{"id":"W4413318882","doi":"10.1109/tcds.2025.3600102","title":"Efficient 2-D/3-D Gaze Estimation Using TGGNet: A Transformer Graph Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Gaze; Transformer; Artificial intelligence; Computer vision; Theoretical computer science; Voltage","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":[0.0001799008,0.0002032978,0.000217069,0.0004316865,0.0004207159,0.0001195072,0.000156784,0.0001119545,0.000003270327],"category_scores_gemma":[0.000004161233,0.0001880555,0.00005869629,0.0006965554,0.0001127604,0.0001101718,0.000002488745,0.0001864611,0.00001608563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009806764,"about_ca_system_score_gemma":0.00009927904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005865663,"about_ca_topic_score_gemma":0.000004632884,"domain_scores_codex":[0.9987655,0.00006372371,0.0002773372,0.0004491884,0.000187253,0.0002570366],"domain_scores_gemma":[0.9995977,0.0001054314,0.00005123133,0.0001013692,0.00008510186,0.00005912076],"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":[0.000251765,0.00235093,0.0005671864,0.0008717505,0.0009941072,0.00004903048,0.006201015,0.08505695,0.01512735,0.01396119,0.0001287867,0.87444],"study_design_scores_gemma":[0.002641164,0.0001849278,0.001870516,0.001202405,0.0001209098,0.0002803937,0.003712087,0.951098,0.03756401,0.0004209063,0.0001246858,0.0007799718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.118698,0.0001207968,0.8775311,0.00003588354,0.0003971249,0.0003957657,0.00001361352,0.0001976824,0.002610066],"genre_scores_gemma":[0.9809583,0.00001296174,0.01867846,0.00007952836,0.000005106732,0.0001010034,0.00000249518,0.000007989616,0.0001541976],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.87366,"threshold_uncertainty_score":0.766868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02164452625989606,"score_gpt":0.2545988458130528,"score_spread":0.2329543195531568,"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."}}