{"id":"W1981167874","doi":"10.1002/pen.20198","title":"Runner balancing by a direct genetic optimization of shrinkage","year":2004,"lang":"en","type":"article","venue":"Polymer Engineering and Science","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Shrinkage; Product (mathematics); Genetic algorithm; Mathematical optimization; Computer science; Quality (philosophy); Materials science; Mathematics; Composite material; Physics","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":[0.000686953,0.0007931439,0.0008938864,0.0007501617,0.0003495525,0.0008859176,0.0007300548,0.0008058905,0.002231784],"category_scores_gemma":[0.001160786,0.0004026216,0.0005545254,0.0005225291,0.0005025585,0.0004719252,0.0006847183,0.0004892608,0.0002833153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008856888,"about_ca_system_score_gemma":0.0008147509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002525888,"about_ca_topic_score_gemma":0.002663511,"domain_scores_codex":[0.9997384,0.00007216223,0.000007530499,0.00006892344,0.00006857569,0.00004451288],"domain_scores_gemma":[0.9997115,0.0001198593,0.00005286014,0.00002219364,0.00007172982,0.00002176816],"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":[0.00005155078,0.00005953444,0.000315241,0.00002805196,0.00002070533,0.00002647343,0.00002600312,0.9702106,0.006271932,0.002238541,0.0001870222,0.02056445],"study_design_scores_gemma":[0.00001669585,0.00004709206,0.00009968713,0.000003589564,0.00001040336,0.000006048675,0.000006556369,0.9973899,0.001382946,0.0006639021,0.0003692523,0.000004034612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3259257,0.00035495,0.6598077,0.0001678538,0.00006374493,0.0001686341,0.00006926525,0.000408408,0.0130338],"genre_scores_gemma":[0.844857,0.0001220274,0.1508537,0.00006483081,0.00001749273,0.0002176245,0.00009168778,0.00009221894,0.003683364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002525888,"threshold_uncertainty_score":0.007466018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002704937979943226,"score_gpt":0.1691096040934532,"score_spread":0.1664046661135099,"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."}}