{"id":"W3204817302","doi":"10.1155/2021/3729379","title":"Multilevel Clustering-Evolutionary Random Support Vector Machine Cluster Algorithm-Based Functional Magnetic Resonance Imaging in Diagnosing Cerebral Ischemic Stroke","year":2021,"lang":"en","type":"article","venue":"Scientific Programming","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Functional magnetic resonance imaging; Support vector machine; Algorithm; Cluster analysis; Magnetic resonance imaging; Cognition; Ischemic stroke; Artificial intelligence; Medicine; Machine learning; Pattern recognition (psychology); Computer science; Internal medicine; Psychiatry; Ischemia; Radiology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003483836,0.0003007531,0.0002593671,0.0002088242,0.0005499385,0.0004357525,0.0002621999,0.00006630352,0.0006170921],"category_scores_gemma":[0.0004724294,0.0002837525,0.0001818681,0.0006293181,0.0002936036,0.0003357544,0.0002795765,0.0002833557,0.00008860368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001347784,"about_ca_system_score_gemma":0.0002159445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001811059,"about_ca_topic_score_gemma":0.00003006424,"domain_scores_codex":[0.9964734,0.0002258923,0.0004248386,0.001365993,0.0006935762,0.0008163142],"domain_scores_gemma":[0.9988754,0.0002974749,0.0001040335,0.0004141243,0.00008688161,0.0002220888],"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":[0.0004730156,0.002042365,0.02191116,0.0000728183,0.000004510794,0.002649827,0.0001167665,0.0006629997,0.1901226,0.00009667535,0.001177266,0.7806699],"study_design_scores_gemma":[0.01674496,0.0002530332,0.04894015,0.000207495,0.00007498333,0.0003091741,0.00006658299,0.7366259,0.14226,0.0003871353,0.05308094,0.001049669],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8231009,0.00865434,0.1447338,0.002786957,0.01217472,0.004220146,0.001781375,0.001206039,0.001341777],"genre_scores_gemma":[0.9821676,0.000005567502,0.0145807,0.0007640391,0.0000791803,0.000247203,0.00015794,0.00003771727,0.001960067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7796203,"threshold_uncertainty_score":0.9999614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02235569115505468,"score_gpt":0.2470679071871991,"score_spread":0.2247122160321444,"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."}}