{"meta":{"query_hash":"6bd2995f78a3","filters":{"topic":"Gaze Tracking and Assistive Technology"},"cohort_total":688,"direct_labels_cover":0,"predictions_cover":688,"exported":688,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/6bd2995f78a3","api":"https://metacan.xera.ac/api/v1/cohort?topic=Gaze+Tracking+and+Assistive+Technology"},"results":[{"id":"W1142781809","doi":"","title":"The Influence of Hockey Visors on Peripheral Vision during Gaze Elevation","year":2002,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Elevation (ballistics); Gaze; Peripheral; Psychology; Medicine; Internal medicine; Mathematics; Geometry","score_opus":0.02651602640402295,"score_gpt":0.3033253426221645,"score_spread":0.27680931621814153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1142781809","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9971398,0.000049422797,0.000085667416,0.0014774228,0.00019216425,0.0001728606,0.0000011078761,0.00012897527,0.00075256213],"genre_scores_gemma":[0.997673,0.00000962346,0.0019918466,0.00010884592,0.000020649448,0.000024747405,2.5638428e-7,0.00000809683,0.00016295262],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9974315,0.00020317806,0.0003870534,0.00074748346,0.0006226302,0.0006081316],"domain_scores_gemma":[0.9982459,0.00033296086,0.00032752924,0.0006065133,0.0003377325,0.00014937967],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00079374656,0.00022570167,0.00022628928,0.00025708645,0.0010363589,0.00012108462,0.0019981165,0.00011285181,0.000014975541],"category_scores_gemma":[0.0010642406,0.00016438043,0.000056676778,0.0019195585,0.005744644,0.0007513344,0.0004355882,0.00036793074,0.000062320774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011248508,0.00015057715,0.02075612,0.000007932804,0.00000931291,0.000048591457,0.001000772,0.00088044413,0.961902,0.0131704565,0.00002453646,0.002037992],"study_design_scores_gemma":[0.00026547152,0.0010408061,0.7275743,0.00007357728,0.0000030150018,0.00012666843,0.000073907,0.013762745,0.24838345,0.008445367,0.000028318198,0.0002224129],"about_ca_topic_score_codex":0.00003166553,"about_ca_topic_score_gemma":0.0000016587653,"teacher_disagreement_score":0.71351856,"about_ca_system_score_codex":0.0001387374,"about_ca_system_score_gemma":0.00009326071,"threshold_uncertainty_score":0.9969612},"labels":[],"label_agreement":null},{"id":"W1173509751","doi":"","title":"Gaze Angle Dependency Of Ocular Dominance In A Pointing Task","year":2002,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Ocular dominance; Optometry; Dominance (genetics); Dependency (UML); Task (project management); Computer science; Psychology; Medicine; Computer vision; Artificial intelligence; Biology; Neuroscience; Engineering","score_opus":0.034990450075728645,"score_gpt":0.29936519982149007,"score_spread":0.2643747497457614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1173509751","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99473166,0.00026349354,0.0008689361,0.0006293258,0.00019617958,0.00016901309,0.0000016343732,0.00009442767,0.0030453368],"genre_scores_gemma":[0.9786628,0.0000053022313,0.021098055,0.000094662864,0.000014759282,0.000024324416,2.9428952e-7,0.00000814763,0.000091680165],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971926,0.00018919638,0.00047816115,0.00094248325,0.00046033706,0.000737193],"domain_scores_gemma":[0.9985647,0.00020058957,0.00031988797,0.0005453007,0.00021706545,0.00015242789],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0011142539,0.00023124645,0.00038874318,0.00055330567,0.0002176735,0.00005060505,0.0019909726,0.00014276785,0.000038414928],"category_scores_gemma":[0.0011322882,0.00022089484,0.000064793196,0.0031920918,0.004099174,0.00079716515,0.0006109306,0.00038593105,0.00006542862],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040616924,0.00036128762,0.13492388,0.000020362651,0.000008991097,0.0006888767,0.001935105,0.000054730866,0.83947366,0.015905844,0.000030864878,0.0065923114],"study_design_scores_gemma":[0.0007130012,0.00083781814,0.3190796,0.00020222997,0.000006597982,0.00074453326,0.00017744915,0.02340217,0.5998752,0.054396104,0.000038800277,0.0005264533],"about_ca_topic_score_codex":0.000116069474,"about_ca_topic_score_gemma":0.0000061317983,"teacher_disagreement_score":0.23959847,"about_ca_system_score_codex":0.000104992534,"about_ca_system_score_gemma":0.00014424963,"threshold_uncertainty_score":0.9986111},"labels":[],"label_agreement":null},{"id":"W1179781901","doi":"","title":"Retrospective Comparison of PRK Results Ssing the VISX Active Eye Tracking System","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Medicine; Optometry; Ophthalmology","score_opus":0.03809489799084564,"score_gpt":0.29261126818785327,"score_spread":0.2545163701970076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1179781901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4031446,0.0002815165,0.47632223,0.0067210416,0.00080652407,0.00053742935,0.000012629101,0.0018750093,0.110299006],"genre_scores_gemma":[0.99236155,0.0000017802331,0.0073092943,0.000025627776,0.0000362183,0.000006326802,4.4200965e-7,0.0000075529997,0.00025123503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853957,0.000090330584,0.0003850006,0.00041033092,0.0002940809,0.00028070516],"domain_scores_gemma":[0.99865156,0.00015343624,0.00032167675,0.0006624626,0.00017663273,0.000034231674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032170556,0.00014609058,0.00030152765,0.000107424115,0.0002593318,0.00008205512,0.000897632,0.0000954729,0.0000056368435],"category_scores_gemma":[0.000134789,0.00009707601,0.000079611294,0.000585411,0.00020840541,0.00023761891,0.00016412727,0.00031789564,0.00003337959],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055554214,0.0006709898,0.024162922,0.000086614724,0.0002147787,0.000034982168,0.028700735,0.00036230765,0.00972429,0.6742913,0.004908729,0.25678676],"study_design_scores_gemma":[0.0016161131,0.0006670091,0.38408855,0.0004477608,0.00005171817,0.000043826534,0.010157415,0.44258535,0.15634316,0.0015577747,0.00183812,0.0006031928],"about_ca_topic_score_codex":0.00004680406,"about_ca_topic_score_gemma":0.000015212611,"teacher_disagreement_score":0.67273355,"about_ca_system_score_codex":0.00013891062,"about_ca_system_score_gemma":0.000012482428,"threshold_uncertainty_score":0.3958645},"labels":[],"label_agreement":null},{"id":"W14285303","doi":"","title":"On the use of attention clues for an autonomous wearable camera 1","year":2003,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Computer science; Wearable technology; Computer vision; Human–computer interaction; Internet privacy; Artificial intelligence; Embedded system","score_opus":0.07236261869250273,"score_gpt":0.2740052136817725,"score_spread":0.20164259498926979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W14285303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.369974,0.000008069496,0.62739265,0.0008866641,0.00009272913,0.00012783085,0.0000010459794,0.00015048664,0.0013665145],"genre_scores_gemma":[0.9549212,0.0000011435726,0.043751262,0.0002365072,0.0000032719504,0.000013349604,3.2765192e-7,0.0000029944579,0.0010699644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954516,0.000042569816,0.000087661705,0.00015289623,0.00005678954,0.00011489934],"domain_scores_gemma":[0.99939424,0.00015602571,0.000043481505,0.0003421131,0.000049528564,0.000014610301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016950436,0.000050178893,0.00006608528,0.000039816896,0.00007721878,0.000038636997,0.00024080428,0.000032575943,0.000009272087],"category_scores_gemma":[0.00008715913,0.00003153799,0.000033219443,0.00009956456,0.00005239267,0.00014290083,0.000016630676,0.00004730002,0.000009707003],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016075007,0.0000673062,0.00035951287,0.0000018935066,0.0000053391723,2.0659677e-7,0.00003539528,0.0001563041,0.0012901317,0.98739487,0.0016972689,0.008990194],"study_design_scores_gemma":[0.0017241765,0.005626606,0.04429447,0.00012598292,0.000041876003,0.000047707505,0.00045187928,0.3647262,0.16110753,0.33683214,0.08408025,0.00094121794],"about_ca_topic_score_codex":0.000037433263,"about_ca_topic_score_gemma":0.00001333179,"teacher_disagreement_score":0.6505627,"about_ca_system_score_codex":0.0000098218925,"about_ca_system_score_gemma":0.000017674009,"threshold_uncertainty_score":0.1286082},"labels":[],"label_agreement":null},{"id":"W1492315901","doi":"","title":"Efficient eye pointing with a fisheye lens","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":107,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IDELIX Software (Canada)","funders":"","keywords":"Computer science; Foveal; Computer vision; Gaze; Eye tracking; Zoom lens; Eye movement; Artificial intelligence; Saccadic masking; Zoom; IRIS (biosensor); Pupil; Lens (geology); Engineering; Optics","score_opus":0.009787171037332162,"score_gpt":0.22082003401558095,"score_spread":0.2110328629782488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492315901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41497412,0.000015950802,0.53850025,0.008825354,0.000041829295,0.000049944807,1.5430871e-7,0.00082005584,0.036772337],"genre_scores_gemma":[0.8538976,3.50835e-7,0.14484134,0.00043296092,0.000025610778,0.000003343378,1.2885084e-7,0.000003997838,0.00079470704],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992563,0.000011853908,0.000095475014,0.0002652295,0.00012620546,0.00024490515],"domain_scores_gemma":[0.9995331,0.000025825995,0.000035633373,0.00033424413,0.000041907984,0.000029298128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000115552226,0.00008371568,0.00008609239,0.00007631112,0.00008472913,0.000055508284,0.00040271424,0.000034282933,0.00002797202],"category_scores_gemma":[0.0000148239,0.000059064623,0.000023616793,0.00024468606,0.000044527347,0.00006868551,0.00011267815,0.00010825514,0.00015586198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010395389,0.0005142464,0.026058413,0.000015753056,0.00004986187,0.0000752378,0.0011527755,0.010064323,0.003571675,0.52613044,0.0034054026,0.42895147],"study_design_scores_gemma":[0.001566348,0.0005032979,0.14174455,0.00008889726,0.000016126933,0.00013389606,0.00017793212,0.7697499,0.031016218,0.00060898566,0.05348638,0.0009074985],"about_ca_topic_score_codex":0.000013113474,"about_ca_topic_score_gemma":0.000020388135,"teacher_disagreement_score":0.7596856,"about_ca_system_score_codex":0.000022691955,"about_ca_system_score_gemma":0.000020032941,"threshold_uncertainty_score":0.24085854},"labels":[],"label_agreement":null},{"id":"W1498479610","doi":"10.1007/978-3-540-73107-8_80","title":"Visual and Auditory Information Specifying an Impending Collision of an Approaching Object","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Collision; Computer science; Object (grammar); Computer vision; Stimulus (psychology); Artificial intelligence; Communication; Psychology; Cognitive psychology; Computer security","score_opus":0.023170123083387267,"score_gpt":0.2812152811868147,"score_spread":0.25804515810342743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498479610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020319253,0.00007050069,0.9772218,0.00004057863,0.00095386297,0.00020699852,0.0000023967834,0.00018710349,0.0009974915],"genre_scores_gemma":[0.6721319,0.000009589758,0.32745948,0.00010442466,0.00026902588,0.0000014587845,0.000006178317,0.000011548871,0.0000063746575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975155,0.00003848073,0.0005242554,0.0007963451,0.0006874028,0.000438021],"domain_scores_gemma":[0.9983158,0.00025992957,0.00043696453,0.0006880463,0.00016953405,0.00012972306],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016243581,0.0003406637,0.00040994788,0.0014798436,0.00028905412,0.00037407735,0.0015637607,0.00035218347,0.0000020881726],"category_scores_gemma":[0.000082007704,0.000319889,0.000049932067,0.0004647787,0.00058383483,0.0022846833,0.0007766425,0.00074106356,0.00000340306],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006801625,0.000019438865,0.00009685309,0.000039453967,0.000004270287,0.000016750935,0.0010406693,0.006170043,0.00044637916,0.012184747,8.110482e-7,0.9799738],"study_design_scores_gemma":[0.00033522525,0.00069862424,0.0038746134,0.0003950747,0.000007970469,0.00013048566,0.000004179895,0.9639192,0.004601341,0.025078788,0.00032678607,0.0006276653],"about_ca_topic_score_codex":0.000027793378,"about_ca_topic_score_gemma":0.000039773462,"teacher_disagreement_score":0.9793461,"about_ca_system_score_codex":0.00018095516,"about_ca_system_score_gemma":0.0002036207,"threshold_uncertainty_score":0.9999253},"labels":[],"label_agreement":null},{"id":"W1508397741","doi":"10.1109/icra.2015.7139816","title":"VIBI: Assistive vision-based interface for robot manipulation","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Joystick; Human–computer interaction; Interface (matter); Task (project management); Computer science; Robot; Robotic arm; Object (grammar); Wheelchair; User interface; Robot control; Artificial intelligence; Computer vision; Simulation; Mobile robot; Engineering","score_opus":0.06074980242893989,"score_gpt":0.3279012676656168,"score_spread":0.2671514652366769,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1508397741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004459893,0.000019414005,0.9892241,0.003246184,0.0002883729,0.00014974167,0.0000011941707,0.00052247767,0.0020886636],"genre_scores_gemma":[0.7844144,8.104919e-8,0.21496314,0.00016607971,0.00001833518,0.000017641458,0.0000025801571,0.00000529035,0.00041245797],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992126,0.000027404503,0.00013916974,0.00030869409,0.00013223679,0.00017986492],"domain_scores_gemma":[0.99921364,0.00013800079,0.00006267265,0.00032601572,0.00019169645,0.00006799017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025207084,0.00009770442,0.000113368405,0.00009958571,0.000059860613,0.00007541595,0.00043602087,0.0000711276,0.000005743642],"category_scores_gemma":[0.0001439708,0.00008163208,0.00004354873,0.00018445273,0.000036288475,0.00017503751,0.000082939,0.00006962994,0.000067098306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014779401,0.00073041226,0.010559331,0.000046112942,0.000072065886,0.000016983407,0.00040374734,0.029886749,0.008637024,0.46965066,0.054590926,0.42525822],"study_design_scores_gemma":[0.001183069,0.0006366134,0.028416883,0.000028881288,0.000008301589,0.0000044231606,0.000056448567,0.9237684,0.02675635,0.01211506,0.006743333,0.0002822224],"about_ca_topic_score_codex":0.000019538005,"about_ca_topic_score_gemma":0.000015110552,"teacher_disagreement_score":0.8938817,"about_ca_system_score_codex":0.00006888329,"about_ca_system_score_gemma":0.00006217696,"threshold_uncertainty_score":0.33288598},"labels":[],"label_agreement":null},{"id":"W1548471706","doi":"10.1007/978-3-642-03655-2_4","title":"The Attentive Hearing Aid: Eye Selection of Auditory Sources for Hearing Impaired Users","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Hearing impaired; Hearing aid; Selection (genetic algorithm); Speech recognition; Audiology; Artificial intelligence; Medicine","score_opus":0.020191731213577466,"score_gpt":0.25473393891741186,"score_spread":0.23454220770383438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548471706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035238706,0.00024279916,0.9929481,0.0010679085,0.0013537329,0.00039069157,0.0000020402638,0.00023542135,0.0002354367],"genre_scores_gemma":[0.7984053,0.000031359832,0.20029172,0.00026920877,0.0005042011,0.00001746474,0.0000016067395,0.000030345489,0.0004487987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997293,0.000033605305,0.0004589896,0.0010349252,0.00054483535,0.0006346604],"domain_scores_gemma":[0.9978101,0.0006592719,0.0004114393,0.00073703274,0.00031025033,0.00007185689],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009861301,0.00036846704,0.00044198395,0.00055027317,0.0006454955,0.00029563898,0.002281277,0.00028719843,0.0000012050365],"category_scores_gemma":[0.00012972078,0.0002920206,0.00018046908,0.0004948715,0.00083026587,0.00029266495,0.0005434696,0.00066516316,0.0000035695243],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019699855,0.000033666198,0.0010560636,0.00004323249,0.0000352274,0.000006929104,0.0005645238,0.014787734,0.0020285656,0.009644984,0.000049173443,0.9717302],"study_design_scores_gemma":[0.0011446995,0.0017436895,0.023934688,0.001399723,0.00005152325,0.000080912665,0.0000036839117,0.7107397,0.033068065,0.21651568,0.009628476,0.0016892053],"about_ca_topic_score_codex":0.000028631277,"about_ca_topic_score_gemma":0.000086763524,"teacher_disagreement_score":0.970041,"about_ca_system_score_codex":0.00026235575,"about_ca_system_score_gemma":0.0003032941,"threshold_uncertainty_score":0.9999532},"labels":[],"label_agreement":null},{"id":"W1557240566","doi":"10.1007/3-540-37620-8_3","title":"Pointing and Visual Feedback for Spatial Interaction in Large-Screen Display Environments","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Visual feedback; Illusion; Computer vision; Artificial intelligence; Optical illusion; Frame (networking); Offset (computer science); Visual perception; Frame of reference; Reference frame; Human–computer interaction; Cognitive psychology; Perception; Psychology; Neuroscience","score_opus":0.012482764544939549,"score_gpt":0.2580336686441565,"score_spread":0.24555090409921695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1557240566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028168296,0.00009312545,0.99511784,0.0005390138,0.00076936866,0.00034973098,0.0000057397015,0.00006167135,0.00024666302],"genre_scores_gemma":[0.8264843,0.000020299876,0.17246477,0.0007034789,0.0001457353,0.000014330018,0.000007706719,0.000027358334,0.00013200281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974028,0.000029785748,0.00039863316,0.001239733,0.00035012857,0.0005789474],"domain_scores_gemma":[0.99891007,0.00032661983,0.00023835718,0.00041273152,0.000036171623,0.00007603121],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00068796834,0.00036668676,0.00039658838,0.0006577081,0.00017730103,0.00023131387,0.0008880124,0.00029704077,0.0000048567576],"category_scores_gemma":[0.00009843607,0.00035384364,0.00006735643,0.00022636795,0.00033415595,0.00038827234,0.0006666819,0.0006482997,0.000007959702],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014383126,0.00007664676,0.0022363255,0.000030601146,0.000010609439,0.000036452497,0.00028242305,0.0015589434,0.00049559807,0.0074601537,0.000009122,0.98778874],"study_design_scores_gemma":[0.0013142677,0.00052151625,0.0118174255,0.000594813,0.000012368046,0.00009657711,0.0000010623667,0.927678,0.0029617548,0.050010927,0.0040027834,0.0009885303],"about_ca_topic_score_codex":0.0000324645,"about_ca_topic_score_gemma":0.00022559798,"teacher_disagreement_score":0.9868002,"about_ca_system_score_codex":0.00021367773,"about_ca_system_score_gemma":0.000066485736,"threshold_uncertainty_score":0.99989134},"labels":[],"label_agreement":null},{"id":"W1561354838","doi":"10.1007/978-3-642-02713-0_33","title":"Impact of Gaze Analysis on the Design of a Caption Production Software","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Task (project management); Workload; Gaze; Software; Process (computing); Eye tracking; Human–computer interaction; Production (economics); Artificial intelligence; Computer vision; Programming language","score_opus":0.026873953807412136,"score_gpt":0.2654193647077874,"score_spread":0.23854541090037523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561354838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022945795,0.00009760459,0.9964752,0.0004556006,0.00021632342,0.00027729018,0.0000031142192,0.00009467296,0.00008559332],"genre_scores_gemma":[0.82330555,0.000017195463,0.17649093,0.000063258725,0.000056920555,0.0000032458443,0.0000015249421,0.0000081660655,0.000053184805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977144,0.00006861672,0.00041115523,0.00087051425,0.000633749,0.0003015446],"domain_scores_gemma":[0.9972557,0.00047952292,0.0005151247,0.0013640388,0.0003457662,0.000039836188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010062713,0.0003081865,0.0005286736,0.0013945318,0.000107556916,0.000063177155,0.0019669393,0.00021356385,0.000007649852],"category_scores_gemma":[0.00028926085,0.00020054939,0.0002698518,0.0017364507,0.00065878994,0.00015832913,0.00021967279,0.0004906673,0.0000038443945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011162737,0.000059801205,0.0004702032,0.000010850644,0.0000924865,0.0000060372627,0.00024692912,0.44042316,0.0006958305,0.004174336,0.000012226065,0.55379695],"study_design_scores_gemma":[0.00030163777,0.0029150895,0.047196742,0.00093830476,0.0002375069,0.000054574637,3.6972457e-7,0.6355877,0.017772902,0.29398525,0.00002515725,0.0009847988],"about_ca_topic_score_codex":0.000032714717,"about_ca_topic_score_gemma":0.000013322899,"teacher_disagreement_score":0.821011,"about_ca_system_score_codex":0.00019586389,"about_ca_system_score_gemma":0.00029218537,"threshold_uncertainty_score":0.8178166},"labels":[],"label_agreement":null},{"id":"W1576259802","doi":"10.1007/978-3-540-92219-3_1","title":"Using the Web and ICT to Enable Persons with Disabilities","year":2008,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Information and Communications Technology; World Wide Web; Computer science","score_opus":0.0774619202872908,"score_gpt":0.29855841477931744,"score_spread":0.22109649449202662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1576259802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006476203,0.00088186644,0.76691014,0.010377984,0.00024073807,0.000921162,0.000024633431,0.0003208258,0.21384646],"genre_scores_gemma":[0.64529014,0.0017451375,0.350057,0.0015006103,0.000027877633,0.00003410012,0.000006148838,0.00001065409,0.001328358],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907887,0.000019859566,0.00026138636,0.0002127099,0.00024212987,0.00018506164],"domain_scores_gemma":[0.9979511,0.00020960507,0.000119396806,0.0014834439,0.00017466667,0.00006177681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043010013,0.00015048611,0.00015814406,0.00046995783,0.0008276257,0.00039763906,0.0020774396,0.00006243959,0.0000012155219],"category_scores_gemma":[0.000033091117,0.00010670003,0.00001688335,0.00036749407,0.0018987725,0.00225494,0.0017000716,0.00030476393,0.0000074329855],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002500567,0.000014306157,0.00050250185,0.000020308877,0.00000814518,5.45512e-7,0.011169541,0.0004695022,0.000008259754,0.88844794,0.00053677324,0.09881968],"study_design_scores_gemma":[0.00034766085,0.00018906272,0.002557325,0.00036081395,0.000010642192,0.00034330384,0.00051597314,0.6706428,0.000024064635,0.0041531823,0.3202811,0.00057408185],"about_ca_topic_score_codex":0.000022997629,"about_ca_topic_score_gemma":0.00002127258,"teacher_disagreement_score":0.88429475,"about_ca_system_score_codex":0.0000781455,"about_ca_system_score_gemma":0.00022137904,"threshold_uncertainty_score":0.69961065},"labels":[],"label_agreement":null},{"id":"W1578489899","doi":"10.1109/cbmsys.1990.109386","title":"The eye wink control interface: using the computer to provide the severely disabled with increased flexibility and comfort","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"CMC Microsystems","keywords":"Interface (matter); Computer science; Interpreter; Flexibility (engineering); Software; User interface; SIGNAL (programming language); Control signal; Human–computer interaction; Detector; Control (management); Artificial intelligence; Operating system; Programming language; Telecommunications","score_opus":0.0211926889848893,"score_gpt":0.2533618449739315,"score_spread":0.2321691559890422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1578489899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39804566,0.000055101496,0.587959,0.01261529,0.00007424344,0.00054374366,0.0000016223013,0.00021291159,0.00049244],"genre_scores_gemma":[0.98861426,0.000002134164,0.009339162,0.0017200136,0.000033381195,0.000023625153,1.1825515e-7,0.000008039075,0.0002592947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986575,0.00019182939,0.00020301671,0.00039428542,0.0002091372,0.00034422474],"domain_scores_gemma":[0.9982749,0.0005553505,0.00007891306,0.00094608415,0.000078279656,0.000066440996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006316432,0.00017779478,0.0001769773,0.00002512119,0.0007461956,0.00038631266,0.001141246,0.00004797019,0.00000801065],"category_scores_gemma":[0.000046305893,0.00006636405,0.000036564612,0.0002927709,0.00046872455,0.00015024074,0.00035993397,0.0002650272,0.000011088058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008165543,0.0010175202,0.30590224,0.00008291908,0.0010288978,0.000060920298,0.0072404123,0.013559722,0.0025705418,0.20398813,0.018058602,0.44567353],"study_design_scores_gemma":[0.0010046638,0.00034907996,0.06301003,0.000036775302,0.000032765558,0.000065529,0.00010054575,0.92260486,0.0005463712,0.0014922735,0.010494706,0.00026241253],"about_ca_topic_score_codex":0.00019908177,"about_ca_topic_score_gemma":0.00019484905,"teacher_disagreement_score":0.9090451,"about_ca_system_score_codex":0.00004189064,"about_ca_system_score_gemma":0.00002461999,"threshold_uncertainty_score":0.5739209},"labels":[],"label_agreement":null},{"id":"W1579510884","doi":"10.1109/ictta.2006.1684488","title":"Infrared-based Human-machine Interaction","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Point (geometry); Human–computer interaction; Infrared; Interface (matter); Simple (philosophy); Head (geology); Simulation; Operating system","score_opus":0.01228962891321053,"score_gpt":0.2590941176850035,"score_spread":0.24680448877179298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579510884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023834514,0.000015782536,0.8451294,0.0011150682,0.00015109415,0.000036939455,4.7585954e-7,0.000989209,0.12872753],"genre_scores_gemma":[0.97582036,1.03462014e-7,0.021092925,0.00019898648,0.000024809853,0.000004702494,0.0000042689117,0.0000033184804,0.0028505288],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99948996,0.000015900727,0.00010995862,0.00018160656,0.00007797163,0.0001246197],"domain_scores_gemma":[0.99960566,0.00003155835,0.0000397291,0.00027687903,0.000031318214,0.0000148774025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006378195,0.000067127774,0.000065554814,0.000113822294,0.00008079475,0.000056378503,0.00032263034,0.000040171144,0.000049311737],"category_scores_gemma":[0.0000070462556,0.00005729507,0.000031025775,0.00017808146,0.000026605845,0.0001473299,0.000050884937,0.00010136787,0.00009193879],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001964859,0.00018366975,0.008443961,0.000005064774,0.00000492594,0.000017441094,0.00000917199,0.00039272843,0.00671113,0.9502203,0.010447983,0.023561688],"study_design_scores_gemma":[0.0020339575,0.0005363421,0.20434983,0.000058167177,0.0000144475,0.00004571414,0.000031234624,0.20183428,0.20914789,0.24951038,0.13145784,0.0009799298],"about_ca_topic_score_codex":0.00014731663,"about_ca_topic_score_gemma":0.00005246661,"teacher_disagreement_score":0.95198584,"about_ca_system_score_codex":0.000024213672,"about_ca_system_score_gemma":0.000010575031,"threshold_uncertainty_score":0.2336425},"labels":[],"label_agreement":null},{"id":"W1587652081","doi":"10.14483/23448393.2307","title":"Desarrollo