{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005160047,0.001053998,0.0008960232,0.002909007,0.0006623709,0.001673291,0.00178645,0.001017597,0.001202055],"category_scores_gemma":[0.03519582,0.001241965,0.001104475,0.001511787,0.0009709543,0.00193301,0.002142579,0.001714637,0.0004693752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008993911,"about_ca_system_score_gemma":0.0009457596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005871859,"about_ca_topic_score_gemma":0.004089802,"domain_scores_codex":[0.9948534,0.002016948,0.0003455714,0.001148783,0.001425002,0.0002102672],"domain_scores_gemma":[0.9831915,0.01101959,0.002191876,0.001363079,0.001960429,0.0002735871],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008323384,0.0001362457,0.06025571,0.0004551627,0.0005955669,0.000541204,0.001161176,0.3354117,0.04644733,0.04111331,0.00154174,0.5115085],"study_design_scores_gemma":[0.00002624888,0.0002529252,0.02769673,0.0001063403,0.00009060672,0.00131766,0.0001566386,0.9247702,0.01074751,0.03117687,0.003407864,0.0002503741],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008745878,0.0001988023,0.9904054,0.00003017841,0.00001055582,0.00002004714,0.00004332499,0.0001551078,0.0003906962],"genre_scores_gemma":[0.3387719,0.0007228964,0.6584318,0.00007478646,0.00008385149,0.0003009425,0.0003064192,0.0001851906,0.001122168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005871859,"threshold_uncertainty_score":0.02728927,"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."}}