{"id":"W7117237560","doi":"10.23977/acss.2025.090409","title":"Deep Bayesian Modeling for Maritime Situational Awareness with Multisource and Heterogeneous Information","year":2025,"lang":"","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Situation awareness; Probabilistic logic; Bayesian network; Inference; Graphical model; Dynamic Bayesian network; Field (mathematics); Bayesian inference; Bayesian probability","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003175397,0.0002830263,0.0004429846,0.000218538,0.0002050461,0.0003223056,0.0001069949,0.000147675,0.000005330803],"category_scores_gemma":[0.00001025535,0.0002824099,0.00003938676,0.0001961469,0.00004331505,0.0009556334,0.00005791036,0.0001379776,8.347812e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006060439,"about_ca_system_score_gemma":0.00004082655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000536461,"about_ca_topic_score_gemma":0.0000635468,"domain_scores_codex":[0.9984082,0.00006854052,0.0007464361,0.0003034358,0.0001835337,0.0002898737],"domain_scores_gemma":[0.9991771,0.0003065898,0.0001055336,0.0001465071,0.0001757548,0.00008848689],"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":[0.00005878948,0.00001486591,0.001345727,0.001546119,0.00004672448,0.000001966956,0.0004578619,0.850465,0.000004953507,0.00109075,0.00000352972,0.1449637],"study_design_scores_gemma":[0.001307773,0.00007828382,0.0001534364,0.001174667,0.00002520405,0.000029552,0.0001161193,0.9943462,0.00001340123,0.0005486952,0.001897894,0.0003087918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01071808,0.01483476,0.9726796,0.00005541716,0.0005374425,0.0009076821,0.00003856684,0.00005026637,0.0001781686],"genre_scores_gemma":[0.9843823,0.001018137,0.0141273,0.000107552,0.0001204562,0.0001302511,0.00007801128,0.00001615447,0.0000198875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9736642,"threshold_uncertainty_score":0.9999628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008069153614836492,"score_gpt":0.2406014818423183,"score_spread":0.2325323282274818,"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."}}