{"id":"W3211283390","doi":"10.5281/zenodo.3998873","title":"Source data for PointNovo","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Data source; Computer science; Data science; Information retrieval","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":[0.001591483,0.003222204,0.001620064,0.003876241,0.0009315117,0.002201328,0.002993721,0.002606933,0.07169881],"category_scores_gemma":[0.006859934,0.0007598925,0.001757026,0.006165254,0.0007508147,0.001251273,0.002327178,0.002160343,0.1149351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001447575,"about_ca_system_score_gemma":0.002503016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0157905,"about_ca_topic_score_gemma":0.02199737,"domain_scores_codex":[0.9983718,0.0002738278,0.0001626551,0.00045931,0.0005192904,0.0002132087],"domain_scores_gemma":[0.997355,0.0005496116,0.0002393751,0.0008747313,0.0006912736,0.000290092],"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":[0.00009577382,0.00004529617,0.0005121952,0.0004260942,0.00003168362,0.00002759283,0.00002405008,0.0009255655,0.0002061947,0.00066191,0.9944915,0.002552226],"study_design_scores_gemma":[0.0005166314,0.00003927295,0.004689875,0.0002219778,0.00004551061,0.00008723178,0.00009083932,0.001400447,0.000879513,0.003893586,0.9880794,0.00005579992],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003136503,0.00004409962,0.0002527361,0.00005216872,0.00003689771,0.00002996972,0.9972973,0.0009813502,0.0009918466],"genre_scores_gemma":[0.0004591884,0.0000253544,0.0005852592,0.00003076039,0.000007273155,0.0001077582,0.9979798,0.0002541072,0.0005505541],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07169881,"threshold_uncertainty_score":0.2398564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06351317257249739,"score_gpt":0.2695143967456252,"score_spread":0.2060012241731278,"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."}}