{"id":"W4298219754","doi":"10.48550/arxiv.1510.00455","title":"Semidefinite relaxations in optimal experiment design with application\\n to substrate injection for hyperpolarized MRI","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Receptor Mechanisms and Signaling","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Relaxation (psychology); Maximization; Norm (philosophy); Quadratic equation; Mathematics; Computer science; Applied mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01153359,0.003020904,0.002257815,0.0008790399,0.0004829692,0.002234908,0.001660682,0.002369474,0.0042176],"category_scores_gemma":[0.0221854,0.002125063,0.001574432,0.0008998261,0.003688582,0.00183756,0.002648247,0.003784913,0.0007672211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001734658,"about_ca_system_score_gemma":0.00274048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00289219,"about_ca_topic_score_gemma":0.002149236,"domain_scores_codex":[0.9955446,0.002777126,0.0001590066,0.0006896619,0.0005839678,0.0002455724],"domain_scores_gemma":[0.9772094,0.01944133,0.001222297,0.0005633268,0.001229613,0.0003340129],"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":[0.0001747078,0.0000743265,0.0001735507,0.0002556052,0.00007061339,0.0001132238,0.00009122214,0.9557602,0.001260212,0.02926891,0.0007063565,0.01205119],"study_design_scores_gemma":[0.00004109153,0.00007375816,0.00003302657,0.00002144693,0.00001028545,0.00001193416,0.00001185875,0.9842338,0.0005526191,0.0143964,0.0006033809,0.00001036884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002170883,0.0002755049,0.9958813,0.0002628428,0.00003140243,0.00007176853,0.00005128538,0.0001137321,0.001141315],"genre_scores_gemma":[0.2619019,0.001112104,0.7284231,0.000772739,0.0001742251,0.001743907,0.0004810313,0.0003586052,0.005032385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01153359,"threshold_uncertainty_score":0.06099617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05988242070470816,"score_gpt":0.212651249832387,"score_spread":0.1527688291276789,"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."}}