{"id":"W6910453624","doi":"10.4230/lipics.cp.2025.7","title":"Optimizing 2D Cutting: A Bin Packing Approach to Minimize Scraps and Maximize Their Reusability","year":2025,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Bin packing problem; Reusability; Scrap; Leverage (statistics); Process (computing); Benchmark (surveying); Linear programming; Bin","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.0007467206,0.0005085372,0.0006006398,0.0004371527,0.0003343729,0.0004878526,0.0004789369,0.0002793815,0.00002226458],"category_scores_gemma":[0.0001870056,0.0004745954,0.0001856281,0.000620743,0.00009928903,0.0006367577,0.0002831599,0.0004451581,0.00002001456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001290691,"about_ca_system_score_gemma":0.00004421292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007827964,"about_ca_topic_score_gemma":0.000003371749,"domain_scores_codex":[0.9975209,0.00004002992,0.001110992,0.0003330867,0.0002403886,0.0007545543],"domain_scores_gemma":[0.9985452,0.0001947845,0.0001494919,0.0006826704,0.0001846256,0.0002432578],"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":[0.0007315262,0.001157478,0.02161802,0.01527452,0.001907517,0.000004564232,0.113088,0.692836,0.0009395946,0.01354169,0.04326094,0.09564019],"study_design_scores_gemma":[0.004327076,0.00008992778,0.0007288713,0.0007315933,0.00009770734,0.00002665763,0.006220168,0.8946264,0.001681241,0.0006612297,0.08966743,0.001141666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04240231,0.0002624628,0.8809321,0.0005103628,0.001205933,0.002504693,0.0004137747,0.001077441,0.0706909],"genre_scores_gemma":[0.5732417,0.0001495427,0.4235211,0.001447984,0.0001095794,0.0003506581,0.0004156157,0.0001285234,0.000635381],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5308393,"threshold_uncertainty_score":0.9997706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141834353432223,"score_gpt":0.2301674662517802,"score_spread":0.2159840309085579,"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."}}