{"id":"W2784138851","doi":"10.1109/bigdata.2017.8258122","title":"Big-data-enabled modelling and optimization of granular speed-based vessel schedule recovery problem","year":2017,"lang":"en","type":"article","venue":"","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Larus Technologies (Canada); University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Computer science; Schedule; Big data; Data mining; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004174672,0.00009416183,0.0001283887,0.00002565544,0.0003052566,0.00005584123,0.0003719149,0.0000550647,0.001437055],"category_scores_gemma":[0.00001860917,0.0000779234,0.00002271953,0.00005942331,0.0001556872,0.0003762243,0.0001229369,0.00005722737,0.00001189724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001276588,"about_ca_system_score_gemma":0.00001781352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009498248,"about_ca_topic_score_gemma":0.00002137552,"domain_scores_codex":[0.9991438,0.00001437213,0.0001945782,0.0003037789,0.0001806494,0.0001628387],"domain_scores_gemma":[0.999115,0.00001765515,0.0001017838,0.000674025,0.000009862933,0.00008169502],"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":[0.00001500029,0.00007999044,0.007811079,0.00002663105,0.00000231194,0.000001578659,0.0000129586,0.9873248,0.001078458,0.00003437503,0.0001659995,0.003446835],"study_design_scores_gemma":[0.0003164664,0.00003049062,0.0007999278,0.00002749448,0.00001667502,7.068906e-7,0.000009386249,0.9956425,0.002049366,0.00022804,0.0007638791,0.0001149996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1344028,0.00003481134,0.8318118,0.0002896495,0.00005445771,0.000226234,0.00001902812,0.00003008978,0.03313112],"genre_scores_gemma":[0.834781,0.00006864627,0.1641531,0.00003666857,0.00001304638,0.000001859822,0.00004040806,0.00001061363,0.0008947306],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7003782,"threshold_uncertainty_score":0.9994758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03636714480368454,"score_gpt":0.2323011868049524,"score_spread":0.1959340420012678,"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."}}