{"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","author_layer_release":"2026-06-26"},"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","authors":[{"name":"Aunnoy K Mutasim","is_ca":true},{"name":"Anil Ufuk Batmaz","is_ca":true},{"name":"Wolfgang Stuerzlinger","is_ca":true}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001752435,0.001076347,0.0008572747,0.0008944516,0.0001996659,0.0007409927,0.0006592159,0.0008336913,0.002444017],"category_scores_gemma":[0.01148502,0.0003240764,0.000461433,0.0003953401,0.0003570767,0.001221077,0.0008917408,0.0003931271,0.0004036539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902937,"about_ca_system_score_gemma":0.0002703758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001841216,"about_ca_topic_score_gemma":0.001890807,"domain_scores_codex":[0.9983316,0.0006550618,0.0002372187,0.0002383607,0.0003837415,0.0001541346],"domain_scores_gemma":[0.9911188,0.007119988,0.0006050084,0.0004365058,0.0005015137,0.0002180021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.03378265,0.002326669,0.01735383,0.005385404,0.0007260152,0.0002375809,0.002318511,0.01363932,0.5234539,0.0005549642,0.001031453,0.3991898],"study_design_scores_gemma":[0.002641589,0.09507374,0.5459841,0.0007164772,0.00225826,0.001668287,0.002647791,0.1294618,0.2107445,0.001574451,0.006478702,0.0007502597],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9734002,0.001011518,0.02327876,0.00003473185,0.00005650491,0.0002424761,0.0003004938,0.0006915215,0.0009838898],"genre_scores_gemma":[0.9712252,0.0005956125,0.02620077,0.00004945124,0.00003258947,0.0002796841,0.0003335286,0.000157089,0.001126165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002444017,"threshold_uncertainty_score":0.009267867,"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","authors":[{"name":"Martin Kocur","is_ca":false},{"name":"Martin Dechant","is_ca":true},{"name":"Michael Lankes","is_ca":false},{"name":"Christian Wolff","is_ca":false},{"name":"Regan L. Mandryk","is_ca":true}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005261425,0.0009443023,0.0005369464,0.0004686642,0.000382818,0.0005874648,0.001019326,0.0008175545,0.005889399],"category_scores_gemma":[0.002287894,0.0002843316,0.0003846916,0.0001557324,0.0002524348,0.0006725832,0.001579668,0.0006542242,0.0009434885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002431582,"about_ca_system_score_gemma":0.0004638382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002872412,"about_ca_topic_score_gemma":0.004077449,"domain_scores_codex":[0.9995828,0.0001236465,0.000022154,0.0000980507,0.0001084543,0.00006483191],"domain_scores_gemma":[0.9992363,0.0003796605,0.00006017049,0.00007732316,0.0001452771,0.0001011951],"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":[0.00349089,0.001759098,0.008092557,0.00142712,0.0002005547,0.0006731024,0.003677533,0.004267519,0.535322,0.001640187,0.007506075,0.4319433],"study_design_scores_gemma":[0.002449393,0.01542528,0.2236411,0.0009680077,0.001300543,0.005707989,0.003046651,0.2905474,0.3614515,0.003994818,0.09072854,0.0007387323],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.660467,0.001356372,0.3074183,0.0004228445,0.000231086,0.001580889,0.0007642797,0.01304133,0.01471797],"genre_scores_gemma":[0.8583593,0.0004145565,0.1313308,0.0002087051,0.00005168594,0.0008363689,0.0003952366,0.0003187563,0.008084548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005889399,"threshold_uncertainty_score":0.01970196,"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","authors":[{"name":"Christina Katsini","is_ca":false},{"name":"George E. Raptis","is_ca":false},{"name":"Andrew Jian-lan Cen","is_ca":true},{"name":"Nalin Asanka Gamagedara Arachchilage","is_ca":false},{"name":"Lennart E. Nacke","is_ca":false}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000618773,0.0003467847,0.000241433,0.0005575122,0.0003109346,0.0007150106,0.0002014142,0.0004514901,0.004775204],"category_scores_gemma":[0.005722043,0.0002002959,0.0002182949,0.000261903,0.0004030704,0.0006827419,0.001049788,0.0004976798,0.0003410176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003139449,"about_ca_system_score_gemma":0.0001990244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002636191,"about_ca_topic_score_gemma":0.00260235,"domain_scores_codex":[0.9995074,0.0001398273,0.00002628151,0.0001233197,0.0001310922,0.00007197505],"domain_scores_gemma":[0.997592,0.0009774872,0.0007636436,0.0002283804,0.0002644945,0.0001741119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003765733,0.001497034,0.4414306,0.0007760102,0.0003007358,0.0006931513,0.01224803,0.00330526,0.3832214,0.00357142,0.002601761,0.1465889],"study_design_scores_gemma":[0.00003453155,0.0006383177,0.9758041,0.00005411922,0.00006270084,0.0005861679,0.001136475,0.005073709,0.01423208,0.001230819,0.001108197,0.00003868047],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928761,0.0001084158,0.003613093,0.00007191124,0.000008142198,0.00003190132,0.00009634201,0.00006297563,0.003131241],"genre_scores_gemma":[0.9979821,0.0000309649,0.00102731,0.00002765931,0.000002306185,0.00002139103,0.00004859783,0.000008811658,0.0008507799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004775204,"threshold_uncertainty_score":0.01597464,"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","authors":[{"name":"Markus