{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001750442,0.0004203327,0.0003397269,0.00221906,0.0002486745,0.001432557,0.0006296037,0.0004229431,0.001717953],"category_scores_gemma":[0.01297059,0.0001938446,0.0005549639,0.001453953,0.0009681281,0.0019547,0.001507892,0.0008145463,0.0002516696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006541793,"about_ca_system_score_gemma":0.0004907774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003699013,"about_ca_topic_score_gemma":0.002258455,"domain_scores_codex":[0.9990745,0.0003511466,0.00006886637,0.0002163979,0.0002172256,0.00007184062],"domain_scores_gemma":[0.9915872,0.005266962,0.001406308,0.0008037138,0.00066228,0.0002736037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003687633,0.0004301879,0.6124291,0.0002853327,0.0002878263,0.0004779034,0.001900915,0.2546736,0.007108178,0.04949706,0.001453526,0.07108761],"study_design_scores_gemma":[0.00001591049,0.0002961813,0.3679605,0.00007483389,0.00004911767,0.0004524364,0.001858237,0.6042402,0.001721358,0.02008602,0.003170669,0.00007453751],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8313118,0.0002437498,0.159787,0.0001512699,0.00002065304,0.0001528608,0.0009542121,0.0001031919,0.007275342],"genre_scores_gemma":[0.9859459,0.00008727467,0.01301545,0.00001445688,0.000008575113,0.00006715504,0.0004801893,0.00001460346,0.0003665107],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003699013,"threshold_uncertainty_score":0.009257317,"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."}}