{"id":"W7134921929","doi":"10.1109/sccc67219.2025.11420801","title":"RF and ELM Applied to Predicting Hair Color Using DNA","year":2025,"lang":"","type":"article","venue":"","topic":"Hair Growth and Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Sample (material); Pattern recognition (psychology); Skin color; Color model; DNA; Class (philosophy)","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":[0.004722989,0.001282487,0.001283544,0.00250235,0.0005136563,0.00102716,0.001305131,0.001229126,0.003062288],"category_scores_gemma":[0.007344665,0.0003559992,0.001630315,0.001385965,0.0004090908,0.0008182322,0.0006556688,0.0013484,0.001410463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009906258,"about_ca_system_score_gemma":0.00112899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01534721,"about_ca_topic_score_gemma":0.008363997,"domain_scores_codex":[0.9990396,0.0003822543,0.00006363066,0.000242908,0.0001441836,0.0001273778],"domain_scores_gemma":[0.9953915,0.0033485,0.0001769262,0.0002509341,0.0007317194,0.0001003994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005521975,0.0003866025,0.02745724,0.0001238484,0.0003769181,0.0002194218,0.000109462,0.6546047,0.003150201,0.001190896,0.002287368,0.3095412],"study_design_scores_gemma":[0.00001124693,0.0000568889,0.002526224,0.00001039846,0.0000247426,0.00003481126,0.00001937707,0.9953789,0.0008854985,0.0007199873,0.0003190562,0.00001288418],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.504186,0.002340381,0.4794466,0.001390006,0.0004209428,0.0003477009,0.001980486,0.004697582,0.005190312],"genre_scores_gemma":[0.8375992,0.0005801611,0.1529226,0.0002828857,0.0001845035,0.0002401713,0.001595443,0.0001601043,0.006434881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01534721,"threshold_uncertainty_score":0.03051579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156872824752736,"score_gpt":0.2792878179850479,"score_spread":0.2677190897375206,"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."}}