{"id":"W2069528928","doi":"10.1145/2168556.2168564","title":"A probabilistic approach for the estimation of angle kappa in infants","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Kappa; Calibration; Probabilistic logic; Visual angle; Range (aeronautics); Viewing angle; Estimation; Cohen's kappa; Statistics; Target range; Mathematics; Computer science; Artificial intelligence; Geometry; Engineering","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":[0.0003265881,0.00004163197,0.00006872314,0.00004884505,0.00002550191,0.000009433493,0.0002979323,0.00003069616,0.000001336642],"category_scores_gemma":[0.0001367006,0.00002546334,0.00001809239,0.0002088336,0.00004322575,0.0001229624,0.00005460601,0.0000417958,0.000001769342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001112765,"about_ca_system_score_gemma":0.0000122885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002045754,"about_ca_topic_score_gemma":0.000003552809,"domain_scores_codex":[0.9995943,0.00001378107,0.000105453,0.00008948386,0.00005754543,0.0001394676],"domain_scores_gemma":[0.9995471,0.0001533428,0.00004004,0.0002242256,0.00002305002,0.00001219423],"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":[0.000006631766,0.0003682194,0.01355901,0.00005701545,0.00000988175,1.026747e-7,0.0007715593,0.004694635,0.0002158345,0.7727914,0.000346343,0.2071793],"study_design_scores_gemma":[0.0002121068,0.00004025651,0.09071115,0.000006490064,0.000003797674,0.000003348481,0.00003850961,0.9003208,0.001304774,0.007177499,0.0001214809,0.00005980092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03723266,0.00005307064,0.9608052,0.0002317408,0.00004831,0.0002317265,5.629556e-7,0.00005944995,0.001337294],"genre_scores_gemma":[0.818424,3.653956e-7,0.1814558,0.00001927534,0.000006921191,0.00005588052,6.165824e-7,0.000001594965,0.00003550982],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8956261,"threshold_uncertainty_score":0.1038365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02987759176069757,"score_gpt":0.2707939106265528,"score_spread":0.2409163188658553,"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."}}