D. Solbach","is_ca":true},{"name":"John K. Tsotsos","is_ca":true}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00158154,0.0004761528,0.0004251784,0.0003233613,0.0003013311,0.001044061,0.0005947525,0.0007982388,0.001431095],"category_scores_gemma":[0.008182711,0.0005145393,0.0002219556,0.0001960746,0.0005699147,0.001459358,0.001504274,0.0006535471,0.0003000729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003167119,"about_ca_system_score_gemma":0.0003762672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001761659,"about_ca_topic_score_gemma":0.002171587,"domain_scores_codex":[0.9990498,0.0002847879,0.00005862858,0.0003625176,0.0001526049,0.00009164122],"domain_scores_gemma":[0.9964653,0.002277717,0.0003774767,0.0003314826,0.0002760701,0.0002718554],"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":[0.005367834,0.002175425,0.02303889,0.0003737357,0.0001181957,0.0002215253,0.006332872,0.009611788,0.8425949,0.003268935,0.0009989628,0.1058969],"study_design_scores_gemma":[0.002031142,0.01159095,0.3218963,0.0001589039,0.0003602465,0.0007482531,0.003203636,0.3401298,0.2916731,0.01831721,0.009541862,0.0003486105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9361368,0.00008498638,0.06127261,0.00005856752,0.00002330788,0.0002649986,0.0001208803,0.0001711724,0.001866541],"genre_scores_gemma":[0.9606729,0.0000766713,0.037459,0.00008899864,0.00001525718,0.0002302554,0.0001888077,0.00004818341,0.001220022],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001761659,"threshold_uncertainty_score":0.008364081,"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)","authors":[{"name":"Maryam Keyvanara","is_ca":true},{"name":"Robert S. Allison","is_ca":false}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004716644,0.0006177912,0.0003251338,0.0002884587,0.000156254,0.0005069857,0.000281102,0.0004544532,0.001697424],"category_scores_gemma":[0.01030077,0.0002899396,0.0002608897,0.0001272212,0.0004273535,0.000374137,0.000557462,0.0004326115,0.0001574345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002455183,"about_ca_system_score_gemma":0.0002411811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384639,"about_ca_topic_score_gemma":0.001018544,"domain_scores_codex":[0.9993729,0.0002330933,0.00005205823,0.000141748,0.00009959741,0.0001005785],"domain_scores_gemma":[0.9927208,0.00559778,0.0006444696,0.0003976328,0.000319481,0.0003197617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.009328717,0.0003178969,0.01080689,0.0003144246,0.00008613993,0.0002204318,0.000509778,0.002879353,0.9435286,0.0001820736,0.0001088508,0.03171678],"study_design_scores_gemma":[0.0004946549,0.01604777,0.5230035,0.0001394055,0.0006593811,0.001326276,0.0005667227,0.03179331,0.424085,0.000507513,0.001220379,0.0001560932],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962585,0.0001707175,0.00293795,0.00001729508,0.00001283317,0.00002031839,0.00004013426,0.00005877526,0.0004835092],"genre_scores_gemma":[0.9979255,0.00007607687,0.001772046,0.00001804157,0.000006183217,0.00001324739,0.00003704672,0.00002410403,0.0001277304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001697424,"threshold_uncertainty_score":0.005678415,"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","authors":[{"name":"Scott A. Stone","is_ca":true},{"name":"Quinn A. Boser","is_ca":true},{"name":"Todd Dawson","is_ca":true},{"name":"Albert H. Vette","is_ca":true},{"name":"Jacqueline S. Hebert","is_ca":true},{"name":"Patrick M. Pilarski","is_ca":true},{"name":"Craig S. Chapman","is_ca":true}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001767236,0.0004771752,0.0003425474,0.0006264484,0.0002141224,0.0006302453,0.0005525431,0.0005258539,0.0007012779],"category_scores_gemma":[0.02742111,0.000321611,0.0002952838,0.0004241534,0.000283261,0.0007822851,0.0007279599,0.0004480603,0.0003429475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004380788,"about_ca_system_score_gemma":0.0003723174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005837951,"about_ca_topic_score_gemma":0.005776162,"domain_scores_codex":[0.998713,0.0003754945,0.0001140631,0.0003352261,0.0003477188,0.0001144563],"domain_scores_gemma":[0.9883723,0.007424999,0.001287541,0.001189048,0.001540042,0.0001860515],"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":[0.004758683,0.0005940669,0.199741,0.0008894273,0.0006071288,0.0002825718,0.002925238,0.07522118,0.3681036,0.0006357739,0.002172035,0.3440693],"study_design_scores_gemma":[0.00008390255,0.00298026,0.6708475,0.0001223383,0.000184885,0.0006163493,0.0005819783,0.1898803,0.1323181,0.0004042229,0.001827113,0.000153083],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9748257,0.0003858316,0.0229355,0.0000722756,0.00002164122,0.00004710811,0.0002795761,0.000600613,0.0008318716],"genre_scores_gemma":[0.9918516,0.0001096805,0.007079636,0.00003684133,0.000004380266,0.00002850632,0.0003350322,0.0001490791,0.0004051973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005837951,"threshold_uncertainty_score":0.01160794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}