{"id":"W3156288946","doi":"10.24908/iqurcp.9984","title":"A System for Visualizing and Assessing Electromagnetic Tracking Error during Computer-‐assisted Surgery","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stylus; Workspace; Computer vision; Computer science; Tracking (education); Visualization; Tracking error; Distortion (music); Observational error; Artificial intelligence; Software; Noise (video); Simulation; Image (mathematics); Mathematics; Robot","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002956805,0.0003435854,0.000498988,0.0008267058,0.001680641,0.002927328,0.001090439,0.0001841752,0.000001827424],"category_scores_gemma":[0.0003367954,0.000351443,0.0001068207,0.001651717,0.0008737653,0.001872941,0.0007264817,0.0005235781,0.00001798547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000454307,"about_ca_system_score_gemma":0.0005572651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000115279,"about_ca_topic_score_gemma":0.00001360684,"domain_scores_codex":[0.9954535,0.0001801119,0.0006918527,0.001271512,0.001025453,0.001377534],"domain_scores_gemma":[0.9953571,0.0008843525,0.0003154454,0.0004353526,0.002623736,0.0003840228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001311406,0.0003839573,0.002493517,0.003801554,0.0002665636,0.00003567293,0.01050108,0.000003812875,0.2650263,0.6237072,0.002497348,0.0911518],"study_design_scores_gemma":[0.003129254,0.001885445,0.03203629,0.00580423,0.0001190005,0.001133072,0.01075965,0.7372871,0.1360846,0.06714195,0.001800075,0.002819329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3620445,0.00005080654,0.6278135,0.007798373,0.0001965945,0.001050052,0.000002995901,0.0006309272,0.0004122817],"genre_scores_gemma":[0.9768175,0.00004847128,0.02205384,0.00004227342,0.0005037903,0.0004098244,0.000006770012,0.0000470159,0.00007052739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7372833,"threshold_uncertainty_score":0.9998938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1630602468551506,"score_gpt":0.4048155231993927,"score_spread":0.2417552763442421,"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."}}