{"id":"W4405394037","doi":"10.1016/j.engappai.2024.109732","title":"From density to geometry: Instance segmentation for reverse engineering of optimized structures","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Reverse engineering; Segmentation; Geometry; Artificial intelligence; Computational geometry; Engineering drawing; Computer vision; Mathematics; Programming language","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":[0.0001413748,0.0002115496,0.0002833432,0.000472602,0.00002772084,0.00002966807,0.0002587178,0.0001122977,0.00003568813],"category_scores_gemma":[0.0001263792,0.0002597013,0.00009012281,0.0009333772,0.00003046289,0.0001412039,0.00003263053,0.0001475908,0.0000110936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009255892,"about_ca_system_score_gemma":0.00001921063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002537011,"about_ca_topic_score_gemma":0.000002494001,"domain_scores_codex":[0.9987658,0.000004398729,0.0005847563,0.0002751025,0.0001467363,0.0002231992],"domain_scores_gemma":[0.9991076,0.0003162715,0.00004151167,0.0003372052,0.0001141354,0.00008330257],"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":[0.000007765531,0.000008771519,0.000002953148,0.0002143684,0.00006837737,3.341978e-7,0.0003527974,0.8741055,0.09732261,0.02028488,0.00007314565,0.007558561],"study_design_scores_gemma":[0.00002136212,0.0000143551,0.00001967633,0.00007052601,0.00002717293,9.505394e-7,0.00008526831,0.6563894,0.3415223,0.0008721258,0.0008105109,0.0001664205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04104475,0.0004927436,0.9564912,0.00004172553,0.0006128981,0.0006260492,0.0001368328,0.000539542,0.00001428766],"genre_scores_gemma":[0.604342,0.00003265784,0.395146,0.000003586272,0.0001302176,0.0002520603,0.00003723758,0.00004995229,0.000006226235],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5632973,"threshold_uncertainty_score":0.9999855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287408244902735,"score_gpt":0.2634540785970831,"score_spread":0.2505799961480558,"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."}}