{"id":"W2042282848","doi":"10.1016/j.ijrobp.2007.07.081","title":"A Multi-Institution Deformable Registration Accuracy Study2","year":2007,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Medicine; DICOM; Voxel; Nuclear medicine; Prostate; Lung; Displacement (psychology); Image registration; Radiology; Image (mathematics); Artificial intelligence; Internal medicine; Computer science; Cancer","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":[0.001815371,0.000159627,0.000366766,0.0002113894,0.0001091866,0.00003592378,0.000280102,0.0001960302,0.0000363403],"category_scores_gemma":[0.001791072,0.0001379715,0.0001947213,0.0001742316,0.0002360918,0.0004325635,0.00004121494,0.0007508156,0.00002806858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007720374,"about_ca_system_score_gemma":0.0005765207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003256906,"about_ca_topic_score_gemma":0.000005772744,"domain_scores_codex":[0.9981984,0.0001006317,0.0008912807,0.0001880071,0.0003670054,0.000254674],"domain_scores_gemma":[0.9974031,0.0003439382,0.001157123,0.0001371495,0.0007727083,0.0001859417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000741742,0.0009484199,0.3009234,0.00001740676,0.0005724847,0.0002126259,0.0007328332,0.001750501,0.02113103,0.01329979,0.00169368,0.6579762],"study_design_scores_gemma":[0.03768075,0.004048387,0.278271,0.0004191955,0.0006743483,0.00429124,0.0007415107,0.1432967,0.01241904,0.01058589,0.506764,0.0008079227],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4513005,0.0003909189,0.5378367,0.004418211,0.00344366,0.0002465261,0.000007574526,0.00005906089,0.002296865],"genre_scores_gemma":[0.9680224,0.0004382225,0.02606823,0.001724995,0.00353972,0.000003256714,0.0000722656,0.0000203904,0.0001105227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6571682,"threshold_uncertainty_score":0.5626315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02542659822357966,"score_gpt":0.3781612521467613,"score_spread":0.3527346539231816,"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."}}