{"id":"W3214998250","doi":"10.18280/ts.380513","title":"Accurate Brain Tumor Recognition Using Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Feature selection; Feature (linguistics); Process (computing); Brain tumor; Class (philosophy); Selection (genetic algorithm); Pathology; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003204818,0.0003374534,0.0002460651,0.0001928223,0.0006924821,0.0002997669,0.0001326781,0.0001813807,0.0003176467],"category_scores_gemma":[0.00004555825,0.0003159188,0.0001004284,0.001234059,0.00006693155,0.0008601702,0.00002282778,0.000696515,0.00002787838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003234061,"about_ca_system_score_gemma":0.0002599905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001080343,"about_ca_topic_score_gemma":0.00005724876,"domain_scores_codex":[0.9974331,0.0003030469,0.0003313007,0.0008766836,0.0006284842,0.0004273679],"domain_scores_gemma":[0.9988126,0.0001289347,0.0003635708,0.0002094836,0.0003299253,0.0001555275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007783682,0.0002835876,0.0000482676,0.00005056856,0.00002242588,0.00004766557,0.0002127497,0.003022716,0.9887688,0.0003254874,0.0005908515,0.005848488],"study_design_scores_gemma":[0.001726165,0.00009038589,0.000394099,0.00008378106,0.00006224617,0.0003739658,0.00009872535,0.4690889,0.5260395,0.0003147707,0.001409284,0.0003181894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.839788,0.00003863963,0.1548219,0.003033302,0.0003079819,0.0007082159,0.00004651143,0.0004296204,0.0008259254],"genre_scores_gemma":[0.9878307,0.00003010375,0.009125698,0.001321161,0.0003332208,0.00006861852,0.0001172973,0.00005815738,0.001115024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4660662,"threshold_uncertainty_score":0.9999293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06266013393252383,"score_gpt":0.2903648687283171,"score_spread":0.2277047347957933,"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."}}