{"id":"W4311915400","doi":"10.48550/arxiv.2202.13867","title":"Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ocean Frontier Institute; Dalhousie University","keywords":"Computer science; Outlier; Convolutional neural network; Artificial intelligence; Artificial neural network; Automatic Identification System; Transmission (telecommunications); Deep learning; Set (abstract data type); Data set; Tracking (education); Noise (video); Machine learning; Pattern recognition (psychology); Data mining; Image (mathematics); Telecommunications","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000584228,0.0006338959,0.0003373546,0.0005540504,0.0002756621,0.0004804614,0.0006292639,0.000614327,0.0006984413],"category_scores_gemma":[0.002852525,0.0003581393,0.0004131959,0.0004876112,0.0003827499,0.0009908014,0.0004408167,0.001211797,0.0002524056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008950065,"about_ca_system_score_gemma":0.0006732852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02566469,"about_ca_topic_score_gemma":0.02349508,"domain_scores_codex":[0.999841,0.00003809711,0.00000923802,0.00005732684,0.0000277926,0.0000265559],"domain_scores_gemma":[0.9993525,0.0003672077,0.00009968261,0.00006283606,0.00008530681,0.00003240025],"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":[0.00004327774,0.00002493515,0.004682797,0.0000141726,0.0000187574,0.00003731319,0.00004073683,0.9763875,0.0009650778,0.0006567481,0.0004012984,0.01672747],"study_design_scores_gemma":[4.993496e-7,0.000002461009,0.0002967168,8.158164e-7,0.000001096094,0.000001560428,0.000003082326,0.9992135,0.0001311452,0.0003133252,0.00003494753,8.92877e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6444961,0.0004121059,0.3501175,0.0008757295,0.00009041811,0.00003223087,0.0006836244,0.00148286,0.001809358],"genre_scores_gemma":[0.9827398,0.00009487922,0.01559504,0.0000403541,0.00002504568,0.0000166939,0.0004631752,0.00003770717,0.000987186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02566469,"threshold_uncertainty_score":0.05103058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1264919250638856,"score_gpt":0.2176908220208655,"score_spread":0.09119889695697997,"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."}}