{"id":"W3007141688","doi":"10.1144/sp500-2019-173","title":"Lessons learned from the monitoring of turbidity currents and guidance for future platform designs","year":2020,"lang":"en","type":"article","venue":"Geological Society London Special Publications","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada","funders":"U.S. Geological Survey; Ocean University of China; Natural Environment Research Council; Sight Research UK","keywords":"Turbidity; Turbidity current; Environmental science; Computer science; Systems engineering; Engineering; Oceanography; Geology; Geomorphology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007515466,0.001206905,0.0005148604,0.0007825333,0.0007791276,0.002677802,0.002889104,0.002994407,0.005309367],"category_scores_gemma":[0.01465551,0.0004424139,0.0004226277,0.0005189906,0.001454542,0.004828493,0.001841224,0.002407362,0.002132372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087614,"about_ca_system_score_gemma":0.002361369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007347927,"about_ca_topic_score_gemma":0.01123755,"domain_scores_codex":[0.9973165,0.001073602,0.0001543284,0.0002860016,0.0009544804,0.000215113],"domain_scores_gemma":[0.9891879,0.003527567,0.000604036,0.001202931,0.004592891,0.0008847349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001841084,0.0003578943,0.01560698,0.001304123,0.00004784655,0.00118091,0.002385764,0.1003976,0.0218052,0.02908423,0.03515903,0.7924864],"study_design_scores_gemma":[0.0001912568,0.00251597,0.01854084,0.003337462,0.0001394371,0.001969408,0.0134863,0.1982445,0.03766859,0.1227459,0.6006674,0.0004928662],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1516971,0.01756287,0.6705543,0.09005272,0.001940685,0.0006202484,0.0006985483,0.003053645,0.06381992],"genre_scores_gemma":[0.4750541,0.00921758,0.4980392,0.002577578,0.0003297531,0.0003221727,0.0004130078,0.0003570868,0.01368959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007515466,"threshold_uncertainty_score":0.03974605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1821829435133274,"score_gpt":0.3046698695459205,"score_spread":0.1224869260325931,"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."}}