{"id":"W2892706121","doi":"10.5194/isprs-archives-xlii-1-101-2018","title":"MODELLING ERRORS IN X-RAY FLUOROSCOPIC IMAGING SYSTEMS USING PHOTOGRAMMETRIC BUNDLE ADJUSTMENT WITH A DATA-DRIVEN SELF-CALIBRATION APPROACH","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Bundle adjustment; Photogrammetry; Calibration; Frame (networking); Bundle; Distortion (music); Parametric statistics; Parametric model","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":[0.001444314,0.0008452676,0.0005544754,0.0008935759,0.0003514166,0.001129328,0.0008815692,0.001068242,0.0007299059],"category_scores_gemma":[0.004462097,0.0007034862,0.001002119,0.0008716023,0.0007007896,0.0008434959,0.0008459202,0.0008041732,0.0002910921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008702037,"about_ca_system_score_gemma":0.001167193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006902243,"about_ca_topic_score_gemma":0.004581115,"domain_scores_codex":[0.9987797,0.0003269698,0.0000978537,0.0003069398,0.0004297176,0.0000588382],"domain_scores_gemma":[0.9983724,0.0005676217,0.0004094446,0.0002247844,0.0003971191,0.00002873623],"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":[0.00003279635,0.00001944736,0.0009145238,0.00004672454,0.00002420835,0.00003077337,0.0001073537,0.9701895,0.004329522,0.001087946,0.00008342891,0.02313379],"study_design_scores_gemma":[0.000004012042,0.00003064075,0.0005644161,0.000006983879,0.000007694393,0.00002720231,0.00001066033,0.9953667,0.003170508,0.0003943905,0.0004043756,0.00001249121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02759909,0.00006771655,0.9714448,0.00003064445,0.00001307543,0.00003948252,0.00002657791,0.0003760731,0.0004024054],"genre_scores_gemma":[0.7159618,0.0001241505,0.2826038,0.00002424571,0.000008743125,0.0001236785,0.0001160975,0.0001110813,0.0009264308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006902243,"threshold_uncertainty_score":0.01372415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03683235820971155,"score_gpt":0.2908678986075182,"score_spread":0.2540355403978066,"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."}}