{"id":"W2770641088","doi":"10.6084/m9.figshare.7859750","title":"Incremental Mixture Importance Sampling With Shotgun Optimization","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Shotgun; Sampling (signal processing); Computer science; Environmental science; Chemistry; Computer vision; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009093659,0.0003701817,0.0003588749,0.00009562234,0.0001356682,0.0004142302,0.001221266,0.0003420453,0.0346917],"category_scores_gemma":[0.0001842339,0.0003152683,0.00009383873,0.0004417939,0.000005803174,0.0003546824,0.0005227212,0.0005236439,0.0001234967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007200298,"about_ca_system_score_gemma":0.0003026239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007139829,"about_ca_topic_score_gemma":0.00003603399,"domain_scores_codex":[0.9980677,0.0000941475,0.000267325,0.0008067269,0.000424009,0.0003401028],"domain_scores_gemma":[0.998138,0.00006493665,0.0002823129,0.001197039,0.0001829824,0.000134778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002318055,0.00002233914,4.112025e-7,0.0001880035,0.00002684557,0.0001221165,0.0000107709,0.0003488376,0.000001553686,0.00001313187,0.9979082,0.001355485],"study_design_scores_gemma":[0.000170462,0.00003850011,0.000006127338,0.001979195,0.00001971873,0.00008267409,0.00000204485,0.003359804,0.00005089831,0.00005269108,0.993748,0.0004898647],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[3.36131e-8,0.0005673206,0.2927224,0.00004799096,0.00008894053,0.0001757221,0.7062272,0.0000604717,0.0001099287],"genre_scores_gemma":[3.35682e-7,0.00001898485,0.3400013,0.000576113,0.0001715866,0.00008313938,0.6591046,0.0000157333,0.00002809783],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04727897,"threshold_uncertainty_score":0.99993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034452851986339,"score_gpt":0.2837403723365439,"score_spread":0.2433958438166805,"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."}}