de una Metodología para seguir y discriminar el Movimiento de un Ojo Humano en Tiempo Real","year":2006,"lang":"es","type":"article","venue":"Repositorio Universidad Distrital","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Robustness (evolution); Impossibility; Artificial intelligence","score_opus":0.011178081029215872,"score_gpt":0.2653047313075316,"score_spread":0.25412665027831577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1587652081","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9459719,0.00037235935,0.037674658,0.0009925863,0.00078345,0.00025767318,0.00007505944,0.0006757324,0.013196588],"genre_scores_gemma":[0.9867524,0.000059132515,0.009781061,0.000023064254,0.00073896657,0.000008369531,0.000060174065,0.000046178728,0.002530644],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9959614,0.00046847103,0.0005674614,0.0011718946,0.0005553136,0.0012754262],"domain_scores_gemma":[0.9976573,0.00031743507,0.00040983217,0.0011057954,0.00019633214,0.00031330268],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045278008,0.0006109703,0.0006010357,0.00036168154,0.000792638,0.00061958085,0.0017701328,0.0006620591,0.000016808279],"category_scores_gemma":[0.00008985278,0.0006741128,0.0003998185,0.0007950545,0.0004856378,0.0005577202,0.0006363766,0.00066656375,0.000051616295],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003800509,0.0024462005,0.3219009,0.00028622194,0.0005858867,0.010530694,0.0026214109,0.0006778382,0.16058996,0.48300004,0.007709645,0.009271152],"study_design_scores_gemma":[0.004735328,0.0016716649,0.79638654,0.00063702674,0.00097364164,0.0018492114,0.002658789,0.005006608,0.15151052,0.012092717,0.019642536,0.0028353855],"about_ca_topic_score_codex":0.0047966368,"about_ca_topic_score_gemma":0.000056617733,"teacher_disagreement_score":0.47448567,"about_ca_system_score_codex":0.0016714372,"about_ca_system_score_gemma":0.0004982734,"threshold_uncertainty_score":0.999571},"labels":[],"label_agreement":null},{"id":"W159670427","doi":"10.1007/978-3-642-16259-6_20","title":"On the Design and Validation of an Intelligent Powered Wheelchair: Lessons from the SmartWheeler Project","year":2010,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wheelchair; Robustness (evolution); Computer science; Human–computer interaction; Probabilistic logic; Engineering; Systems engineering; Simulation; Artificial intelligence; World Wide Web","score_opus":0.04501364438996974,"score_gpt":0.3012433895851609,"score_spread":0.25622974519519115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W159670427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018724805,0.0041972576,0.9705093,0.0012921118,0.0007796524,0.00087327353,0.000010829474,0.000169647,0.0034431631],"genre_scores_gemma":[0.9653138,0.0020796154,0.031420235,0.00023979286,0.00013833676,0.00001555585,0.000015737132,0.000042750686,0.00073417596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982195,0.0001359718,0.00048432962,0.0006531842,0.0002563797,0.00025064524],"domain_scores_gemma":[0.9961471,0.0026671195,0.00040138295,0.0006453399,0.00010266901,0.000036378933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089457683,0.0003349914,0.00037102963,0.0001589561,0.00024773722,0.00012415684,0.00090924813,0.00024108995,0.000009477854],"category_scores_gemma":[0.00019173526,0.00020945763,0.00006827717,0.00009286887,0.00041520613,0.00016717921,0.0003846732,0.00088349934,0.0000045326715],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023536179,0.00004166774,0.00018423238,0.000017096494,0.000037243917,0.0000070186093,0.0019097001,0.00057934603,0.0000873895,0.51673836,0.000023167882,0.48035127],"study_design_scores_gemma":[0.00032286343,0.0006539281,0.00039243733,0.0015219412,0.000061896535,0.000031533764,0.0004763137,0.069183715,0.01875753,0.8715248,0.03610106,0.0009719684],"about_ca_topic_score_codex":0.00004702948,"about_ca_topic_score_gemma":0.00006978102,"teacher_disagreement_score":0.946589,"about_ca_system_score_codex":0.000029579927,"about_ca_system_score_gemma":0.000053359807,"threshold_uncertainty_score":0.85414344},"labels":[],"label_agreement":null},{"id":"W1710332795","doi":"","title":"Eye-tracking studie av vektoranalys på LTH","year":2011,"lang":"no","type":"article","venue":"Lund University Publications (Lund University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science","score_opus":0.05277719580134074,"score_gpt":0.23580923059804806,"score_spread":0.18303203479670732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1710332795","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016513627,0.00015152953,0.12684143,0.0037938934,0.000636627,0.00042062032,0.00009329915,0.0011914591,0.85035753],"genre_scores_gemma":[0.28894424,0.0004366916,0.013428147,0.00016461211,0.0001330893,5.810144e-7,0.00006629172,0.000050091596,0.6967763],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99598336,0.00043417115,0.00036847594,0.0016084109,0.0005043381,0.0011012234],"domain_scores_gemma":[0.9954622,0.0002059173,0.0005942692,0.0020366274,0.0011581394,0.0005428915],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00044276135,0.0005790247,0.0006324767,0.0032167207,0.0026907348,0.0003002336,0.004462364,0.0005377563,0.00046441104],"category_scores_gemma":[0.00011974498,0.00077229657,0.00043227285,0.0071726176,0.0009829355,0.002851876,0.0016923678,0.0009653527,0.00049610453],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046895617,0.00082477287,0.014386196,0.000031668773,0.00054613047,0.00025510188,0.002340757,0.000018426874,0.000067584864,0.9745504,0.0032962407,0.0036357949],"study_design_scores_gemma":[0.0011981042,0.00019447997,0.05087473,0.000065115084,0.00057202944,0.000014146383,0.0051831477,0.0012478891,0.00007764701,0.00062974717,0.93913555,0.00080738385],"about_ca_topic_score_codex":0.0011678162,"about_ca_topic_score_gemma":0.0008221772,"teacher_disagreement_score":0.9739207,"about_ca_system_score_codex":0.0010987576,"about_ca_system_score_gemma":0.00069486944,"threshold_uncertainty_score":0.9994728},"labels":[],"label_agreement":null},{"id":"W1767744422","doi":"","title":"Double Modality Computer Interface for Learners with Special Needs","year":2007,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Cursor (databases); Human–computer interaction; Gesture; Modality (human–computer interaction); Interface (matter); Gesture recognition; User interface; Head (geology); Speech recognition; Artificial intelligence; Operating system","score_opus":0.2281023093637138,"score_gpt":0.5321579090144642,"score_spread":0.30405559965075046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1767744422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38210002,0.00047429735,0.6122585,0.0005569647,0.00078476424,0.00037528662,0.0000055683813,0.00012899294,0.0033156094],"genre_scores_gemma":[0.97713345,0.00010385184,0.021602847,0.00021657326,0.00054290175,0.000017075701,0.0000024275803,0.00003097526,0.00034990557],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976306,0.00007788754,0.0006497631,0.0005428824,0.00050699414,0.00059190404],"domain_scores_gemma":[0.99780786,0.00030210792,0.00062403874,0.0006836939,0.00037096572,0.00021132681],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001859462,0.00031136427,0.00063503307,0.0009120639,0.00027065238,0.0012667492,0.0050174114,0.00014218861,0.00035665225],"category_scores_gemma":[0.000043184016,0.0002580486,0.0001542467,0.0013790469,0.00024503324,0.0019204121,0.0012395253,0.0004707982,0.000013171174],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022283315,0.0011754793,0.46639052,0.00018560314,0.00068572,0.00014401591,0.0016095262,0.003193646,0.02647811,0.03504696,0.07378926,0.3890728],"study_design_scores_gemma":[0.0054022465,0.00026390844,0.74043477,0.0006169161,0.00011136011,0.00013138502,0.00031418787,0.004538531,0.15912998,0.030109491,0.057279587,0.0016676377],"about_ca_topic_score_codex":0.00024832113,"about_ca_topic_score_gemma":0.0000813082,"teacher_disagreement_score":0.59503347,"about_ca_system_score_codex":0.000117898526,"about_ca_system_score_gemma":0.000121796154,"threshold_uncertainty_score":0.9999872},"labels":[],"label_agreement":null},{"id":"W1824291197","doi":"10.18100/ijamec.87797","title":"A novel test of implicit memory; an eye tracking study","year":2014,"lang":"en","type":"article","venue":"International Journal of Applied Mathematics Electronics and Computers","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Novelty; Preference; Repeated measures design; Analysis of variance; Audiology; Test (biology); Psychology; Eye tracking; Artificial intelligence; Medicine; Computer science; Mathematics; Internal medicine; Statistics; Social psychology","score_opus":0.011118653868041305,"score_gpt":0.2686403983556827,"score_spread":0.2575217444876414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1824291197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40061048,0.000016181608,0.5987946,0.00017108156,0.0001062922,0.00006180644,5.613251e-7,0.000022016846,0.0002169961],"genre_scores_gemma":[0.870741,0.000009219489,0.12911163,0.00006387124,0.000061449915,0.0000018967639,3.7072638e-7,0.000008719794,0.0000018598674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987234,0.000010031068,0.0005124488,0.00018028401,0.00039918587,0.0001746591],"domain_scores_gemma":[0.99858016,0.00022789507,0.00059097714,0.00022840034,0.00031247578,0.000060097675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000620035,0.00013424075,0.00030006576,0.00023560318,0.00004251661,0.00011788658,0.0011841864,0.00004443222,8.968197e-7],"category_scores_gemma":[0.00003784416,0.00011559292,0.000058234466,0.0001203897,0.00004481117,0.0001518789,0.00015724877,0.0002255791,6.104647e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000337661,0.0033644661,0.0005045951,0.000035289748,0.0004106716,0.000014081978,0.0042826533,0.0015600107,0.106605515,0.6779553,0.000037245776,0.20519635],"study_design_scores_gemma":[0.007906981,0.0070850495,0.014285251,0.0003161659,0.00018176678,0.0010284039,0.0018453471,0.6539606,0.04772382,0.26403445,0.0006546788,0.0009775017],"about_ca_topic_score_codex":0.0000029291666,"about_ca_topic_score_gemma":0.000004164192,"teacher_disagreement_score":0.65240055,"about_ca_system_score_codex":0.00004271105,"about_ca_system_score_gemma":0.00006100659,"threshold_uncertainty_score":0.47137424},"labels":[],"label_agreement":null},{"id":"W1833779271","doi":"","title":"Using eye-tracking data for high-level user modeling in adaptive interfaces","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; User interface; User modeling; User interface design; Eye tracking; Interface (matter); Mental model; Interface metaphor; Natural user interface; Software; Tracking (education); Post-WIMP; User experience design; Artificial intelligence; Cognitive science","score_opus":0.2887163377803316,"score_gpt":0.3803703030238187,"score_spread":0.09165396524348707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1833779271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27934274,0.00003332673,0.7198761,0.00018926279,0.00016436874,0.00010779042,0.0000064126684,0.00016980164,0.00011016242],"genre_scores_gemma":[0.58563095,8.9901727e-7,0.41424975,0.000051043127,0.000022067949,0.0000015848175,0.0000028472432,0.000006884535,0.000033960157],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985577,0.000018475794,0.00029909986,0.0005767555,0.00012984364,0.00041813037],"domain_scores_gemma":[0.9988947,0.00013990384,0.00007106612,0.0007631576,0.00009456901,0.000036620128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090573286,0.0001390261,0.00018292344,0.00024791568,0.00008997942,0.000085504435,0.001526178,0.000102095306,0.0000027239037],"category_scores_gemma":[0.000097510274,0.00012681163,0.000020592755,0.00032635828,0.000039142295,0.00075309194,0.00069137727,0.00018150746,0.000004846873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012826447,0.00044747704,0.011249286,0.00005511437,0.00011534332,0.00008371805,0.0012582629,0.048886556,0.036132537,0.5505412,0.00033185972,0.35077038],"study_design_scores_gemma":[0.00031625558,0.000039781014,0.0019359443,0.000055400425,0.0000046572513,0.0000041865096,0.0002307231,0.98277,0.008947147,0.005451596,0.000057893594,0.00018639982],"about_ca_topic_score_codex":0.00041178337,"about_ca_topic_score_gemma":0.00064460456,"teacher_disagreement_score":0.9338835,"about_ca_system_score_codex":0.00006800283,"about_ca_system_score_gemma":0.00004117724,"threshold_uncertainty_score":0.51712286},"labels":[],"label_agreement":null},{"id":"W1838354524","doi":"10.1109/iros.2001.976391","title":"Sensing and control of a robotic prosthetic eye for ocular implant","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Electrooculography; Eye movement; Artificial intelligence; Computer science; Computer vision; Sensor fusion; Artificial neural network; Control system; SIGNAL (programming language); Engineering","score_opus":0.014709341314498355,"score_gpt":0.22327581160519688,"score_spread":0.20856647029069852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1838354524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034565832,0.00013085722,0.9631901,0.0016797285,0.00004258632,0.00014924876,7.6620074e-7,0.000103455284,0.00013742392],"genre_scores_gemma":[0.9541519,0.0000023970704,0.045627274,0.000099635836,0.0000045823003,0.0000023430987,1.076416e-7,0.0000029138412,0.000108843175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995411,0.000012303255,0.00010097215,0.00015790084,0.000049221813,0.00013852185],"domain_scores_gemma":[0.99967307,0.000054699816,0.000039294522,0.00017129414,0.000039462175,0.000022191645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082903316,0.000057004865,0.00013018673,0.000050314644,0.000038642804,0.000019181683,0.00011536171,0.000033668315,0.0000018140283],"category_scores_gemma":[0.000024397217,0.00004458559,0.000026620928,0.000068379646,0.00005067519,0.000039493192,0.000028634255,0.00003463186,0.0000026672715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016287977,0.00023579139,0.0065299044,0.00016751899,0.000110437315,0.00007175745,0.00066881883,0.00025687285,0.076903254,0.23570414,0.0009571208,0.6783781],"study_design_scores_gemma":[0.0010949869,0.00031709747,0.0035043673,0.000031065454,0.000019008778,0.00015394868,0.000016871714,0.9766663,0.012492393,0.0050287526,0.0005287094,0.00014652267],"about_ca_topic_score_codex":0.0000072549974,"about_ca_topic_score_gemma":0.0000021506664,"teacher_disagreement_score":0.9764094,"about_ca_system_score_codex":0.000004643203,"about_ca_system_score_gemma":0.0000034854768,"threshold_uncertainty_score":0.18181476},"labels":[],"label_agreement":null},{"id":"W1838803699","doi":"10.1007/978-3-540-73110-8_85","title":"Evaluating Eye Tracking with ISO 9241 - Part 9","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":134,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Throughput; Eye tracking; Fixation (population genetics); Tracking (education); Dwell time; Selection (genetic algorithm); Point (geometry); Task (project management); Tracking system; Artificial intelligence; Computer vision; Computer hardware; Simulation; Operating system; Engineering","score_opus":0.0581028780014639,"score_gpt":0.3296150350680574,"score_spread":0.2715121570665935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1838803699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009798154,0.00032835142,0.9915367,0.0007302314,0.0011419941,0.000316603,0.0000019055956,0.0005404637,0.0044239727],"genre_scores_gemma":[0.26803046,0.000015341135,0.7297863,0.0011477467,0.0005063474,0.000009755899,0.000003616025,0.0000558851,0.00044458624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949079,0.00003998307,0.00059233385,0.0020208652,0.001393769,0.001045156],"domain_scores_gemma":[0.9968097,0.00052474195,0.00043937302,0.0016475482,0.0004137427,0.00016488727],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0020698104,0.0006481504,0.000626894,0.0011180841,0.000413572,0.00054774416,0.0036057027,0.00045439173,0.000022954377],"category_scores_gemma":[0.0001523256,0.00053886493,0.00011501429,0.00096619496,0.0011601313,0.00053748407,0.00094667106,0.001463105,0.000055758974],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000686604,0.000027604994,0.00035938623,0.000024125176,0.000014206986,0.00020191408,0.00036384654,0.010412621,0.00014544567,0.015903896,0.000008210762,0.97253186],"study_design_scores_gemma":[0.0014708841,0.0022545476,0.0034037437,0.0033259036,0.00006216285,0.00055611716,0.0000014105389,0.77254105,0.008681337,0.19675623,0.007448033,0.0034986024],"about_ca_topic_score_codex":0.000016928103,"about_ca_topic_score_gemma":0.00010611169,"teacher_disagreement_score":0.9690333,"about_ca_system_score_codex":0.00031234007,"about_ca_system_score_gemma":0.0005452407,"threshold_uncertainty_score":0.99970627},"labels":[],"label_agreement":null},{"id":"W1904573686","doi":"10.1111/cogs.12246","title":"An Analysis of the Time Course of Lexical Processing During Reading","year":2015,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; University of Southampton","keywords":"Eye movement; Fixation (population genetics); Word processing; Reading (process); Psychology; Cognitive psychology; Control (management); Computer science; Communication; Natural language processing; Artificial intelligence; Linguistics; Biology","score_opus":0.02458552771880461,"score_gpt":0.31372760398017113,"score_spread":0.2891420762613665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1904573686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9412813,0.000025451272,0.057618942,0.00010854125,0.000041750907,0.00004905079,0.00000401042,0.00005442712,0.00081652036],"genre_scores_gemma":[0.99817747,3.143273e-7,0.0017655444,0.000021629568,0.0000063783295,0.0000018535619,3.434425e-7,0.0000021594524,0.00002430549],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9988413,0.000052610318,0.00016685342,0.00033666138,0.00039338166,0.00020918429],"domain_scores_gemma":[0.998666,0.000052291896,0.00018071108,0.00033165436,0.00068836025,0.00008099018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007904086,0.00007375809,0.00018326688,0.0002844145,0.00016083744,0.000046135025,0.0012440968,0.000030368263,0.0000021355015],"category_scores_gemma":[0.0003801845,0.000050663057,0.00005316298,0.003940926,0.0014545361,0.00048091894,0.00025466896,0.00009286577,0.0000029517194],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030932864,0.000663384,0.32704985,0.00003463719,0.00014880569,0.000012846925,0.007365002,0.00086279836,0.52693915,0.014269166,0.0000101435735,0.12261328],"study_design_scores_gemma":[0.00015172028,0.00007186609,0.616216,0.000071464696,0.000085060274,0.000005448398,0.0004916354,0.13430408,0.24784721,0.0006615153,0.0000011282451,0.00009286709],"about_ca_topic_score_codex":0.000009081126,"about_ca_topic_score_gemma":0.00000253882,"teacher_disagreement_score":0.28916615,"about_ca_system_score_codex":0.000030592157,"about_ca_system_score_gemma":0.00030355938,"threshold_uncertainty_score":0.5359299},"labels":[],"label_agreement":null},{"id":"W1911048249","doi":"10.1002/cta.1848","title":"Analogue portable electrooculogram real‐time signal processor","year":2012,"lang":"en","type":"article","venue":"International Journal of Circuit Theory and Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Decodes; SIGNAL (programming language); Computer science; Power consumption; Computer hardware; Power (physics); Interface (matter); Wearable computer; Voltage; Real-time computing; Electrical engineering; Embedded system; Engineering; Telecommunications; Decoding methods","score_opus":0.009523540862362572,"score_gpt":0.26351461360509554,"score_spread":0.25399107274273297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1911048249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023363149,0.00041699846,0.97097516,0.0003176515,0.00009402778,0.00008215644,0.000005491271,0.00006923521,0.0046761273],"genre_scores_gemma":[0.99841225,0.00008269922,0.0008381209,0.00013590041,0.00031326854,0.000021649235,0.000003914046,0.0000055426167,0.00018667257],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9991403,0.000060415077,0.0002782889,0.00011925799,0.0002183147,0.0001833796],"domain_scores_gemma":[0.9989668,0.00019080653,0.00029050262,0.0001359083,0.0003217224,0.00009424397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086305634,0.000085481566,0.0001279456,0.00018247597,0.00008574477,0.00007546087,0.0007665224,0.000054202887,0.000034867608],"category_scores_gemma":[0.000041438838,0.0000735968,0.000059411832,0.00018758804,0.0000849791,0.00047675092,0.000054773376,0.000173088,0.000024192525],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006882882,0.00014027185,0.0015085774,0.0000022083282,0.00007010227,0.0000030628914,0.00008626104,0.000004809227,0.0069270157,0.9227948,0.00011736811,0.068338595],"study_design_scores_gemma":[0.00044080758,0.00012066809,0.014457019,0.000035040724,0.00004500899,0.00096443883,0.000077380835,0.00008086298,0.006948808,0.9458445,0.030766506,0.00021900865],"about_ca_topic_score_codex":0.0000017547179,"about_ca_topic_score_gemma":3.0535801e-7,"teacher_disagreement_score":0.9750491,"about_ca_system_score_codex":0.00003306289,"about_ca_system_score_gemma":0.000058331494,"threshold_uncertainty_score":0.30011904},"labels":[],"label_agreement":null},{"id":"W1938263175","doi":"10.3389/fncom.2015.00072","title":"A kinematic model for 3-D head-free gaze-shifts","year":2015,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Sciences and Engineering Research Council; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Saccade; Head (geology); Kinematics; Computer science; Orientation (vector space); Eye movement; Fixation (population genetics); Rotation (mathematics); Computer vision; Artificial intelligence; Physics; Mathematics; Geometry; Geology","score_opus":0.07020397183885306,"score_gpt":0.3021781047203559,"score_spread":0.23197413288150281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1938263175","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006888225,0.000051651205,0.98734206,0.003973335,0.001166792,0.00023632863,0.000008325479,0.00020444788,0.00012883345],"genre_scores_gemma":[0.4622181,7.374389e-7,0.5370764,0.0005371249,0.000010346691,0.00003379003,0.0000016430486,0.0000057118814,0.00011610758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998335,0.000042833908,0.0002683552,0.00056332105,0.0004306528,0.0003598502],"domain_scores_gemma":[0.9991057,0.00010548775,0.00009994351,0.00042391304,0.00014213023,0.00012283846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043942282,0.00014217292,0.00019606766,0.00032077573,0.00010876618,0.00011460829,0.0017355036,0.00005210892,1.4794152e-7],"category_scores_gemma":[0.00069182506,0.0001421171,0.000048181093,0.00075666775,0.00019771056,0.00043352283,0.00027930056,0.00013995709,0.0000032517833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000115468065,0.00012242672,0.0022253718,0.000018781495,0.000001480408,0.000012109352,0.00024753614,0.8982828,0.000060659746,0.07905096,0.015103696,0.004862623],"study_design_scores_gemma":[0.00048014353,0.000061797175,0.0036758806,0.000013276576,0.0000010473373,0.000007401398,0.000008298659,0.67840123,0.000009139749,0.31711385,0.00012474653,0.00010319171],"about_ca_topic_score_codex":0.0000024937422,"about_ca_topic_score_gemma":0.0000021209855,"teacher_disagreement_score":0.4553299,"about_ca_system_score_codex":0.00008049499,"about_ca_system_score_gemma":0.0002705582,"threshold_uncertainty_score":0.5795367},"labels":[],"label_agreement":null},{"id":"W1963708897","doi":"10.4137/jen.s13448","title":"Design and Application of a Novel Virtual Reality Navigational Technology (VRNChair)","year":2014,"lang":"en","type":"article","venue":"Journal of Experimental Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Joystick; Virtual reality; Computer science; Human–computer interaction; Simulator sickness; Wheelchair; Task (project management); Virtual machine; Simulation; Engineering","score_opus":0.027623221071235507,"score_gpt":0.2994957368743775,"score_spread":0.27187251580314203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963708897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23080337,0.00004044863,0.76822793,0.0007143673,0.00012041488,0.00004899224,3.3233366e-7,0.000024124976,0.000020011343],"genre_scores_gemma":[0.94449973,0.0000031880843,0.055368204,0.00010563319,0.000013453046,0.0000033363049,5.9377903e-8,0.0000027994372,0.0000035934033],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905485,0.00004551641,0.0002966953,0.00021386704,0.00026351583,0.00012557788],"domain_scores_gemma":[0.99917704,0.00008032082,0.00038220035,0.00020702193,0.00010043578,0.000052975523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004963394,0.00007960807,0.00015197787,0.00019068133,0.00008426881,0.000025760237,0.000664181,0.000050159048,3.3090873e-7],"category_scores_gemma":[0.00012788396,0.00006849564,0.00002783526,0.00042126962,0.00044685727,0.00028826002,0.0001614266,0.00014175317,4.1800476e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007883396,0.00017655817,0.00026012096,0.000001203063,9.66327e-7,0.0000015669866,0.0000599841,0.00035193327,0.9168702,0.07311025,0.00001154193,0.009147794],"study_design_scores_gemma":[0.00057550985,0.0013316665,0.0047118664,0.000020892521,0.0000025657864,0.0004692724,0.00004728564,0.18415694,0.80590624,0.0024333112,0.00024395442,0.00010053267],"about_ca_topic_score_codex":0.0000021206029,"about_ca_topic_score_gemma":5.807714e-8,"teacher_disagreement_score":0.71369636,"about_ca_system_score_codex":0.000021877271,"about_ca_system_score_gemma":0.000040679286,"threshold_uncertainty_score":0.2793171},"labels":[],"label_agreement":null},{"id":"W1965245095","doi":"10.1145/1178823.1178847","title":"Use of eye movements for video game control","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Eye tracking; Eye movement; Video game; Computer science; Control (management); Human–computer interaction; Immersion (mathematics); Artificial intelligence; Computer vision; Multimedia","score_opus":0.02101451989327043,"score_gpt":0.2488525620216684,"score_spread":0.22783804212839795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965245095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075069375,0.000010726955,0.92353433,0.00053685415,0.000071434304,0.00012225877,0.000005229824,0.00016284855,0.0004869354],"genre_scores_gemma":[0.9494954,2.4472834e-7,0.04866247,0.00032266634,0.000011830443,0.000014661127,7.2958704e-7,0.0000030197314,0.0014890025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99946994,0.000009088629,0.00014125399,0.0001597253,0.00007478149,0.0001452372],"domain_scores_gemma":[0.999532,0.00007411407,0.000062194216,0.0002481312,0.000070285154,0.000013278672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007073916,0.00005698118,0.00010831578,0.000058100773,0.000019635232,0.000025639247,0.00027707958,0.000029724457,0.00000461207],"category_scores_gemma":[0.00002894374,0.000047122347,0.000044276097,0.00009275529,0.00003400724,0.00012829926,0.000037647176,0.000028225331,0.0000069205403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014433514,0.00025657902,0.044945862,0.000015922957,0.000035096116,0.0000039029987,0.000016807773,0.00032235176,0.03171065,0.89282227,0.008068116,0.02178803],"study_design_scores_gemma":[0.0050164424,0.00071857334,0.3593812,0.00004475817,0.000026888516,0.0000040871446,0.000013206502,0.21138756,0.18048146,0.111294106,0.13103184,0.00059989485],"about_ca_topic_score_codex":0.00010003885,"about_ca_topic_score_gemma":0.000013810339,"teacher_disagreement_score":0.87487185,"about_ca_system_score_codex":0.000009187259,"about_ca_system_score_gemma":0.000010613246,"threshold_uncertainty_score":0.19215935},"labels":[],"label_agreement":null},{"id":"W1965400586","doi":"10.1109/iros.2014.6943065","title":"A