{"id":"W3109582891","doi":"10.1155/2020/8897700","title":"Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Task (project management); Process (computing); Collision; Artificial intelligence; Set (abstract data type); Channel (broadcasting); TRACE (psycholinguistics); Machine learning; Data mining; Engineering; Computer security","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":[0.0002297642,0.0001407233,0.0001928696,0.0001192708,0.00008855439,0.00002356819,0.00007067322,0.00008587039,0.00003295693],"category_scores_gemma":[0.00004899895,0.0001485242,0.0001440491,0.0001397776,0.00001245929,0.0004934724,6.24303e-7,0.0003412967,8.232071e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001179212,"about_ca_system_score_gemma":0.0000261306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001570724,"about_ca_topic_score_gemma":0.00002493782,"domain_scores_codex":[0.9988689,0.00003845006,0.0005733439,0.0001257855,0.0002634615,0.0001300748],"domain_scores_gemma":[0.9993487,0.0000890611,0.000211491,0.00005402027,0.0001959321,0.0001007878],"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":[0.0003476537,0.00002119477,0.0344875,0.0001148089,0.00002356627,0.000007336784,0.001800764,0.9272168,0.01847918,0.000003485402,0.000002364274,0.01749539],"study_design_scores_gemma":[0.002484308,0.0006167232,0.4299486,0.0003907667,0.0003331288,0.000004612462,0.001074422,0.53804,0.0266917,0.00004088268,0.00007884664,0.0002959908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5965339,0.00002661139,0.4029149,0.00002629833,0.0002796949,0.0001391011,0.00002147503,0.0000480278,0.00001002111],"genre_scores_gemma":[0.9952657,0.0000290299,0.004209956,0.00004212281,0.000198743,0.000008478923,0.0002096495,0.00003427808,0.000001993534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3987318,"threshold_uncertainty_score":0.6056643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02130320536815273,"score_gpt":0.2686216889703736,"score_spread":0.2473184836022209,"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."}}