{"id":"W4388945936","doi":"10.3390/make5040085","title":"FCIoU: A Targeted Approach for Improving Minority Class Detection in Semantic Segmentation Systems","year":2023,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intersection (aeronautics); Segmentation; Class (philosophy); Function (biology); Computer science; Terrain; Artificial intelligence; Machine learning; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000483916,0.0001282132,0.0001357614,0.0002514582,0.0003462418,0.0001074923,0.0001044018,0.00007890058,4.236306e-7],"category_scores_gemma":[0.00008606692,0.000133213,0.00003280842,0.0008064976,0.00001510774,0.000399728,0.00006011964,0.0003068862,0.00001263945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009285375,"about_ca_system_score_gemma":0.00001564089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001151304,"about_ca_topic_score_gemma":0.00009048518,"domain_scores_codex":[0.9988779,0.0001406367,0.0002281234,0.0004251861,0.00009158778,0.0002365489],"domain_scores_gemma":[0.9994135,0.0002091049,0.0001405125,0.0001317268,0.00005633437,0.00004879639],"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":[0.00005018872,0.0001662392,0.007831006,0.0005634404,0.00001661373,0.000002693242,0.001210837,0.08856986,0.1808825,0.001502784,0.00006896033,0.7191349],"study_design_scores_gemma":[0.0003834894,0.00006724244,0.005131976,0.000009443464,0.000007967667,0.00001622933,0.0001298902,0.991069,0.001238825,0.0001496165,0.001658723,0.0001375707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06385902,0.0004627345,0.9339926,0.00006397667,0.0003181727,0.0005602666,0.000001253111,0.000477904,0.0002641376],"genre_scores_gemma":[0.989948,0.00003345309,0.008625791,0.000004291548,0.0001496655,0.0003398482,0.00005245884,0.00001376812,0.0008327277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.926089,"threshold_uncertainty_score":0.5432267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01779713589971847,"score_gpt":0.2931307677395185,"score_spread":0.2753336318398,"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."}}