risk assessment infrastructure for powered wheelchair motion commands without full sensor coverage","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wheelchair; Joystick; Computer science; Tree traversal; Real-time computing; Motion (physics); Simulation; Artificial intelligence; Human–computer interaction; Computer vision","score_opus":0.006847736444384076,"score_gpt":0.25250082908737764,"score_spread":0.24565309264299356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965400586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080650344,0.0000044487842,0.9140003,0.0012816003,0.0002873917,0.00021362447,0.000010245228,0.00047736068,0.0030746877],"genre_scores_gemma":[0.8033282,0.0000035544037,0.19608526,0.00025541507,0.00004637436,0.000021112804,0.000007045162,0.00000910015,0.00024390142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988442,0.00009260893,0.00019793616,0.00040976622,0.00016700011,0.00028845755],"domain_scores_gemma":[0.9989454,0.00012630688,0.00015169979,0.00058360584,0.00012830524,0.00006464114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037814246,0.00016884551,0.0002235479,0.0001068796,0.00019265458,0.00012438819,0.0005030172,0.00013206499,0.000017039627],"category_scores_gemma":[0.00008670388,0.00013598523,0.00007848662,0.00015219451,0.000049518367,0.00018614574,0.00013973714,0.00022840509,0.000013155312],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005410127,0.0002578397,0.16161397,0.00006445119,0.00012777069,0.0000046667205,0.00034369584,0.0015474411,0.010631381,0.45443657,0.008770672,0.36214742],"study_design_scores_gemma":[0.0027828468,0.001018118,0.4187967,0.000026712043,0.00003287825,0.000040905015,0.000047281115,0.47095272,0.0033514143,0.077555545,0.024840916,0.00055398134],"about_ca_topic_score_codex":0.000016591774,"about_ca_topic_score_gemma":0.00001957402,"teacher_disagreement_score":0.7226779,"about_ca_system_score_codex":0.0000544787,"about_ca_system_score_gemma":0.00002693651,"threshold_uncertainty_score":0.5545317},"labels":[],"label_agreement":null},{"id":"W1965772020","doi":"10.1109/tcst.2010.2084577","title":"An Orientation Estimator for the Wheelchair's Caster Wheels","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Caster; Wheelchair; Orientation (vector space); Kinematics; Simulation; Engineering; Observer (physics); Biomechanics; Computer science; Control theory (sociology); Mechanical engineering; Mathematics; Control (management); Artificial intelligence; Physics","score_opus":0.009797697849225826,"score_gpt":0.2593089765779013,"score_spread":0.24951127872867548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965772020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021365223,0.00007181092,0.9671857,0.005542746,0.0039017857,0.0008219,0.000028755014,0.0010470664,0.000035055516],"genre_scores_gemma":[0.9891469,0.0000029840999,0.009647898,0.00013760978,0.0000755995,0.0007815524,9.127645e-7,0.000023092145,0.00018349124],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984155,0.00006277057,0.00034600054,0.00058434054,0.00017932894,0.0004120244],"domain_scores_gemma":[0.9977651,0.00037954006,0.0001549979,0.0013493106,0.0002960703,0.00005498351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038668484,0.00022384517,0.00030712402,0.00038453544,0.00050606887,0.00017304787,0.0010977216,0.00044284578,0.000008425328],"category_scores_gemma":[0.00003019771,0.00017012027,0.0001083566,0.00042070894,0.00022419366,0.0002788308,0.0000026773703,0.00062341645,0.000053455657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010232724,0.0008811313,0.0003943153,0.00006566746,0.00040650502,0.000043496268,0.0003306218,0.016953826,0.29526126,0.38926363,0.00052468106,0.29577252],"study_design_scores_gemma":[0.004496014,0.0012820108,0.00045264882,0.000059763523,0.00016068113,0.0007348271,0.000550962,0.9052686,0.069230504,0.0042711264,0.012764377,0.0007284657],"about_ca_topic_score_codex":0.000038212915,"about_ca_topic_score_gemma":0.00026758344,"teacher_disagreement_score":0.96778166,"about_ca_system_score_codex":0.000040258554,"about_ca_system_score_gemma":0.000080986094,"threshold_uncertainty_score":0.69373035},"labels":[],"label_agreement":null},{"id":"W1969105404","doi":"10.1016/j.brainres.2008.12.006","title":"Mixed pro and antisaccade performance in children and adults","year":2008,"lang":"en","type":"article","venue":"Brain Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Western Hospital; York University; University of Waterloo","funders":"","keywords":"Saccade; Antisaccade task; Eye movement; Task (project management); Psychology; Eye tracking; Audiology; Fixation (population genetics); Cognitive psychology; Developmental psychology; Computer science; Medicine; Neuroscience; Artificial intelligence; Population; Engineering","score_opus":0.060629331895443915,"score_gpt":0.3164992537844168,"score_spread":0.2558699218889729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969105404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99611425,0.00025524115,0.00032915757,0.0027721508,0.000013625337,0.00018034443,8.289469e-7,0.000069383284,0.00026499038],"genre_scores_gemma":[0.99695086,0.00021231775,0.0025828253,0.000035710604,0.000014226096,0.000014563119,7.458775e-7,0.0000044514177,0.00018428374],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99886024,0.00010723047,0.00009603014,0.00035543522,0.00023038902,0.00035066617],"domain_scores_gemma":[0.99949616,0.00014897138,0.00001453723,0.00023630296,0.00004756205,0.000056482793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007048191,0.000065760985,0.00009577451,0.0002901953,0.00017706484,0.00003825829,0.000377374,0.00006884606,0.0000013655442],"category_scores_gemma":[0.00018127725,0.000058802325,0.000007189128,0.0005018281,0.00033959575,0.00016639174,0.0003589197,0.0003692741,0.000011650544],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010163445,0.00004484528,0.85495585,0.000014942423,0.0000036385936,0.00003048907,0.00038040357,7.8723616e-7,0.00034837314,0.0039171013,0.00090628635,0.13938713],"study_design_scores_gemma":[0.00043933484,0.00012288897,0.99477345,0.000032316715,1.4075219e-7,0.000173178,0.00001252991,0.00285839,0.0010327215,0.0002659848,0.00022064177,0.00006844998],"about_ca_topic_score_codex":0.00007804717,"about_ca_topic_score_gemma":0.000018878201,"teacher_disagreement_score":0.13981758,"about_ca_system_score_codex":0.0000137724,"about_ca_system_score_gemma":0.000042848835,"threshold_uncertainty_score":0.23978892},"labels":[],"label_agreement":null},{"id":"W1969423440","doi":"10.7490/f1000research.1095769.1","title":"The effects of speed and direction on eye-hand coordination for moving targets","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Open peer review; Plant biology; Eye–hand coordination; Neuroscience; Physiology; Optometry; Physical medicine and rehabilitation; Medicine; Biology; Computer science","score_opus":0.005447375837416153,"score_gpt":0.23223998950905594,"score_spread":0.2267926136716398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969423440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29309097,0.000045575922,0.7039499,0.0013930956,0.00029450486,0.00014881935,1.2732353e-7,0.00010017743,0.0009768335],"genre_scores_gemma":[0.9964907,0.000003157215,0.0030354618,0.00003059402,0.000014870217,0.0000056964404,1.8890465e-7,0.0000021158128,0.00041717538],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996663,0.000024986479,0.00006032146,0.00011858042,0.00005016979,0.00007967007],"domain_scores_gemma":[0.999259,0.000523177,0.00004410204,0.00012033945,0.000042454954,0.000010955113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021289,0.000041806394,0.0000593471,0.000037990783,0.0001485779,0.00003699666,0.00011264766,0.000027488019,1.4061747e-7],"category_scores_gemma":[0.000315761,0.000026698368,0.000014792319,0.00006484035,0.000044553868,0.000049294566,0.000029260502,0.0000309527,8.429267e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001008087,0.000037544418,0.0019956194,0.000049310835,0.000012418298,1.778122e-7,0.00008444569,0.000010433345,0.07511546,0.31743804,0.00074796996,0.6044985],"study_design_scores_gemma":[0.0008958711,0.0010541627,0.16863693,0.00005583588,0.0000095928535,0.0000015706249,0.000013205891,0.15159692,0.64538854,0.025844298,0.006359912,0.000143145],"about_ca_topic_score_codex":0.000008138846,"about_ca_topic_score_gemma":0.000005992312,"teacher_disagreement_score":0.7033998,"about_ca_system_score_codex":0.000007092989,"about_ca_system_score_gemma":0.000002949754,"threshold_uncertainty_score":0.11427561},"labels":[],"label_agreement":null},{"id":"W1970346247","doi":"10.1145/1280720.1280879","title":"NeuroFloat","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Visualization; Virtual reality; Human–computer interaction; Brain–computer interface; Optical head-mounted display; User interface; Electroencephalography; Interface (matter); Graphical user interface; Computer graphics (images); Artificial intelligence; Neuroscience; Operating system","score_opus":0.01022319407224826,"score_gpt":0.24186309083521285,"score_spread":0.2316398967629646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970346247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033994917,0.000008208453,0.9038959,0.0011229499,0.00014589487,0.0000126470495,2.1880686e-8,0.0005726735,0.060246762],"genre_scores_gemma":[0.9528406,5.029752e-7,0.045790892,0.00046903914,0.0000133244275,2.8160247e-7,4.3693397e-8,0.0000013618647,0.0008839175],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99963874,0.0000036488238,0.00005203766,0.000116260555,0.000053394517,0.0001358943],"domain_scores_gemma":[0.9997063,0.00003152753,0.000010520009,0.0002120907,0.000014980519,0.000024533898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013511765,0.000031573654,0.00003183989,0.000052509567,0.000028910805,0.000016927499,0.00033708906,0.000021487476,0.000009480523],"category_scores_gemma":[0.000013345357,0.000025545649,0.000013770154,0.00017088714,0.000017908791,0.0000644508,0.000070347065,0.000052060783,0.00016646957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.712251e-7,0.000015679174,0.002953593,4.6036564e-7,0.0000011643069,0.000029328025,0.0000152301955,4.1481644e-7,0.0029584656,0.82825863,0.0015344133,0.16423227],"study_design_scores_gemma":[0.0004450519,0.00019669639,0.5635097,0.000005843484,0.00000261358,0.00012589141,0.00003554996,0.0035835213,0.2104465,0.047106788,0.17413917,0.00040267603],"about_ca_topic_score_codex":0.0000043486284,"about_ca_topic_score_gemma":0.0000044594944,"teacher_disagreement_score":0.9188457,"about_ca_system_score_codex":0.000004736738,"about_ca_system_score_gemma":0.000004508622,"threshold_uncertainty_score":0.21396852},"labels":[],"label_agreement":null},{"id":"W1972123475","doi":"10.1117/12.703344","title":"Improving video captioning for deaf and hearing-impaired people based on eye movement and attention overload","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Closed captioning; Computer science; Eye tracking; Eye movement; Fixation (population genetics); Computer vision; Adaptation (eye); Artificial intelligence; Motion (physics); Gaze; Speech recognition; Psychology","score_opus":0.009511405496094397,"score_gpt":0.22677060340086552,"score_spread":0.21725919790477113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972123475","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9762945,0.000041431595,0.020286575,0.002482336,0.00013917933,0.0004565965,0.000009709365,0.00013456012,0.00015511442],"genre_scores_gemma":[0.78715837,0.000008988827,0.21248831,0.00012767411,0.00008966731,0.000069260874,0.0000021551941,0.000020976702,0.000034605728],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983516,1.5032667e-8,0.00043755487,0.00044947964,0.00039297823,0.00036841427],"domain_scores_gemma":[0.99866503,0.0001929394,0.00028589572,0.000059882594,0.0007018635,0.00009439535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009832934,0.00022772323,0.0002737367,0.0001463919,0.00014879755,0.00016163534,0.0005100608,0.00014556368,7.4615696e-7],"category_scores_gemma":[0.00042716876,0.00020307381,0.00023222489,0.00022498835,0.00012361967,0.00040763183,0.00017543681,0.0002031158,2.0975621e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007175756,0.00009748706,0.005303781,0.000390596,0.00010266485,7.931303e-8,0.00013155211,0.0000919991,0.47478718,0.51518553,0.00015326559,0.0036841102],"study_design_scores_gemma":[0.0029595173,0.0013615693,0.09903266,0.00058130326,0.00012265403,0.000011671122,0.0009544219,0.76130664,0.12787728,0.004796839,0.00038456297,0.00061088055],"about_ca_topic_score_codex":0.000022973063,"about_ca_topic_score_gemma":8.2896315e-7,"teacher_disagreement_score":0.7612147,"about_ca_system_score_codex":0.00012858158,"about_ca_system_score_gemma":0.000019615303,"threshold_uncertainty_score":0.828111},"labels":[],"label_agreement":null},{"id":"W1973618798","doi":"10.1145/985921.985927","title":"Eye contact sensing glasses for attention-sensitive wearable video blogging","year":2004,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Wearable computer; Eye contact; Computer science; Computer vision; Psychology; Communication","score_opus":0.015008676111683739,"score_gpt":0.26063984194968026,"score_spread":0.24563116583799652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973618798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15346901,0.000024165354,0.8397556,0.0038317172,0.00023703305,0.00014316026,0.0000012211223,0.0006351063,0.0019029622],"genre_scores_gemma":[0.83588845,0.00000341248,0.16337635,0.00032984672,0.000035870893,0.0000036403594,0.0000011715072,0.0000078729045,0.00035340383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998965,0.000020403077,0.00016538666,0.00039049468,0.000111198395,0.00034746854],"domain_scores_gemma":[0.99930036,0.00013827295,0.00007146652,0.00029370157,0.00014926735,0.00004695105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019769806,0.00012920938,0.0001754589,0.00011731594,0.00023308076,0.00012470254,0.00023572438,0.000074160445,0.0000021209494],"category_scores_gemma":[0.00008521473,0.00011712596,0.00008744044,0.00021368597,0.000046824898,0.00026859003,0.00008786021,0.00011060028,0.000052498213],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021649932,0.00016910829,0.0028692856,0.000040000785,0.0001281397,0.00012131178,0.00031560694,0.000528332,0.15325029,0.76018876,0.0006757262,0.08169178],"study_design_scores_gemma":[0.007649468,0.0011902769,0.13001,0.00086784933,0.00010319343,0.00036318525,0.0011971503,0.07822661,0.61441356,0.15530382,0.008668319,0.0020065815],"about_ca_topic_score_codex":0.00011113486,"about_ca_topic_score_gemma":0.000047857786,"teacher_disagreement_score":0.6824194,"about_ca_system_score_codex":0.00007380157,"about_ca_system_score_gemma":0.000060420916,"threshold_uncertainty_score":0.4776258},"labels":[],"label_agreement":null},{"id":"W1974043473","doi":"10.1080/09541440440000122","title":"Allocating visual attention to grouped objects","year":2004,"lang":"en","type":"article","venue":"The European Journal of Cognitive Psychology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychology; Cognitive psychology; Visual attention; Cognitive science; Communication; Neuroscience; Cognition","score_opus":0.02384482629817248,"score_gpt":0.3185293305904294,"score_spread":0.29468450429225695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974043473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41101506,0.000049674672,0.5759584,0.004602317,0.0004339624,0.00006193108,3.3991833e-7,0.000049354738,0.007828953],"genre_scores_gemma":[0.99078923,0.000009674458,0.0065552895,0.0023858864,0.00021876194,0.0000011264599,3.7270203e-7,0.000014743694,0.000024933988],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982288,0.00072986627,0.00038279605,0.00021907022,0.00017857761,0.00026088193],"domain_scores_gemma":[0.99889237,0.00015438086,0.00033525148,0.0001982938,0.0003333818,0.000086296146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017481368,0.00012636221,0.00017750164,0.00021972317,0.00013413705,0.000050214196,0.00092953886,0.000025111212,0.000004440746],"category_scores_gemma":[0.00037591704,0.00008890103,0.00009227906,0.00040076606,0.00013510577,0.00014482845,0.00015379177,0.00040067796,0.00030189336],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022020192,0.0007294682,0.0014241086,0.000007745759,0.00025773194,0.0015902771,0.005133545,0.0001063394,0.058009636,0.01267199,0.00066132884,0.9191876],"study_design_scores_gemma":[0.005764403,0.005752542,0.96297526,0.0006060873,0.000087032495,0.004299862,0.0010461218,0.00004907225,0.0040969597,0.01407229,0.00076521473,0.0004851234],"about_ca_topic_score_codex":0.0000012102163,"about_ca_topic_score_gemma":0.0000014943587,"teacher_disagreement_score":0.9615512,"about_ca_system_score_codex":0.00002221261,"about_ca_system_score_gemma":0.000029018127,"threshold_uncertainty_score":0.3880329},"labels":[],"label_agreement":null},{"id":"W1977224048","doi":"10.1167/14.1.8","title":"The relationship between delay period eye movements and visuospatial memory","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"University of Washington; Canadian Institutes of Health Research; University of California, Santa Cruz","keywords":"Period (music); Eye movement; Psychology; Cognitive psychology; Audiology; Neuroscience; Medicine; Art","score_opus":0.019157431136989228,"score_gpt":0.29948968952300414,"score_spread":0.28033225838601494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977224048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8214344,0.00010495582,0.17503375,0.0030259218,0.00020797843,0.000025069492,1.7334634e-7,0.000017947212,0.00014983083],"genre_scores_gemma":[0.99476516,0.000006106943,0.005005907,0.000044402208,0.000105531515,3.144365e-7,1.0922066e-7,0.0000031056647,0.00006937995],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992588,0.00009496194,0.00023849384,0.00008793157,0.00021435738,0.000105438085],"domain_scores_gemma":[0.99908817,0.00037559253,0.00024087024,0.0001666447,0.00008162695,0.000047088568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086354144,0.00005757917,0.000102852006,0.00007428486,0.00026293204,0.00010644886,0.00037723486,0.000049490794,9.091668e-7],"category_scores_gemma":[0.00033718479,0.000035115783,0.000037354395,0.00009726374,0.00005618415,0.00019924136,0.00010469713,0.00022418301,0.000005434097],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014892436,0.000041360054,0.5456149,0.000006374249,0.000024359832,0.000011205933,0.00034260217,0.000023709339,0.0011753531,0.014121052,0.00063051464,0.43799368],"study_design_scores_gemma":[0.00029171383,0.00032644247,0.98716444,0.00003427032,0.0000054423886,0.000016837763,0.000016506536,0.0015653872,0.00014126812,0.008678563,0.0017147921,0.000044361102],"about_ca_topic_score_codex":0.0000023109003,"about_ca_topic_score_gemma":0.0000013073943,"teacher_disagreement_score":0.4415495,"about_ca_system_score_codex":0.00001828481,"about_ca_system_score_gemma":0.000018306995,"threshold_uncertainty_score":0.20222872},"labels":[],"label_agreement":null},{"id":"W1977247366","doi":"10.1109/jtehm.2014.2365773","title":"Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use","year":2014,"lang":"en","type":"review","venue":"IEEE Journal of Translational Engineering in Health and Medicine","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Wheelchair; Computer science; Wavelet; Task (project management); Data mining; Artificial intelligence; Pattern recognition (psychology); Real-time computing; Engineering","score_opus":0.039326333725030464,"score_gpt":0.3181195564836848,"score_spread":0.2787932227586543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977247366","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067816772,0.81271344,0.17920914,0.0005906455,0.0004725037,0.00019925073,0.0000015855978,0.000029949968,0.0000018283182],"genre_scores_gemma":[0.19263348,0.8029889,0.004246213,0.0000136993895,0.00009467942,0.0000046863743,0.0000014478591,0.000013292744,0.0000036359952],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99837816,0.000072344665,0.0010305487,0.00014452645,0.00023045033,0.00014395326],"domain_scores_gemma":[0.9987099,0.00033316604,0.0006482985,0.000109838606,0.00007017188,0.00012858628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007284056,0.00015577373,0.00085126923,0.00071891356,0.000029105246,0.0000058359683,0.00014043719,0.00012281658,8.47672e-7],"category_scores_gemma":[0.00014443892,0.000121836405,0.000058423833,0.00025828034,0.000034070024,0.0001511201,0.0000064827295,0.0003824339,1.535102e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008317795,0.00003330299,0.00020409348,0.020862998,0.000056547367,0.0000062147456,0.00031004965,0.00050845096,0.000072310104,0.0011042356,0.0000054692887,0.97682804],"study_design_scores_gemma":[0.009918437,0.004547554,0.23442951,0.2937369,0.00046872534,0.0053396723,0.0000515655,0.23955354,0.000037928985,0.0013593044,0.20909372,0.0014631397],"about_ca_topic_score_codex":0.0000068741615,"about_ca_topic_score_gemma":0.000002395836,"teacher_disagreement_score":0.97536486,"about_ca_system_score_codex":0.000053822554,"about_ca_system_score_gemma":0.00014201777,"threshold_uncertainty_score":0.49683446},"labels":[],"label_agreement":null},{"id":"W1977432432","doi":"10.1145/636772.636796","title":"Interacting with groups of computers","year":2003,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Computer graphics (images)","score_opus":0.035506416514565084,"score_gpt":0.2750711930883131,"score_spread":0.23956477657374803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977432432","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66436744,0.0008419285,0.23603049,0.077644855,0.0005432082,0.00041010743,0.0000038760213,0.00032460288,0.019833507],"genre_scores_gemma":[0.6431304,0.0000057593725,0.3568276,0.000023451486,6.444546e-7,0.000002152788,1.6431281e-7,0.0000017219628,0.000008080783],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99954706,0.00010986941,0.00013441856,0.00007723591,0.000065435954,0.000066002925],"domain_scores_gemma":[0.982387,0.00045804927,0.00018035156,0.016881244,0.00008389347,0.0000094651095],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.00017625475,0.000044079203,0.00008749038,0.00004577506,0.000092654824,0.00000905651,0.020061146,0.000018727667,7.474316e-7],"category_scores_gemma":[0.0019778677,0.000029578214,0.00003311585,0.00031342744,0.00022497503,0.000078622244,0.0061221495,0.00012965716,0.000001586901],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028269133,0.00025025907,0.031698894,0.000017362685,0.000059027654,2.2808301e-7,0.0010162168,0.00015749365,0.003797184,0.9516648,0.0019842712,0.009351404],"study_design_scores_gemma":[0.002022024,0.00055159244,0.23062848,0.0014901839,0.0001042365,0.00019649322,0.0017224159,0.019240566,0.18527682,0.5357458,0.02224769,0.0007737023],"about_ca_topic_score_codex":0.0000125037495,"about_ca_topic_score_gemma":0.000010249696,"teacher_disagreement_score":0.41591904,"about_ca_system_score_codex":0.000010077609,"about_ca_system_score_gemma":0.000024443532,"threshold_uncertainty_score":0.9852408},"labels":[],"label_agreement":null},{"id":"W1979454848","doi":"10.1145/1822327.1822340","title":"The relationship between scan path direction and cognitive processing","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Cognition; Computer science; Neurophysiology; Perception; Interface (matter); Encoding (memory); Path (computing); Spatial cognition; Task (project management); Path integration; Eye movement; Information processing; Human–computer interaction; Cognitive psychology; Artificial intelligence; Psychology; Neuroscience; Engineering","score_opus":0.027511139562318026,"score_gpt":0.2802606034477822,"score_spread":0.25274946388546415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979454848","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8106648,0.000035789144,0.18289644,0.002034457,0.00009832896,0.000055831828,3.9356235e-7,0.00031664982,0.0038973608],"genre_scores_gemma":[0.9955774,5.270465e-7,0.0040431553,0.000021295411,0.000033425793,0.000006022963,4.2311837e-7,0.0000023518273,0.00031543334],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9996033,0.000020294085,0.000065964574,0.00014741476,0.00006124872,0.0001017937],"domain_scores_gemma":[0.9993827,0.00040384257,0.000036871737,0.000103144936,0.000049396844,0.000024094232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022275413,0.00004507146,0.000040705985,0.00003295158,0.00044841188,0.00013503665,0.00014424029,0.000047498965,6.3227753e-7],"category_scores_gemma":[0.0002861893,0.000028878963,0.000008732673,0.00016649734,0.00011558311,0.00014746544,0.000055215347,0.00021600677,0.000006525343],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.8740925e-7,0.0000029129567,0.5347451,0.0000010519158,0.0000013960479,2.4679736e-7,0.00006613169,8.8529735e-9,0.00005876309,0.043488096,0.000016249607,0.42161977],"study_design_scores_gemma":[0.000058667123,0.00001917541,0.980967,0.000008520177,0.0000037149816,0.000005417233,0.00003075049,0.00053605216,0.00034074986,0.017694587,0.0002939366,0.000041466225],"about_ca_topic_score_codex":0.000012514068,"about_ca_topic_score_gemma":0.00007293841,"teacher_disagreement_score":0.4462219,"about_ca_system_score_codex":0.0000035925916,"about_ca_system_score_gemma":0.000020037161,"threshold_uncertainty_score":0.34488672},"labels":[],"label_agreement":null},{"id":"W1980120944","doi":"10.1145/1117309.1117345","title":"Speech-augmented eye gaze interaction with small closely spaced targets","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Computer science; Eye tracking; Augmented reality; Speech recognition; Human–computer interaction; Artificial intelligence; Computer