{"id":"W4390825212","doi":"10.1115/1.4064489","title":"Thermally Driven Multi-Objective Packing Optimization Using Acceleration Fields","year":2024,"lang":"en","type":"article","venue":"Journal of Mechanical Design","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Acceleration; Computer science; Structural engineering; Mathematical optimization; Mechanical engineering; Engineering; Materials science; Physics; Mathematics; Classical mechanics","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":[0.000408665,0.0001100799,0.0001547097,0.0001475914,0.00004725564,0.0001642205,0.0001014411,0.0001127582,0.0001398014],"category_scores_gemma":[0.00006205772,0.00009496392,0.00008754127,0.00020956,0.00000656704,0.0003891765,0.00001114462,0.000292541,0.000005972888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009607722,"about_ca_system_score_gemma":0.00005444321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001092394,"about_ca_topic_score_gemma":8.17866e-7,"domain_scores_codex":[0.9991633,0.00008745455,0.0003564743,0.00008610071,0.0001787112,0.0001279478],"domain_scores_gemma":[0.9995629,0.0001133904,0.00006814658,0.0000661363,0.0001254543,0.00006392923],"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":[0.00001098771,0.00001366734,0.000002546731,0.00002678274,0.00006280078,0.0000194862,0.0002643072,0.9885556,0.008480233,0.0002227588,0.0001547375,0.002186165],"study_design_scores_gemma":[0.0002284665,0.00007843742,0.000005854702,0.0001936599,0.00004134379,0.00005856986,0.00002683456,0.9942539,0.004751294,0.0001736991,0.00008157918,0.0001063984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008574398,0.0003012779,0.9974697,0.00006688474,0.0009600075,0.0001094698,9.854728e-7,0.000104877,0.0001293146],"genre_scores_gemma":[0.6736215,0.0001718309,0.3259071,0.00004878705,0.0001900596,0.000001318332,0.000001210798,0.00003255277,0.00002572661],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.672764,"threshold_uncertainty_score":0.3872516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06041497718554498,"score_gpt":0.2788482014512427,"score_spread":0.2184332242656978,"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."}}