{"id":"W2963222815","doi":"10.1007/978-3-319-94776-1_18","title":"Approximation Algorithms for Two-Machine Flow-Shop Scheduling with a Conflict Graph","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Vertex cover; Combinatorics; Time complexity; Disjoint sets; Job shop scheduling; Flow shop scheduling; Approximation algorithm; Discrete mathematics; Algorithm; Mathematics; Schedule","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":[0.002921819,0.002305937,0.003029252,0.001420774,0.001230569,0.003120774,0.005603005,0.003012687,0.006516279],"category_scores_gemma":[0.009626894,0.001618693,0.002069286,0.003986403,0.001307004,0.004465348,0.002518029,0.00425197,0.001081165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003717153,"about_ca_system_score_gemma":0.003185674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006752245,"about_ca_topic_score_gemma":0.005506793,"domain_scores_codex":[0.9981907,0.000703065,0.00008937948,0.0003190614,0.0003870936,0.000310694],"domain_scores_gemma":[0.9936738,0.004624285,0.0003281577,0.000675611,0.0003771748,0.0003210304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005220843,0.0003117894,0.0004085567,0.0003295721,0.00009478519,0.00006026902,0.0001208983,0.8537241,0.0008222293,0.03945692,0.006823711,0.09732501],"study_design_scores_gemma":[0.00004533498,0.00002359827,0.0000464953,0.00001320412,0.00001230031,0.00001908265,0.00001475721,0.9723985,0.0001606759,0.02675392,0.0005063684,0.000005686006],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01718074,0.001224972,0.9741178,0.0006230962,0.0002120281,0.0001318341,0.0002520322,0.0009473477,0.005310212],"genre_scores_gemma":[0.2680508,0.0009553176,0.7239392,0.0003246737,0.000238607,0.0003659502,0.0009399379,0.0005050022,0.004680559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006752245,"threshold_uncertainty_score":0.02696997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799213227543936,"score_gpt":0.2452845668499694,"score_spread":0.2272924345745301,"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."}}