{"id":"W2156909660","doi":"10.1120/jacmp.v11i3.3175","title":"Assessment of a commercially available automatic deformable registration system","year":2010,"lang":"en","type":"article","venue":"Journal of Applied Clinical Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Cancer Foundation; University of Alberta","funders":"","keywords":"Image registration; Computer science; Protocol (science); Artificial intelligence; Imaging phantom; Computer vision; Modality (human–computer interaction); Transformation (genetics); Range (aeronautics); Image (mathematics); Nuclear medicine; Medicine","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":[0.005628922,0.0001425857,0.0006569283,0.00006135089,0.00006219221,0.0000606264,0.001346847,0.0002345908,0.0001360903],"category_scores_gemma":[0.0007138136,0.0001079657,0.0002173897,0.0003157934,0.0003049393,0.0003977245,0.000180897,0.001353842,0.00002548782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005340256,"about_ca_system_score_gemma":0.001177737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006064969,"about_ca_topic_score_gemma":0.000002066196,"domain_scores_codex":[0.995244,0.0001275419,0.002252725,0.0002052983,0.001949424,0.00022097],"domain_scores_gemma":[0.9957925,0.0009661051,0.001849237,0.0005298146,0.000371569,0.0004907866],"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":[0.00003396944,0.00167453,0.0008463826,0.0004670604,0.0001731885,0.00006964622,0.0001525511,0.00001643981,0.006174091,0.2094057,0.02304076,0.7579457],"study_design_scores_gemma":[0.0139689,0.004860658,0.0151299,0.002554235,0.000509927,0.0004925863,0.0004302419,0.7376172,0.1103968,0.1080973,0.004390818,0.001551366],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01012303,0.000006434797,0.9740407,0.0004487608,0.0009260931,0.0001952836,9.417948e-7,0.00008835334,0.01417043],"genre_scores_gemma":[0.6068429,0.00002154185,0.3919432,0.000665734,0.000490529,0.000006911655,0.000001480777,0.000009115665,0.0000186272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7563943,"threshold_uncertainty_score":0.5881847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03393541882435368,"score_gpt":0.3748182151467843,"score_spread":0.3408827963224306,"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."}}