{"id":"W3160246689","doi":"10.1016/j.ress.2021.107752","title":"An empirical ship domain based on evasive maneuver and perceived collision risk","year":2021,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Canada First Research Excellence Fund; Ocean Frontier Institute; China Scholarship Council; Aalto-Yliopisto; National Natural Science Foundation of China; National Science Foundation","keywords":"Collision; Automatic Identification System; Domain (mathematical analysis); Proxy (statistics); Computer science; Identification (biology); Process (computing); Sample (material); Ship motions; Margin (machine learning); Marine engineering; Point (geometry); Collision avoidance; Operations research; Engineering; Data mining; Hull; Computer security; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009118651,0.0003395238,0.0004512418,0.00009273443,0.0001398957,0.00006929672,0.0001451245,0.000270267,0.00007059852],"category_scores_gemma":[0.0002312663,0.0003438337,0.0001323188,0.0003328934,0.00003134445,0.000131581,0.00002435399,0.0004723596,0.0000371253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005582927,"about_ca_system_score_gemma":0.00005894174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001937049,"about_ca_topic_score_gemma":0.000007260136,"domain_scores_codex":[0.9978694,0.000265681,0.0005515395,0.0005434675,0.0004051505,0.000364762],"domain_scores_gemma":[0.9981801,0.0005074163,0.00004328274,0.0008141789,0.0001379179,0.0003171166],"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":[0.00007646007,0.00007624958,0.006303212,0.0009236772,0.00002669629,0.00004783966,0.0003134226,0.9887668,0.002378474,0.0002997106,0.0001184386,0.0006690343],"study_design_scores_gemma":[0.0009228087,0.00007376345,0.1418069,0.000279855,0.00003022158,0.00002441763,0.0001785919,0.851769,0.000699367,0.00001523931,0.003824763,0.0003750388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9343213,0.00009290792,0.0594711,0.0001973091,0.0006849338,0.000570438,0.000188817,0.001325427,0.0031477],"genre_scores_gemma":[0.9944569,0.0000496003,0.005124676,0.00004351357,0.0001122555,0.00003357568,0.00008799534,0.00006570613,0.00002573397],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1369978,"threshold_uncertainty_score":0.9999014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005378508815715582,"score_gpt":0.2199113513115351,"score_spread":0.2145328424958195,"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."}}