{"id":"W4409884738","doi":"10.1145/3706598.3713910","title":"SocialEyes: Scaling Mobile Eye-tracking to Multi-person Social Settings","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Eye tracking; Scaling; Computer vision; Artificial intelligence; Human–computer interaction; Mathematics","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":[0.0007026179,0.001061313,0.0008015216,0.001874426,0.0003063532,0.0007943643,0.0006525674,0.0006070868,0.003501217],"category_scores_gemma":[0.003635484,0.0003017866,0.0005686377,0.001009473,0.0001525984,0.001053713,0.001637323,0.0003299922,0.001698839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003122351,"about_ca_system_score_gemma":0.0002780172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007496438,"about_ca_topic_score_gemma":0.01159074,"domain_scores_codex":[0.9991758,0.0001775044,0.00005153317,0.000260431,0.0002484301,0.00008617984],"domain_scores_gemma":[0.9989172,0.0003291286,0.00009432506,0.0001862068,0.0003654617,0.0001076819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003188934,0.001297093,0.07519159,0.001112046,0.001409674,0.0005571248,0.001003017,0.02068393,0.1262386,0.001060409,0.02200565,0.7462521],"study_design_scores_gemma":[0.0005341787,0.002759106,0.3240215,0.0002505427,0.0007064211,0.002632842,0.00151081,0.574039,0.06330321,0.004173113,0.02570516,0.000364031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8240122,0.001505509,0.1404157,0.0002020404,0.0006016588,0.000826311,0.008294039,0.01709769,0.007044909],"genre_scores_gemma":[0.9073217,0.0004225927,0.08199184,0.0001755684,0.0001261312,0.0004970088,0.005311483,0.0005322305,0.003621591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007496438,"threshold_uncertainty_score":0.01490563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02179899332018128,"score_gpt":0.333864495318591,"score_spread":0.3120655019984098,"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."}}