{"id":"W4393733384","doi":"10.5281/zenodo.7488069","title":"Human pseudoDB: simulated database of human genetic variants","year":2022,"lang":"pt","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Database; Computer science","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":[0.0009332133,0.0009165433,0.0007521223,0.001123238,0.0003814806,0.0009575516,0.002206164,0.001558026,0.01311265],"category_scores_gemma":[0.005800992,0.0003990223,0.0008908423,0.002192082,0.0003163491,0.0004364244,0.000770798,0.001189038,0.006423658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008410508,"about_ca_system_score_gemma":0.001674874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052969,"about_ca_topic_score_gemma":0.01560985,"domain_scores_codex":[0.9994703,0.0001675545,0.00004923617,0.0001534313,0.0001140057,0.00004549356],"domain_scores_gemma":[0.9981788,0.0009023217,0.00008961826,0.00043122,0.0002050882,0.0001929276],"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":[0.001753708,0.0002948357,0.0175549,0.001198927,0.000435501,0.0005890317,0.00007354716,0.02400273,0.001505577,0.0035919,0.9331451,0.01585415],"study_design_scores_gemma":[0.005670391,0.0006179737,0.05006899,0.0006108195,0.0006135253,0.00388202,0.0003600361,0.1143076,0.008599479,0.02663667,0.7884672,0.0001653356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01140071,0.000323057,0.002359944,0.0004361369,0.00008885143,0.00005809574,0.9824741,0.001248629,0.001610385],"genre_scores_gemma":[0.01519284,0.0001173954,0.002729909,0.0001824326,0.00001032762,0.0001309543,0.9809926,0.00006828735,0.0005752267],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01311265,"threshold_uncertainty_score":0.04386616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.207540571167175,"score_gpt":0.3577449677550974,"score_spread":0.1502043965879224,"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."}}