{"id":"W2049637703","doi":"10.1118/1.4815379","title":"TU‐C‐141‐02: Development of a Hyrbid Biomechanical Model Based Deformable Image Registration; Application in Lung","year":2013,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Image registration; Interpolation (computer graphics); Mathematics; Range (aeronautics); Reduction (mathematics); Image (mathematics); Nuclear medicine; Artificial intelligence; Computer vision; Computer science; Medicine; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001744244,0.0005781659,0.0005450041,0.0008848408,0.0001904678,0.0007343604,0.00112698,0.001037773,0.00321146],"category_scores_gemma":[0.002064067,0.0004638455,0.0006084086,0.0005370007,0.0002952343,0.0004884832,0.0008804867,0.0006626179,0.001416415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003793928,"about_ca_system_score_gemma":0.0009217142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001955031,"about_ca_topic_score_gemma":0.001987435,"domain_scores_codex":[0.9993234,0.0001258472,0.00003823992,0.0001146084,0.000371672,0.00002633023],"domain_scores_gemma":[0.9994484,0.0001691954,0.00005598969,0.0001218328,0.0001611482,0.00004344211],"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":[0.0003334691,0.000301449,0.003708015,0.000416465,0.00014815,0.0002554274,0.0002122754,0.1297426,0.2598451,0.003874,0.003945115,0.5972179],"study_design_scores_gemma":[0.00004893228,0.0004793382,0.004610993,0.0000309457,0.00004302625,0.0006303143,0.00002874652,0.8949212,0.08627807,0.000530454,0.0123252,0.00007276801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03008129,0.0004283614,0.9640313,0.0001039897,0.00005537507,0.0002186274,0.0001917816,0.003205993,0.001683236],"genre_scores_gemma":[0.1430923,0.0003467222,0.8508494,0.00008339118,0.0000304592,0.0003592846,0.000772004,0.0006052536,0.003861099],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00321146,"threshold_uncertainty_score":0.01074344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008849467491033072,"score_gpt":0.2318158505423784,"score_spread":0.2229663830513453,"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."}}