{"id":"W1984681595","doi":"10.3758/s13428-014-0544-1","title":"A simple algorithm for the offline recalibration of eye-tracking data through best-fitting linear transformation","year":2014,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Economic and Social Research Council","keywords":"Computer science; Eye tracking; MATLAB; Artificial intelligence; Transformation (genetics); Computer vision; Algorithm; Eye movement; Tracking (education)","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":[0.01321101,0.0001259388,0.0002325297,0.0001633346,0.0004557476,0.0001241795,0.002204257,0.0001320051,0.000005751126],"category_scores_gemma":[0.001713748,0.00009312831,0.00007097358,0.0007792538,0.0002224928,0.0008974133,0.0004635464,0.0004721422,0.000003608798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002992201,"about_ca_system_score_gemma":0.00008554702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001528819,"about_ca_topic_score_gemma":0.00001271852,"domain_scores_codex":[0.996904,0.001204172,0.0004664541,0.0004690996,0.0005075291,0.0004487017],"domain_scores_gemma":[0.9951755,0.002673267,0.0001492252,0.001452668,0.0005026356,0.00004674154],"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":[0.00000446297,0.00009420087,0.0001831215,0.00002269903,0.000008401713,5.022836e-7,0.0004460555,0.00002347551,0.01160359,0.006788867,0.00007195725,0.9807526],"study_design_scores_gemma":[0.0003944776,0.0003002211,0.002038889,0.00003758724,0.00002772214,0.000004751999,0.0002934015,0.8938765,0.08092231,0.005091466,0.01688416,0.0001285275],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005067919,0.000105031,0.9923224,0.001424802,0.0001334842,0.00070269,0.00003041432,0.0001274653,0.00008583406],"genre_scores_gemma":[0.08858836,0.00003206225,0.9109723,0.00002610202,0.000115082,0.0001564164,0.00005276216,0.00001668684,0.00004026085],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9806241,"threshold_uncertainty_score":0.4578698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4242559375345631,"score_gpt":0.5898099072666028,"score_spread":0.1655539697320397,"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."}}