{"id":"W2496185103","doi":"10.1007/978-3-319-42836-9_38","title":"Towards Scheduling to Minimize the Total Penalties of Tardiness of Delivered Data in Maritime CPSs (Invited Paper)","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Opportunistic and Delay-Tolerant Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Tardiness; Computer science; Initialization; Upload; Scheduling (production processes); Crossover; Real-time computing; Network packet; Heuristic; Mathematical optimization; Job shop scheduling; Distributed computing; Operations research; Computer network; Schedule; Artificial intelligence; Operating system; 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":[0.0009132696,0.001059819,0.0007652909,0.0004373867,0.0004930557,0.001150291,0.001368954,0.0008031387,0.003146003],"category_scores_gemma":[0.001572354,0.0004515352,0.0006647339,0.000889837,0.0004728069,0.0008610032,0.0009511531,0.001501751,0.0005640024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131111,"about_ca_system_score_gemma":0.001758841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004827328,"about_ca_topic_score_gemma":0.004460145,"domain_scores_codex":[0.9997137,0.00006532876,0.00001322915,0.00007082395,0.00007237057,0.00006449837],"domain_scores_gemma":[0.9995529,0.0001916982,0.0000475228,0.0000363401,0.0001192519,0.00005222237],"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":[0.0001508837,0.0001111616,0.000412207,0.0003986056,0.00007737787,0.00009347341,0.0001689066,0.7979621,0.01051963,0.05648953,0.01229109,0.121325],"study_design_scores_gemma":[0.00001220273,0.00008627765,0.0002254912,0.00003070061,0.00001911792,0.00003600253,0.00004971567,0.9603963,0.001502988,0.03056174,0.007069432,0.0000101126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0244864,0.001932466,0.9626247,0.0009766378,0.0006158021,0.00009391262,0.0001384422,0.0003763665,0.008755343],"genre_scores_gemma":[0.4570021,0.00326404,0.5140725,0.000488921,0.000697597,0.0001815544,0.0003801291,0.0006818916,0.02323122],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004827328,"threshold_uncertainty_score":0.01052439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02993447698549282,"score_gpt":0.2527816455067911,"score_spread":0.2228471685212982,"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."}}