{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":6,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":6,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"4eda3b2a715c","filters":{"venue":"ACM Symposium on Eye Tracking Research and Applications"}},"results":[{"id":"W3164626843","doi":"10.1145/3448018.3457998","title":"Pinch, Click, or Dwell: Comparing Different Selection Techniques for Eye-Gaze-Based Pointing in Virtual Reality","year":2021,"lang":"en","type":"article","venue":"ACM Symposium on Eye Tracking Research and Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Gaze; Dwell time; Selection (genetic algorithm); Virtual reality; Computer vision; Artificial intelligence; Pinch; Computer graphics (images); Human–computer interaction; Eye tracking; Psychology; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.09156642605013683,"gpt":0.4008738250494436,"spread":0.3093073989993068,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115055,0.0001959198,0.0002920234,0.0004182574,0.0006602498,0.0003268706,0.0008643985,0.0001565005,0.000003816586],"category_scores_gemma":[0.0004066807,0.0001774172,0.00006181672,0.001139306,0.0001879708,0.0001745953,0.000345591,0.000671814,0.000006602262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001757057,"about_ca_system_score_gemma":0.0001576197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000691545,"about_ca_topic_score_gemma":0.0002959531,"domain_scores_codex":[0.997543,0.0002018107,0.0003882518,0.0008736128,0.0003633197,0.0006299685],"domain_scores_gemma":[0.9973444,0.001157506,0.000101471,0.0008780464,0.0003972841,0.0001213069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002668508,0.003205181,0.1125397,0.0003884334,0.00006558486,0.00002983136,0.0005026778,0.0005397304,0.2928594,0.3170638,0.0007417534,0.2717971],"study_design_scores_gemma":[0.002187948,0.001752339,0.168328,0.0006726924,0.00002174663,0.00002087726,0.0003275681,0.07283702,0.706025,0.03412043,0.01280566,0.000900735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2871607,0.00004789286,0.6954554,0.0146113,0.0000434237,0.001241825,0.00001217884,0.0007277137,0.0006996165],"genre_scores_gemma":[0.9885938,0.00005934311,0.009752469,0.0001099027,0.00009930979,0.001151201,0.00002832233,0.00002123094,0.0001844274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7014331,"threshold_uncertainty_score":0.7234865,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3033938431","doi":"10.1145/3379156.3391841","title":"Eye Caramba: Gaze-based Assistance for Virtual Reality Aiming and Throwing Tasks in Games","year":2020,"lang":"en","type":"article","venue":"ACM Symposium on Eye Tracking Research and Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Gaze; Human–computer interaction; Virtual reality; Computer science; Eye tracking; Throwing; Modality (human–computer interaction); Natural (archaeology); Multimedia; Artificial intelligence; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.08764614001397125,"gpt":0.3826528556737595,"spread":0.2950067156597883,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008861754,0.0001758338,0.0002473348,0.0002111107,0.0005216272,0.0002730004,0.0008914145,0.0001273489,9.675725e-7],"category_scores_gemma":[0.0004195194,0.0001594062,0.00004179779,0.0007798158,0.0003475138,0.0002202952,0.0002593001,0.0005461784,0.000004873007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006909185,"about_ca_system_score_gemma":0.00009972163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004254143,"about_ca_topic_score_gemma":0.00003404433,"domain_scores_codex":[0.9978393,0.0001091322,0.000285209,0.0008793643,0.0003315767,0.0005553636],"domain_scores_gemma":[0.9977521,0.001091032,0.00007343847,0.0007122477,0.0001798575,0.0001913178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002378218,0.0008632495,0.06287866,0.0004960063,0.00006609144,0.00003348876,0.002542069,0.001034771,0.1274358,0.4739437,0.001152804,0.3293155],"study_design_scores_gemma":[0.008142316,0.00509453,0.5162286,0.00115234,0.00006172172,0.00001252897,0.002453933,0.1821462,0.113827,0.09306955,0.07520949,0.002601796],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2743903,0.0004086476,0.6004646,0.1219323,0.00004733775,0.001589223,0.00005361828,0.0005176421,0.0005961978],"genre_scores_gemma":[0.9920557,0.00007245311,0.006729726,0.0003861097,0.00007936622,0.0006135621,0.0000111536,0.00001762973,0.00003433459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7176653,"threshold_uncertainty_score":0.6500396,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3164170294","doi":"10.1145/3448018.3458615","title":"Eye-GUAna: