{"id":"W3165249856","doi":"10.48550/arxiv.2105.12703","title":"Exploring dual information in distance metric learning for clustering","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Dual (grammatical number); Cluster analysis; Metric (unit); Computer science; Artificial intelligence; Mathematics; Business; Marketing","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":[0.006842424,0.001411039,0.002538895,0.003363138,0.001285894,0.003442943,0.002547876,0.002680796,0.00182911],"category_scores_gemma":[0.03169442,0.0008953401,0.001271185,0.003801244,0.002880493,0.005568647,0.006108846,0.003465718,0.000859303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002102396,"about_ca_system_score_gemma":0.001666645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00223543,"about_ca_topic_score_gemma":0.002018381,"domain_scores_codex":[0.9925608,0.003834059,0.0003800292,0.001219139,0.001773384,0.0002325172],"domain_scores_gemma":[0.987228,0.007937101,0.0009276756,0.001862627,0.001544043,0.0005004036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000509779,0.0002697817,0.003386895,0.0005306859,0.0002868077,0.0002122379,0.0008442748,0.3901214,0.004439287,0.3256132,0.005627233,0.2681584],"study_design_scores_gemma":[0.00001931462,0.00005954158,0.0002122173,0.00003125642,0.0000161493,0.0000656596,0.00004863463,0.7871178,0.0007870193,0.2097615,0.001852741,0.00002825097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01024084,0.000611325,0.9872602,0.0003999894,0.00002969127,0.00003130081,0.0000783439,0.0001461868,0.00120207],"genre_scores_gemma":[0.3551243,0.00109584,0.6395524,0.0004152927,0.0002283935,0.000242961,0.0008830341,0.0002324436,0.002225414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006842424,"threshold_uncertainty_score":0.03618658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530994065869715,"score_gpt":0.1976105859296889,"score_spread":0.04451117934271742,"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."}}