{"id":"W4206442543","doi":"10.18280/ts.380629","title":"Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Euclidean distance; Segmentation; Face (sociological concept); Artificial intelligence; Computer science; Pattern recognition (psychology); Human skin; Measure (data warehouse); Computer vision; Skin color; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002260544,0.0002067468,0.0002151603,0.00005819015,0.0003560896,0.0002675097,0.0003674191,0.00005169075,0.0001848715],"category_scores_gemma":[0.00004095557,0.000210042,0.0001685357,0.0002126417,0.00003975151,0.0006131128,0.00007684036,0.00006883862,0.00001213865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018913,"about_ca_system_score_gemma":0.00008286388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004010971,"about_ca_topic_score_gemma":0.0002800762,"domain_scores_codex":[0.9981489,0.00007631215,0.000403335,0.0006643562,0.0003445922,0.0003625525],"domain_scores_gemma":[0.9988073,0.0003764524,0.0001946587,0.0003074442,0.000180362,0.0001337515],"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":[0.002050365,0.00117178,0.0009745184,0.0005388181,0.0005091219,0.00005028806,0.01533817,0.00146852,0.1590992,0.2696144,0.1748339,0.374351],"study_design_scores_gemma":[0.02126213,0.001732647,0.005404121,0.0004511363,0.0001700341,0.000008524671,0.005553744,0.02176164,0.7725325,0.07263893,0.09695411,0.001530529],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02409511,0.0001007198,0.973077,0.0009727586,0.0003973969,0.0008080118,0.0001548155,0.00009035832,0.0003038061],"genre_scores_gemma":[0.8005828,0.00002204152,0.1951921,0.0004779223,0.0005096945,0.0005727368,0.0009030651,0.0000342808,0.001705424],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.777885,"threshold_uncertainty_score":0.8565264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05870582638371101,"score_gpt":0.3269719322831284,"score_spread":0.2682661058994174,"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."}}