vision","score_opus":0.013017030152982112,"score_gpt":0.22727463571522913,"score_spread":0.21425760556224702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980120944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2345475,0.000031197596,0.7364168,0.0030860596,0.00022598628,0.00012571146,7.7777486e-7,0.0011321311,0.02443383],"genre_scores_gemma":[0.8614448,0.0000016655731,0.13469313,0.00014225386,0.000040690273,0.0000081793605,0.0000038389403,0.0000089383775,0.0036565564],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989909,0.000030285384,0.00015593406,0.00038329573,0.00014326433,0.0002963055],"domain_scores_gemma":[0.9993715,0.000030856885,0.00008883714,0.0003839746,0.00008506203,0.000039744446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000104031824,0.00015188726,0.00014258,0.00014711164,0.000082007384,0.00009357483,0.00040730977,0.00006973611,0.00003450427],"category_scores_gemma":[0.000010403457,0.000115360475,0.00003810028,0.00032670802,0.000052516043,0.0002067213,0.000094377196,0.00017760546,0.00014896001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000181033,0.0017483796,0.065268606,0.00006167025,0.00020900047,0.0007691039,0.00027443067,0.00083557935,0.10053827,0.52008325,0.028106192,0.28192452],"study_design_scores_gemma":[0.003772673,0.0015363463,0.35117438,0.00019155591,0.000053249605,0.00032825378,0.00021493887,0.037778243,0.4775999,0.016464163,0.10921361,0.0016726806],"about_ca_topic_score_codex":0.0003691847,"about_ca_topic_score_gemma":0.0004830847,"teacher_disagreement_score":0.6268973,"about_ca_system_score_codex":0.000052157993,"about_ca_system_score_gemma":0.000024236959,"threshold_uncertainty_score":0.47042635},"labels":[],"label_agreement":null},{"id":"W1983712136","doi":"10.7490/f1000research.1093250.1","title":"Distinct stages of word identification during reading: Evidence from eye movements","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Open peer review; Plant biology; Identification (biology); Eye movement; Reading (process); Neuroscience; Word (group theory); Word identification; Biology; Psychology; Physiology; Word recognition; Cognitive science; Linguistics; Ecology; Philosophy; Botany","score_opus":0.024104977207615903,"score_gpt":0.2699539023199288,"score_spread":0.24584892511231288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983712136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85869634,0.000035091824,0.14015652,0.00047741947,0.00010190527,0.00009145014,0.0000013497765,0.00021330363,0.00022662015],"genre_scores_gemma":[0.9871029,0.000008840542,0.011071869,0.000014510556,0.000012567835,0.000019681556,0.0000014266993,0.000004150046,0.0017640204],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99905413,0.000028615095,0.00026141797,0.0003184203,0.00017918003,0.00015825611],"domain_scores_gemma":[0.9990588,0.00009356642,0.00015194585,0.000562405,0.00009901308,0.000034286768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012096467,0.000084685096,0.00011591656,0.000085789856,0.000062084604,0.000090296475,0.0007415384,0.00004119621,0.000086382934],"category_scores_gemma":[0.0001294787,0.00007332702,0.000030036175,0.00021986947,0.00005430792,0.0005625565,0.00021646441,0.0000719344,0.0001463392],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021010158,0.00007097762,0.51502645,0.00002237604,0.000024722132,0.0000027082024,0.00025245253,0.0000060329357,0.44595942,0.0038828966,0.00026190112,0.03448799],"study_design_scores_gemma":[0.00006445456,0.000011159382,0.8345696,0.00005640658,0.000002070364,1.8951712e-7,0.000027233391,0.0010871234,0.16102219,0.003078125,0.000011421166,0.000070042355],"about_ca_topic_score_codex":0.0006820037,"about_ca_topic_score_gemma":0.000013113594,"teacher_disagreement_score":0.31954315,"about_ca_system_score_codex":0.000028294795,"about_ca_system_score_gemma":0.000010889113,"threshold_uncertainty_score":0.29901892},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"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)","score_opus":0.4242559375345631,"score_gpt":0.5898099072666028,"score_spread":0.16555396973203967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984681595","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050679194,0.00010503104,0.9923224,0.0014248017,0.0001334842,0.00070269,0.000030414316,0.0001274653,0.00008583406],"genre_scores_gemma":[0.08858836,0.000032062246,0.9109723,0.000026102016,0.00011508204,0.00015641643,0.000052762156,0.000016686845,0.000040260853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996904,0.0012041719,0.00046645408,0.00046909956,0.00050752907,0.00044870167],"domain_scores_gemma":[0.9951755,0.0026732667,0.00014922518,0.0014526678,0.0005026356,0.000046741538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013211007,0.00012593878,0.00023252965,0.00016333461,0.0004557476,0.00012417954,0.002204257,0.0001320051,0.000005751126],"category_scores_gemma":[0.0017137476,0.00009312831,0.000070973576,0.0007792538,0.00022249276,0.0008974133,0.00046354643,0.0004721422,0.0000036087981],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000446297,0.00009420087,0.00018312149,0.000022699025,0.000008401713,5.0228357e-7,0.00044605552,0.000023475508,0.01160359,0.0067888666,0.00007195725,0.98075265],"study_design_scores_gemma":[0.00039447763,0.0003002211,0.002038889,0.000037587244,0.00002772214,0.0000047519993,0.00029340154,0.8938765,0.08092231,0.005091466,0.016884161,0.00012852754],"about_ca_topic_score_codex":0.00015288193,"about_ca_topic_score_gemma":0.000012718522,"teacher_disagreement_score":0.98062414,"about_ca_system_score_codex":0.000029922008,"about_ca_system_score_gemma":0.00008554702,"threshold_uncertainty_score":0.4578698},"labels":[],"label_agreement":null},{"id":"W1985412887","doi":"10.1167/13.9.159","title":"Motor effort predicts memory use in active visual search","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Visual search; Gaze; Computer science; Eye tracking; Cognitive psychology; Eye movement; Recall; Psychology; Stimulus (psychology); Artificial intelligence","score_opus":0.01981245122498245,"score_gpt":0.303238176046005,"score_spread":0.2834257248210226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985412887","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98557794,0.000028993509,0.013240361,0.00071071176,0.00022649877,0.00008735908,3.305727e-7,0.000028887094,0.00009890655],"genre_scores_gemma":[0.9915149,0.000014069288,0.008253226,0.00006139927,0.000053994565,0.0000014834231,1.3844432e-7,0.000004897613,0.0000958424],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99898803,0.000059806425,0.00027677845,0.00013656558,0.00034397858,0.00019480998],"domain_scores_gemma":[0.9992686,0.00011833533,0.00014969216,0.0001614244,0.00022076527,0.00008118678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037658008,0.000078551726,0.00016869043,0.00037512046,0.00003858038,0.000098060154,0.00047226026,0.00008120619,0.000016559066],"category_scores_gemma":[0.000102671904,0.000058538844,0.00005962638,0.00025570032,0.000042969383,0.0011117767,0.00015192559,0.00039597598,0.000040203926],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014807154,0.0011105723,0.05977101,0.000029881305,0.000062651,0.0004873991,0.0011771542,0.0003376358,0.14855537,0.0010647445,0.004316271,0.78293926],"study_design_scores_gemma":[0.00053540344,0.00089342165,0.9764717,0.00010678321,0.0000024046187,0.000079725825,0.000056179546,0.013956015,0.007220584,0.00047911776,0.00012916674,0.000069468835],"about_ca_topic_score_codex":0.000045129815,"about_ca_topic_score_gemma":0.0000036658805,"teacher_disagreement_score":0.9167007,"about_ca_system_score_codex":0.00007752685,"about_ca_system_score_gemma":0.00006615044,"threshold_uncertainty_score":0.23871447},"labels":[],"label_agreement":null},{"id":"W1988287720","doi":"10.1167/6.6.734","title":"Egocentric distance estimation requires eye-head position signals","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Canadian Institutes of Health Research","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Eye movement; Gaze","score_opus":0.010392405194858682,"score_gpt":0.3086640883417914,"score_spread":0.2982716831469327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988287720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42187655,0.000095386,0.5745013,0.0028742896,0.00047427183,0.000027149808,4.3690204e-7,0.00004154774,0.00010907328],"genre_scores_gemma":[0.93265456,0.0000150734295,0.067160465,0.000059158454,0.000077832636,3.9289586e-7,4.9244846e-7,0.000003829571,0.000028170372],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991067,0.000038460992,0.00031385617,0.000121204175,0.00028675457,0.00013302286],"domain_scores_gemma":[0.9990662,0.00006923868,0.00039358917,0.00020337902,0.00020993364,0.000057686822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045232187,0.00007564976,0.00013404796,0.00020513954,0.000095576856,0.00009418004,0.00044440958,0.00007874242,0.000010825919],"category_scores_gemma":[0.00015243565,0.000058718007,0.00006484412,0.00028403918,0.00003919419,0.00064805424,0.00004853401,0.00032416143,0.000019281231],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039337043,0.00028574845,0.0032208022,0.000017626087,0.00001527635,0.00011371094,0.00014638962,0.00042219792,0.39876765,0.032125127,0.0022619246,0.5625842],"study_design_scores_gemma":[0.001397061,0.0016377605,0.7134022,0.0004746887,0.0000348376,0.00068040314,0.000021739539,0.14964744,0.052033447,0.075046875,0.005237039,0.00038651604],"about_ca_topic_score_codex":0.0000019338977,"about_ca_topic_score_gemma":0.0000025048823,"teacher_disagreement_score":0.7101814,"about_ca_system_score_codex":0.00003603017,"about_ca_system_score_gemma":0.000036708705,"threshold_uncertainty_score":0.23944509},"labels":[],"label_agreement":null},{"id":"W1988554548","doi":"10.1167/13.9.403","title":"Detecting Gaze Direction in the Horizontal and Vertical Periphery","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaze; Visual field; Horizontal and vertical; Face (sociological concept); Eccentricity (behavior); Computer vision; Geology; Head (geology); Artificial intelligence; Asymmetry; Psychology; Computer science; Geodesy; Physics; Neuroscience; Social psychology","score_opus":0.00954392693491751,"score_gpt":0.25448709481444576,"score_spread":0.24494316787952825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988554548","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9845087,0.0001400914,0.013185493,0.0017587384,0.0001662323,0.000027336784,1.1141682e-8,0.000011086215,0.00020233924],"genre_scores_gemma":[0.9969713,0.000015878884,0.0029153472,0.000049210306,0.00004376178,7.075734e-7,1.3634016e-8,0.0000015790673,0.0000022106112],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999502,0.00006201812,0.0001512638,0.000068277135,0.00012873516,0.00008773029],"domain_scores_gemma":[0.99970526,0.00009970409,0.000051018556,0.000079881655,0.000042552078,0.000021605598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038832182,0.0000408736,0.00007503631,0.000078392724,0.00005331727,0.00008806169,0.00019998068,0.000037042842,0.0000037868372],"category_scores_gemma":[0.000105859275,0.000023936556,0.000025330977,0.00013456475,0.000026139682,0.0003068539,0.000042292697,0.0002303946,0.0000033954882],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009872289,0.000101393314,0.01439036,0.0000050847093,0.0000061399833,0.000048828602,0.00068068336,0.000007555634,0.0714061,0.0012976377,0.00043475072,0.9116116],"study_design_scores_gemma":[0.00022087367,0.0005070128,0.98874915,0.000052754247,0.000002534594,0.00046080202,0.00019551213,0.0067844526,0.000976769,0.0017279587,0.0002779623,0.000044241682],"about_ca_topic_score_codex":0.000016485079,"about_ca_topic_score_gemma":0.000005211672,"teacher_disagreement_score":0.97435874,"about_ca_system_score_codex":0.000021121621,"about_ca_system_score_gemma":0.000009157671,"threshold_uncertainty_score":0.10009626},"labels":[],"label_agreement":null},{"id":"W1988801299","doi":"10.1117/12.472952","title":"&lt;title&gt;Survey on advanced personal portable communication systems&lt;/title&gt;","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation, Science and Economic Development Canada","funders":"","keywords":"Software portability; Wearable computer; Computer science; Human–computer interaction; Wireless; Workspace; Wearable technology; Multimedia; Embedded system; Telecommunications; Operating system","score_opus":0.017858325728941558,"score_gpt":0.22902922352665123,"score_spread":0.21117089779770967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988801299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94698036,0.00043857758,0.00029019435,0.0018302065,0.00037686562,0.00025342777,0.000037663835,0.00019480304,0.049597904],"genre_scores_gemma":[0.94029504,0.00017473457,0.056257773,0.00007922623,0.00013352916,0.000055993314,0.000009357147,0.000033040615,0.0029612842],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892026,3.056744e-8,0.00027571354,0.00023544664,0.00036020874,0.00020834102],"domain_scores_gemma":[0.99897736,0.00009305829,0.00018419477,0.0000795577,0.0006170147,0.00004881219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003529295,0.00015035602,0.00019801437,0.00007278462,0.0000650495,0.000078390265,0.0009357339,0.00011831563,0.000040005107],"category_scores_gemma":[0.00023354223,0.00012844613,0.00017571243,0.00023943478,0.0001024068,0.00021133307,0.000116762785,0.00020810487,0.000038060312],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007436264,0.000080158345,0.000121354205,0.00006662461,0.000096060394,8.59104e-8,0.00005881311,0.000036565085,0.02390658,0.9329723,0.04089978,0.0017542423],"study_design_scores_gemma":[0.0022810635,0.0009835446,0.012135855,0.0012489296,0.0001498118,0.00006251168,0.0004233088,0.65022624,0.026396364,0.0044915574,0.3001279,0.0014729119],"about_ca_topic_score_codex":0.000003408901,"about_ca_topic_score_gemma":9.769959e-8,"teacher_disagreement_score":0.92848074,"about_ca_system_score_codex":0.000084973755,"about_ca_system_score_gemma":0.000011502371,"threshold_uncertainty_score":0.5237881},"labels":[],"label_agreement":null},{"id":"W1989649021","doi":"10.1080/13506285.2013.876481","title":"Eye tracking research and technology: Towards objective measurement of data quality","year":2014,"lang":"en","type":"article","venue":"Visual Cognition","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Eye tracking; Computer vision; Artificial intelligence; Eye movement; Computer science; Gaze; Saccade; Eye tracking on the ISS; Tracking (education); Motion (physics); Psychology","score_opus":0.26077510509540663,"score_gpt":0.46068540981198963,"score_spread":0.199910304716583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989649021","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6138707,0.0001388256,0.38170758,0.0019674273,0.00008573898,0.00021994351,0.000011093358,0.00036776255,0.0016309497],"genre_scores_gemma":[0.9941241,0.000009713722,0.005792039,0.000016230228,0.000025728006,0.0000119569395,0.000008504566,0.0000055697096,0.000006159452],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99823755,0.00024222428,0.000224539,0.0005057953,0.0005527442,0.00023711899],"domain_scores_gemma":[0.9981295,0.00011611644,0.00010227821,0.0005458416,0.0010712058,0.000035060915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038088353,0.000090258975,0.00017441843,0.00039887967,0.00015458767,0.000052461914,0.00066549634,0.0001273286,0.000001947835],"category_scores_gemma":[0.0009274697,0.000085887405,0.000012580656,0.00082041667,0.0003383916,0.00034504855,0.000639479,0.00029395765,0.000009276353],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021271238,0.00034356464,0.004208855,0.000072427996,0.00004816684,0.0000022745637,0.00019960066,2.0663633e-7,0.09320827,0.056337737,0.00006901126,0.8454886],"study_design_scores_gemma":[0.0015926845,0.00228632,0.26760352,0.00042121467,0.000044566525,0.000014947134,0.0009058665,0.01182861,0.4858624,0.2281802,0.0007487153,0.0005109455],"about_ca_topic_score_codex":0.000045670484,"about_ca_topic_score_gemma":0.00003175762,"teacher_disagreement_score":0.8449777,"about_ca_system_score_codex":0.000037314654,"about_ca_system_score_gemma":0.0000638657,"threshold_uncertainty_score":0.35023868},"labels":[],"label_agreement":null},{"id":"W1993601943","doi":"10.1117/12.813452","title":"Hands-free interactive image segmentation using eyegaze","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Computer vision; Segmentation; Artificial intelligence; Zoom; Image segmentation; Graphical user interface; Contrast (vision); User interface; Set (abstract data type); Segmentation-based object categorization; Pixel; Feature (linguistics); Object (grammar); Scale-space segmentation","score_opus":0.012267667502315041,"score_gpt":0.2505859760544681,"score_spread":0.23831830855215305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993601943","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9882566,0.00003731601,0.005083161,0.0041936613,0.00020679056,0.0003341022,0.000013747881,0.00020778194,0.0016668949],"genre_scores_gemma":[0.5227014,0.000017291211,0.47682917,0.00014430651,0.0001712384,0.000032107066,0.0000027864512,0.000022735756,0.00007902483],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99804425,1.6977689e-8,0.0005497602,0.00045653887,0.0005627943,0.00038664235],"domain_scores_gemma":[0.997839,0.000107825945,0.0004327917,0.00010838127,0.0014246381,0.00008741407],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004407093,0.00028849248,0.00034740253,0.00016176573,0.000109110944,0.00021824226,0.0019554815,0.00016279797,0.0000042214656],"category_scores_gemma":[0.0004751811,0.00024896642,0.0004383165,0.0004537756,0.0001806025,0.001244675,0.00028319468,0.00034440478,0.0000016066433],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034689143,0.00010945049,0.00014118505,0.000066101726,0.00013792848,1.8084033e-7,0.00020471586,0.000034592005,0.5865514,0.4102274,0.0012975787,0.0011947335],"study_design_scores_gemma":[0.0034211243,0.0010282961,0.004671391,0.00049407466,0.00016111194,0.00006595096,0.001152749,0.17378177,0.78950775,0.024207288,0.0008083688,0.00070012506],"about_ca_topic_score_codex":0.000006423009,"about_ca_topic_score_gemma":9.309043e-8,"teacher_disagreement_score":0.471746,"about_ca_system_score_codex":0.00020994036,"about_ca_system_score_gemma":0.00003280835,"threshold_uncertainty_score":0.99999624},"labels":[],"label_agreement":null},{"id":"W1994221098","doi":"10.1007/s10209-010-0188-6","title":"BlinkWrite: efficient text entry using eye blinks","year":2010,"lang":"en","type":"article","venue":"Universal Access in the Information Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Text entry; Computer science; Interval (graph theory); Modality (human–computer interaction); Contrast (vision); Character (mathematics); Eye tracking; Speech recognition; Human–computer interaction; Artificial intelligence; Mathematics","score_opus":0.016963078314768832,"score_gpt":0.284488425815609,"score_spread":0.26752534750084017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994221098","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6200582,0.0000072086336,0.36892605,0.0020728484,0.0005114567,0.00017564974,0.0000038821586,0.0002408456,0.0080039],"genre_scores_gemma":[0.9866359,0.0000057376205,0.01237215,0.0009131387,0.000033934833,0.0000023199875,0.000004317679,0.0000025628785,0.000029924311],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991289,0.00003056449,0.00022197767,0.00012354883,0.00026814232,0.00022690215],"domain_scores_gemma":[0.99921685,0.000089503534,0.0001536487,0.00040275083,0.000108544155,0.000028702583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005103759,0.000104569604,0.00009151447,0.00012597501,0.00023178021,0.0003951423,0.0018479074,0.00016470128,0.000015287236],"category_scores_gemma":[0.000043369284,0.000079908204,0.000084054576,0.0008476607,0.00013904436,0.0018009141,0.00036729628,0.0006353655,0.000033089662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022232955,0.0004310315,0.020927899,0.000098326374,0.000071666924,0.000015402884,0.047737572,0.026639653,0.0016498036,0.7692796,0.008788707,0.124338135],"study_design_scores_gemma":[0.0009707272,0.000024160654,0.016002843,0.000026460571,0.000010488232,0.000020978907,0.003446492,0.9444034,0.0012081316,0.0019186231,0.031663083,0.0003045817],"about_ca_topic_score_codex":0.00004056901,"about_ca_topic_score_gemma":0.00000464123,"teacher_disagreement_score":0.91776377,"about_ca_system_score_codex":0.000059697515,"about_ca_system_score_gemma":0.000074911826,"threshold_uncertainty_score":0.3810366},"labels":[],"label_agreement":null},{"id":"W1994892719","doi":"10.1145/1773965.1773969","title":"Pilot gaze and glideslope control","year":2008,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Australian Research Council","keywords":"Runway; Touchdown; Gaze; Cockpit; Fixation (population genetics); Computer science; Flight simulator; Aeronautics; Computer vision; Eye movement; Simulation; Artificial intelligence; Engineering; Medicine; Geography","score_opus":0.026805477350489097,"score_gpt":0.23525989125022448,"score_spread":0.20845441389973537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994892719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.163816,0.000009342334,0.83325046,0.0010992297,0.00010911166,0.00014902976,0.0000031984305,0.0004928271,0.0010708014],"genre_scores_gemma":[0.9731203,0.00008379487,0.026091862,0.00044931966,0.00002312968,0.00006363452,0.0000014272914,0.000010067151,0.00015649853],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991014,0.000025501618,0.00014644951,0.00037690307,0.00014643974,0.00020330212],"domain_scores_gemma":[0.9992426,0.00008365406,0.000036621455,0.00054228655,0.000030464122,0.0000643857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009727249,0.00013982675,0.00014301286,0.0001558859,0.00036691915,0.000033152344,0.00037492265,0.00006691562,0.00006730636],"category_scores_gemma":[0.0000074153163,0.00013512581,0.000037121412,0.00021983865,0.00013779376,0.00013635277,0.000007815064,0.00025362565,0.00023318597],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001374967,0.0005879159,0.00045716987,0.000015560008,0.000054443546,0.000017169115,0.0012043996,0.00066746806,0.12581535,0.011439645,0.00044818808,0.8591552],"study_design_scores_gemma":[0.013107157,0.0036359616,0.9008467,0.000101903315,0.00015827345,0.0011346505,0.0008172311,0.020445136,0.018009245,0.030869352,0.008459953,0.0024144375],"about_ca_topic_score_codex":0.000021549135,"about_ca_topic_score_gemma":0.000011564966,"teacher_disagreement_score":0.90038955,"about_ca_system_score_codex":0.00004295391,"about_ca_system_score_gemma":0.000019227302,"threshold_uncertainty_score":0.55102706},"labels":[],"label_agreement":null},{"id":"W1995383499","doi":"10.3758/s13428-011-0078-8","title":"Exploiting human sensitivity to gaze for tracking the eyes","year":2011,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Gaze; Eye movement; Eye tracking; Saccade; Fixation (population genetics); Computer vision; Covert; Computer science; Artificial intelligence; Eye–hand coordination; Visual search; Psychology; Optometry; Cognitive psychology; Medicine; Population","score_opus":0.6849314061675018,"score_gpt":0.6082399704273977,"score_spread":0.07669143574010406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995383499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2597966,0.000023534416,0.7379359,0.0005707902,0.00012737076,0.00062236213,0.0000027703445,0.00027695496,0.00064373703],"genre_scores_gemma":[0.55605596,8.343115e-7,0.44332558,0.000037040274,0.00004524456,0.00038752696,4.538601e-7,0.000013789453,0.00013359624],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960656,0.0018848359,0.0002430158,0.0005897505,0.00040130122,0.00081550534],"domain_scores_gemma":[0.9967578,0.0016129843,0.00005874071,0.00096175954,0.00046642876,0.0001422595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014782162,0.00014830352,0.00021232489,0.00032102413,0.0009508668,0.00018573066,0.0012420306,0.000101110505,0.000012551117],"category_scores_gemma":[0.0015158969,0.00011093326,0.000111174886,0.00076674164,0.0002527097,0.00022102088,0.0006675229,0.00059399544,0.000020974121],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068892173,0.00014187602,0.003253894,0.000009622111,0.000007839385,0.000044000855,0.002096463,6.1370787e-7,0.20182475,0.050344735,0.00020545062,0.7420639],"study_design_scores_gemma":[0.0003541244,0.0006328621,0.29585555,0.00007662028,0.000020023817,0.00006195468,0.001417751,0.00079797336,0.6781983,0.01777416,0.0043821186,0.00042857954],"about_ca_topic_score_codex":0.00021525547,"about_ca_topic_score_gemma":0.00005099591,"teacher_disagreement_score":0.74163526,"about_ca_system_score_codex":0.00006788393,"about_ca_system_score_gemma":0.000055447348,"threshold_uncertainty_score":0.73133945},"labels":[],"label_agreement":null},{"id":"W1995424040","doi":"10.1145/1344471.1344482","title":"Improving hands-free menu selection using eyegaze glances and fixations","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Usability; Computer science; Interface (matter); Selection (genetic algorithm); Dwell time; Human–computer interaction; User interface; Eye tracking; Artificial intelligence; Operating system; Psychology","score_opus":0.02589439042896128,"score_gpt":0.2384784590749441,"score_spread":0.21258406864598284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995424040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4694718,0.00004088873,0.5291202,0.00030957547,0.000052949053,0.000029694827,2.2195977e-7,0.00027484578,0.0006997861],"genre_scores_gemma":[0.884321,0.00000811388,0.11528626,0.000052502695,0.000025103509,0.0000026659814,2.4252932e-7,0.000002743078,0.00030136862],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946886,0.000014449477,0.00008686057,0.00020896168,0.000078166246,0.00014268758],"domain_scores_gemma":[0.999668,0.000031117055,0.000047095244,0.00018303163,0.00004425407,0.000026510848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006820982,0.00006415971,0.00007365567,0.00008846754,0.00031090423,0.00004614979,0.0002324091,0.000042713382,0.000003826821],"category_scores_gemma":[0.000050177583,0.00005612779,0.000014274244,0.00020088062,0.000063482774,0.00027410893,0.000119604825,0.00007657526,0.000002911669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014915441,0.00026034552,0.36636785,0.000057459976,0.00008825601,0.00005848765,0.002073343,0.00042518412,0.10805445,0.29399723,0.0036172776,0.2249852],"study_design_scores_gemma":[0.0021289252,0.00032733593,0.19369811,0.000029381856,0.000018743967,0.00075739494,0.00006209003,0.73408014,0.05456236,0.011827378,0.0019588035,0.0005493665],"about_ca_topic_score_codex":0.00017271077,"about_ca_topic_score_gemma":0.00010344019,"teacher_disagreement_score":0.7336549,"about_ca_system_score_codex":0.000020176489,"about_ca_system_score_gemma":0.000032590306,"threshold_uncertainty_score":0.23912553},"labels":[],"label_agreement":null},{"id":"W1995949108","doi":"10.3109/17483107.2011.625072","title":"Driving performance in a power wheelchair simulator","year":2011,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; McGill University","funders":"","keywords":"Wheelchair; Joystick; Driving simulator; Simulation; Task (project management); Computer science; Virtual reality; Power (physics); Engineering; Human–computer