{"id":"W2125300226","doi":"10.1109/10.959326","title":"Feature extraction of chromosomes from 3-D confocal microscope images","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Feature extraction; Phase congruency; Computer science; Segmentation; Computer vision; Feature (linguistics); Image segmentation; Microscope; Feature vector; Optics; Physics","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":[0.00006556019,0.0001502442,0.00017192,0.0001236619,0.00003373938,0.0000137363,0.000130864,0.0002143805,0.00009782637],"category_scores_gemma":[0.00001211253,0.0001473196,0.00014038,0.0001923548,0.00008447235,0.000008233742,0.000002255166,0.0001912918,0.000007699256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969583,"about_ca_system_score_gemma":0.0000232215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004986787,"about_ca_topic_score_gemma":0.000007370312,"domain_scores_codex":[0.9992303,0.00001422396,0.0001695139,0.0002568586,0.0001619312,0.0001671786],"domain_scores_gemma":[0.9995283,0.00002484405,0.00004354546,0.0002657807,0.00004278498,0.00009468404],"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":[0.00003316419,0.0001131626,0.000008065831,0.000008890572,0.00008918373,0.000009816412,0.000008369771,0.0006636549,0.9887701,4.62514e-7,0.001479585,0.008815598],"study_design_scores_gemma":[0.0002558189,0.0001349169,0.0001256254,0.00003068856,0.00004386307,0.00002235172,0.00001205861,0.001482357,0.9571089,0.000001828979,0.04064311,0.0001384872],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1864146,0.0002641828,0.8127578,0.0001636131,0.0001277983,0.00007723955,0.00003417962,0.00006249332,0.00009804727],"genre_scores_gemma":[0.9927007,0.0007731768,0.005762669,0.00005676576,0.0001373399,0.00001676865,0.00007099431,0.00002520836,0.000456355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8069952,"threshold_uncertainty_score":0.6007518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004292013496390081,"score_gpt":0.2423737716652988,"score_spread":0.2380817581689088,"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."}}