{"id":"W2151593109","doi":"10.1109/isic.2002.1157770","title":"An adaptive fuzzy algorithm for cut sequencing of solid wood in furniture component production","year":2003,"lang":"en","type":"article","venue":"","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Adaptability; Production (economics); Fuzzy logic; Component (thermodynamics); Algorithm; Process (computing); Computer science; STRIPS; Solid wood; Artificial intelligence; Materials science; Layer (electronics); Composite material","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008359748,0.0006950711,0.0008221721,0.0006012717,0.0005190604,0.0005763774,0.001139594,0.001004006,0.001460768],"category_scores_gemma":[0.001326952,0.0003583962,0.0005955935,0.0006410907,0.0005442387,0.0006203977,0.0004810787,0.000785736,0.000201283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008005576,"about_ca_system_score_gemma":0.001151088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007355516,"about_ca_topic_score_gemma":0.006046914,"domain_scores_codex":[0.9996578,0.00006340783,0.00002412269,0.00008775845,0.000112139,0.00005488715],"domain_scores_gemma":[0.9995995,0.0002062484,0.00004129431,0.00001932884,0.0001047535,0.00002878058],"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":[0.0001218354,0.00006651333,0.0005399435,0.00006017845,0.00003317252,0.00004705105,0.00007973688,0.8683718,0.00515739,0.004990079,0.0005689478,0.1199634],"study_design_scores_gemma":[0.00001223935,0.00003617259,0.00006739626,0.000003020614,0.000004788857,0.000009240849,0.000004216663,0.9978359,0.0007338718,0.001062754,0.0002269605,0.000003444587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0222759,0.0001058246,0.9766053,0.00003883111,0.00001864338,0.00004285941,0.0000172869,0.0001415414,0.0007538861],"genre_scores_gemma":[0.4041222,0.0001411632,0.5929412,0.00006569584,0.00002441892,0.000192287,0.0001160236,0.00004500309,0.002352006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007355516,"threshold_uncertainty_score":0.01462537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02288766735146257,"score_gpt":0.245421220449971,"score_spread":0.2225335530985084,"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."}}