{"id":"W4295067019","doi":"10.3758/s13428-022-01958-6","title":"Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture","year":2022,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer vision; Computer science; Artificial intelligence; Gaze; Eye tracking; Monocular; Motion capture; Motion (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":["sts"],"consensus_categories":[],"category_scores_codex":[0.007013314,0.0001784494,0.0002587706,0.000515182,0.001498418,0.0003241528,0.000856672,0.0001043652,0.00005486666],"category_scores_gemma":[0.0005311446,0.0001795262,0.00006164364,0.001239726,0.0001948249,0.0003293748,0.001233185,0.001338178,0.000002051483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000328347,"about_ca_system_score_gemma":0.0001511175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002500656,"about_ca_topic_score_gemma":0.000007237343,"domain_scores_codex":[0.9942128,0.003408828,0.0002885588,0.0007183012,0.0006744713,0.0006970377],"domain_scores_gemma":[0.9985466,0.0004232506,0.0001093003,0.0005827561,0.0002138976,0.0001242036],"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":[0.00000719726,0.0001193754,0.006694335,0.00001836441,0.00001166305,0.0001559692,0.001040684,0.001026167,0.3853573,0.001222327,0.00002884122,0.6043177],"study_design_scores_gemma":[0.001488562,0.0004494178,0.06649519,0.00007402037,0.00006114614,0.0004729153,0.002110429,0.8752515,0.0489376,0.001172934,0.00251086,0.0009754818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5312739,0.0004014135,0.4674763,0.0001777637,0.0002140644,0.0002400341,0.000003419138,0.000183754,0.00002931459],"genre_scores_gemma":[0.5723819,0.000006239333,0.4273566,0.00001653951,0.00003674997,0.0001173096,0.00000252548,0.0000172045,0.00006494323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8742253,"threshold_uncertainty_score":0.9998015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2522700265680609,"score_gpt":0.5296659863595778,"score_spread":0.277395959791517,"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."}}