,0.00004934971,0.001286937,0.0002283693,0.0001567336,0.0001340889,0.0003514172,0.5483153,0.0004188835,0.3753126,0.002595762,0.0710825],"study_design_scores_gemma":[0.00001715542,0.00001064006,0.0001847876,0.00002896776,0.00002041788,0.00002130972,0.00002132857,0.795112,0.0001027252,0.2029935,0.001470848,0.00001624879],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002076759,0.0004910218,0.996043,0.0001899481,0.00002944864,0.00004967708,0.00009902236,0.0001427473,0.0008783213],"genre_scores_gemma":[0.1875979,0.001783125,0.7999835,0.0004039295,0.0003595778,0.0008402191,0.0008155543,0.0002379624,0.007978242],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0247146,"threshold_uncertainty_score":0.05478472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0291136702754589,"score_gpt":0.3347031105932653,"score_spread":0.3055894403178064,"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."}}