{"id":"W1560677248","doi":"10.1007/978-3-642-15384-6_57","title":"Prognosis of Breast Cancer Using Genetic Programming","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Breast cancer; Genetic programming; Computer science; Cancer; Lung cancer; Machine learning; Artificial intelligence; Medicine; Oncology; Internal 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002773837,0.0003189367,0.0003409744,0.0004307851,0.0002516003,0.0001975388,0.002201529,0.0002454724,0.00002398341],"category_scores_gemma":[0.000008974738,0.0002956143,0.0001070287,0.000678408,0.000761949,0.0003419713,0.0008286149,0.0005781642,0.000004519875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001440584,"about_ca_system_score_gemma":0.0007109285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001092721,"about_ca_topic_score_gemma":0.00006456618,"domain_scores_codex":[0.9974608,0.00001220676,0.0004370809,0.000992245,0.0006526741,0.0004450291],"domain_scores_gemma":[0.9981849,0.00009208621,0.0003316483,0.0008714874,0.0003947841,0.0001250892],"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":[0.000001177556,0.00005728985,0.0003897332,0.0000424091,0.00001033608,0.000009811622,0.0002633034,0.01693748,0.001920129,0.006097121,0.000001715897,0.9742695],"study_design_scores_gemma":[0.0001580719,0.00006084702,0.002866246,0.0004437975,0.00001905835,0.0002160515,1.294388e-7,0.9396191,0.002247516,0.05285815,0.0009094044,0.0006016677],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006319828,0.0003836202,0.9970066,0.0006957177,0.000618882,0.0004430373,0.00001583387,0.00007613648,0.0001282034],"genre_scores_gemma":[0.0655682,0.00003575223,0.9338014,0.0001227106,0.0003727568,0.00003049871,0.000001564511,0.00002103238,0.00004613979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9736678,"threshold_uncertainty_score":0.9999496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752465085875869,"score_gpt":0.2615564378247187,"score_spread":0.24403178696596,"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."}}