{"id":"W2964383556","doi":"10.1126/science.365.6452.416","title":"Bringing machine learning to the masses","year":2019,"lang":"en","type":"article","venue":"Science","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Federation of Science Journalists","funders":"","keywords":"Computer science; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0008128621,0.00005809153,0.00004756368,0.00005023645,0.0001701584,0.00008066965,0.0005535478,0.00002731955,0.00004075719],"category_scores_gemma":[0.0004426769,0.00003671495,0.00002467837,0.0002647613,0.0001981208,0.000004155635,0.0003987185,0.0000917057,0.0003270097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009722811,"about_ca_system_score_gemma":0.0001163162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001377015,"about_ca_topic_score_gemma":0.00001643869,"domain_scores_codex":[0.9990045,0.00001486192,0.000092467,0.0001926209,0.0003791098,0.0003164348],"domain_scores_gemma":[0.9995108,0.00001143247,0.0000216478,0.0002566731,0.00007508454,0.0001244262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001212491,0.00001246444,0.01595926,0.0000152487,0.000004825008,5.816911e-7,0.0002648644,0.000348087,0.9419756,0.0000426994,0.0007685794,0.04059564],"study_design_scores_gemma":[0.0001482738,0.0003460367,0.01263617,0.000013581,0.000001872728,0.000007981964,0.0004115403,0.00760921,0.2279183,0.00002613378,0.7507335,0.0001474327],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893603,0.0002044342,0.001207886,0.001177431,0.000268749,0.0001606473,0.000002427263,0.000008933673,0.007609227],"genre_scores_gemma":[0.993591,0.00009233184,0.001010537,0.0005240071,0.00008705587,0.000002852981,0.000004427341,0.000004081182,0.004683737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.749965,"threshold_uncertainty_score":0.4203157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01178403370375122,"score_gpt":0.2869225088155536,"score_spread":0.2751384751118024,"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."}}