{"id":"W4254840640","doi":"10.22541/au.158162243.31921234","title":"Arrival Sequencing and Scheduling using an Evolutionary Approach in a 4D Environment","year":2020,"lang":"en","type":"dataset","venue":"Authorea","topic":"Assembly Line Balancing Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Runway; Air traffic control; Schedule; Computer science; Separation (statistics); Scheduling (production processes); International airport; Trajectory optimization; Real-time computing; Operations research; Simulation; Mathematical optimization; Optimal control; Engineering; Transport engineering; Mathematics; Geography; Aerospace 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":[0.0008204186,0.001279899,0.000751336,0.001821384,0.0005064034,0.001089976,0.002360304,0.001506143,0.005769068],"category_scores_gemma":[0.002437848,0.0005433852,0.001604576,0.002799908,0.0003436311,0.0006833868,0.0007987497,0.001021249,0.002834231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251327,"about_ca_system_score_gemma":0.0008664784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03057037,"about_ca_topic_score_gemma":0.04902309,"domain_scores_codex":[0.9994809,0.0001567385,0.0000439885,0.0001858701,0.00007732306,0.00005519162],"domain_scores_gemma":[0.9993792,0.0002940547,0.00004172636,0.0001504461,0.0001012742,0.00003332854],"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":[0.0009237645,0.0008016954,0.01767544,0.002407782,0.0005389586,0.000500451,0.0001654711,0.5934545,0.002132792,0.005360228,0.2908387,0.08520015],"study_design_scores_gemma":[0.0009439401,0.0003722354,0.02227148,0.0002604626,0.0001445977,0.0003619188,0.0003143021,0.7260023,0.002883995,0.01236727,0.2339746,0.0001028801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08913856,0.00129858,0.0293519,0.001168779,0.0002829721,0.0005128635,0.8650011,0.004908609,0.00833661],"genre_scores_gemma":[0.07330314,0.0003886681,0.06045249,0.0002141677,0.00002813942,0.000825415,0.8620567,0.0001520779,0.002579133],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03057037,"threshold_uncertainty_score":0.06078482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901989771848879,"score_gpt":0.2331841676243848,"score_spread":0.204164269905896,"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."}}