{"id":"W6963294807","doi":"10.17879/satura-2019-3098","title":"Enough","year":2020,"lang":"en","type":"article","venue":"Universitäts- und Landesbibliothek Münster","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Anglo American (Canada)","funders":"","keywords":"Selection (genetic algorithm); Process (computing); Identification (biology)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001404411,0.0003100181,0.000320557,0.001338414,0.0001850366,0.0001736884,0.0006062399,0.0001415582,0.005579134],"category_scores_gemma":[0.00004430527,0.0002935823,0.0001732858,0.004380419,0.0001442645,0.001214561,0.0002210963,0.0002919586,0.01369918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001351778,"about_ca_system_score_gemma":0.0000962267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007055103,"about_ca_topic_score_gemma":0.0000353034,"domain_scores_codex":[0.9981004,0.0001164908,0.0002093341,0.0005456104,0.0004727966,0.0005553695],"domain_scores_gemma":[0.998809,0.00009741598,0.0001141324,0.0003930292,0.0001184482,0.0004679907],"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":[0.00332924,0.000676156,0.05917328,0.0002989166,0.002074924,0.002172019,0.03386175,0.001093584,0.04362763,0.05739571,0.789524,0.006772785],"study_design_scores_gemma":[0.002836876,0.0002188492,0.001464618,0.00002726977,0.000178824,0.0000160254,0.001111783,0.0008747521,0.001452661,0.0001570875,0.9910893,0.0005719315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2558821,0.001069116,0.006449425,0.009331778,0.0004178117,0.0008242512,0.0003251473,0.002023398,0.7236769],"genre_scores_gemma":[0.9888949,0.00004700029,0.0009304789,0.005089779,0.000338613,0.000001311259,0.00003862702,0.0001208683,0.004538456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7330127,"threshold_uncertainty_score":0.9999517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03482058962928101,"score_gpt":0.2511123233302808,"score_spread":0.2162917337009998,"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."}}