{"id":"W1964011778","doi":"10.1016/j.marpolbul.2014.06.036","title":"A Monte Carlo simulation based two-stage adaptive resonance theory mapping approach for offshore oil spill vulnerability index classification","year":2014,"lang":"en","type":"article","venue":"Marine Pollution Bulletin","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo method; Submarine pipeline; Oil spill; Centroid; Computer science; Adaptive resonance theory; Stage (stratigraphy); Feature (linguistics); Vulnerability (computing); Index (typography); Environmental science; Data mining; Petroleum engineering; Statistics; Marine engineering; Artificial intelligence; Geology; Engineering; Mathematics; Artificial neural network; Geotechnical engineering","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":[0.0008359783,0.0004459131,0.0007862321,0.0009669823,0.0005861815,0.0006838666,0.00122995,0.001055902,0.003050402],"category_scores_gemma":[0.002424028,0.0005122577,0.0006692545,0.0006080652,0.000390529,0.0006872409,0.0007372402,0.0006885444,0.0003347391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006163897,"about_ca_system_score_gemma":0.001250767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01797591,"about_ca_topic_score_gemma":0.01550268,"domain_scores_codex":[0.999763,0.00008157071,0.00001240795,0.00004251557,0.00006686355,0.00003372692],"domain_scores_gemma":[0.9986678,0.0009405165,0.00006877534,0.00005876118,0.0002182681,0.00004587595],"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":[0.00005065804,0.00004608193,0.0007950563,0.00001559823,0.0000196341,0.00003409112,0.00002881611,0.980356,0.0007288701,0.001583074,0.000207997,0.01613404],"study_design_scores_gemma":[0.00000176143,0.000004176462,0.00004446637,7.552805e-7,0.000001616638,0.000003181536,0.000001979923,0.9996506,0.00005363029,0.000211207,0.00002520695,0.000001389664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07283036,0.0001198525,0.9228845,0.0001460271,0.00003221476,0.00007488684,0.0000625666,0.0005562104,0.003293399],"genre_scores_gemma":[0.8203973,0.00007049404,0.1767614,0.0000896624,0.00002378515,0.00017942,0.0001540329,0.00007606893,0.002247917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01797591,"threshold_uncertainty_score":0.03574258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269823254039331,"score_gpt":0.2410516225355558,"score_spread":0.2183533899951625,"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."}}