{"id":"W4226370827","doi":"10.5750/ijme.v159ia3.1025","title":"RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS","year":2021,"lang":"en","type":"article","venue":"The International Journal of Maritime Engineering","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Arctic; Fault tree analysis; Accident (philosophy); The arctic; Submarine pipeline; Markov chain; Environmental science; Collision; Risk analysis (engineering); Computer science; Marine engineering; Engineering; Oceanography; Geology; Business; Reliability engineering; Computer security","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":[0.002215337,0.00007216477,0.0003198561,0.0008683169,0.0000177144,0.00007161068,0.0007025236,0.00003004344,0.0003600804],"category_scores_gemma":[0.001249563,0.00004828936,0.0004372943,0.00105555,0.00001822931,0.0002242079,0.00002636704,0.000203206,0.000003986089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007012717,"about_ca_system_score_gemma":0.00004752044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001342553,"about_ca_topic_score_gemma":0.000364298,"domain_scores_codex":[0.9974182,0.00008844128,0.0009708943,0.0001085725,0.001324072,0.00008987878],"domain_scores_gemma":[0.9976568,0.0008798094,0.000420424,0.0001695091,0.0008344197,0.00003904896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00004282192,0.00003317816,0.2198141,0.000001101454,0.001215984,0.00008986413,0.0006340787,0.770712,0.0005434401,0.0003046104,0.00003953926,0.0065693],"study_design_scores_gemma":[0.0002621168,0.00001215346,0.9159357,0.0000351971,0.0005064054,0.00001786733,0.0005059281,0.07840806,0.001554069,0.002333404,0.0003674892,0.00006160657],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726669,0.000248085,0.02487507,0.001822826,0.0003018788,0.00001698492,0.00001539821,0.000002408527,0.00005051368],"genre_scores_gemma":[0.9983336,0.000401135,0.001079567,0.00003031686,0.00005974326,5.441851e-7,0.000008174599,0.000004256602,0.00008271771],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6961216,"threshold_uncertainty_score":0.3942629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192769885523775,"score_gpt":0.2973501838629422,"score_spread":0.2780731953105647,"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."}}