interaction","score_opus":0.011100024208031715,"score_gpt":0.23680373223786624,"score_spread":0.22570370802983453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995949108","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9779636,0.000054248165,0.01659462,0.003121807,0.00012572514,0.00027902584,0.0000016447577,0.0006833039,0.0011759843],"genre_scores_gemma":[0.969828,0.0000059998447,0.030003132,0.000041854735,0.0000044171,0.00008995462,7.4639115e-7,0.0000092001665,0.000016671742],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981969,0.000114811504,0.00041409384,0.0007608427,0.00013583434,0.00037754682],"domain_scores_gemma":[0.9986136,0.00038843256,0.00011896356,0.00069792196,0.00011725342,0.00006380854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005412591,0.00021058027,0.00031509582,0.00039845254,0.00014103376,0.000021645898,0.0005190714,0.00030847336,0.00002200933],"category_scores_gemma":[0.00080362486,0.00019408642,0.000060273527,0.0010247257,0.0016821914,0.00039148345,0.00029994972,0.00039290218,0.000020706066],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012000096,0.00031502912,0.8977128,0.000025321558,0.000007257645,0.0000024685485,0.00047761717,0.00000473761,0.00009788744,0.07520245,0.0000060904677,0.026136318],"study_design_scores_gemma":[0.00040259492,0.0005031233,0.98519313,0.000049222257,0.0000032778623,0.000007893219,0.0005301068,0.002241103,0.0002504554,0.010332353,0.0002706912,0.00021606544],"about_ca_topic_score_codex":0.00002120732,"about_ca_topic_score_gemma":0.00006910404,"teacher_disagreement_score":0.08748029,"about_ca_system_score_codex":0.00013997519,"about_ca_system_score_gemma":0.000035071516,"threshold_uncertainty_score":0.79146147},"labels":[],"label_agreement":null},{"id":"W1998166345","doi":"10.1145/1743666.1743728","title":"Contingency evaluation of gaze-contingent displays for real-time visual field simulations","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Contingency; Computer science; Visual field; Field (mathematics); Computer vision; Artificial intelligence; Human–computer interaction; Computer graphics (images); Psychology; Mathematics","score_opus":0.021623567064580105,"score_gpt":0.3345266388639048,"score_spread":0.3129030717993247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998166345","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.695105,0.0000055885394,0.2992048,0.00074496906,0.00025506,0.00029323465,0.0000031603695,0.00016682471,0.0042213304],"genre_scores_gemma":[0.95538265,6.8743213e-7,0.04426265,0.000028080507,0.000039573923,0.000023836827,0.000004880407,0.000004734385,0.00025289395],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991326,0.000029088647,0.00023067335,0.00023163062,0.00021136425,0.00016464874],"domain_scores_gemma":[0.9987359,0.00037750386,0.00012481061,0.00030302937,0.0004291387,0.000029588937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006449627,0.00007988718,0.00012971381,0.000091714675,0.00008471774,0.000026879645,0.00031447117,0.00009308981,0.00011146932],"category_scores_gemma":[0.00059737504,0.000071137234,0.000059008282,0.00014915993,0.000030838288,0.00012497768,0.00006648681,0.00008958513,0.000011984082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010057461,0.00025830703,0.010705295,0.000017352464,0.000039624803,3.7896646e-7,0.00024290387,0.00024404908,0.55918175,0.22866571,0.0013714238,0.19926314],"study_design_scores_gemma":[0.00062852114,0.00019052399,0.022332951,0.000011706028,0.00003914738,0.0000014307354,0.000011435205,0.86004645,0.10395954,0.012288334,0.0003462845,0.00014369204],"about_ca_topic_score_codex":0.000043382734,"about_ca_topic_score_gemma":0.00009911599,"teacher_disagreement_score":0.85980237,"about_ca_system_score_codex":0.000009386667,"about_ca_system_score_gemma":0.00006913123,"threshold_uncertainty_score":0.29008922},"labels":[],"label_agreement":null},{"id":"W1998230972","doi":"10.1016/j.neuroscience.2006.09.006","title":"Gaze fixation patterns for negotiating complex ground terrain","year":2006,"lang":"en","type":"article","venue":"Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":185,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Terrain; Gaze; Fixation (population genetics); Computer science; Computer vision; Artificial intelligence; Salient; Eye movement; Geography; Cartography; Biology","score_opus":0.03889617961719088,"score_gpt":0.2759784457591816,"score_spread":0.23708226614199074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998230972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19599003,0.000005834649,0.8017311,0.0010458664,0.0004549267,0.0001293736,0.0000050239123,0.00029525397,0.0003426149],"genre_scores_gemma":[0.9844159,7.836082e-7,0.014792684,0.0005187338,0.000059057944,0.000024727025,0.0000032810049,0.0000060691727,0.00017879604],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99881417,0.00003015423,0.0001700703,0.0004671284,0.00019061613,0.00032784918],"domain_scores_gemma":[0.9993828,0.00009832272,0.00011123689,0.00032499575,0.00005130528,0.00003131884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000181864,0.00010253893,0.00009504025,0.000095418225,0.00028351927,0.0002509751,0.00080177764,0.000037037637,0.0000015421653],"category_scores_gemma":[0.00011279801,0.00009760789,0.00003619111,0.00033058395,0.00009616569,0.00034488324,0.0001253725,0.000085063846,0.0000053333006],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005982847,0.00032686355,0.099951,0.000049305996,0.0000018277485,0.00002813165,0.00026573683,0.0036647462,0.33739862,0.4635842,0.0048875753,0.089835994],"study_design_scores_gemma":[0.00019853354,0.00011053449,0.6886301,0.000008350138,0.0000015270033,0.000018921444,0.000008647306,0.30053157,0.0012143701,0.003882836,0.00524879,0.0001458135],"about_ca_topic_score_codex":0.000110022644,"about_ca_topic_score_gemma":0.000015249471,"teacher_disagreement_score":0.78842586,"about_ca_system_score_codex":0.00002446783,"about_ca_system_score_gemma":0.000022308406,"threshold_uncertainty_score":0.3980334},"labels":[],"label_agreement":null},{"id":"W1998447267","doi":"10.3200/jmbr.41.2.117-127","title":"Saccadic Trajectories Receive Online Correction: Evidence for a Feedback-Based System of Oculomotor Control","year":2009,"lang":"en","type":"article","venue":"Journal of Motor Behavior","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saccadic masking; Kinematics; Saccade; Eye movement; Saccadic suppression of image displacement; Psychology; Trajectory; Saccadic eye movement; Computer science; Motor control; Eye tracking; Artificial intelligence; Computer vision; Communication; Neuroscience; Physics","score_opus":0.038669589404166885,"score_gpt":0.3091268619289003,"score_spread":0.27045727252473345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998447267","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71127766,0.0004785227,0.28429773,0.001079464,0.0022166092,0.00049516285,0.000030759034,0.00011757419,0.0000065086774],"genre_scores_gemma":[0.97266126,0.000007837696,0.026957428,0.00006980076,0.00022102614,0.000019216439,6.590466e-7,0.000008905972,0.000053893244],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983377,0.00009787206,0.0007320152,0.00022046996,0.0003705153,0.00024144074],"domain_scores_gemma":[0.99732137,0.00042844054,0.0009457552,0.00032880995,0.000868275,0.0001073547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058912556,0.00017592717,0.0005242728,0.00029931124,0.00009156738,0.000048216647,0.0007915589,0.0001516694,0.0000040128743],"category_scores_gemma":[0.00038153277,0.00014509271,0.00031542825,0.00031735093,0.00007642388,0.0003369224,0.000013151249,0.0003061238,0.000001348415],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0033192614,0.004986128,0.07520156,0.00042080344,0.00026486133,0.00042933537,0.0008272185,0.0005198091,0.40985143,0.0033214334,0.004613224,0.4962449],"study_design_scores_gemma":[0.0061603957,0.012291729,0.9213344,0.002186978,0.00055929134,0.00065725634,0.00032361769,0.008760963,0.046419013,0.000093280454,0.00071803614,0.0004950393],"about_ca_topic_score_codex":0.000010836977,"about_ca_topic_score_gemma":0.0000046245905,"teacher_disagreement_score":0.8461328,"about_ca_system_score_codex":0.00019668932,"about_ca_system_score_gemma":0.0002580542,"threshold_uncertainty_score":0.5916709},"labels":[],"label_agreement":null},{"id":"W2002429320","doi":"10.3758/s13428-013-0327-0","title":"Pupil diameter measurement errors as a function of gaze direction in corneal reflection eyetrackers","year":2013,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Pupillometry; Gaze; Pupillary response; Pupil; Eye tracking; Psychology; Nonverbal communication; Cognitive psychology; Computer vision; Reflection (computer programming); Task (project management); Artificial intelligence; Computer science; Communication","score_opus":0.3221110839369203,"score_gpt":0.5182101067454136,"score_spread":0.19609902280849328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002429320","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.660297,0.00011992235,0.33635226,0.00048985187,0.0005671523,0.00077677687,5.3487673e-7,0.00035749725,0.0010390169],"genre_scores_gemma":[0.861295,0.00001677989,0.13792023,0.000013402832,0.000022132494,0.00055131665,8.615917e-7,0.000018797855,0.00016151981],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9953907,0.0021517528,0.00039542763,0.00055708934,0.00096913474,0.0005358558],"domain_scores_gemma":[0.9981115,0.0002733346,0.00011240446,0.00060641667,0.0007878356,0.00010849615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00830446,0.00014689949,0.00024658008,0.0010445924,0.00011199655,0.0000876354,0.00051979464,0.00018969511,0.000050816063],"category_scores_gemma":[0.0010129103,0.00013655877,0.000086509106,0.0018282802,0.0002137367,0.00042975627,0.00018238762,0.00074023183,0.000056178473],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021263793,0.00030371675,0.012971772,0.000014099432,0.000009449155,0.0000060703846,0.00014977137,0.0000046027835,0.31209075,0.00084127253,0.00025537904,0.67333186],"study_design_scores_gemma":[0.00043440948,0.00085769984,0.9106695,0.00006344055,0.000011206637,0.000014152655,0.00020468642,0.0010022268,0.08098774,0.0045094956,0.0010655288,0.00017988915],"about_ca_topic_score_codex":0.0018669221,"about_ca_topic_score_gemma":0.0001561983,"teacher_disagreement_score":0.89769775,"about_ca_system_score_codex":0.0004220768,"about_ca_system_score_gemma":0.00013225502,"threshold_uncertainty_score":0.55687046},"labels":[],"label_agreement":null},{"id":"W2006728390","doi":"10.1115/detc2008-49453","title":"Quasimoro: A Telerobot for the Augmentation of Wheelchair Users","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; McGill University","keywords":"Teleoperation; Telerobotics; Payload (computing); Robot; Task (project management); Wheelchair; Computer science; Human–computer interaction; Architecture; Grippers; Mobile robot; Simulation; Engineering; Artificial intelligence; Systems engineering","score_opus":0.0479759712208497,"score_gpt":0.26905212735380074,"score_spread":0.22107615613295103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006728390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045742303,0.000030595053,0.95073104,0.002747591,0.00010621786,0.00012851141,6.666333e-7,0.00013909576,0.0003739944],"genre_scores_gemma":[0.9427516,0.00000888459,0.056606166,0.00017499982,0.000009612099,0.00002115243,3.8053713e-7,0.0000025032011,0.00042471057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995837,0.000010097869,0.000102768856,0.00012178301,0.00008085932,0.000100783385],"domain_scores_gemma":[0.9994914,0.00015035988,0.000051490875,0.00024227578,0.00005242177,0.0000120318555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009443381,0.000046916775,0.00006756668,0.000038739934,0.00010235229,0.0000074415766,0.0003920383,0.000025813542,0.000004305691],"category_scores_gemma":[0.000026492375,0.000029701632,0.00004037149,0.00014583087,0.000082039856,0.00008258729,0.000040881354,0.000035982885,0.0000056013623],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031117896,0.00027145116,0.015610371,0.000032897442,0.00010414794,0.0000058439905,0.0020572112,0.001075859,0.0142263025,0.6922343,0.02564218,0.24870832],"study_design_scores_gemma":[0.0039468114,0.0014255843,0.43076515,0.00005052732,0.000047027796,0.00012928368,0.0012482004,0.22803228,0.29060468,0.018444961,0.024580706,0.0007248054],"about_ca_topic_score_codex":0.00004513201,"about_ca_topic_score_gemma":0.000016104994,"teacher_disagreement_score":0.8970093,"about_ca_system_score_codex":0.000010015575,"about_ca_system_score_gemma":0.000023398408,"threshold_uncertainty_score":0.12111974},"labels":[],"label_agreement":null},{"id":"W2006991636","doi":"10.1145/2168556.2168596","title":"Shifts in reported gaze position due to changes in pupil size","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pupil; Computer vision; Fixation (population genetics); Eye tracking; Gaze; Artificial intelligence; Computer science; BitTorrent tracker; Eye movement; Artifact (error); IRIS (biosensor); Optics; Physics; Medicine; Biometrics","score_opus":0.02383270773402104,"score_gpt":0.2676141941496207,"score_spread":0.24378148641559963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006991636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95723855,0.000035378973,0.027024116,0.011175995,0.00021373224,0.0001299748,3.8345988e-7,0.00029200906,0.0038898627],"genre_scores_gemma":[0.9840327,0.0000016618487,0.01507201,0.0006668674,0.000025373118,0.000024815672,6.372339e-7,0.0000040357554,0.00017191454],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991603,0.000037360023,0.00015373722,0.00022489091,0.00010139918,0.00032231235],"domain_scores_gemma":[0.999509,0.000066724606,0.000038527607,0.00030584418,0.000021044467,0.000058881436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000376498,0.000079643076,0.00012484564,0.00021261506,0.000018989484,0.00002431469,0.00026907376,0.00007360551,0.000023141583],"category_scores_gemma":[0.00012913394,0.00007286153,0.0000134741085,0.00062317244,0.000014445978,0.00018798241,0.00012551535,0.00011356007,0.00006631597],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002562502,0.0013237463,0.53121907,0.000030043151,0.000020111425,0.0008090266,0.003865999,0.000034990113,0.045313183,0.22007748,0.0024667673,0.19481398],"study_design_scores_gemma":[0.00015650559,0.000050288283,0.9900626,0.000025329213,8.7101506e-7,0.000032302338,0.000030393068,0.00029358498,0.0072153904,0.0016006666,0.0004183333,0.00011374437],"about_ca_topic_score_codex":0.00018553012,"about_ca_topic_score_gemma":0.0010627676,"teacher_disagreement_score":0.45884356,"about_ca_system_score_codex":0.0000595205,"about_ca_system_score_gemma":0.000012923761,"threshold_uncertainty_score":0.2971207},"labels":[],"label_agreement":null},{"id":"W2007044209","doi":"10.1007/s10278-009-9247-z","title":"Evaluating Eyegaze Targeting to Improve Mouse Pointing for Radiology Tasks","year":2009,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Computer science; Medical physics; Radiology information systems; Medicine; Radiology","score_opus":0.022201744457296977,"score_gpt":0.3246296953241046,"score_spread":0.30242795086680757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007044209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25308245,0.00018628626,0.73988175,0.0058875647,0.0003659593,0.00012196544,0.0000028723014,0.00010903413,0.00036209248],"genre_scores_gemma":[0.8061114,6.6680263e-7,0.19313388,0.00048634,0.0002048144,0.0000016682195,6.4322495e-7,0.000009362485,0.00005121346],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99845195,0.000028487017,0.0005930929,0.00026903593,0.00021531487,0.0004421156],"domain_scores_gemma":[0.9985465,0.00022240434,0.0005023044,0.0002093216,0.0004031185,0.00011637428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096687075,0.00015473657,0.00030028555,0.00029434706,0.00013033219,0.00043738075,0.00074722926,0.000039796338,8.156136e-7],"category_scores_gemma":[0.0017156547,0.00013626713,0.00016128035,0.00023951715,0.000031252763,0.0012642925,0.000120673816,0.00027082954,0.0000054139396],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020589932,0.000063484185,0.0015187638,0.00000947879,0.000018191544,0.00003779911,0.00026975147,0.0003352431,0.19583169,0.002031078,0.0009113965,0.7989525],"study_design_scores_gemma":[0.011096794,0.011016552,0.01585997,0.00090011815,0.00019934295,0.0053270115,0.0014555799,0.35898075,0.39475262,0.18715039,0.009921278,0.0033395793],"about_ca_topic_score_codex":6.141663e-7,"about_ca_topic_score_gemma":6.790912e-8,"teacher_disagreement_score":0.79561293,"about_ca_system_score_codex":0.000077491204,"about_ca_system_score_gemma":0.000071951334,"threshold_uncertainty_score":0.5556813},"labels":[],"label_agreement":null},{"id":"W2008571347","doi":"10.4262/denkiseiko.74.155","title":"Development of an Electric Powered Wheelchair in which the Angle of Recline of the User's Body Controls the Direction","year":2003,"lang":"en","type":"article","venue":"DENKI-SEIKO","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wheelchair; Quarter (Canadian coin); Tilt (camera); Population; Life style; Work (physics); Control (management); Human–computer interaction; Computer science; Simulation; Engineering; Psychology; Physical medicine and rehabilitation; Applied psychology; Sociology; Geography; Mechanical engineering; Medicine; Artificial intelligence; World Wide Web","score_opus":0.011529510629729865,"score_gpt":0.24025918020440615,"score_spread":0.22872966957467628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008571347","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9883297,0.00023234154,0.009372304,0.0008556209,0.00019691074,0.0002774561,0.0000016046149,0.00004046458,0.0006936124],"genre_scores_gemma":[0.9969337,0.0000096908125,0.00289706,0.0000588468,0.0000081346125,0.000019288758,6.959139e-7,0.000006168211,0.00006640686],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986298,0.0002871304,0.0004150518,0.0002135346,0.0002504898,0.00020395961],"domain_scores_gemma":[0.9986317,0.00018463517,0.0003085338,0.00067589973,0.00018237437,0.000016848604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010769961,0.000108982385,0.00022231828,0.00010930397,0.000103877064,0.000014621486,0.0009435484,0.000079880505,0.0000049119035],"category_scores_gemma":[0.00030561344,0.00005713752,0.00005081238,0.00129124,0.00008451788,0.00008197799,0.000086103784,0.00020487126,0.0000016521657],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013334716,0.0018721455,0.10777439,0.000077706,0.00027317353,0.000006619949,0.008962305,0.00055798463,0.55236363,0.20988828,0.00047681996,0.11761362],"study_design_scores_gemma":[0.00097367313,0.00023152496,0.56311005,0.00007307317,0.000019933334,0.000018689338,0.00024895076,0.0042854403,0.41934747,0.0030604624,0.008454033,0.00017670095],"about_ca_topic_score_codex":0.00007354694,"about_ca_topic_score_gemma":0.00063446234,"teacher_disagreement_score":0.45533568,"about_ca_system_score_codex":0.000039973318,"about_ca_system_score_gemma":0.00017190138,"threshold_uncertainty_score":0.23300005},"labels":[],"label_agreement":null},{"id":"W2008836539","doi":"10.7490/f1000research.1477.1","title":"Eye Movement in Face Change Detection Task","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Open peer review; Plant biology; Face (sociological concept); Task (project management); Movement (music); Neuroscience; Eye movement; Optometry; Medicine; Biology; Physiology; Physical medicine and rehabilitation; Engineering; Art; Aesthetics; Philosophy","score_opus":0.054802699776498785,"score_gpt":0.2451757784758578,"score_spread":0.19037307869935902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008836539","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24476942,0.000031208565,0.747008,0.0006009805,0.00021615159,0.000121325815,2.3945637e-7,0.00044066497,0.0068119965],"genre_scores_gemma":[0.99165446,0.0000037909188,0.007760539,0.0003012825,0.000008775139,0.0000292587,9.3113805e-8,0.0000022696986,0.00023952995],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995151,0.000014060637,0.00007752754,0.00018477814,0.00005987099,0.0001486662],"domain_scores_gemma":[0.99972385,0.0000040741015,0.000022628827,0.00021767431,0.000012682036,0.00001906051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009384373,0.000054441483,0.00005600333,0.000108212385,0.000026067659,0.0000107019805,0.00028928445,0.000041724146,0.000014992552],"category_scores_gemma":[0.0000057199477,0.000047193018,0.000014574693,0.00024845047,0.000016211221,0.00014334777,0.000099164434,0.00007278217,0.00008558043],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046959244,0.00024340957,0.044369515,0.0000073827077,0.000008755173,0.000031243126,0.0028478713,0.0000012737313,0.012675293,0.10935211,0.00006491402,0.83039355],"study_design_scores_gemma":[0.0003165697,0.00020874622,0.85503966,0.000011732286,0.0000015310861,0.0000020863206,0.00012747059,0.0074048345,0.12177662,0.013344084,0.0015631008,0.00020355791],"about_ca_topic_score_codex":0.00033465688,"about_ca_topic_score_gemma":0.00028348085,"teacher_disagreement_score":0.83019,"about_ca_system_score_codex":0.000026164438,"about_ca_system_score_gemma":0.000003756417,"threshold_uncertainty_score":0.19244753},"labels":[],"label_agreement":null},{"id":"W2009384932","doi":"10.1167/9.8.446","title":"Gaze behaviour in the natural environment: Eye movements in video versus the real world","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; CLIPS; Perspective (graphical); Session (web analytics); Eye movement; Eye tracking; Set (abstract data type); Natural (archaeology); Pace; Point (geometry); Sitting; Psychology; Computer science; Computer vision; Cognitive psychology; Artificial intelligence; Medicine; Geography","score_opus":0.011549167894492712,"score_gpt":0.28765009660558927,"score_spread":0.27610092871109654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009384932","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9946872,0.00006191777,0.00031495458,0.0037854274,0.00076999434,0.000056982157,2.5582477e-7,0.000007616565,0.0003156674],"genre_scores_gemma":[0.9984072,0.00003263842,0.0013287119,0.00011712738,0.00005532303,0.0000013676063,1.6596732e-7,0.0000031289715,0.0000543334],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990284,0.00008991336,0.00027256834,0.000115060684,0.0003319985,0.00016209626],"domain_scores_gemma":[0.99931896,0.00015198307,0.00019362288,0.0002967729,0.000017789027,0.000020853631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097346725,0.0000772572,0.000105387,0.00018930278,0.00006132324,0.00006291703,0.0010817143,0.000045660647,0.000008032488],"category_scores_gemma":[0.000045353114,0.000040132934,0.00005384923,0.00025515733,0.000058682846,0.00021509458,0.00013046032,0.00094458694,0.000008771833],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037701233,0.0014681396,0.5724743,0.000007752019,0.000040460887,0.00081346533,0.0041348953,0.00028642788,0.060754124,0.018773364,0.0021325226,0.33873758],"study_design_scores_gemma":[0.0008070284,0.0001624565,0.9953563,0.000028014587,0.0000026935631,0.0000096646645,0.00008705501,0.00081207266,0.0003538413,0.00092349993,0.0014062945,0.000051076586],"about_ca_topic_score_codex":0.00004495173,"about_ca_topic_score_gemma":0.00029192358,"teacher_disagreement_score":0.42288202,"about_ca_system_score_codex":0.00004507007,"about_ca_system_score_gemma":0.000018974342,"threshold_uncertainty_score":0.4103813},"labels":[],"label_agreement":null},{"id":"W2010854031","doi":"10.1145/1117309.1117349","title":"A single camera eye-gaze tracking system with free head motion","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":218,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer vision; Computer science; Artificial intelligence; Head (geology); Eye tracking; Tracking (education); Computer graphics (images); Motion (physics); Tracking system; Geology; Psychology","score_opus":0.015402846022890989,"score_gpt":0.2202798767266287,"score_spread":0.2048770307037377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010854031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18245797,0.000046121637,0.80130005,0.0010441826,0.00011278262,0.000087086206,0.0000011420516,0.0014525817,0.013498118],"genre_scores_gemma":[0.95426697,1.9108198e-7,0.045109414,0.000050324044,0.000055581135,0.000009725651,0.000001645402,0.000010383317,0.0004957786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99887294,0.00003550592,0.00018372701,0.0003920506,0.00021108525,0.0003046771],"domain_scores_gemma":[0.9991259,0.0000307182,0.00008534974,0.00063091225,0.000091769696,0.000035375404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013165243,0.00014369863,0.00016665368,0.00012825622,0.00014372873,0.00015061015,0.00063137943,0.00007869377,0.0000042082734],"category_scores_gemma":[0.000010594644,0.00011064234,0.000038640057,0.00038526618,0.00006926404,0.00027637955,0.00009705197,0.0001296167,0.00004248834],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012283507,0.0004783755,0.030561298,0.000084903535,0.00003355183,0.00019804809,0.00015802289,0.00074022176,0.017712329,0.8292517,0.0018501695,0.11891907],"study_design_scores_gemma":[0.006006396,0.0023007863,0.61178976,0.0011494965,0.00008306459,0.0011076215,0.0009803205,0.16330384,0.19282529,0.009717974,0.008035867,0.0026995665],"about_ca_topic_score_codex":0.00065313233,"about_ca_topic_score_gemma":0.00041068337,"teacher_disagreement_score":0.81953377,"about_ca_system_score_codex":0.000110948684,"about_ca_system_score_gemma":0.000020647602,"threshold_uncertainty_score":0.45118636},"labels":[],"label_agreement":null},{"id":"W2011667597","doi":"10.1027/1618-3169.54.4.264","title":"Eye Movements and Serial Memory for Visual-Spatial Information","year":2007,"lang":"en","type":"article","venue":"Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Moncton","funders":"","keywords":"Recall; Serial position effect; Fixation (population genetics); Eye movement; Encoding (memory); Context-dependent memory; Free recall; Eye tracking; Psychology; Cognitive psychology; Recall test; Rapid serial visual presentation; Communication; Computer science; Cognition; Artificial intelligence; Neuroscience; Medicine","score_opus":0.020112830853366518,"score_gpt":0.37960904360655995,"score_spread":0.35949621275319343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011667597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29654896,0.0012832484,0.66905516,0.0008800013,0.0048005846,0.0015231457,0.000017218326,0.001000208,0.024891447],"genre_scores_gemma":[0.9683418,0.000048807735,0.027074648,0.0035914257,0.00025711046,0.0003566379,0.000059418853,0.00004583751,0.00022426697],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9958504,0.000097930795,0.0011013197,0.0012230421,0.000420882,0.0013064557],"domain_scores_gemma":[0.9979577,0.00014672092,0.00045142238,0.0009924774,0.00014292075,0.0003087527],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009861963,0.0006626926,0.00058817456,0.0006123175,0.0005189865,0.00020364007,0.0012974489,0.00057773414,0.00011968894],"category_scores_gemma":[0.00007507832,0.0006497221,0.0002069681,0.00042638354,0.0005083672,0.0014054974,0.0004177126,0.00043190134,0.00020158077],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026915069,0.0033751088,0.0033674778,0.000047128386,0.0003803623,0.00005259575,0.007759912,0.000007689083,0.5705933,0.028471157,0.014103127,0.36915064],"study_design_scores_gemma":[0.015573678,0.008628706,0.027920444,0.00005813923,0.000023986095,0.00017398925,0.0056678066,0.0010262702,0.8251807,0.0008656675,0.11290585,0.0019747498],"about_ca_topic_score_codex":0.000080818056,"about_ca_topic_score_gemma":0.0000069484686,"teacher_disagreement_score":0.67179286,"about_ca_system_score_codex":0.000053182743,"about_ca_system_score_gemma":0.000040903935,"threshold_uncertainty_score":0.9995954},"labels":[],"label_agreement":null},{"id":"W2013399540","doi":"10.1145/1095034.1095043","title":"ViewPointer","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Headset; Computer