{"id":"W3011700626","doi":"10.23919/fusion43075.2019.9011180","title":"Relevance and Importance in Deep Learning for Open Source Data Processing to Enhance Context","year":2019,"lang":"en","type":"article","venue":"","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Université de Montréal","funders":"","keywords":"Relevance (law); Computer science; Context (archaeology); Quality (philosophy); Information quality; Data quality; Open source; Set (abstract data type); Artificial intelligence; Sensor fusion; Data science; Data mining; Information retrieval; Information system; Engineering; Software","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":[0.004540496,0.000828666,0.0007481292,0.003040251,0.0006530539,0.001933975,0.0009440563,0.001078071,0.0009558398],"category_scores_gemma":[0.0132946,0.0003139354,0.0009013682,0.002044135,0.001086184,0.003721097,0.002567781,0.00237372,0.0002602018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001391911,"about_ca_system_score_gemma":0.001078179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003072529,"about_ca_topic_score_gemma":0.00504698,"domain_scores_codex":[0.9975556,0.0008542726,0.000246099,0.0004879438,0.0006907061,0.0001654552],"domain_scores_gemma":[0.9953045,0.002612997,0.0005577698,0.0003756615,0.0009778519,0.0001711776],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006813604,0.00069633,0.01361987,0.0006891008,0.0002715235,0.0003430043,0.001127757,0.1311941,0.02978024,0.03211994,0.004518816,0.7849579],"study_design_scores_gemma":[0.0000244526,0.0001544038,0.005708479,0.00009124499,0.00008304276,0.00009794557,0.0001846343,0.9222141,0.01468904,0.05391009,0.002783517,0.00005908712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06495369,0.001399232,0.9298804,0.0008745156,0.0000970866,0.0001566727,0.0001764951,0.0007658343,0.001696138],"genre_scores_gemma":[0.7430499,0.0004967848,0.2541669,0.0002639455,0.0001507676,0.0001427795,0.0003122976,0.00008074316,0.001335816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004540496,"threshold_uncertainty_score":0.02401274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01712360829158502,"score_gpt":0.2922582846404627,"score_spread":0.2751346763488777,"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."}}