{"id":"W3139791528","doi":"10.1109/iccspa49915.2021.9385767","title":"Spatiotemporal Prediction Using Hierarchical Bayesian Modeling","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Gaussian process; Covariance; Bayesian probability; Kriging; Computer science; Gaussian; Artificial intelligence; Covariance function; Pattern recognition (psychology); Data mining; Exponential function; Bayesian inference; Algorithm; Machine learning; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000100431,0.00009924781,0.0001078579,0.00005706636,0.0001414937,0.0002873434,0.0003069297,0.00006395919,0.00007170545],"category_scores_gemma":[0.00002573561,0.00009103285,0.00004742461,0.000395894,0.00001668424,0.0006876616,0.0001810682,0.0001583012,0.00001240184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002987012,"about_ca_system_score_gemma":0.0003180486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000398256,"about_ca_topic_score_gemma":0.00001276591,"domain_scores_codex":[0.9989198,0.00003668861,0.0002149126,0.000371903,0.0002300326,0.0002266637],"domain_scores_gemma":[0.9993753,0.00001380294,0.00003305722,0.000352123,0.0001171888,0.0001085739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008456291,0.0002315493,0.006194787,0.0001045656,0.00004130905,0.0002391533,0.0008151332,0.02891832,0.005308401,0.8763875,0.0003683761,0.08138248],"study_design_scores_gemma":[0.0001038873,0.00001585899,0.000180371,0.00002184417,0.000002962663,0.00007526827,0.00001595228,0.9626806,0.001701774,0.03499646,0.0001007131,0.0001042483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01044365,0.00004702634,0.9826512,0.001372896,0.0002132037,0.00003830514,0.000001446297,0.0001772048,0.005055002],"genre_scores_gemma":[0.6937941,0.000005577704,0.3058058,0.0001971979,0.00006590806,0.000001459147,0.000002807649,0.000004499984,0.0001226166],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9337623,"threshold_uncertainty_score":0.3712212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03054838603105757,"score_gpt":0.2570726136288241,"score_spread":0.2265242275977666,"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."}}