{"id":"W2951487100","doi":"10.48550/arxiv.1906.10096","title":"Audio-Visual Kinship Verification","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Kinship; Audio visual; Computer science; Sociology; Multimedia; Anthropology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006844576,0.0008348688,0.0008176839,0.001421999,0.0006634026,0.0005620498,0.00116746,0.001127062,0.004758645],"category_scores_gemma":[0.002515419,0.0001773258,0.000488113,0.0007042493,0.0004087076,0.001099913,0.001714707,0.000676849,0.002447793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004625462,"about_ca_system_score_gemma":0.0006046535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004707463,"about_ca_topic_score_gemma":0.01185038,"domain_scores_codex":[0.998896,0.0001269974,0.00005785412,0.0004715679,0.0003012497,0.000146223],"domain_scores_gemma":[0.9990007,0.0001785785,0.0001466465,0.0003299802,0.0002508983,0.00009325048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001725722,0.0006738924,0.03446995,0.0009945162,0.0002776454,0.001314211,0.0003859092,0.02362592,0.0881776,0.005240707,0.05617248,0.7869415],"study_design_scores_gemma":[0.0001960498,0.0009111643,0.1249106,0.0002643667,0.0002597396,0.006965207,0.001407966,0.6247018,0.1490025,0.01643971,0.07475535,0.0001855454],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6412047,0.004567256,0.2798407,0.0005025975,0.0005177956,0.0009369805,0.03073445,0.007738359,0.03395711],"genre_scores_gemma":[0.8538852,0.0004939563,0.09256127,0.0002436934,0.000103276,0.0002414999,0.04228788,0.0001233632,0.01005985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004758645,"threshold_uncertainty_score":0.01591927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0970102276245375,"score_gpt":0.200140152783388,"score_spread":0.1031299251588505,"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."}}