{"id":"W4210299395","doi":"10.1088/1757-899x/1218/1/012010","title":"Tightly-Packed Repetitive Schedules: A Tetris Challenge","year":2022,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Resource-Constrained Project Scheduling","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Schedule; Visualization; Job shop scheduling; Distributed computing; Operations research; Industrial engineering; Artificial intelligence; Operations management; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006269498,0.000290883,0.0004752305,0.0007704913,0.00100573,0.001428186,0.001421613,0.00005899614,0.001770583],"category_scores_gemma":[0.004129361,0.0002525315,0.00004892138,0.001816896,0.0007606896,0.001416503,0.001105359,0.0002648299,0.00005291406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001283673,"about_ca_system_score_gemma":0.000506945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003879727,"about_ca_topic_score_gemma":0.000004804052,"domain_scores_codex":[0.9952698,0.000130063,0.0006933248,0.0009832104,0.002203339,0.0007202166],"domain_scores_gemma":[0.9979684,0.0003159659,0.0002354082,0.000680793,0.000543848,0.000255606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009060949,0.00003928355,0.0001467954,0.00003692056,0.00001646483,0.0000665625,0.006285546,0.000982396,0.8906731,0.0948495,0.00006117421,0.006751672],"study_design_scores_gemma":[0.001592116,0.001486995,0.007159367,0.0002302425,0.00004667072,0.0007683875,0.06865175,0.0106072,0.8153314,0.01831468,0.07324009,0.002571081],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928669,0.0001175846,0.0007050165,0.001084292,0.001062914,0.0002821452,0.0000700388,0.0001627109,0.003648363],"genre_scores_gemma":[0.9959211,0.00006007263,0.003387578,0.00008171541,0.0001102845,0.00009957119,0.00000357367,0.00002029067,0.0003158152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07653482,"threshold_uncertainty_score":0.9999927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06091720703342447,"score_gpt":0.2984117952257453,"score_spread":0.2374945881923208,"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."}}