{"id":"W2425827634","doi":"","title":"The Conference Biomath 2016","year":2016,"lang":"en","type":"article","venue":"Biomath Communications","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bulgarian; Gratitude; Political science; Library science; Multidisciplinary approach; Social science; Sociology; Psychology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004745404,0.0001208343,0.00008811247,0.00003966339,0.0004806626,0.00007508179,0.001687819,0.0001226913,0.00002128606],"category_scores_gemma":[0.0004245948,0.0000616603,0.00007864384,0.0001102096,0.001017549,0.000005682401,0.0008781714,0.00005157875,0.0003241619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001726397,"about_ca_system_score_gemma":0.0002166303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001692487,"about_ca_topic_score_gemma":0.0001246855,"domain_scores_codex":[0.9988794,0.0001081918,0.0002980705,0.0001626126,0.0002212751,0.0003304024],"domain_scores_gemma":[0.9972651,0.0001391273,0.00009384045,0.002122527,0.0002269879,0.0001523926],"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":[0.00003111892,0.0001245347,0.0005584858,0.0000127182,0.0000819007,4.04523e-7,0.0000844719,2.554474e-8,0.7139175,0.008373893,0.05495552,0.2218594],"study_design_scores_gemma":[0.0003738037,0.0001351064,0.002676542,0.00003552677,0.000007830769,0.000008347881,0.0001244705,0.00008972471,0.03456528,0.001026081,0.9607965,0.0001608037],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4742519,0.03214605,0.0861434,0.2358517,0.00237925,0.003652873,0.001161068,0.0004921741,0.1639217],"genre_scores_gemma":[0.9725866,0.0177335,0.003282082,0.0002027459,0.00009846914,0.00006623659,0.00007540761,0.00001551064,0.005939405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9058409,"threshold_uncertainty_score":0.4166554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04526920426064305,"score_gpt":0.3217361331099121,"score_spread":0.276466928849269,"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."}}