{"id":"W2141462346","doi":"10.1109/tbme.2008.919722","title":"Investigation of the Cross-Ratios Method for Point-of-Gaze Estimation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Cross-ratio; Gaze; Artificial intelligence; Computer science; Computer vision; Point (geometry); Plane (geometry); Function (biology); Image plane; Mathematics; Image (mathematics); Geometry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002042378,0.00008338285,0.0001293453,0.00015773,0.00008552179,0.000006899568,0.0003102262,0.00009672742,0.000002373062],"category_scores_gemma":[0.00005224222,0.00006550098,0.00008826318,0.0004850662,0.0001439656,0.0001214998,0.000002419971,0.0001386902,0.000001171023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000256451,"about_ca_system_score_gemma":0.00005193623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008281535,"about_ca_topic_score_gemma":6.414729e-7,"domain_scores_codex":[0.9992571,0.00001856326,0.0002590792,0.0001542015,0.0001912196,0.000119796],"domain_scores_gemma":[0.999338,0.0002243807,0.00007754708,0.000249875,0.00006788615,0.00004230099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002017405,0.0001866546,0.0001319381,0.0002486852,0.0001065738,0.000002507699,0.00137809,0.4445036,0.4222289,0.01128025,0.0001460074,0.1197666],"study_design_scores_gemma":[0.0002127287,0.00007281856,0.001138864,0.0000403493,0.000006257219,0.00001315723,0.00000214866,0.6579014,0.3401475,0.0003574882,0.00005332961,0.00005392515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04121933,0.000006834256,0.9574577,0.0005941434,0.0004489617,0.0001324919,0.000008795347,0.0001272286,0.000004471634],"genre_scores_gemma":[0.7152365,0.000002203863,0.2846887,0.00001933819,0.000008903254,0.00002344669,6.786418e-7,0.000005026248,0.00001519431],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6740173,"threshold_uncertainty_score":0.2671052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0220845246737225,"score_gpt":0.2678226525835098,"score_spread":0.2457381279097873,"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."}}