{"id":"W2801981053","doi":"10.1142/s2339547818500036","title":"A mixed-integer optimization approach for homogeneous magnet design","year":2018,"lang":"en","type":"article","venue":"TECHNOLOGY","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Princess Margaret Cancer Foundation","keywords":"Magnet; Computer science; Integer programming; Mathematical optimization; Linear particle accelerator; Linear programming; Physics; Algorithm; Mathematics; Beam (structure); Optics","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":[0.00006586494,0.00009347744,0.0001550482,0.0001660327,0.00008235905,0.000003715209,0.0001080693,0.0002091145,0.00005211768],"category_scores_gemma":[0.00005873917,0.00008329474,0.00003364149,0.000313587,0.0002023181,0.00001934619,0.00003482567,0.00008851499,0.0000110124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003444839,"about_ca_system_score_gemma":0.00002707393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001333035,"about_ca_topic_score_gemma":4.175287e-7,"domain_scores_codex":[0.9993835,0.000004719812,0.0001341975,0.0002386858,0.00004590195,0.0001930092],"domain_scores_gemma":[0.9993832,0.00001764136,0.00004902069,0.0003622114,0.0001543605,0.00003363391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009495417,0.001848059,0.0003717376,0.0001806463,0.0001105609,0.00002361815,0.0001678974,0.04085207,0.0856278,0.46392,0.08845443,0.3174936],"study_design_scores_gemma":[0.001355705,0.002767988,0.00001118953,0.00002687632,0.0001346171,0.0005445213,0.0001934279,0.7012759,0.2168629,0.02075967,0.05574331,0.0003238448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003469881,0.0001027884,0.9940307,0.00109834,0.00002159558,0.001091354,0.000006373059,0.0009168542,0.002385073],"genre_scores_gemma":[0.1127896,0.00002942578,0.8853697,0.0001966714,0.00008627905,0.000777542,0.00003466146,0.00002494902,0.0006911168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6604239,"threshold_uncertainty_score":0.3396661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04439141993843738,"score_gpt":0.3094912065622726,"score_spread":0.2650997866238353,"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."}}