{"id":"W809203089","doi":"10.1007/s11081-015-9305-y","title":"Creating annual delivery programs of liquefied natural gas","year":2015,"lang":"en","type":"article","venue":"Optimization and Engineering","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Natural Sciences and Engineering Research Council of Canada; Norges Forskningsråd","keywords":"Liquefied natural gas; Integer programming; Revenue; Operations research; Fleet management; Schedule; Linear programming; Computer science; Time horizon; Scheduling (production processes); Port (circuit theory); Procurement; Supply chain; Natural gas; Business; Operations management; Finance; Engineering; Waste management; Marketing","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":[],"consensus_categories":[],"category_scores_codex":[0.00008037957,0.00005503459,0.00006081111,0.0000181999,0.00002533975,0.000008360913,0.00003739591,0.00002537367,0.0001328679],"category_scores_gemma":[0.00001677968,0.00004990672,0.00001225323,0.0001173282,0.00002096034,0.0001147482,0.00002309248,0.00004035723,0.000001928719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001462101,"about_ca_system_score_gemma":0.0000041081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006855486,"about_ca_topic_score_gemma":0.000001827116,"domain_scores_codex":[0.9996282,0.000003528271,0.00009758553,0.00008671536,0.0000908493,0.00009310872],"domain_scores_gemma":[0.9998376,0.000006416908,0.00001910592,0.00004886957,0.00001082337,0.00007713294],"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":[0.000003976269,0.00001759007,0.004688106,0.000008298248,0.000001629643,0.000001003188,0.0004127651,0.9904407,0.0003778326,0.00002982249,0.00003876412,0.003979528],"study_design_scores_gemma":[0.0001701538,0.00003282535,0.002082991,0.00002093813,0.000004609178,0.000004176889,0.0001248427,0.9964652,0.0001931974,0.000001828436,0.0008170807,0.00008213526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9294258,0.0001173896,0.06130996,0.00004804239,0.00008788974,0.0001253762,0.000002328439,0.00009101175,0.008792136],"genre_scores_gemma":[0.950588,0.00001900984,0.04915505,0.000007588149,0.000008580707,0.000002616655,0.00001065295,0.000005967941,0.0002025398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02116213,"threshold_uncertainty_score":0.2035137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005837351310503501,"score_gpt":0.1798060458275832,"score_spread":0.1739686945170797,"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."}}