{"id":"W2087193821","doi":"10.1007/s11548-010-0415-x","title":"C-arm rotation encoding with accelerometers","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Accelerometer; Robotic arm; Computer vision; Computer science; Artificial intelligence; Rotation (mathematics); Tracking (education); Offset (computer science); Euler angles; Physics","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":[0.0003108709,0.001386303,0.0004336741,0.0008279399,0.0002325529,0.0009335637,0.0004935279,0.0008157487,0.008786507],"category_scores_gemma":[0.002511228,0.0004693296,0.0002521336,0.001602894,0.0002189991,0.0009301929,0.0009088174,0.0006820916,0.003825298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001372489,"about_ca_system_score_gemma":0.000448315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818891,"about_ca_topic_score_gemma":0.002491787,"domain_scores_codex":[0.9995202,0.00008586378,0.00003382649,0.00008780497,0.0002197314,0.00005260745],"domain_scores_gemma":[0.9992879,0.0001330069,0.00009042218,0.000140183,0.0003097526,0.00003879756],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001316355,0.0001782811,0.004994194,0.0004494179,0.00007278754,0.0002979898,0.000245729,0.01286509,0.1664526,0.004291344,0.0116991,0.7971371],"study_design_scores_gemma":[0.0002773428,0.001029654,0.04115516,0.0005593926,0.0002240119,0.003082022,0.0004468531,0.3734296,0.4952305,0.005902284,0.07832357,0.0003395918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05509468,0.0009274125,0.9212866,0.0004397304,0.0008291309,0.0001994993,0.001863502,0.004371422,0.01498809],"genre_scores_gemma":[0.6131697,0.001237327,0.3626366,0.0003526472,0.0002701716,0.0002579067,0.00185732,0.0004195552,0.01979883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008786507,"threshold_uncertainty_score":0.02939379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02047631676670192,"score_gpt":0.263207552416415,"score_spread":0.2427312356497131,"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."}}