{"id":"W4214775388","doi":"10.36227/techrxiv.19089995.v1","title":"Spectral image clustering on dual-energy CT scans using functional regression mixtures","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; Agence Nationale de la Recherche","keywords":"Cluster analysis; Artificial intelligence; Spectral clustering; Computer science; Context (archaeology); Voxel; Pattern recognition (psychology); Energy (signal processing); Data mining; Nuclear medicine; Mathematics; Medicine; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002437874,0.0009538475,0.0008223421,0.002958284,0.0005535714,0.001405811,0.00137686,0.001359099,0.001242568],"category_scores_gemma":[0.007932799,0.000589024,0.00165379,0.001395036,0.000830557,0.001436641,0.001584881,0.001066879,0.001117409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009575813,"about_ca_system_score_gemma":0.0008845688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00369213,"about_ca_topic_score_gemma":0.004050791,"domain_scores_codex":[0.9989183,0.0003574075,0.00006639821,0.0002981837,0.0002674307,0.00009219594],"domain_scores_gemma":[0.997422,0.001092464,0.0003527576,0.0004836051,0.0005599392,0.00008929293],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000350984,0.0001819628,0.003964958,0.000148465,0.0001793679,0.0001773766,0.0003570559,0.5838832,0.03668461,0.00874536,0.001443917,0.3638827],"study_design_scores_gemma":[0.000005009729,0.00002214411,0.0009086826,0.000008656953,0.00001127791,0.00006882899,0.00003066801,0.9889396,0.005655353,0.003843935,0.0004873811,0.00001850743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02617628,0.00007694346,0.9724981,0.00008079856,0.00001131669,0.00003951297,0.00005960929,0.0006983519,0.000359124],"genre_scores_gemma":[0.2823431,0.0002138182,0.7147466,0.00008026016,0.00003352378,0.0001206843,0.0006193782,0.0004391661,0.001403451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00369213,"threshold_uncertainty_score":0.01289284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01918473891598844,"score_gpt":0.2549701072241266,"score_spread":0.2357853683081381,"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."}}