{"id":"W4408201618","doi":"10.3390/app15052830","title":"Models to Identify Small Brain White Matter Hyperintensity Lesions","year":2025,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Hyperintensity; Segmentation; Context (archaeology); White matter; Medicine; Artificial intelligence; Magnetic resonance imaging; Computer science; Pattern recognition (psychology); Radiology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003825678,0.0001006214,0.0001027658,0.0002060083,0.000574577,0.0001960658,0.0004830742,0.0000382674,0.000119321],"category_scores_gemma":[0.00009310521,0.00008828888,0.00003669335,0.001157382,0.0002779026,0.0001232113,0.0001362731,0.0001070225,0.0006606421],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000288292,"about_ca_system_score_gemma":0.00005219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001495097,"about_ca_topic_score_gemma":0.00003441838,"domain_scores_codex":[0.9987289,0.00004668292,0.0001708315,0.0005790562,0.0002230552,0.0002515239],"domain_scores_gemma":[0.9994348,0.0001520259,0.00004360135,0.0002622447,0.00002639595,0.00008087824],"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":[0.00001206669,0.000035586,0.0005902462,0.000006005052,8.891311e-7,0.000001022776,0.0003460838,0.0007916306,0.8779714,0.1073145,0.00969362,0.003236997],"study_design_scores_gemma":[0.0004164641,0.00006131367,0.1625131,0.00003964776,0.00001304194,0.00002231273,0.002346476,0.01395435,0.7167844,0.08628794,0.01697302,0.0005879158],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7275105,0.000003131418,0.04579965,0.02408362,0.0004536408,0.0003314783,0.000003561057,0.0001524564,0.201662],"genre_scores_gemma":[0.9663855,0.000001204046,0.0008274133,0.02683926,0.00001823323,0.00005446466,2.731205e-7,0.000004565599,0.005869051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.238875,"threshold_uncertainty_score":0.8491437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0977056648071548,"score_gpt":0.3142707164477433,"score_spread":0.2165650516405885,"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."}}