{"id":"W4415948050","doi":"10.6026/973206300213866","title":"Machine learning for predicting antimicrobial efficacy of periodontal gel formulations in vitro biofilm models","year":2025,"lang":"en","type":"article","venue":"Bioinformation","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Biofilm; Gradient boosting; Boosting (machine learning); Antimicrobial; Periodontal disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002911858,0.00009536971,0.0001801043,0.00032242,0.0001842511,0.00004317238,0.0001230484,0.0001520401,0.00003254545],"category_scores_gemma":[0.0001473678,0.00009579758,0.00009304775,0.0002576254,0.00004917907,0.0005359039,0.00006587908,0.0001826208,0.00001645829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006395461,"about_ca_system_score_gemma":0.00007340966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002464983,"about_ca_topic_score_gemma":0.0001518835,"domain_scores_codex":[0.9991175,0.0000372324,0.0004525578,0.0001128279,0.00006538582,0.000214506],"domain_scores_gemma":[0.9995528,0.0001027388,0.00012439,0.0001090357,0.00009095626,0.00002004941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002757067,0.0002849294,0.1270588,0.001025677,0.0001298571,0.000002758826,0.002897841,0.01969639,0.7785406,0.004588729,0.0008924755,0.06212488],"study_design_scores_gemma":[0.004307835,0.00009256182,0.07361651,0.0001580357,0.00002289079,0.00001595268,0.0002108693,0.8085806,0.1104896,0.000144008,0.002158531,0.0002026569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9762942,0.00008927086,0.02003726,0.00007368248,0.0002389379,0.0005469355,0.0002308453,0.00004770316,0.002441133],"genre_scores_gemma":[0.9959219,0.00001418763,0.002418833,0.00002607913,0.00002503134,0.00001899443,0.00100436,0.000006410864,0.0005642351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7888842,"threshold_uncertainty_score":0.3906512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02132391860788595,"score_gpt":0.2965179139249522,"score_spread":0.2751939953170662,"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."}}