vision; Wearable computer; Context (archaeology); Artificial intelligence; Bluetooth; Object (grammar); Mobile device; Wireless; Embedded system","score_opus":0.009146022645638971,"score_gpt":0.2320200081583207,"score_spread":0.22287398551268173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013399540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056954324,0.000034141292,0.8933575,0.01793184,0.00006279328,0.0000158691,4.7127173e-8,0.00059557887,0.08230678],"genre_scores_gemma":[0.86380875,0.0000013754076,0.13244933,0.0012263393,0.000022548902,0.0000014486745,4.718525e-8,0.0000011771486,0.0024889878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997054,0.0000052696905,0.000049433576,0.0001088017,0.000039274008,0.000091867565],"domain_scores_gemma":[0.99974823,0.00000934344,0.0000093700155,0.00020407139,0.000012362545,0.000016647282],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000046789046,0.000031837673,0.00003609438,0.000032423337,0.000019794648,0.00002078797,0.00034192784,0.000018375216,0.00007404803],"category_scores_gemma":[0.0000058220658,0.000024087087,0.000017598917,0.00007725534,0.000014094978,0.000112249654,0.00007505754,0.00004394441,0.0010342367],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.3290168e-7,0.000017450377,0.00028460423,4.0915086e-7,0.0000017833827,0.0000016242368,0.000021998818,0.0000020763337,0.00039881162,0.54862726,0.009193577,0.44145027],"study_design_scores_gemma":[0.00025481274,0.000058856946,0.01474051,0.0000071554823,0.0000015039991,0.0000509734,0.0000076037377,0.0218525,0.033720877,0.01286623,0.9162356,0.00020334238],"about_ca_topic_score_codex":0.0000014795075,"about_ca_topic_score_gemma":0.000004587217,"teacher_disagreement_score":0.9070421,"about_ca_system_score_codex":0.000007654916,"about_ca_system_score_gemma":0.000004375494,"threshold_uncertainty_score":0.9997436},"labels":[],"label_agreement":null},{"id":"W2014160492","doi":"10.1145/765891.766086","title":"AuraMirror","year":2003,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science","score_opus":0.01382201138760096,"score_gpt":0.23028595535054308,"score_spread":0.2164639439629421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014160492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012571118,0.000016004513,0.8290834,0.0009874854,0.000131159,0.000018935652,3.9597985e-8,0.00054584607,0.15664603],"genre_scores_gemma":[0.87578857,5.049942e-7,0.12212246,0.00019836746,0.0000017356867,0.000001733577,3.259648e-8,0.0000012088439,0.001885372],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996776,0.000013324575,0.00004194419,0.000117864445,0.00004355791,0.00010570346],"domain_scores_gemma":[0.9997151,0.000015025439,0.000009623332,0.00022631879,0.000014524064,0.000019394942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006730301,0.000033408272,0.00003636252,0.000031427437,0.00003141986,0.00002202207,0.000245349,0.000022732358,0.000039733677],"category_scores_gemma":[0.000030444578,0.000026314585,0.000014468956,0.00013937418,0.000017206528,0.000063985404,0.000023003257,0.000041536314,0.00026380795],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.5409588e-8,0.000009601514,0.00072955294,2.8972244e-7,0.000001018761,0.0000028418053,0.000006995609,3.0835847e-7,0.00035705284,0.98844707,0.000776816,0.009668432],"study_design_scores_gemma":[0.00065182155,0.0001723357,0.0531426,0.000007956546,0.0000037876673,0.0001530865,0.00006369207,0.00382299,0.12028245,0.300972,0.52018666,0.0005405793],"about_ca_topic_score_codex":0.0000026462183,"about_ca_topic_score_gemma":0.0000015101217,"teacher_disagreement_score":0.8632175,"about_ca_system_score_codex":0.0000055646865,"about_ca_system_score_gemma":0.00001140814,"threshold_uncertainty_score":0.33908054},"labels":[],"label_agreement":null},{"id":"W2014215901","doi":"10.1145/1095034.1095058","title":"Predictive interaction using the delphian desktop","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"WIMP; Computer science; Traverse; Cursor (databases); Human–computer interaction; Computer graphics (images); Post-WIMP; Computer vision; User experience design; User interface design; Detector; Graphical user interface testing","score_opus":0.0356858652888817,"score_gpt":0.2946250149767506,"score_spread":0.2589391496878689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014215901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08575353,0.00001707545,0.90447664,0.005108553,0.0001509132,0.000046059595,1.9145354e-7,0.00030597966,0.0041410644],"genre_scores_gemma":[0.94882196,0.0000014614284,0.050644837,0.00033502845,0.0000634326,0.000003001607,1.3948573e-7,0.000002446683,0.00012767674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995281,0.000026691001,0.00008480487,0.00015595302,0.00007681089,0.00012760922],"domain_scores_gemma":[0.9995967,0.00004372391,0.00003717822,0.00026431563,0.00003954706,0.000018510491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011141697,0.00005551475,0.00004693563,0.00004850304,0.00012413127,0.0000561456,0.000404429,0.000033016095,0.000015808922],"category_scores_gemma":[0.000018659362,0.00003481874,0.000025366382,0.00016888848,0.000044977427,0.00028869338,0.00010272571,0.00012402647,0.000039206538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012285928,0.00016637432,0.0047899396,0.0000035077246,0.000059557275,0.0000085962665,0.0014392736,0.0053648907,0.008821734,0.26533195,0.0029322882,0.7110696],"study_design_scores_gemma":[0.00015060311,0.0000648922,0.0087236995,0.000011828599,0.000007584383,0.00008605175,0.00024603325,0.95165336,0.02184854,0.0026903637,0.014391336,0.00012570043],"about_ca_topic_score_codex":0.000030796673,"about_ca_topic_score_gemma":0.000028999188,"teacher_disagreement_score":0.94628847,"about_ca_system_score_codex":0.000050739847,"about_ca_system_score_gemma":0.000016873735,"threshold_uncertainty_score":0.1419867},"labels":[],"label_agreement":null},{"id":"W2018337957","doi":"10.1145/968363.968384","title":"ECSGlasses and EyePliances","year":2004,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Human–computer interaction; Wearable computer; Eye contact; Modality (human–computer interaction); Phone; Process (computing); Encoding (memory); Wearable technology; Artificial intelligence; Communication; Psychology; Embedded system","score_opus":0.009470469223518711,"score_gpt":0.2285228749871745,"score_spread":0.21905240576365578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018337957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2452367,0.00018585147,0.7287826,0.0082613,0.000112054,0.000030489831,1.4997647e-7,0.00066744175,0.016723407],"genre_scores_gemma":[0.9195047,0.0000139620715,0.080106325,0.00021069586,0.000006933583,0.0000015879028,4.2695657e-8,0.0000010796238,0.00015464441],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968976,0.000002977319,0.00003955497,0.00013934074,0.000039895698,0.00008847943],"domain_scores_gemma":[0.99980885,0.000012924045,0.000011042227,0.00013558513,0.000011567604,0.000020017425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034061275,0.000037346697,0.000043206353,0.000029253344,0.00004212881,0.000042060925,0.00019528171,0.00002075399,0.0000028342674],"category_scores_gemma":[0.000010255012,0.000028553182,0.000007601269,0.00009462141,0.000045361376,0.00012190431,0.00006783273,0.000036058547,0.000034125133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.9444396e-7,0.000013946932,0.0031892913,0.0000018526965,0.0000023189496,0.0000066275793,0.000029491206,0.0000063947723,0.00051561743,0.9515896,0.00011379497,0.044530857],"study_design_scores_gemma":[0.00095744355,0.00027181185,0.28836426,0.00003737514,0.0000044775034,0.00012723973,0.000096019074,0.0008957379,0.066153295,0.62305504,0.019622732,0.00041457269],"about_ca_topic_score_codex":0.000019363855,"about_ca_topic_score_gemma":0.000011795198,"teacher_disagreement_score":0.674268,"about_ca_system_score_codex":0.0000054297534,"about_ca_system_score_gemma":0.000012138707,"threshold_uncertainty_score":0.116436504},"labels":[],"label_agreement":null},{"id":"W2018341796","doi":"10.3928/15394492-20120928-01","title":"Occupational Therapists and Observation: What are You Looking At?","year":2012,"lang":"en","type":"article","venue":"OTJR Occupational Therapy Journal of Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Psychology; Occupational therapy; Eye movement; Saccade; Observational study; Fixation (population genetics); Clinical psychology; Cognitive psychology; Applied psychology; Medicine; Psychiatry","score_opus":0.20989890064417027,"score_gpt":0.4246854218096044,"score_spread":0.21478652116543412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018341796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96707124,0.010699827,0.016689843,0.004763966,0.0004888717,0.00012682077,0.0000034882635,0.000040362575,0.00011559578],"genre_scores_gemma":[0.9851142,0.0017374228,0.012176258,0.00027120486,0.000455543,0.000008933339,0.000003871246,0.000012369324,0.00022021028],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99738795,0.000325022,0.0003799953,0.00020874178,0.00127247,0.00042583456],"domain_scores_gemma":[0.9973866,0.0006325009,0.00034413216,0.0002757863,0.0011931087,0.00016788536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029767745,0.00013738073,0.00019937455,0.00049702544,0.00041765682,0.00029118833,0.00068934367,0.00010988568,0.00008774632],"category_scores_gemma":[0.00017913045,0.00011009882,0.00008028821,0.00062234723,0.00022759289,0.0020171155,0.0001650582,0.00046843648,0.000023369306],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029784767,0.00022668981,0.86724824,0.000011178596,0.00010309303,0.000016259726,0.0006858134,0.000018866693,0.0022562942,0.027968388,0.0017663469,0.099400975],"study_design_scores_gemma":[0.0007585107,0.00019813013,0.9748815,0.00010605346,0.0000018529119,0.00020043956,0.00014368405,0.00026234164,0.0018211008,0.004224705,0.017272143,0.0001295753],"about_ca_topic_score_codex":0.000004853289,"about_ca_topic_score_gemma":0.000002834812,"teacher_disagreement_score":0.10763322,"about_ca_system_score_codex":0.0001664995,"about_ca_system_score_gemma":0.00021167967,"threshold_uncertainty_score":0.44896996},"labels":[],"label_agreement":null},{"id":"W2020971920","doi":"10.1167/13.9.1347","title":"Cognitive Load Modulates Microsaccade Rate and Pupil Size","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pupil size; Pupil; Cognition; Audiology; Cognitive load; Fixation (population genetics); Psychology; Pupil diameter; Microsaccade; Pupillary response; Task (project management); Subtraction; Cognitive psychology; Eye movement; Arithmetic; Mathematics; Neuroscience; Engineering; Medicine","score_opus":0.009496858827184654,"score_gpt":0.2628205010569348,"score_spread":0.2533236422297501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020971920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9777077,0.00039117644,0.018554028,0.0029983781,0.00015258828,0.00004605909,4.1558545e-7,0.000028181972,0.00012149809],"genre_scores_gemma":[0.990079,0.00007874494,0.009520074,0.00019083264,0.000035255194,5.9146566e-7,4.976464e-8,0.0000037681414,0.00009168653],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993375,0.000047698493,0.00021509307,0.00012300338,0.00014225642,0.000134471],"domain_scores_gemma":[0.99904734,0.00024705747,0.00021741322,0.00009328244,0.00032987303,0.000065034605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032202108,0.000078375044,0.00015099396,0.00007220995,0.0000633668,0.0001326159,0.00025908696,0.000060676128,0.000015140737],"category_scores_gemma":[0.00025191394,0.000056312685,0.00004191865,0.0001199328,0.00006398018,0.000490068,0.000107605134,0.00021045581,0.000036253965],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003052282,0.00017813289,0.005864801,0.000015957256,0.00005488141,0.00009149729,0.0004594303,0.000009573734,0.4321685,0.00095605786,0.0036344703,0.5565362],"study_design_scores_gemma":[0.001187027,0.0007490853,0.9438527,0.0002570408,0.000015752314,0.00035820692,0.00008155849,0.0051221633,0.030686606,0.016887946,0.000632838,0.00016902835],"about_ca_topic_score_codex":0.000009747055,"about_ca_topic_score_gemma":5.766541e-7,"teacher_disagreement_score":0.9379879,"about_ca_system_score_codex":0.000021833326,"about_ca_system_score_gemma":0.00003500832,"threshold_uncertainty_score":0.22963646},"labels":[],"label_agreement":null},{"id":"W2021256293","doi":"10.1145/1344471.1344484","title":"Eye typing using word and letter prediction and a fixation algorithm","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Fixation (population genetics); Correctness; Word (group theory); Algorithm; Typing; Artificial intelligence; Speech recognition; Mathematics","score_opus":0.024489996159509548,"score_gpt":0.23994036109794678,"score_spread":0.21545036493843722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021256293","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3747089,0.000037410427,0.624334,0.00059485045,0.00005328422,0.00002425011,2.5637257e-7,0.00013674212,0.00011034405],"genre_scores_gemma":[0.81366956,0.000015003872,0.18594936,0.00022723876,0.000036249443,0.0000013746852,3.885634e-7,0.0000023005507,0.00009853683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996171,0.000012076313,0.0000660246,0.0001706637,0.000044499317,0.000089638],"domain_scores_gemma":[0.9998304,0.000013496183,0.000024642393,0.00009254019,0.00001957809,0.00001931811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000056689514,0.00004819735,0.000052231917,0.00006852983,0.00013061284,0.000028496346,0.00005752323,0.000039789018,0.0000017965764],"category_scores_gemma":[0.000008007764,0.000042764757,0.0000067226997,0.00010394768,0.000054443524,0.00020796167,0.00006237409,0.00005968574,0.0000018537062],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000025675324,0.000036282043,0.08590199,0.0000115143575,0.000019724932,0.00002587575,0.00066559063,0.000028479863,0.00823445,0.004849334,0.001029437,0.8991948],"study_design_scores_gemma":[0.0003000069,0.000045196368,0.37018678,0.000021791218,0.000004984571,0.00018655277,0.000015891554,0.62448144,0.0016893228,0.0011491398,0.0017885581,0.00013035892],"about_ca_topic_score_codex":0.00002042581,"about_ca_topic_score_gemma":0.000001226072,"teacher_disagreement_score":0.8990644,"about_ca_system_score_codex":0.0000105001845,"about_ca_system_score_gemma":0.0000069145563,"threshold_uncertainty_score":0.17438962},"labels":[],"label_agreement":null},{"id":"W2023508164","doi":"10.1371/journal.pone.0111197","title":"Smaller Is Better: Drift in Gaze Measurements due to Pupil Dynamics","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"National Natural Science Foundation of China","keywords":"Pupil; Gaze; Eye tracking; Optics; Computer vision; Tracking (education); BitTorrent tracker; Optometry; Population; Pupil diameter; Eye movement; Position (finance); Artificial intelligence; Computer science; Mathematics; Physics; Psychology; Medicine","score_opus":0.05890305208906619,"score_gpt":0.22655086919708484,"score_spread":0.16764781710801865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023508164","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86282855,0.000014723546,0.120761834,0.013147851,0.000079941616,0.00015166462,0.0000021372775,0.0002885979,0.0027246946],"genre_scores_gemma":[0.9441531,0.0000014515579,0.053794578,0.0017759398,0.000029821838,0.000024650506,0.0000013867292,0.000010624783,0.00020848615],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987056,0.00005274229,0.00018474105,0.0003892999,0.00034509323,0.00032247373],"domain_scores_gemma":[0.9991874,0.000036035344,0.00004720652,0.0005741944,0.00008101179,0.0000741281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030999994,0.00012441947,0.00022338319,0.00018730291,0.000048005902,0.000054383654,0.0007923378,0.00008438726,0.000013278964],"category_scores_gemma":[0.00010607969,0.00012665027,0.000027596097,0.00035178254,0.000027197984,0.000099922734,0.00021743542,0.00018819034,0.0003107288],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023264965,0.0047603934,0.728902,0.00014218185,0.00030131865,0.00006945887,0.0014005172,0.00004783262,0.030577226,0.029259203,0.0025421043,0.20197451],"study_design_scores_gemma":[0.0017861625,0.00066509267,0.7791721,0.0006816256,0.00004951508,0.0000097436205,0.00002622206,0.119312644,0.0810752,0.014705977,0.0014150083,0.0011006732],"about_ca_topic_score_codex":0.00004037772,"about_ca_topic_score_gemma":0.00020930902,"teacher_disagreement_score":0.20087384,"about_ca_system_score_codex":0.000077870915,"about_ca_system_score_gemma":0.000016025053,"threshold_uncertainty_score":0.51646477},"labels":[],"label_agreement":null},{"id":"W2025371974","doi":"10.1167/14.10.859","title":"Revaluating the Visual Short-Term Memory Benefit for 3-D Stimuli","year":2014,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Stimulus (psychology); Psychology; Orientation (vector space); Cognition; Cognitive psychology; Mathematics; Communication; Arithmetic; Artificial intelligence; Computer science; Geometry","score_opus":0.03494169714289464,"score_gpt":0.3553582928881674,"score_spread":0.32041659574527276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025371974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5077002,0.000111434056,0.4890079,0.0025071106,0.00043209994,0.00007005631,2.427663e-7,0.000030173936,0.0001407477],"genre_scores_gemma":[0.97127163,0.0000069527205,0.028282976,0.00015333944,0.00023441922,0.0000015583654,1.740168e-7,0.000005900896,0.00004304637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904734,0.000058785714,0.00032324076,0.00012734585,0.0002868935,0.00015637804],"domain_scores_gemma":[0.9988284,0.0004081916,0.00025106658,0.00022249398,0.00024787604,0.00004197053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017611783,0.000080158075,0.00016341332,0.00009348122,0.00017471104,0.00008581109,0.00069652946,0.000053616735,0.0000034854852],"category_scores_gemma":[0.00032007234,0.000047427075,0.00011293774,0.00011761323,0.000038418966,0.00019635368,0.00011029696,0.00020288878,0.000004430494],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001805021,0.00006819246,0.0008756661,0.000011678016,0.000016503573,0.000003983451,0.00015277398,0.0004036516,0.020430988,0.004342194,0.0010096508,0.9726667],"study_design_scores_gemma":[0.0021164352,0.0063201454,0.3446611,0.0006612891,0.00010301615,0.00043436882,0.00013448803,0.5870502,0.017768515,0.034510788,0.005795999,0.00044365082],"about_ca_topic_score_codex":6.830325e-7,"about_ca_topic_score_gemma":0.0000010130179,"teacher_disagreement_score":0.97222304,"about_ca_system_score_codex":0.000027122493,"about_ca_system_score_gemma":0.000026075202,"threshold_uncertainty_score":0.19340199},"labels":[],"label_agreement":null},{"id":"W2025420702","doi":"10.3109/17483107.2011.629330","title":"Evaluation of an ambient noise insensitive hum-based powered wheelchair controller","year":2011,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique; Holland Bloorview Kids Rehabilitation Hospital; Université du Québec à Montréal","funders":"","keywords":"Hum; Wheelchair; Noise (video); Controller (irrigation); Ambient noise level; Computer science; Acoustics; Automotive engineering; Engineering; Physics; Biology; Sound (geography); Art; Artificial intelligence","score_opus":0.028761669149006425,"score_gpt":0.28237279895202644,"score_spread":0.25361112980302003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025420702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9584287,0.000073190065,0.03764664,0.001942186,0.0001280787,0.0007515506,0.00002005318,0.0004991269,0.0005105132],"genre_scores_gemma":[0.9614478,0.0000012622988,0.038303293,0.000053135624,0.0000067999786,0.00016677087,0.0000073576116,0.000010461302,0.000003114526],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99702036,0.0007660937,0.0005523385,0.0008567157,0.00050775555,0.00029675895],"domain_scores_gemma":[0.99685144,0.0004828144,0.00030588824,0.0008291038,0.0014449286,0.00008584196],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.002554659,0.0002446316,0.0004533241,0.0004095543,0.00016435835,0.000017861246,0.00043733625,0.00035469895,0.000023643346],"category_scores_gemma":[0.0022974883,0.00022140871,0.000110816814,0.00079229206,0.002772287,0.0003021845,0.00013526272,0.00026569542,0.0000067127594],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031010978,0.004212235,0.47128913,0.000095478965,0.00015159359,0.000003869807,0.0019136971,0.00007428289,0.0101931,0.15945804,0.00002302414,0.35227546],"study_design_scores_gemma":[0.0018449103,0.001564429,0.9250893,0.000038570728,0.00007267752,0.0000045758716,0.0011441758,0.02502714,0.007404565,0.037547633,0.000024311004,0.00023773534],"about_ca_topic_score_codex":0.00009595295,"about_ca_topic_score_gemma":0.00010937507,"teacher_disagreement_score":0.45380017,"about_ca_system_score_codex":0.00021004847,"about_ca_system_score_gemma":0.00013923619,"threshold_uncertainty_score":0.9999416},"labels":[],"label_agreement":null},{"id":"W2026937303","doi":"10.1109/rose.2012.6402633","title":"Gaze estimation using Kinect/PTZ camera","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer vision; Gaze; Artificial intelligence; Computer science; Orientation (vector space); Position (finance); Head (geology); Calibration; Multinomial logistic regression; Mathematics; Statistics","score_opus":0.033605722980399995,"score_gpt":0.286727441025584,"score_spread":0.253121718045184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026937303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17022787,0.000044020286,0.82601136,0.00038030758,0.00022529568,0.000029606223,1.4297778e-7,0.0003956013,0.0026858128],"genre_scores_gemma":[0.7419028,4.5893404e-7,0.25781924,0.00011738588,0.000029585073,0.000001522487,3.5338792e-7,0.0000029412367,0.00012569681],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943626,0.000018235669,0.00008981735,0.00012832964,0.000089793415,0.00023755337],"domain_scores_gemma":[0.9996025,0.000029181043,0.00003650795,0.00025826157,0.000027303302,0.00004624657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013453624,0.000067975794,0.0000722195,0.00007782564,0.00006612383,0.00003696864,0.00025969726,0.00004380254,0.000023620167],"category_scores_gemma":[0.000033386634,0.00005785156,0.000022465072,0.00021716763,0.000026658501,0.00038862257,0.0000886958,0.000074141084,0.00012482794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017607916,0.00019696877,0.037108775,0.000014471277,0.000022370787,0.0000064642113,0.0006131483,0.0020953354,0.012203711,0.60415125,0.001859989,0.34172574],"study_design_scores_gemma":[0.00023777504,0.00005718609,0.059496112,0.000021424803,0.000010299245,0.00013987596,0.000034345572,0.913907,0.01880111,0.00471319,0.0022618491,0.00031982738],"about_ca_topic_score_codex":0.000041570307,"about_ca_topic_score_gemma":0.0000016426462,"teacher_disagreement_score":0.91181165,"about_ca_system_score_codex":0.000029467243,"about_ca_system_score_gemma":0.000013217947,"threshold_uncertainty_score":0.23591183},"labels":[],"label_agreement":null},{"id":"W2028264897","doi":"10.1117/12.527092","title":"A focus of attention mechanism for gaze control within a framework for intelligent image analysis tools","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Focus (optics); Computer science; Computer vision; Object (grammar); Segmentation; Gaze; Image segmentation; Cluster analysis; Image (mathematics); Object detection; Field (mathematics); Mathematics","score_opus":0.01348796836394159,"score_gpt":0.24520162899069356,"score_spread":0.23171366062675197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028264897","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5638703,0.000024744762,0.4324254,0.0027174293,0.00012911581,0.0006366366,0.0000637271,0.00008767428,0.000044937817],"genre_scores_gemma":[0.5067622,0.000010347806,0.49279284,0.00004633106,0.00007712753,0.00027368034,0.000003519684,0.000020404996,0.000013505057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99774426,2.2097417e-8,0.0008444809,0.00050734245,0.00050199137,0.00040190257],"domain_scores_gemma":[0.99594265,0.00033383333,0.0007077928,0.000106402855,0.0028220485,0.000087287015],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00082768605,0.00029499969,0.00061135675,0.00023839998,0.000090964895,0.0001461903,0.0014678923,0.00026003286,0.0000016237332],"category_scores_gemma":[0.0015000725,0.0002517671,0.0012754939,0.0006612825,0.00019193316,0.00053326087,0.00014503334,0.00025185721,4.8866696e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006758324,0.00013499382,0.000069127935,0.00023783834,0.0010095517,4.045519e-8,0.00014784152,0.00027013614,0.25007954,0.7474904,0.000056685705,0.00043623685],"study_design_scores_gemma":[0.0023573844,0.0010734774,0.0005603184,0.00045871295,0.0009234881,0.0000083105115,0.00080613606,0.12294369,0.5224394,0.34788284,0.00009254745,0.00045368445],"about_ca_topic_score_codex":0.000009688347,"about_ca_topic_score_gemma":3.7358768e-7,"teacher_disagreement_score":0.3996076,"about_ca_system_score_codex":0.00016470598,"about_ca_system_score_gemma":0.000045284374,"threshold_uncertainty_score":0.99999344},"labels":[],"label_agreement":null},{"id":"W2030079037","doi":"10.1371/journal.pone.0059244","title":"Eye Exercises Enhance Accuracy and Letter Recognition, but Not Reaction Time, in a Modified Rapid Serial Visual Presentation Task","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadians Living with HIV; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rapid serial visual presentation; Eye movement; Task (project management); Computer science; Serial reaction time; Visual search; Presentation (obstetrics); Cognition; Audiology; Psychology; Medicine; Artificial intelligence; Neuroscience; Surgery","score_opus":0.03525100974852008,"score_gpt":0.2585852417275469,"score_spread":0.2233342319790268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030079037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.992855,0.000015133722,0.003875891,0.0025035525,0.000056933117,0.00033064332,0.0000031564407,0.00020337274,0.00015629827],"genre_scores_gemma":[0.9923254,0.000038375,0.0068907165,0.0002613565,0.00010063997,0.00014933449,0.000014716499,0.000007921845,0.00021149288],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900484,0.00008454605,0.00019943817,0.00034442852,0.00017513092,0.00019160788],"domain_scores_gemma":[0.9994248,0.00013679371,0.000104273546,0.00018929371,0.00010889345,0.000035920628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001307174,0.00010678213,0.00016452788,0.0001454881,0.00007304349,0.00008565824,0.00017597034,0.00008916254,0.000020652185],"category_scores_gemma":[0.00019510054,0.00010866948,0.000016130796,0.00019133562,0.000051264724,0.0008295559,0.000078627854,0.00017972,0.00026894143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002732191,0.00039864515,0.0003793864,0.000020527885,0.000015677431,0.0000026655166,0.00014811968,6.6053116e-7,0.95719326,0.000022913215,0.0002562711,0.04153457],"study_design_scores_gemma":[0.00080540753,0.00018014605,0.124920376,0.00014286571,0.00002583713,0.0000027625695,0.000023889295,0.016818382,0.854749,0.0020285815,0.000022453882,0.00028028752],"about_ca_topic_score_codex":0.00014999205,"about_ca_topic_score_gemma":0.0000073826614,"teacher_disagreement_score":0.12454099,"about_ca_system_score_codex":0.000026330588,"about_ca_system_score_gemma":0.000014598225,"threshold_uncertainty_score":0.4431413},"labels":[],"label_agreement":null},{"id":"W2031255826","doi":"10.1145/1344471.1344504","title":"Real-time