{"id":"W4289438365","doi":"10.48550/arxiv.1810.01577","title":"Moment-Sum-Of-Squares Approach For Fast Risk Estimation In Uncertain\\n Environments","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Probabilistic logic; Bounded function; Mathematics; Moment (physics); Chebyshev filter; Mathematical optimization; Explained sum of squares; Probability distribution; Chebyshev nodes; Polynomial; Applied mathematics; Statistics; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001844586,0.00148737,0.001486513,0.0007865023,0.0004695663,0.0009564584,0.001400638,0.001247775,0.002377839],"category_scores_gemma":[0.007011105,0.0009899306,0.001067101,0.0007686723,0.001048305,0.001545143,0.001841823,0.002264234,0.0006854113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008488202,"about_ca_system_score_gemma":0.00119022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004568375,"about_ca_topic_score_gemma":0.003182048,"domain_scores_codex":[0.9990668,0.0003928517,0.00004203655,0.0001690453,0.0002519777,0.00007734528],"domain_scores_gemma":[0.9965546,0.002664969,0.0002185115,0.0001690353,0.0003036455,0.00008927844],"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":[0.00008593779,0.00002516253,0.0003529789,0.0001021056,0.00007126498,0.00007809256,0.0000573334,0.9447653,0.002058808,0.01701188,0.0007630713,0.03462805],"study_design_scores_gemma":[0.000002530487,0.00001021389,0.00003681038,0.000003276499,0.000003269925,0.00001160627,0.000003731172,0.9946942,0.0002923515,0.004707577,0.0002298364,0.000004594193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0014669,0.00009028271,0.9979882,0.00005053319,0.00001032034,0.000008122515,0.0000154085,0.000126743,0.00024339],"genre_scores_gemma":[0.3048793,0.0006363339,0.6893101,0.0001911032,0.0001643236,0.0002433743,0.0003057217,0.0004034697,0.003866321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004568375,"threshold_uncertainty_score":0.009755194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06879104267056715,"score_gpt":0.2261371348595242,"score_spread":0.1573460921889571,"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."}}