{"id":"W2938541447","doi":"10.11591/ijeecs.v14.i3.pp1493-1498","title":"A performance study of the suitability of Adaptive boosting in Red Acne detection","year":2019,"lang":"en","type":"article","venue":"Indonesian Journal of Electrical Engineering and Computer Science","topic":"melanin and skin pigmentation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Boosting (machine learning); AdaBoost; Acne; Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Dermatology; Classifier (UML); Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.000393648,0.00003873284,0.00008681048,0.00006726458,0.00001669054,0.000005024147,0.0001293113,0.0000183353,1.422904e-7],"category_scores_gemma":[0.00003310979,0.00002776715,0.00001822665,0.000293646,0.00002352761,0.000009711041,0.00003458576,0.00007378604,1.835603e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001168259,"about_ca_system_score_gemma":0.00003654112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002782113,"about_ca_topic_score_gemma":8.329014e-7,"domain_scores_codex":[0.9994953,0.00002280325,0.0001895893,0.00007955296,0.0001373788,0.00007535976],"domain_scores_gemma":[0.9997119,0.00001555596,0.0001073712,0.0000734548,0.00007060725,0.0000211529],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007416473,0.00007919397,0.1812103,0.00001349458,0.000007160063,5.633567e-7,0.0002249145,0.01484039,0.7899164,0.000005952109,2.947014e-7,0.01362715],"study_design_scores_gemma":[0.0004641035,0.001776798,0.6718181,0.00002949209,0.000003093688,0.00002966168,0.00002679343,0.1331204,0.1926935,0.000002190782,7.662696e-7,0.00003511492],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924416,0.00003422543,0.007349945,0.000004026022,0.00009371415,0.00007039788,5.97417e-8,6.158567e-7,0.000005420564],"genre_scores_gemma":[0.999563,0.00000328802,0.0004073815,0.000002416916,0.00002115007,4.425971e-7,2.444927e-8,0.000001560951,7.598967e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5972229,"threshold_uncertainty_score":0.1132311,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004893342883287972,"score_gpt":0.1981193913345179,"score_spread":0.19322604845123,"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."}}