{"id":"W6945674312","doi":"10.25549/one-ouc16848307","title":"Donald M., letters (1960-1972)","year":2021,"lang":"en","type":"dataset","venue":"University of Southern California Digital Library","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ridiculous; Government (linguistics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008054851,0.001334055,0.0009870647,0.003981255,0.0008646136,0.002504852,0.001418034,0.001216117,0.1612917],"category_scores_gemma":[0.00802001,0.000559102,0.000572507,0.006858515,0.0002918037,0.001267985,0.001423848,0.001082116,0.2205774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009741399,"about_ca_system_score_gemma":0.001978739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02927322,"about_ca_topic_score_gemma":0.06160919,"domain_scores_codex":[0.9993474,0.00009453556,0.0000625199,0.0002137884,0.0001730403,0.0001086586],"domain_scores_gemma":[0.997968,0.0005771348,0.0001962457,0.0003367047,0.0006129979,0.0003088255],"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":[0.00002369112,0.000003833085,0.0003717922,0.0001529505,0.000004798018,0.000007062888,0.000006281456,0.00003455424,0.00001749187,0.00009490321,0.9978883,0.001394356],"study_design_scores_gemma":[0.00008885468,0.000006250148,0.002779029,0.0002016712,0.0000111904,0.00002758137,0.00005844769,0.00007730621,0.00009075765,0.0003354277,0.9963133,0.0000102883],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007335761,0.00009361737,0.00002534052,0.00009753939,0.00003710697,0.000004281758,0.9982698,0.0001626994,0.001236297],"genre_scores_gemma":[0.0004270538,0.0001334486,0.0001454586,0.0001003416,0.00001749918,0.00003638679,0.9961196,0.0001220444,0.002898198],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1612917,"threshold_uncertainty_score":0.5395746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003700823868020259,"score_gpt":0.1659693910165292,"score_spread":0.1622685671485089,"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."}}