{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007206115,0.00009027351,0.0000917112,0.0001004487,0.0000469204,0.0000352235,0.00009472747,0.00002580443,0.000008148251],"category_scores_gemma":[0.00001404737,0.00008831474,0.00001190441,0.000234074,0.0000454986,0.0001513557,0.00001846737,0.00004523026,6.754315e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002339783,"about_ca_system_score_gemma":0.00001356374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002286798,"about_ca_topic_score_gemma":2.699089e-7,"domain_scores_codex":[0.999439,0.000001184009,0.0001165659,0.0001338432,0.0001327293,0.0001766492],"domain_scores_gemma":[0.9998007,0.000008003478,0.00001545847,0.00009287271,0.00001645986,0.00006647571],"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":[3.552738e-7,0.000003232704,0.00005438199,0.0000458403,0.000002352574,3.391555e-7,0.0001370383,0.9754192,0.02365861,0.00004781766,0.000003011471,0.0006278897],"study_design_scores_gemma":[0.0001203859,0.00001065124,0.0006489987,0.00003721514,0.000004769615,0.000003463867,0.000006039106,0.7201255,0.2788916,0.000003736516,0.00003114,0.0001165243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.503084,0.003080452,0.4922995,0.00001896345,0.0002194459,0.0000658489,0.000004807745,0.0002533555,0.0009736177],"genre_scores_gemma":[0.9881231,0.0001556151,0.01165753,0.000006162276,0.00001486317,0.000004314152,0.000001355058,0.00001357175,0.00002348563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4850391,"threshold_uncertainty_score":0.360137,"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."}}