Higher Gaze-Based Entropy and Increased Password Space in Graphical User Authentication Through Gamification","year":2021,"lang":"en","type":"article","venue":"ACM Symposium on Eye Tracking Research and Applications","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Password; Computer science; Gaze; Human–computer interaction; Eye tracking; Cognitive password; Authentication (law); Entropy (arrow of time); Process (computing); Artificial intelligence; Computer security; Password strength; One-time password","retraction":null,"screen_n_in":null,"score":{"opus":0.04849467029769218,"gpt":0.3530283808702054,"spread":0.3045337105725132,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009012325,0.0001913935,0.0002224797,0.0003231537,0.0004250364,0.0005701228,0.000731744,0.0001502979,0.00001782301],"category_scores_gemma":[0.0001760093,0.0001918099,0.00004990391,0.001586636,0.0002422563,0.0004062961,0.000210088,0.0004513597,0.00005109841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008879167,"about_ca_system_score_gemma":0.0001512843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000121464,"about_ca_topic_score_gemma":0.00005903746,"domain_scores_codex":[0.9970988,0.0004309657,0.0004071777,0.0009102064,0.0006733355,0.0004794995],"domain_scores_gemma":[0.9970309,0.0007141161,0.0001025827,0.001498798,0.0004263143,0.0002273142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00003012664,0.00100709,0.02900423,0.0001155125,0.00002590221,0.000009430357,0.004197272,0.0000041258,0.07438152,0.8893197,0.000269314,0.00163574],"study_design_scores_gemma":[0.003557456,0.000348363,0.6877649,0.0004254716,0.00004482764,0.0000137157,0.000752877,0.02543264,0.03592068,0.1705884,0.07410472,0.001045958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8470144,0.0008143871,0.05323817,0.09562238,0.0001222209,0.001866317,0.00002258744,0.0003023593,0.0009971709],"genre_scores_gemma":[0.9951652,0.0003320619,0.003036621,0.0003063501,0.00008376026,0.0006485329,0.00006704953,0.00002025477,0.0003402215],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7187313,"threshold_uncertainty_score":0.7821781,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3165101072","doi":"10.1145/3450341.3458496","title":"Tracking Active Observers in 3D Visuo-Cognitive Tasks","year":2021,"lang":"en","type":"article","venue":"ACM Symposium on Eye Tracking Research and Applications","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Active vision; Gaze; Computer science; Active perception; Perception; Tracking (education); Computer vision; Artificial intelligence; Eye tracking; Cognition; Psychology; Robot","retraction":null,"screen_n_in":null,"score":{"opus":0.08753215093995723,"gpt":0.4064605416121349,"spread":0.3189283906721777,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006343856,0.0001422249,0.0001558693,0.0003197721,0.0005452597,0.0003950351,0.0005630619,0.00009893354,0.00001909742],"category_scores_gemma":[0.0002718261,0.0001479184,0.0000528968,0.0016233,0.0001330841,0.000579623,0.0002888607,0.0005915446,0.00008711172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001114573,"about_ca_system_score_gemma":0.0001226386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004962826,"about_ca_topic_score_gemma":0.00008820207,"domain_scores_codex":[0.9977558,0.0002298282,0.0002528053,0.0007453053,0.0005339719,0.0004822877],"domain_scores_gemma":[0.9980851,0.0006144869,0.00005751467,0.0005864052,0.0004900674,0.0001664159],"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":[0.0001076173,0.002214647,0.01290856,0.0001180294,0.00007349052,0.000115122,0.004664491,0.0001505413,0.1755601,0.1023881,0.0001351159,0.7015642],"study_design_scores_gemma":[0.003961304,0.001398298,0.5382123,0.0007615363,0.00003106081,0.00008475099,0.006844707,0.01326657,0.366351,0.05446175,0.01321441,0.001412285],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9189292,0.0001886318,0.06344732,0.008321765,0.0001400833,0.001091825,0.0000206281,0.0002541922,0.007606406],"genre_scores_gemma":[0.9979204,0.0002053262,0.0008474877,0.0001936641,0.00008036234,0.0003785957,0.00002046873,0.00001378916,0.000339904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7001519,"threshold_uncertainty_score":0.6031937,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3022119923","doi":"10.1145/3379155.3391318","title":"Effect