{"id":"W4408006890","doi":"10.18280/ts.420101","title":"Hybrid Ensemble Model with Bagging (HEMBAGG) Leveraging Machine Learning for Breast Carcinoma Diagnosis","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Ensemble learning; Computer science; Artificial intelligence; Machine learning; Breast carcinoma; Bootstrap aggregating; Ensemble forecasting; Breast cancer; Medicine; Internal medicine; Cancer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399125,0.001008706,0.00172375,0.001505057,0.0005891987,0.0009872166,0.001440636,0.001170124,0.001060569],"category_scores_gemma":[0.002268088,0.0003530004,0.001082418,0.001568502,0.0002222586,0.001484674,0.001260345,0.001453643,0.001057812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002897371,"about_ca_system_score_gemma":0.0009214358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005780625,"about_ca_topic_score_gemma":0.009440699,"domain_scores_codex":[0.9991553,0.0002493954,0.00004707636,0.0001631427,0.0002400321,0.0001450997],"domain_scores_gemma":[0.9990624,0.0004036543,0.00004729666,0.0001305707,0.0003013887,0.00005468914],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005022099,0.00045403,0.007954584,0.0001418074,0.0004541682,0.0001311995,0.00009567264,0.1630424,0.01140243,0.001666159,0.01024369,0.8039116],"study_design_scores_gemma":[0.000009640391,0.0001221728,0.001299909,0.0000170093,0.0001065192,0.00008054568,0.00002112731,0.9909473,0.003208494,0.00240983,0.001757658,0.00001976074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09424087,0.007195691,0.8900267,0.0007772925,0.0006034292,0.00008305704,0.0007964433,0.0040319,0.002244518],"genre_scores_gemma":[0.7945428,0.001866698,0.1932886,0.0009180704,0.0004344165,0.0001291595,0.002642379,0.0002085562,0.005969306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005780625,"threshold_uncertainty_score":0.01149398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01568797068429395,"score_gpt":0.2261899447998361,"score_spread":0.2105019741155421,"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."}}