{"id":"W2995722603","doi":"","title":"Ingredients for Life","year":2019,"lang":"en","type":"article","venue":"AGU Fall Meeting 2019","topic":"Nutrition, Genetics, and Disease","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001421418,0.0001185063,0.0001111471,0.00002818043,0.00005625044,0.00002272821,0.0001590372,0.0001059768,0.000007688852],"category_scores_gemma":[0.0002030337,0.0001223477,0.0001045616,0.00003206402,0.00002437266,0.000002534616,0.00006710605,0.00003390742,0.0001026779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006622883,"about_ca_system_score_gemma":0.00006252727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006523512,"about_ca_topic_score_gemma":0.00004086468,"domain_scores_codex":[0.9991464,0.0000234914,0.0001610136,0.0003209094,0.00009858665,0.0002495734],"domain_scores_gemma":[0.9993631,0.0000179691,0.00006764158,0.0003079236,0.0001107958,0.000132523],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004698066,0.0002643811,0.5096857,0.0002330179,0.0001163914,0.000001155462,0.0001003143,0.0001885759,0.3334759,0.0002002429,0.1540536,0.001210807],"study_design_scores_gemma":[0.00981183,0.0017466,0.05187201,0.0002182646,0.0001498192,0.000007950105,0.0004716271,0.0006209674,0.1224263,0.001146006,0.810057,0.001471581],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938844,0.001481717,0.0001870376,0.0001225782,0.0005732032,0.0003486861,0.00005161495,0.00001881956,0.003331981],"genre_scores_gemma":[0.9937586,0.0001345452,0.001221141,0.0008616023,0.0005801817,0.00002632485,0.0002908714,0.00002904234,0.003097677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6560034,"threshold_uncertainty_score":0.4989194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008350811569907605,"score_gpt":0.2413707780483821,"score_spread":0.2330199664784745,"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."}}