{"id":"W4393491580","doi":"10.5281/zenodo.7489312","title":"Human genetic variants using dbSNP and PseudoDB","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"dbSNP; Genetics; Biology; Gene; Single-nucleotide polymorphism; Genotype","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":[0.0009418556,0.001759471,0.001631712,0.002658574,0.0007395413,0.001832029,0.002161127,0.001800328,0.02603283],"category_scores_gemma":[0.004778442,0.0005379892,0.001289673,0.005047004,0.0003594898,0.0005862731,0.001306456,0.001596709,0.01680882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007982829,"about_ca_system_score_gemma":0.001795133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01664029,"about_ca_topic_score_gemma":0.0250806,"domain_scores_codex":[0.9989533,0.0001744325,0.0001748949,0.0003899806,0.0002033729,0.0001038405],"domain_scores_gemma":[0.998552,0.0005600799,0.0001786008,0.0003279065,0.0001938498,0.0001876879],"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":[0.001096649,0.00012036,0.008794229,0.002537581,0.0005321976,0.0006977509,0.00008374748,0.002315345,0.00149518,0.002317094,0.9687226,0.01128729],"study_design_scores_gemma":[0.002161617,0.0001387306,0.03499328,0.0007038507,0.0005410398,0.002038694,0.0001648721,0.002928274,0.002659259,0.007913855,0.9456366,0.0001199539],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001212163,0.0001631147,0.0003024698,0.00005740521,0.00001719214,0.00001759412,0.9974929,0.0002304262,0.0005067812],"genre_scores_gemma":[0.001664752,0.00007250948,0.0005686834,0.00005204623,0.000004940572,0.00006979683,0.9972833,0.00004826126,0.0002356697],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02603283,"threshold_uncertainty_score":0.08708853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02663421569344258,"score_gpt":0.257493596622877,"score_spread":0.2308593809294344,"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."}}