of a Constant Camera Rotation on the Visibility of Transsaccadic Camera Shifts","year":2020,"lang":"en","type":"article","venue":"ACM Symposium on Eye Tracking Research and Applications","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Gaze; Saccade; Saccadic masking; Rotation (mathematics); Fixation (population genetics); Eye movement; Computer graphics (images)","retraction":null,"screen_n_in":null,"score":{"opus":0.1021777872390664,"gpt":0.4134227898472519,"spread":0.3112450026081856,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141739,0.0001219432,0.0001891338,0.00009304452,0.0003724384,0.00006113744,0.0004366318,0.00005974869,0.00005814455],"category_scores_gemma":[0.001297273,0.00008366806,0.000054138,0.0006901476,0.0004876651,0.00007909083,0.00004494299,0.0004015997,0.00003044447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002050563,"about_ca_system_score_gemma":0.00005901349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001721912,"about_ca_topic_score_gemma":0.000001874914,"domain_scores_codex":[0.9979374,0.0005363562,0.0002843767,0.0004347345,0.0005750599,0.000232052],"domain_scores_gemma":[0.9965848,0.002590847,0.0001013733,0.0004295735,0.0001574987,0.0001358876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003525629,0.0001577411,0.0001650447,0.0001762463,0.000003884527,5.230685e-7,0.001376475,0.00003290883,0.9661372,0.01612971,0.00004762871,0.01542007],"study_design_scores_gemma":[0.0004332355,0.002447442,0.0005496487,0.00007792481,0.000009793664,6.603138e-7,0.0002113189,0.0008993435,0.9915756,0.003407331,0.0002970025,0.00009067333],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862733,0.00002054701,0.001149069,0.009804672,0.00001557654,0.001200917,0.00004931194,0.00004296347,0.001443586],"genre_scores_gemma":[0.9989972,0.00009431064,0.00002992865,0.0005699205,0.00004322114,0.0002275824,0.000004167782,0.0000139915,0.00001973727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02543842,"threshold_uncertainty_score":0.3411884,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3164168002","doi":"10.1145/3450341.3458880","title":"Sub-centimeter 3D gaze vector accuracy on real-world tasks: an investigation of eye and motion capture calibration routines","year":2021,"lang":"en","type":"article","venue":"ACM Symposium on Eye Tracking Research and Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Glenrose Rehabilitation Hospital; Alberta Health Services; Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Computer vision; Computer science; Gaze; Artificial intelligence; Eye tracking; Fixation (population genetics); Calibration; Motion capture; Monocular; Task (project management); Reference frame; Eye movement; Frame (networking); Motion (physics); Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04852354243660974,"gpt":0.348596981867638,"spread":0.3000734394310283,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005754326,0.0001716807,0.0001977137,0.0003738605,0.0004005073,0.0002792099,0.0005106188,0.0001311855,0.00000433671],"category_scores_gemma":[0.0002242571,0.0001627942,0.00003243675,0.001063865,0.0002849169,0.000509968,0.0002048625,0.0004295355,0.000007616137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005289382,"about_ca_system_score_gemma":0.00009032091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001203183,"about_ca_topic_score_gemma":0.0001029358,"domain_scores_codex":[0.9979588,0.000248488,0.0003047719,0.0007237995,0.0004235807,0.0003405408],"domain_scores_gemma":[0.9976622,0.0005908298,0.0001385494,0.00101571,0.0004309313,0.0001618183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004125118,0.0006431991,0.06130982,0.0001530152,0.00004281862,0.0000114824,0.00122184,0.0001334503,0.6769715,0.2000616,0.0003218974,0.05908814],"study_design_scores_gemma":[0.0008510682,0.0005820088,0.4668299,0.0002723874,0.0000279389,0.00000978573,0.0002149979,0.01162096,0.486451,0.03176086,0.0009040236,0.0004750634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964519,0.000099941,0.02265188,0.0115173,0.00005121784,0.0005397403,0.00002601396,0.0002330514,0.0003618863],"genre_scores_gemma":[0.9950263,0.000206154,0.004120037,0.0001084029,0.00009113876,0.0001599985,0.00009530061,0.00001745852,0.0001751971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4055201,"threshold_uncertainty_score":0.6638556,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}