{"id":"W1982852503","doi":"10.1109/tro.2014.2343073","title":"Determining the Time Delay Between Inertial and Visual Sensor Measurements","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada; University of Southern California; National Science Foundation","keywords":"Inertial measurement unit; Calibration; Computer science; Computer vision; Inertial frame of reference; Orientation (vector space); Artificial intelligence; Real-time computing; Mathematics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001909699,0.0001218507,0.000121025,0.00006843294,0.0003250857,0.0001086497,0.0002306774,0.00003583846,0.000004866077],"category_scores_gemma":[0.00001159612,0.00009112824,0.00004115084,0.0001508239,0.00005104641,0.0002485216,0.00000484578,0.0001787686,0.00005914621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002087246,"about_ca_system_score_gemma":0.00001382112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001695823,"about_ca_topic_score_gemma":0.000001140036,"domain_scores_codex":[0.9990773,0.00008235307,0.0001673251,0.000228004,0.0002471554,0.0001979261],"domain_scores_gemma":[0.9994162,0.0001747175,0.00004611988,0.0002203409,0.00005936105,0.00008330501],"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":[0.000006556221,0.00008438367,0.0006700185,0.000006222938,0.00003357465,0.000001399292,0.0002552955,0.1559663,0.002478533,0.00004192493,0.00005548915,0.8404003],"study_design_scores_gemma":[0.0005210157,0.000144133,0.001634718,0.0000235832,0.0000294203,0.000007303168,0.000006253646,0.9902992,0.006801785,0.00008754836,0.000257578,0.0001874973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005987327,0.000003329241,0.9928199,0.0006206425,0.0002561251,0.00008302375,0.000001006124,0.00009619564,0.0001324534],"genre_scores_gemma":[0.9072076,0.000002886871,0.09201497,0.0005666043,0.00006121909,0.00000284024,3.663223e-7,0.00001243323,0.0001310447],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9012203,"threshold_uncertainty_score":0.3716102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03126140432456483,"score_gpt":0.2848655630361163,"score_spread":0.2536041587115515,"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."}}