{"id":"W4303022251","doi":"10.1007/s10696-022-09471-w","title":"Deep learning models for vessel’s ETA prediction: bulk ports perspective","year":2022,"lang":"en","type":"article","venue":"Flexible Services and Manufacturing Journal","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Leverage (statistics); Automatic Identification System; Arrival time; Port (circuit theory); Deep learning; Convolutional neural network; Artificial intelligence; Operations research; Time of arrival; Artificial neural network; Machine learning; Data mining; Telecommunications; Channel (broadcasting); Engineering; Transport engineering","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":[0.0005074641,0.0007675165,0.0005718373,0.0005078808,0.0002143964,0.0008294142,0.0008932107,0.0009291773,0.001666827],"category_scores_gemma":[0.001699245,0.0003680843,0.0005033239,0.0008468776,0.0002999039,0.001107816,0.0005180024,0.001732515,0.0004485883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000708681,"about_ca_system_score_gemma":0.0008342349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01773494,"about_ca_topic_score_gemma":0.01645831,"domain_scores_codex":[0.999868,0.00002251642,0.000007445989,0.00004986343,0.00002046285,0.00003167914],"domain_scores_gemma":[0.9994734,0.0002846273,0.00006437533,0.00004086801,0.0001074396,0.00002935345],"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":[0.00005072356,0.00004830199,0.002511897,0.00002444513,0.00003370174,0.00004419768,0.00001779538,0.9607062,0.0006809338,0.00296859,0.001353059,0.03156009],"study_design_scores_gemma":[8.1192e-7,0.000002833776,0.0001326638,0.000001660688,0.000002063928,0.000002076092,0.000001698076,0.9988779,0.00008466926,0.0008027375,0.0000895296,0.00000130693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2736799,0.002684383,0.7118062,0.001797565,0.0002475476,0.00003297265,0.001410716,0.001344558,0.006996179],"genre_scores_gemma":[0.9682557,0.000704125,0.02411343,0.00013711,0.00009933765,0.00003139062,0.001170257,0.00004794973,0.00544066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01773494,"threshold_uncertainty_score":0.03526342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01493948679344528,"score_gpt":0.2136755450374902,"score_spread":0.1987360582440449,"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."}}