simulation of visual defects with gaze-contingent display","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Visual field; Computer vision; Gaze; Artificial intelligence; Gaze-contingency paradigm; Virtual reality; OpenGL; Stereoscopy; Optical head-mounted display; Visualization; Projection (relational algebra); Distortion (music); Human visual system model; GLARE; Computer graphics (images); Visual perception; Psychology; Image (mathematics); Algorithm","score_opus":0.01233248781809094,"score_gpt":0.25324743662153143,"score_spread":0.2409149488034405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031255826","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7328649,0.0000045861725,0.26396176,0.00004326821,0.00001917539,0.00005084547,2.1131244e-7,0.00022894464,0.002826325],"genre_scores_gemma":[0.98170537,0.0000031483612,0.017830404,0.000015459067,0.000009906577,0.0000025037048,9.3830675e-7,0.0000048628654,0.0004273826],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934345,0.000021854636,0.00012646713,0.0002072393,0.00014948037,0.00015149536],"domain_scores_gemma":[0.9995008,0.000095295625,0.00007984062,0.00022699698,0.000066400586,0.000030641546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007613061,0.00008126938,0.0001305901,0.00007809631,0.00006385506,0.00000912103,0.00021756391,0.000043482534,0.000011310101],"category_scores_gemma":[0.000020174572,0.000059280053,0.000028770703,0.00023154495,0.00007474632,0.00011703983,0.0000616441,0.00005193348,0.000033492623],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018036205,0.001998671,0.47671786,0.00010327255,0.00023621925,0.00037093915,0.0025795929,0.07080916,0.23841913,0.1409808,0.0014801858,0.06612381],"study_design_scores_gemma":[0.0007877976,0.00084634346,0.33972314,0.000047303158,0.000012908328,0.000045982702,0.000013311452,0.59984136,0.05780833,0.00037125422,0.00020885645,0.00029338975],"about_ca_topic_score_codex":0.00003492911,"about_ca_topic_score_gemma":0.0000054849797,"teacher_disagreement_score":0.52903223,"about_ca_system_score_codex":0.000013308952,"about_ca_system_score_gemma":0.00002825639,"threshold_uncertainty_score":0.24173704},"labels":[],"label_agreement":null},{"id":"W2031734938","doi":"10.1145/1743666.1743742","title":"BlinkWrite2","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Text entry; Modality (human–computer interaction); Computer science; Human–computer interaction","score_opus":0.006044382257825499,"score_gpt":0.22551444174689578,"score_spread":0.21947005948907028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031734938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14924884,0.0000045720244,0.75073975,0.00623093,0.0005825852,0.000026837975,1.276973e-7,0.0012437998,0.091922574],"genre_scores_gemma":[0.8689838,2.5737174e-7,0.1298132,0.0001760806,0.00002035456,0.0000014360032,8.63837e-8,0.00000128549,0.0010035],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996826,0.0000033477925,0.000043290715,0.00012410233,0.000047553174,0.000099111225],"domain_scores_gemma":[0.99961066,0.000018833296,0.000010259086,0.0003139375,0.000021930427,0.000024390798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006963886,0.000033138174,0.000034928416,0.000044848075,0.000033674958,0.000035918692,0.0004831225,0.000041957494,0.000054095453],"category_scores_gemma":[0.0000220752,0.000025983836,0.000015238735,0.00013084325,0.00002976775,0.00008226968,0.00008299993,0.0001515572,0.0003194691],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[7.15361e-8,0.000012662774,0.0018862233,2.9297394e-7,9.102622e-7,0.000003117049,0.0000074827776,9.218496e-8,0.013695547,0.9180874,0.0014604403,0.06484573],"study_design_scores_gemma":[0.00066578994,0.00014453124,0.16617976,0.0000049451087,0.000003851095,0.00018533888,0.000023719807,0.02468245,0.15577555,0.20912498,0.4426148,0.0005942901],"about_ca_topic_score_codex":0.0000043517293,"about_ca_topic_score_gemma":0.000014371593,"teacher_disagreement_score":0.71973497,"about_ca_system_score_codex":0.0000012552202,"about_ca_system_score_gemma":0.00000936157,"threshold_uncertainty_score":0.41062358},"labels":[],"label_agreement":null},{"id":"W2033695446","doi":"10.1145/1743666.1743715","title":"Listing's and Donders' laws and the estimation of the point-of-gaze","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"","keywords":"Listing (finance); Gaze; Point (geometry); Estimation; Computer science; Artificial intelligence; Mathematics; Economics; Geometry; Management","score_opus":0.004959878200549474,"score_gpt":0.21068464967548745,"score_spread":0.20572477147493798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033695446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84513426,0.00004106947,0.14122786,0.009426566,0.0001460115,0.000102654565,4.939178e-7,0.00006178916,0.003859275],"genre_scores_gemma":[0.9772399,0.0000020296213,0.02259096,0.00007097826,0.0000041577678,0.0000013822698,4.811091e-8,0.0000012837331,0.00008927945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999668,0.000023893403,0.00009257097,0.00009520462,0.00006339235,0.00005694676],"domain_scores_gemma":[0.99943787,0.00018631254,0.00007227239,0.0002628672,0.000031026957,0.000009626625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027893062,0.000040101775,0.000074139556,0.00002053934,0.000060644143,0.000021013682,0.00027516868,0.000034261946,0.0000023515477],"category_scores_gemma":[0.00017146832,0.000018916813,0.000015538812,0.00009724457,0.00044581632,0.00006137099,0.00016789074,0.00010662276,4.3851753e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024880476,0.0000132830255,0.0032455663,0.000009262335,0.000004699919,1.4294562e-7,0.00033955896,0.0000051066695,0.0026014145,0.9461268,0.000106694475,0.047544956],"study_design_scores_gemma":[0.0025055641,0.00011250444,0.33256635,0.00007004115,0.00003435311,0.0000938206,0.00025463474,0.31211796,0.064306505,0.28711358,0.0005999628,0.0002247445],"about_ca_topic_score_codex":0.000079127654,"about_ca_topic_score_gemma":0.000048486796,"teacher_disagreement_score":0.6590133,"about_ca_system_score_codex":0.0000010154669,"about_ca_system_score_gemma":0.000010066131,"threshold_uncertainty_score":0.16426288},"labels":[],"label_agreement":null},{"id":"W2033995932","doi":"10.1145/1743666.1743672","title":"User-calibration-free remote gaze estimation system","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer science; Calibration; Computer vision; Estimation; Artificial intelligence; Human–computer interaction; Computer graphics (images); Engineering; Statistics; Mathematics","score_opus":0.008509326026478957,"score_gpt":0.22846682863809714,"score_spread":0.21995750261161817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033995932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033018705,0.0000030316837,0.95431715,0.0026659456,0.0005233763,0.00006877595,6.531682e-7,0.001515979,0.007886407],"genre_scores_gemma":[0.6450915,1.8578848e-7,0.35436258,0.00006097722,0.000025348329,0.0000017657782,9.920334e-7,0.0000036091405,0.00045305662],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929476,0.000019237039,0.00014883907,0.00024394525,0.00014044349,0.00015277782],"domain_scores_gemma":[0.99891543,0.000053827658,0.000057269204,0.00087559345,0.00005366367,0.000044212367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017367887,0.00008225902,0.000089204805,0.00008664227,0.000094300296,0.00012631099,0.0008934984,0.00009802679,0.000014909011],"category_scores_gemma":[0.000106625324,0.00006928772,0.00002818655,0.00022900656,0.000037470658,0.00033500424,0.0001678271,0.00017643059,0.000121456855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.979355e-7,0.000011810928,0.0003125396,0.000010001199,0.0000037457419,0.0000053550666,0.000027376092,0.000059810805,0.0037955076,0.9421284,0.0020948492,0.051549997],"study_design_scores_gemma":[0.00018932395,0.000026295309,0.004507313,0.000015159763,0.0000028801217,0.00004822424,0.000012791733,0.9733912,0.010098842,0.010159341,0.0014246241,0.00012401887],"about_ca_topic_score_codex":0.00004008663,"about_ca_topic_score_gemma":0.0000546068,"teacher_disagreement_score":0.9733314,"about_ca_system_score_codex":0.0000138554105,"about_ca_system_score_gemma":0.000028786919,"threshold_uncertainty_score":0.28254712},"labels":[],"label_agreement":null},{"id":"W2035385895","doi":"10.1109/tbme.2011.2108295","title":"Online Removal of Eye Movement and Blink EEG Artifacts Using a High-Speed Eye Tracker","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Neil Squire Society; University of British Columbia","funders":"","keywords":"Eye movement; Computer vision; Electrooculography; Electroencephalography; Eye tracking; Artificial intelligence; Computer science; Psychology; Neuroscience","score_opus":0.02639394091989412,"score_gpt":0.2438981828744726,"score_spread":0.2175042419545785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035385895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4004034,0.00002209106,0.59885067,0.00016376401,0.0003063172,0.00005461603,0.000009206584,0.00018138647,0.000008565205],"genre_scores_gemma":[0.87734747,0.000018592755,0.12252257,0.000049285274,0.00001882587,0.0000027541653,0.0000011112396,0.0000136838435,0.00002571405],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987969,0.000015866222,0.00031849157,0.0003212071,0.00025594822,0.00029163528],"domain_scores_gemma":[0.9994014,0.000040165196,0.000060018898,0.0003054015,0.000042382067,0.00015064703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014534064,0.00017636942,0.00023881263,0.00038482866,0.000057759935,0.00001526922,0.00028511902,0.00015267165,0.000021466909],"category_scores_gemma":[0.000010540547,0.00016439465,0.00006115953,0.00048722164,0.00012889807,0.00013680573,0.000006755439,0.00030799265,0.000004880945],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052321346,0.0025593396,0.000045968245,0.0001700694,0.00030140733,0.00034737316,0.0011216697,0.01260946,0.7131509,0.0044234754,0.000013622813,0.2652044],"study_design_scores_gemma":[0.0014320597,0.00060043647,0.01016692,0.00026909725,0.00007041538,0.000079115,0.00005387708,0.7378119,0.2480285,0.00044966032,0.00049910974,0.0005388819],"about_ca_topic_score_codex":0.00007045279,"about_ca_topic_score_gemma":0.0000032542466,"teacher_disagreement_score":0.72520244,"about_ca_system_score_codex":0.000043395677,"about_ca_system_score_gemma":0.000031276028,"threshold_uncertainty_score":0.6703819},"labels":[],"label_agreement":null},{"id":"W2036298055","doi":"10.1145/765891.765981","title":"EyePliances","year":2003,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Gaze; Human–computer interaction; Context (archaeology); Eye contact; Eye tracking; Mechanism (biology); Computer vision; Artificial intelligence; Communication; Psychology","score_opus":0.014364373586284217,"score_gpt":0.2359422950031564,"score_spread":0.22157792141687216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036298055","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074115037,0.000047561596,0.78039336,0.0008138274,0.00013755486,0.000014579318,3.2334373e-8,0.0004256128,0.21075594],"genre_scores_gemma":[0.8983122,0.0000017748348,0.09973874,0.00018212407,0.0000028553382,0.0000017955643,2.0601991e-8,9.523212e-7,0.001759496],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996947,0.000011020616,0.000038685186,0.00011791151,0.000041757132,0.000095934825],"domain_scores_gemma":[0.9997467,0.000014715672,0.000009981846,0.00020005442,0.000012667666,0.000015923368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065477274,0.00003082775,0.0000350758,0.000024439592,0.00003268565,0.00002297211,0.000249478,0.000018199737,0.00003456216],"category_scores_gemma":[0.000024673272,0.000023822495,0.000012396906,0.00013603676,0.00001913224,0.00007416126,0.00001859302,0.00003463572,0.00021315186],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.128266e-8,0.000007252586,0.001821033,3.3449902e-7,9.550001e-7,0.000001942396,0.000005774051,6.494086e-7,0.00016644363,0.982588,0.0009795699,0.014428045],"study_design_scores_gemma":[0.00039302386,0.0001212988,0.034818098,0.00000893447,0.0000023770524,0.00006773895,0.000055099128,0.0018574263,0.121345215,0.30729795,0.5336397,0.00039310355],"about_ca_topic_score_codex":0.0000018512792,"about_ca_topic_score_gemma":0.0000013238174,"teacher_disagreement_score":0.89090073,"about_ca_system_score_codex":0.0000038613903,"about_ca_system_score_gemma":0.000010045544,"threshold_uncertainty_score":0.2739707},"labels":[],"label_agreement":null},{"id":"W2037215996","doi":"10.3758/s13414-012-0298-8","title":"Erratum to: Improved top-down control reduces oculomotor capture: The case of action video game players","year":2012,"lang":"en","type":"erratum","venue":"Attention Perception & Psychophysics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Video game; Action (physics); Control (management); Computer science; Psychology; Multimedia; Artificial intelligence; Physics","score_opus":0.018076069547445173,"score_gpt":0.2892010940933005,"score_spread":0.27112502454585535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037215996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14133379,0.00060188485,0.70493066,0.01379758,0.1267795,0.0032089874,0.0002932289,0.0015271327,0.007527252],"genre_scores_gemma":[0.96577436,0.0000934127,0.0013296382,0.0007054112,0.0019129452,0.00019437657,0.00012518706,0.000059277012,0.029805385],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970799,0.00026033036,0.0006862631,0.000948435,0.0004243154,0.0006007537],"domain_scores_gemma":[0.99700266,0.00007528833,0.0008190559,0.0014564495,0.00047304976,0.00017349808],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040941034,0.0005540951,0.00059788383,0.00030736072,0.0003177572,0.00020033141,0.0011341324,0.0007341127,0.000070465045],"category_scores_gemma":[0.00005061475,0.00044930383,0.00049782207,0.00079776946,0.00022843962,0.0006033231,0.00013953894,0.0013784829,0.00025739212],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080357815,0.00046597206,0.000042007545,0.00012392322,0.0002655391,0.000028656365,0.0015610026,0.000031062216,0.12540035,0.0019027723,0.722191,0.1479074],"study_design_scores_gemma":[0.014249389,0.0049496605,0.12533851,0.002886887,0.0038063626,0.004199778,0.017012887,0.06526726,0.0033064056,0.024837509,0.7228264,0.011318945],"about_ca_topic_score_codex":0.00025603888,"about_ca_topic_score_gemma":0.0000633371,"teacher_disagreement_score":0.8244406,"about_ca_system_score_codex":0.00019594203,"about_ca_system_score_gemma":0.0001187697,"threshold_uncertainty_score":0.99979585},"labels":[],"label_agreement":null},{"id":"W2038219465","doi":"10.1145/642611.642702","title":"GAZE-2","year":2003,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer vision; Gaze; Parallax; Eye contact; Computer science; Artificial intelligence; Eye tracking; Perception; Computer graphics (images); Communication; Psychology","score_opus":0.011621290177702796,"score_gpt":0.2200949384350219,"score_spread":0.2084736482573191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038219465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055459356,0.000024450923,0.79513186,0.0006023496,0.00010196656,0.0000132342275,2.7164234e-8,0.0004136945,0.19816646],"genre_scores_gemma":[0.901554,0.0000011007578,0.096444905,0.00020676029,0.0000026832668,0.0000014999208,2.9113467e-8,0.0000012054089,0.0017878509],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996857,0.000013033676,0.0000403096,0.00011522838,0.000042877953,0.00010283546],"domain_scores_gemma":[0.99971825,0.000014801473,0.000008816638,0.00022548842,0.000013830922,0.000018786055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007019188,0.000032268366,0.000035312034,0.00003156698,0.000029353365,0.000020782993,0.000236698,0.000021595868,0.00004244517],"category_scores_gemma":[0.000028711851,0.000025751155,0.0000139047115,0.00013753322,0.000015889329,0.00005936269,0.000022992008,0.000040938943,0.0002587807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.005062e-8,0.000008754328,0.0009091463,2.5956882e-7,9.70029e-7,0.0000028172676,0.000006049712,6.3548083e-7,0.0002673155,0.9858786,0.0012346711,0.011690752],"study_design_scores_gemma":[0.0005552766,0.0001404717,0.029710753,0.000008783493,0.0000031264158,0.00014310215,0.000041726,0.0028758713,0.12637958,0.29962033,0.54006326,0.0004577471],"about_ca_topic_score_codex":0.0000018391785,"about_ca_topic_score_gemma":0.000001036824,"teacher_disagreement_score":0.896008,"about_ca_system_score_codex":0.0000049147357,"about_ca_system_score_gemma":0.000010633354,"threshold_uncertainty_score":0.33261886},"labels":[],"label_agreement":null},{"id":"W2038977635","doi":"10.1080/07370024.2010.500146","title":"Providing Dynamic Visual Information for Collaborative Tasks: Experiments With Automatic Camera Control","year":2010,"lang":"en","type":"article","venue":"Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Task (project management); Human–computer interaction; Focus (optics); Control (management); Point (geometry); Orientation (vector space); Negotiation; Multimedia; Artificial intelligence; Systems engineering; Engineering","score_opus":0.011482658663356403,"score_gpt":0.3047139496860821,"score_spread":0.2932312910227257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038977635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3183725,0.0000028261682,0.6794594,0.00022920704,0.0008779082,0.00050106854,0.0000037841644,0.0004334118,0.00011989826],"genre_scores_gemma":[0.9324335,2.3333195e-7,0.066971704,0.00019740572,0.000093284165,0.0002429577,0.000025780946,0.000011318786,0.00002382267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998872,0.00004246657,0.00032766486,0.00030694233,0.00018576995,0.00026517044],"domain_scores_gemma":[0.9989048,0.00011090613,0.0003305917,0.0002911232,0.00031089937,0.00005164967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016224236,0.00020461188,0.0002167101,0.0002829599,0.00029729863,0.00043287151,0.00038502703,0.00009848921,0.000011029404],"category_scores_gemma":[0.000019244964,0.00017599482,0.000050309493,0.0002266752,0.000058869606,0.0021020877,0.000062860796,0.00029284562,0.000039619947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024697214,0.0010244331,0.0015395884,0.00025907383,0.0005437777,0.000022677079,0.012848757,0.0014973679,0.14887741,0.05959338,0.0037356168,0.7698109],"study_design_scores_gemma":[0.0021856716,0.0011279973,0.0036897857,0.000095674346,0.000022166803,0.000058976297,0.00028157546,0.9738867,0.014341154,0.00043404833,0.0035046628,0.00037155653],"about_ca_topic_score_codex":0.000014322261,"about_ca_topic_score_gemma":0.000033505363,"teacher_disagreement_score":0.97238934,"about_ca_system_score_codex":0.00010494582,"about_ca_system_score_gemma":0.00005877964,"threshold_uncertainty_score":0.71768606},"labels":[],"label_agreement":null},{"id":"W2040362090","doi":"10.1167/13.9.343","title":"Eye-hand coordination: Differential effects of obstacle position on reach trajectories, grasp and gaze locations.","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gaze; Workspace; Computer vision; Object (grammar); GRASP; Computer science; Trajectory; Obstacle; Artificial intelligence; Eye–hand coordination; Task (project management); Path (computing); Perception; Orientation (vector space); Position (finance); Psychology; Robot; Mathematics; Engineering; Physics; Geography; Geometry","score_opus":0.0058296581417845205,"score_gpt":0.24647845306921026,"score_spread":0.24064879492742575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040362090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8844436,0.00017087946,0.113688104,0.0012613139,0.0003086495,0.00007667544,3.517164e-7,0.000016457578,0.00003401073],"genre_scores_gemma":[0.99727833,0.000020488385,0.002591793,0.000021726784,0.000047621423,0.000001769521,8.13532e-7,0.000003765605,0.00003366039],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992861,0.000058621885,0.00023956658,0.00011004067,0.0002159121,0.00008971547],"domain_scores_gemma":[0.9991608,0.00013781108,0.00025990343,0.00014190261,0.0002533717,0.000046156994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010340944,0.00007617622,0.00015878674,0.00018077015,0.00008290838,0.00007511251,0.00019345242,0.000062614476,0.0000048172806],"category_scores_gemma":[0.000079351004,0.00005817143,0.000040646602,0.00016385068,0.00006387787,0.00032221637,0.000036252015,0.00014619593,0.0000032008447],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004940837,0.0007789501,0.005832452,0.00012294823,0.000059896563,0.000024628482,0.0005432006,0.000026830066,0.6365325,0.019171627,0.00290817,0.3339494],"study_design_scores_gemma":[0.0008417769,0.0016810787,0.9142596,0.00028487376,0.000018418874,0.000049435617,0.000018580784,0.0019126702,0.07608021,0.0046895253,0.00007792186,0.000085918655],"about_ca_topic_score_codex":0.000012984523,"about_ca_topic_score_gemma":8.597612e-7,"teacher_disagreement_score":0.9084271,"about_ca_system_score_codex":0.000033183704,"about_ca_system_score_gemma":0.000021327944,"threshold_uncertainty_score":0.2372162},"labels":[],"label_agreement":null},{"id":"W2040379008","doi":"10.1109/icdim.2010.5664741","title":"Quantitative measurements of cognitive processing involved with gaze fixations","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Gaze; Cognition; Neurophysiology; Eye movement; Fixation (population genetics); Computer science; Task (project management); Cognitive load; Cognitive psychology; Computer vision; Artificial intelligence; Psychology; Neuroscience; Medicine; Engineering","score_opus":0.062173045296396466,"score_gpt":0.3025700037730705,"score_spread":0.240396958476674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040379008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4343251,0.0000099249055,0.56004816,0.00028987014,0.00004186393,0.000088021436,0.000001067968,0.00016398478,0.0050319796],"genre_scores_gemma":[0.8417792,1.3771995e-7,0.15810315,0.00003888023,0.000003662767,0.000010688127,0.0000011617841,0.0000035570238,0.000059563234],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99935097,0.000019267545,0.00011858381,0.000202448,0.00018041728,0.00012832308],"domain_scores_gemma":[0.99916255,0.00007384146,0.00011399636,0.00015795983,0.0004625325,0.000029095914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015604401,0.000077401375,0.00010522192,0.000102127255,0.000079854435,0.0000310294,0.00028550258,0.00004234986,0.0000105158015],"category_scores_gemma":[0.00017697443,0.00005644836,0.000016741695,0.00037162367,0.00016451953,0.00023765363,0.000047061294,0.00014890778,0.000010482003],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006785536,0.00095704105,0.32241982,0.000074823416,0.00017705774,0.000011841365,0.0053122486,0.000015667141,0.16427736,0.34141746,0.00016313173,0.16510569],"study_design_scores_gemma":[0.0023553965,0.0017685747,0.49739745,0.00053013465,0.00006553789,0.000018542672,0.0017094695,0.017388718,0.46521553,0.012864758,0.000062667445,0.00062321004],"about_ca_topic_score_codex":0.000022413176,"about_ca_topic_score_gemma":0.00040337045,"teacher_disagreement_score":0.40745407,"about_ca_system_score_codex":0.000005490298,"about_ca_system_score_gemma":0.00009145499,"threshold_uncertainty_score":0.23018973},"labels":[],"label_agreement":null},{"id":"W2041212402","doi":"10.1145/2168556.2168609","title":"A general framework for extension of a tracking range of user-calibration-free remote eye-gaze tracking systems","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Computer science; Eye tracking; Tracking (education); Gaze; Calibration; Tracking system; Stereo camera; Range (aeronautics); Mathematics; Engineering; Kalman filter","score_opus":0.04520925293459969,"score_gpt":0.29742288216964674,"score_spread":0.25221362923504703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041212402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19080949,0.000429977,0.80719817,0.00041031826,0.0005273744,0.00028419716,0.0000065587096,0.00021565835,0.000118274605],"genre_scores_gemma":[0.64871556,0.000006893554,0.35101974,0.00004437083,0.00011181314,0.000007779919,0.0000012773727,0.000013205115,0.00007933998],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998318,0.000082425446,0.0005546741,0.000335981,0.00028772323,0.00042116383],"domain_scores_gemma":[0.997966,0.00036807227,0.00037446563,0.0009548973,0.00025867065,0.0000779127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007002126,0.00018011968,0.00042290185,0.0001993553,0.00008965246,0.000054767985,0.00086219923,0.00023990123,0.000005714705],"category_scores_gemma":[0.00048788136,0.00015443772,0.00015316307,0.0004072153,0.000083584215,0.00060621835,0.00017351631,0.00017283857,0.0000020031896],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035848097,0.00024452942,0.015142367,0.0002739179,0.00006445229,0.0000025612417,0.00087752845,0.00031272508,0.030233093,0.90081656,0.0005480888,0.05144836],"study_design_scores_gemma":[0.0036878234,0.00093172956,0.22398603,0.0020900378,0.00017621415,0.000097241646,0.0006322805,0.51795685,0.182659,0.06338458,0.0029195778,0.0014786234],"about_ca_topic_score_codex":0.00008937686,"about_ca_topic_score_gemma":0.000005669958,"teacher_disagreement_score":0.83743197,"about_ca_system_score_codex":0.000025896457,"about_ca_system_score_gemma":0.00003549634,"threshold_uncertainty_score":0.62977874},"labels":[],"label_agreement":null},{"id":"W2041257003","doi":"10.1007/s10209-006-0034-z","title":"Effects of feedback and dwell time on eye typing speed and accuracy","year":2006,"lang":"en","type":"article","venue":"Universal Access in the Information Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Typing; Computer science; Dwell time; Gaze; Auditory feedback; Visual feedback; Words per minute; Process (computing); Eye tracking; Human–computer interaction; Artificial intelligence; Speech recognition; Psychology; Audiology; Medicine","score_opus":0.006145147353700687,"score_gpt":0.23200853460648166,"score_spread":0.22586338725278096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041257003","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97900236,0.000028862702,0.014595855,0.00087012287,0.000040622657,0.00016229041,0.0000015062537,0.00007266927,0.0052256845],"genre_scores_gemma":[0.99854654,0.000023301804,0.0011035977,0.00027821184,0.000007924289,6.319937e-7,0.0000019256659,0.0000012977275,0.00003657678],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99959695,0.000024439834,0.0001221114,0.00007138468,0.00009169194,0.00009341965],"domain_scores_gemma":[0.9994719,0.0002542018,0.00011555349,0.000115243434,0.000033456086,0.000009639731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017196474,0.0000640462,0.00007970638,0.000058349044,0.00007181903,0.00010755517,0.0003995035,0.00005410639,0.0000012281535],"category_scores_gemma":[0.000029345456,0.000048428803,0.00002357609,0.00024945554,0.000092048955,0.0013076626,0.00015042047,0.000111165995,0.00000636434],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010258281,0.00042986337,0.034774788,0.0010473138,0.00011999767,0.00001279549,0.036705058,0.0028802282,0.0043782187,0.62938005,0.018840851,0.27132824],"study_design_scores_gemma":[0.0041921968,0.00024910067,0.7697042,0.00030735007,0.00003319804,0.000014973755,0.0020110768,0.17264257,0.022585543,0.017229231,0.010451766,0.00057877105],"about_ca_topic_score_codex":0.000044547895,"about_ca_topic_score_gemma":7.348385e-7,"teacher_disagreement_score":0.73492944,"about_ca_system_score_codex":0.000021711316,"about_ca_system_score_gemma":0.000011621167,"threshold_uncertainty_score":0.19748694},"labels":[],"label_agreement":null},{"id":"W2041600651","doi":"10.1145/1452392.1452443","title":"A