{"id":"W3019459555","doi":"10.1007/s11548-020-02143-w","title":"Hand-eye coordination-based implicit re-calibration method for gaze tracking on ultrasound machines: a statistical approach","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Gaze; Computer science; Eye tracking; Computer vision; Calibration; Artificial intelligence; Benchmark (surveying); Tracking (education); Tracking system; Human–computer interaction; Kalman filter; Mathematics; Statistics; Psychology","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":[0.0008529046,0.0001888676,0.0004813773,0.0003501328,0.0001293351,0.0002140272,0.000556689,0.0001593511,0.000006885252],"category_scores_gemma":[0.0005469444,0.0001588074,0.0001849949,0.0001762404,0.0001248469,0.0002479194,0.0000426197,0.0003376295,8.271489e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004744546,"about_ca_system_score_gemma":0.0001250617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003131247,"about_ca_topic_score_gemma":5.864755e-7,"domain_scores_codex":[0.9981723,0.0003364644,0.0006554446,0.0003548682,0.0002735732,0.0002073737],"domain_scores_gemma":[0.9943076,0.004594773,0.000477114,0.0001245519,0.0003705767,0.0001253458],"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":[0.001719248,0.001010205,0.05430705,0.0001459918,0.001676783,0.0004666087,0.001077052,0.01728369,0.009426664,0.1208249,0.03615488,0.7559069],"study_design_scores_gemma":[0.001304927,0.0004789926,0.06539427,0.0000646581,0.00003773562,0.0007159961,0.0000158845,0.926618,0.0007356961,0.002783258,0.001597773,0.0002528038],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01170092,0.00008034983,0.9747995,0.01239598,0.0008100743,0.00009674773,0.0000220935,0.00006175048,0.00003257467],"genre_scores_gemma":[0.7204155,0.000005433582,0.276446,0.002672577,0.0004068442,0.000007053701,0.00003423933,0.000009503668,0.00000287471],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9093343,"threshold_uncertainty_score":0.647598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03678703279457799,"score_gpt":0.3078458196009692,"score_spread":0.2710587868063912,"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."}}