{"id":"W2246794939","doi":"10.5281/zenodo.17877","title":"librosa: 0.4.0 release candidate 2","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","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":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001354642,0.00009222006,0.0001006583,0.000114816,0.003124572,0.0008729023,0.0009916457,0.000071938,0.0116689],"category_scores_gemma":[0.002498158,0.0001009868,0.00003756774,0.0005973529,0.0005320438,0.0004516176,0.0005314854,0.0001955789,0.02452112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002598551,"about_ca_system_score_gemma":0.00002080395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007965878,"about_ca_topic_score_gemma":0.00002093541,"domain_scores_codex":[0.9979874,0.0005422106,0.0002007033,0.0002907345,0.0005503516,0.0004285637],"domain_scores_gemma":[0.9984126,0.00002901908,0.00007642791,0.0003202875,0.0007008241,0.0004608291],"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":[0.00008701397,0.0001303546,0.0000185137,0.00001073258,0.00002050129,0.00002964433,0.02359024,0.0001046074,0.000514348,0.1655028,0.67787,0.1321213],"study_design_scores_gemma":[0.00009738575,0.000084507,0.00002849332,0.000009739508,0.000005465725,0.000008085295,0.004185997,0.0001223673,0.000390181,0.002567068,0.9923745,0.0001262287],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03691837,0.00008602082,0.002783744,0.004040337,0.0003985243,0.0004530267,0.00007609084,0.001542347,0.9537016],"genre_scores_gemma":[0.9930003,0.00006627967,0.0002373701,0.0002480667,0.0004605155,2.858037e-8,0.0002281875,0.0006858889,0.005073323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.956082,"threshold_uncertainty_score":0.9981732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1041197447678246,"score_gpt":0.326787031651135,"score_spread":0.2226672868833104,"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."}}