{"id":"W4220815390","doi":"10.3390/e24040492","title":"Estimation of the Covariance Matrix in Hierarchical Bayesian Spatio-Temporal Modeling via Dimension Expansion","year":2022,"lang":"en","type":"article","venue":"Entropy","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Covariance; Covariance matrix; Dimension (graph theory); Missing data; Bayesian probability; Applied mathematics; Computer science; Entropy (arrow of time); Estimation of covariance matrices; Mathematics; Algorithm; Statistical physics; Data mining; 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":[0.0003818104,0.0000643013,0.00008544747,0.00002148735,0.0002062234,0.000004560162,0.0001424489,0.00002178862,0.0002594601],"category_scores_gemma":[0.00003559218,0.00005332232,0.00003534058,0.0001850435,0.00004204579,0.00007626713,0.0002486679,0.0001908807,0.00001012493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766835,"about_ca_system_score_gemma":0.000009262986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006960534,"about_ca_topic_score_gemma":0.00001343958,"domain_scores_codex":[0.9989401,0.0001675843,0.0002246121,0.0001611874,0.0003674288,0.0001390961],"domain_scores_gemma":[0.9996844,0.00003249963,0.00008568752,0.0001684181,0.00000224842,0.00002672439],"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":[0.00003417111,0.00004501501,0.0291469,0.000004251793,9.575554e-7,0.000001518164,0.00077683,0.9608176,0.003370338,0.0002165848,0.0000340418,0.005551767],"study_design_scores_gemma":[0.0001857893,0.00003422622,0.005009444,0.00001338841,0.000002626106,0.000003021225,0.00007762721,0.9914542,0.0009085541,0.002195064,0.0000560446,0.00006001481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8690339,0.00001124944,0.1303047,0.0002950854,0.0001881983,0.0001146832,0.000003464888,0.00001456354,0.00003416901],"genre_scores_gemma":[0.9914948,6.357931e-7,0.008371478,0.00002346794,0.00002282072,0.00001175868,0.000007346111,0.000007268108,0.00006043121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1224609,"threshold_uncertainty_score":0.2840906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409670684186892,"score_gpt":0.2528970238325174,"score_spread":0.2388003169906485,"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."}}