{"id":"W2793838739","doi":"10.5539/mas.v12n3p56","title":"A Hybrid Methodology for Automation the Diagnosis of Leukemia Based on Quantitative and Morphological Feature Analysis","year":2018,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Leukemia; Chronic leukemia; Acute leukemia; Pathology; Medicine; Pathological; Automation; Artificial intelligence; Pattern recognition (psychology); Computer science; Radiology; Internal medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010393,0.0001143474,0.0002025246,0.0002597025,0.0002598681,0.000195432,0.000959683,0.00002411438,0.000002118651],"category_scores_gemma":[0.0004911837,0.00007501903,0.00007298094,0.001106492,0.001380034,0.0002841094,0.0001943918,0.00005585369,0.00000322307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004575325,"about_ca_system_score_gemma":0.0001435062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009620328,"about_ca_topic_score_gemma":0.000003594934,"domain_scores_codex":[0.9986342,0.00005482115,0.0001412296,0.0005557437,0.0003670121,0.0002469611],"domain_scores_gemma":[0.9981254,0.0009916221,0.0001323297,0.0005094493,0.0001635914,0.00007755883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000313476,0.0005421648,0.004426072,0.00005998035,0.0002164444,0.00001090239,0.003175148,0.02038654,0.1060989,0.6867018,0.001919298,0.1761492],"study_design_scores_gemma":[0.0001811946,0.0001700106,0.009613905,0.000004305351,0.00005842872,0.000001722077,0.00001757796,0.9219247,0.03585707,0.03204099,0.00003843966,0.00009172032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1665274,0.00002400756,0.831632,0.0006681709,0.00003914524,0.0002387385,0.0000188636,0.00006185694,0.0007898653],"genre_scores_gemma":[0.7708923,5.955076e-7,0.2283945,0.0006054955,0.000007803244,0.00008663289,0.000002101531,0.000002886448,0.00000769726],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9015381,"threshold_uncertainty_score":0.5084791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06540221403611313,"score_gpt":0.3335048165581269,"score_spread":0.2681026025220138,"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."}}