{"id":"W3000225146","doi":"10.1002/nav.21885","title":"Schedule design for liner services under vessel speed reduction incentive programs","year":2020,"lang":"en","type":"article","venue":"Naval Research Logistics (NRL)","topic":"Maritime Transport Emissions and Efficiency","field":"Environmental Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"HEC Montréal","funders":"National Natural Science Foundation of China","keywords":"Schedule; Reduction (mathematics); Minification; Computer science; Limit (mathematics); Nonlinear system; Piecewise linear function; Cost reduction; Incentive; Operations research; Mathematical optimization; Piecewise; Engineering; Mathematics; Business; Economics","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":[0.001062206,0.000838626,0.0008916228,0.0006251312,0.0004501999,0.0009345249,0.0009171496,0.0007071,0.00603571],"category_scores_gemma":[0.002859475,0.0004126742,0.0005292862,0.00061274,0.0004080106,0.000666919,0.0006592784,0.0007298135,0.0003776656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001706501,"about_ca_system_score_gemma":0.0023266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104739,"about_ca_topic_score_gemma":0.008100984,"domain_scores_codex":[0.999588,0.0001590477,0.00001257231,0.00006898152,0.00007378346,0.00009760664],"domain_scores_gemma":[0.9989976,0.0004720021,0.0002047043,0.00004606451,0.0001626843,0.0001168892],"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":[0.00005849107,0.00004287595,0.0003433,0.00006282367,0.000008929023,0.00004077113,0.00003541095,0.9806246,0.0008885124,0.004728014,0.0005336433,0.01263257],"study_design_scores_gemma":[0.00001106068,0.00003823846,0.00006429223,0.000002632602,0.00000369687,0.000004255071,0.00001854969,0.9985613,0.0001904778,0.0008504821,0.0002530586,0.000001926764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1673997,0.0001262831,0.8228663,0.0002831203,0.00005199134,0.0004137341,0.0002914076,0.0003681926,0.008199193],"genre_scores_gemma":[0.8588744,0.0001743774,0.1358034,0.0000414292,0.00002205208,0.0002287629,0.0002606288,0.00008265197,0.004512305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0104739,"threshold_uncertainty_score":0.02082586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2018412727594271,"score_gpt":0.374165840108919,"score_spread":0.1723245673494918,"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."}}