Fitts Law comparison of eye tracking and manual input in the selection of visual targets","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Stylus; Eye tracking; Computer science; Dwell time; Selection (genetic algorithm); Tracking (education); Computer vision; Artificial intelligence; Eye movement; Tracking error; Medicine; Psychology","score_opus":0.026773807566785938,"score_gpt":0.3216670883796394,"score_spread":0.2948932808128535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041600651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9581355,0.00005657556,0.040539127,0.0002447949,0.000026366952,0.00005690529,2.2355408e-7,0.00004630425,0.0008942318],"genre_scores_gemma":[0.9948097,0.000003891076,0.0051106974,0.000050254395,0.0000063734947,0.0000023942303,3.0679948e-7,0.0000019771296,0.000014397662],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935615,0.00005608139,0.00019724907,0.00014627531,0.00012866277,0.0001156033],"domain_scores_gemma":[0.99968046,0.00007812462,0.000082144994,0.000105798754,0.000042736163,0.000010754512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018479077,0.00006153587,0.00015493551,0.0000874796,0.00005842493,0.000008514224,0.00023646302,0.000051615436,0.000002351892],"category_scores_gemma":[0.000013331613,0.000044189976,0.000019736715,0.00027988237,0.00013861767,0.00011272628,0.000053491578,0.0001229296,8.4220835e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021414262,0.0008851616,0.75041384,0.00004530728,0.00002266313,0.00001387109,0.007810136,0.00013390933,0.01743524,0.19322406,0.00035262434,0.02964179],"study_design_scores_gemma":[0.00037925367,0.0004364739,0.8679629,0.000022289962,0.0000037941702,0.00003404124,0.00036517522,0.03242142,0.09705705,0.0009946318,0.0002181838,0.00010481034],"about_ca_topic_score_codex":0.00017657723,"about_ca_topic_score_gemma":0.00019705223,"teacher_disagreement_score":0.19222943,"about_ca_system_score_codex":0.0000073229303,"about_ca_system_score_gemma":0.000012718295,"threshold_uncertainty_score":0.18020149},"labels":[],"label_agreement":null},{"id":"W2041837016","doi":"10.1145/2168556.2168632","title":"Typing with eye-gaze and tooth-clicks","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Toronto Rehabilitation Institute; University of Toronto","funders":"Toronto Rehabilitation Institute","keywords":"Computer science; Clicker; Gaze; Mechanism (biology); Dwell time; Human–computer interaction; Computer vision; Psychology","score_opus":0.013712700055342553,"score_gpt":0.2391147530709317,"score_spread":0.22540205301558913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041837016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39639047,0.00024430722,0.5732685,0.0017116125,0.00011355534,0.00005384987,1.831908e-7,0.0005661394,0.027651422],"genre_scores_gemma":[0.94664884,0.0000037675181,0.052355718,0.00023371827,0.000024215626,0.0000028989668,1.11549824e-7,0.0000032215146,0.00072748744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995373,0.0000100832385,0.000052293573,0.00013792013,0.000049315568,0.00021307867],"domain_scores_gemma":[0.9996779,0.000024761686,0.000020389563,0.00020787044,0.000016869355,0.00005222368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010049672,0.000062366984,0.0000685246,0.000044458233,0.0000572613,0.000037999536,0.00018563982,0.00003496517,0.000011159217],"category_scores_gemma":[0.000009534908,0.00004295572,0.000008225501,0.00012154276,0.00005245219,0.00023998827,0.00010044314,0.000072883755,0.000041413783],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030760232,0.00005970864,0.3523143,0.000008085232,0.000017129067,0.00000385086,0.00038941344,0.0000011027911,0.0010893842,0.5020857,0.0007582034,0.14327008],"study_design_scores_gemma":[0.0004922821,0.00017714693,0.94446117,0.000036627465,0.000009745921,0.00010085802,0.00011752436,0.0016401146,0.007787547,0.001971874,0.042808507,0.00039662607],"about_ca_topic_score_codex":0.000012182434,"about_ca_topic_score_gemma":0.000004375143,"teacher_disagreement_score":0.5921469,"about_ca_system_score_codex":0.0000069988346,"about_ca_system_score_gemma":0.000007777211,"threshold_uncertainty_score":0.17516834},"labels":[],"label_agreement":null},{"id":"W2043164136","doi":"10.1109/3dui.2010.5444711","title":"Comparison of multimodal interactions in perspective-corrected multi-display environment","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Perspective (graphical); Gesture; Gaze; Human–computer interaction; Cursor (databases); Multimodal interaction; Task (project management); Interaction technique; Modal; Artificial intelligence; Computer vision; Affordance; Engineering","score_opus":0.028384297333156845,"score_gpt":0.3373326147438408,"score_spread":0.30894831741068396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043164136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.59128565,0.000012915165,0.40610862,0.00070283166,0.00038616618,0.000097895325,0.0000013531835,0.00015300576,0.0012515314],"genre_scores_gemma":[0.8493815,0.0000010550564,0.15043901,0.000015161785,0.000007872065,0.000009800904,6.8317445e-7,0.0000035637502,0.00014133465],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999231,0.000028906737,0.00020694766,0.0002785663,0.00009470766,0.00015988301],"domain_scores_gemma":[0.9994042,0.00009116009,0.000078707366,0.00035512014,0.000037065976,0.00003377111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000087053486,0.00009332206,0.00016742494,0.00018002816,0.00003804502,0.000014584658,0.00042950173,0.00005143454,0.0000807936],"category_scores_gemma":[0.00006196634,0.00008334598,0.00004022802,0.00017753817,0.00009851012,0.00013061218,0.00014111809,0.0004177899,0.00005509677],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013938206,0.0029569312,0.44949886,0.000004978902,0.00003122701,0.0000096437925,0.0040550525,0.00041640495,0.37550342,0.122472085,0.0002541777,0.04478328],"study_design_scores_gemma":[0.0005299833,0.0000555426,0.6135595,0.000008589053,0.0000022910724,0.000007399657,0.0005802159,0.33754793,0.046555683,0.00028746587,0.00072184694,0.00014359147],"about_ca_topic_score_codex":0.0006124804,"about_ca_topic_score_gemma":0.001258986,"teacher_disagreement_score":0.3371315,"about_ca_system_score_codex":0.000047914953,"about_ca_system_score_gemma":0.000016560794,"threshold_uncertainty_score":0.33987504},"labels":[],"label_agreement":null},{"id":"W2043663275","doi":"10.1159/000141008","title":"A FIBRE-ANALYSIS OF THE LARYNGEAL NERVE-SUPPLY IN MAN","year":2008,"lang":"en","type":"article","venue":"Acta Anatomica","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Nerve fibre; Superior laryngeal nerve; Recurrent laryngeal nerve; Anatomy; Medicine; Larynx; Internal medicine","score_opus":0.013496374998702964,"score_gpt":0.22782628322294982,"score_spread":0.21432990822424686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043663275","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9947655,0.00004531829,0.0007038831,0.0035381329,0.000045190598,0.000047478017,0.000003814604,0.00006306273,0.00078758015],"genre_scores_gemma":[0.9983506,0.000009666629,0.0013425048,0.0001544354,0.0000046285973,0.0000038084036,0.0000012761395,0.0000036062045,0.00012944503],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991838,0.000055797103,0.00019105453,0.00023709294,0.00014665954,0.00018559223],"domain_scores_gemma":[0.9991331,0.00006584186,0.000090897185,0.0006569681,0.000028110535,0.000025068483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110592584,0.000082019375,0.00022982563,0.00027933106,0.00006804229,0.000009982384,0.0011137434,0.00006690768,0.000021242382],"category_scores_gemma":[0.00003394183,0.00005950069,0.0001507085,0.0015231712,0.00012158165,0.000095924406,0.00024274644,0.00015608231,0.000009052396],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009905289,0.00024060358,0.9597136,0.000009102992,0.00044460208,0.00009406341,0.0011153362,0.000103052735,0.0075110216,0.020768754,0.0036164199,0.0063735885],"study_design_scores_gemma":[0.00016463305,0.000018277437,0.9793245,0.0000069338516,0.00004746128,0.000012156133,0.000016431251,0.014760708,0.004094376,0.0005395021,0.00093010376,0.000084908126],"about_ca_topic_score_codex":0.00030110503,"about_ca_topic_score_gemma":0.00016202376,"teacher_disagreement_score":0.020229252,"about_ca_system_score_codex":0.000028957958,"about_ca_system_score_gemma":0.000048865248,"threshold_uncertainty_score":0.24263677},"labels":[],"label_agreement":null},{"id":"W2044228361","doi":"10.3389/fnagi.2014.00312","title":"What's on TV? Detecting age-related neurodegenerative eye disease using eye movement scanpaths","year":2014,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Interquartile range; Eye movement; Medicine; Receiver operating characteristic; Glaucoma; Artificial intelligence; Confidence interval; Ophthalmology; Saccade; Audiology; Computer science; Internal medicine","score_opus":0.017243135045812,"score_gpt":0.2593497203685154,"score_spread":0.2421065853227034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044228361","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52224773,0.00007103861,0.47073644,0.0011704542,0.0052176896,0.00016163026,7.587835e-7,0.00031277226,0.00008148626],"genre_scores_gemma":[0.97908765,0.000020403771,0.018531216,0.0021259908,0.000037121015,0.000014145691,5.0358733e-7,0.000020491134,0.00016245895],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692094,0.00029685453,0.0003542812,0.0012437959,0.00047783827,0.0007062965],"domain_scores_gemma":[0.99868727,0.00006100901,0.00019959954,0.000818618,0.000045285058,0.00018819711],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006451612,0.00028886134,0.00027367225,0.0005472418,0.00045865006,0.0006159057,0.0014651314,0.000063181215,8.329792e-7],"category_scores_gemma":[0.0004902482,0.00028811328,0.00007457711,0.00137652,0.0003769546,0.001050424,0.00040744,0.00048935984,0.0000045085044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004331129,0.00066498993,0.37448543,0.00007349152,0.000014813194,0.0010818774,0.002491992,0.17919832,0.17411344,0.021181772,0.00052287424,0.24612768],"study_design_scores_gemma":[0.00036241164,0.00013376315,0.08254211,0.00018891753,0.000006550083,0.0000016001278,0.00007035137,0.9003225,0.010928383,0.004764359,0.00029255106,0.0003865204],"about_ca_topic_score_codex":0.000024600959,"about_ca_topic_score_gemma":0.0000032542891,"teacher_disagreement_score":0.7211242,"about_ca_system_score_codex":0.00015548857,"about_ca_system_score_gemma":0.00007299752,"threshold_uncertainty_score":0.9999571},"labels":[],"label_agreement":null},{"id":"W2045526515","doi":"","title":"Evaluation of minimal mechanical effort during the ramp access of manual wheelchair","year":2004,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"","keywords":"Wheelchair; Computer science; Manual wheelchair; World Wide Web","score_opus":0.02766311970073985,"score_gpt":0.3114929356807639,"score_spread":0.28382981598002405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045526515","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96392953,0.000067411536,0.032816973,0.002361454,0.00018961758,0.0002002368,0.0000020017783,0.00009728521,0.00033546964],"genre_scores_gemma":[0.9966164,0.0000043159007,0.0032483842,0.000025418516,0.0000300735,0.000017983664,5.7798957e-7,0.0000072564953,0.000049539307],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99842954,0.00009312858,0.00024353678,0.00029395212,0.00071671756,0.00022313112],"domain_scores_gemma":[0.99886143,0.000062386694,0.00020531844,0.00057699333,0.0002591968,0.00003468124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012713115,0.00011827148,0.00019491914,0.0001202,0.00008240333,0.000034467295,0.0011222749,0.00009630704,0.000015465435],"category_scores_gemma":[0.00017296313,0.00008830067,0.00007850247,0.00037329408,0.00011808068,0.00022197432,0.00038806093,0.0001683891,0.000011687092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024683998,0.0015980833,0.022033103,0.0003088268,0.00049873407,0.00005932486,0.006356096,0.0246393,0.2589437,0.56079304,0.0004875393,0.12403538],"study_design_scores_gemma":[0.0021045816,0.00027033477,0.31193006,0.00012895331,0.00008186032,0.000040257397,0.00021370326,0.011985414,0.65684134,0.016011827,0.00012955493,0.00026210042],"about_ca_topic_score_codex":0.000042448068,"about_ca_topic_score_gemma":0.00007027018,"teacher_disagreement_score":0.54478127,"about_ca_system_score_codex":0.00007968851,"about_ca_system_score_gemma":0.00013754083,"threshold_uncertainty_score":0.36007968},"labels":[],"label_agreement":null},{"id":"W2047001392","doi":"10.1145/2254556.2254676","title":"Comparing cognitive effort in spatial learning of text entry keyboards and ShapeWriters","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Representativeness heuristic; Human–computer interaction; Cognitive architecture; Cognition; Interactive kiosk; Control (management); Artificial intelligence; World Wide Web; Psychology","score_opus":0.019662785853267903,"score_gpt":0.2529731525565795,"score_spread":0.23331036670331157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047001392","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79524,0.00006823689,0.19857961,0.00006983205,0.00005572055,0.000043193664,1.5561565e-7,0.00008025463,0.0058629946],"genre_scores_gemma":[0.99467427,0.000005616496,0.0052174963,0.000030975283,0.000014501665,0.0000019850875,6.9802246e-7,0.0000033503613,0.00005113064],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993697,0.0000278523,0.00013960332,0.0001503901,0.000089517525,0.00022294257],"domain_scores_gemma":[0.9997198,0.00008399737,0.00005309801,0.00007750808,0.00002462421,0.000040965497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021147964,0.00007437725,0.0001511845,0.00012847995,0.000032521595,0.000016823058,0.00014901796,0.00004819006,0.000009714368],"category_scores_gemma":[0.000040445284,0.000068125744,0.000017967563,0.00014531917,0.000081131795,0.00018159271,0.00018622083,0.0001692884,0.000007995524],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004513421,0.000045134333,0.9271332,0.000010056528,0.000005164166,0.0000018156454,0.0006043691,0.0000066380876,0.00022300909,0.009088433,0.00000646727,0.062871195],"study_design_scores_gemma":[0.00043841166,0.00005598599,0.9719356,0.000053335134,0.000003858623,0.000009561102,0.00031141803,0.023760071,0.0031703874,0.000090753914,0.00007371379,0.00009696218],"about_ca_topic_score_codex":0.00018153289,"about_ca_topic_score_gemma":0.000027370626,"teacher_disagreement_score":0.19943425,"about_ca_system_score_codex":0.000012388958,"about_ca_system_score_gemma":0.000009079585,"threshold_uncertainty_score":0.27780873},"labels":[],"label_agreement":null},{"id":"W2047980137","doi":"10.3758/bf03195478","title":"Saliency of peripheral targets in gaze-contingent multiresolutional displays","year":2002,"lang":"en","type":"article","venue":"Behavior Research Methods, Instruments, & Computers","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Window (computing); Gaze; Computer science; Luminance; Computer vision; Bottleneck; Artificial intelligence; Perception; Psychology; Neuroscience","score_opus":0.1717972736600134,"score_gpt":0.4492476297380134,"score_spread":0.277450356078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047980137","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60789514,0.00040283523,0.38876384,0.0006761718,0.00082404487,0.0006849126,0.000011979032,0.00020559088,0.00053545466],"genre_scores_gemma":[0.5848259,0.00003985544,0.41487548,0.000023129347,0.000025766565,0.00009935276,0.0000035309113,0.000013291787,0.00009371344],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99512434,0.0012951894,0.0006998564,0.0008511397,0.0009814624,0.0010479889],"domain_scores_gemma":[0.9981778,0.00036616274,0.00017701671,0.0008423255,0.0002421113,0.00019456826],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0024841009,0.000266303,0.00044075085,0.0008622831,0.00024005983,0.00011390649,0.001957019,0.000164566,0.000081526545],"category_scores_gemma":[0.00024750328,0.00026537135,0.00015106815,0.001262889,0.0005257581,0.00039209187,0.0010518505,0.00082066236,0.000025315157],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001309288,0.0012439908,0.09424158,0.000031151136,0.000025647589,0.00009245472,0.00087454007,0.000057552323,0.011371749,0.012959531,0.0007552062,0.8783335],"study_design_scores_gemma":[0.0036640568,0.0009774658,0.72929794,0.0003644472,0.000018066461,0.00012072249,0.00028154158,0.23199758,0.021059029,0.0021056877,0.009131669,0.0009818092],"about_ca_topic_score_codex":0.0003274889,"about_ca_topic_score_gemma":0.000025555342,"teacher_disagreement_score":0.8773517,"about_ca_system_score_codex":0.00032995222,"about_ca_system_score_gemma":0.0000702386,"threshold_uncertainty_score":0.99997985},"labels":[],"label_agreement":null},{"id":"W2048325557","doi":"10.1145/1631097.1631103","title":"Examining the feasibility of face gesture detection using a wheelchair mounted camera","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Wheelchair; Gesture; Computer science; Human–computer interaction; Gesture recognition; User interface; Face (sociological concept); Embedded system; Computer vision; Operating system","score_opus":0.0620170677466031,"score_gpt":0.3033204966610936,"score_spread":0.24130342891449053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048325557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56709164,0.000026565114,0.43194234,0.0004237422,0.000048142607,0.00006197829,2.1367674e-7,0.00016650786,0.0002388659],"genre_scores_gemma":[0.9896382,8.381448e-7,0.010150373,0.00014763698,0.000012380207,0.0000010726791,1.1733338e-7,0.000002379976,0.000046990146],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9992448,0.0000605978,0.00015108622,0.00025278746,0.00013013544,0.00016057817],"domain_scores_gemma":[0.9992714,0.000055071174,0.00009137826,0.00048668717,0.000075324664,0.000020152456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026553893,0.00008909362,0.0001192953,0.000060455717,0.00011556446,0.000031984622,0.00045529567,0.00007392798,0.0000024841884],"category_scores_gemma":[0.00006642841,0.000058525184,0.000033795946,0.00040468143,0.00006658532,0.00013437094,0.000067913956,0.00017966007,0.0000021387968],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024237876,0.00030057546,0.043646872,0.000018961598,0.000040710907,0.000012171112,0.0023504351,0.004766289,0.4727216,0.02107275,0.00007388329,0.45497152],"study_design_scores_gemma":[0.0003300847,0.00050313264,0.6506567,0.000029012612,0.000011715826,0.000075272415,0.000537358,0.2798881,0.06364831,0.0040650577,0.000050222505,0.00020506495],"about_ca_topic_score_codex":0.000088352244,"about_ca_topic_score_gemma":0.000029180368,"teacher_disagreement_score":0.6070098,"about_ca_system_score_codex":0.000045804336,"about_ca_system_score_gemma":0.000023083032,"threshold_uncertainty_score":0.23865877},"labels":[],"label_agreement":null},{"id":"W2050132566","doi":"10.1109/wimob.2013.6673429","title":"Two gaze-detection methods for power reduction in near-to eye displays for wearable computing","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Recon Instruments (Canada)","funders":"","keywords":"Polling; Computer science; Wearable computer; Interrupt; Gaze; Eye tracking; Detector; Reduction (mathematics); Computer vision; Power consumption; Power (physics); Computer hardware; Artificial intelligence; Embedded system; Microcontroller; Telecommunications","score_opus":0.019193000671597342,"score_gpt":0.3517346000061365,"score_spread":0.33254159933453914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050132566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084399074,0.000018660632,0.91191167,0.0016346219,0.0005726502,0.00069711776,4.1849148e-7,0.0003241998,0.00044160118],"genre_scores_gemma":[0.5034127,2.943009e-7,0.4961303,0.00005685553,0.000022225986,0.000104432125,3.4723192e-7,0.0000067158485,0.0002661203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886066,0.000059658407,0.00022239672,0.0004384898,0.000060015427,0.00035875983],"domain_scores_gemma":[0.9992776,0.00017813529,0.00006373231,0.00029715564,0.00013018293,0.000053225493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071059406,0.00011612098,0.00016868771,0.00016302007,0.00018185456,0.00015821749,0.00032905934,0.00007782342,0.000008497711],"category_scores_gemma":[0.00015019832,0.000106286854,0.000061607476,0.0004153147,0.000031100808,0.00027368538,0.00009671959,0.000112565176,0.00003467488],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015272182,0.00008239974,0.00030861222,0.000019483237,0.00001248619,2.2119559e-7,0.00034967888,0.0013951015,0.12023066,0.021159636,0.0006355765,0.85579085],"study_design_scores_gemma":[0.00092418777,0.00047507416,0.011457408,0.000048536942,0.0000056262184,0.000011275173,0.00017609962,0.8544006,0.105683744,0.022426305,0.004070687,0.00032048483],"about_ca_topic_score_codex":0.00021546138,"about_ca_topic_score_gemma":0.000020550131,"teacher_disagreement_score":0.85547036,"about_ca_system_score_codex":0.000068130605,"about_ca_system_score_gemma":0.000020744952,"threshold_uncertainty_score":0.43342522},"labels":[],"label_agreement":null},{"id":"W2051224158","doi":"10.3390/ijerph110202244","title":"Exploring Powered Wheelchair Users and Their Caregivers’ Perspectives on Potential Intelligent Power Wheelchair Use: A Qualitative Study","year":2014,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Institut Universitaire de Gériatrie de Montréal; Institut de Readaptation Gingras Lindsay de Montreal; Université Laval; Université de Montréal; Polytechnique Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"Centre for Interdisciplinary Research in Rehabilitation","keywords":"Wheelchair; Manual wheelchair; Human factors and ergonomics; Psychology; Power (physics); Physical medicine and rehabilitation; Qualitative research; Poison control; Applied psychology; Medicine; Computer science; Medical emergency; Sociology; World Wide Web","score_opus":0.15801285220264505,"score_gpt":0.38287363415857395,"score_spread":0.2248607819559289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051224158","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96682817,0.0002980161,0.020336911,0.012031712,0.0002592907,0.00015577083,0.00001436889,0.00001853285,0.000057230078],"genre_scores_gemma":[0.9981492,0.0010725844,0.0005424263,0.0001149533,0.00006910564,0.000006944724,0.000001342549,0.000009497657,0.000033927867],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99716353,0.0008095823,0.000343702,0.0003583265,0.0009263355,0.00039854168],"domain_scores_gemma":[0.9986415,0.00042619059,0.00020537736,0.00019444281,0.00017433493,0.0003581812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027787697,0.00014731081,0.00022257281,0.0006808186,0.00021831968,0.00032113158,0.000668472,0.000037831807,0.000019387537],"category_scores_gemma":[0.00034147067,0.00011490034,0.000053577034,0.00013228106,0.0003446974,0.00095701544,0.00039161858,0.000592233,0.0000050805247],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034234088,0.005510371,0.008504386,0.000015133975,0.0007527479,0.00017989802,0.6303752,0.000023467397,0.0014598857,0.052781105,0.0004019051,0.2996536],"study_design_scores_gemma":[0.001998854,0.010423924,0.1363873,0.00010774424,0.0000022730944,0.00020368556,0.84116787,0.00046586187,0.00022854829,0.0030218381,0.005722873,0.00026919294],"about_ca_topic_score_codex":0.000108054744,"about_ca_topic_score_gemma":0.0000147717155,"teacher_disagreement_score":0.2993844,"about_ca_system_score_codex":0.00044126963,"about_ca_system_score_gemma":0.0001101773,"threshold_uncertainty_score":0.46855},"labels":[],"label_agreement":null},{"id":"W2051230341","doi":"10.1017/s0140525x04300026","title":"Visual context can influence on-line control","year":2004,"lang":"en","type":"article","venue":"Behavioral and Brain Sciences","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; McMaster University","funders":"","keywords":"Context (archaeology); Control (management); Line (geometry); Cognitive psychology; Computer science; Movement control; Movement (music); Psychology; Physical medicine and rehabilitation; Communication; Artificial intelligence; Medicine; History; Aesthetics; Mathematics","score_opus":0.04358919831214666,"score_gpt":0.3353921962327212,"score_spread":0.29180299792057457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051230341","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9759122,0.000050030667,0.008967494,0.014592022,0.00011258138,0.00009103197,0.0000041348276,0.00017872144,0.000091782924],"genre_scores_gemma":[0.99614036,0.000002286642,0.0015032169,0.00227416,0.000019173252,0.000010164029,3.3117738e-7,0.0000024393937,0.000047844733],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.998823,0.000028569195,0.00014802424,0.0004409119,0.00025607712,0.00030342795],"domain_scores_gemma":[0.99957067,0.000074089985,0.00006173467,0.00015456113,0.00004860074,0.00009033009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031310256,0.00012646541,0.00014828071,0.000119127646,0.00034138391,0.0001791751,0.0006123206,0.000056468285,0.0000029046387],"category_scores_gemma":[0.000048289974,0.000092120084,0.000030263773,0.00040774443,0.00076053076,0.00025345257,0.00009219834,0.00012688673,0.000015855114],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001964306,0.00074476213,0.064027674,0.0000074351037,0.000008073266,0.00015589414,0.001014947,0.0010191324,0.019363884,0.37628508,0.00019631577,0.5371572],"study_design_scores_gemma":[0.008605222,0.017352948,0.8402332,0.0003545767,0.000043624666,0.000410736,0.0013217976,0.006564322,0.045939665,0.072883405,0.0041120322,0.002178479],"about_ca_topic_score_codex":0.00037145667,"about_ca_topic_score_gemma":0.00015937157,"teacher_disagreement_score":0.77620554,"about_ca_system_score_codex":0.000021492846,"about_ca_system_score_gemma":0.0000951081,"threshold_uncertainty_score":0.3756548},"labels":[],"label_agreement":null},{"id":"W2053376448","doi":"10.1145/1943403.1943510","title":"2nd workshop on eye gaze in intelligent human machine interaction","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Human–computer interaction; Computer science; Eye tracking; Cognitive science; Psychology; Artificial intelligence","score_opus":0.07127125074459768,"score_gpt":0.3136380265367053,"score_spread":0.24236677579210764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053376448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40567753,0.000043913067,0.44951725,0.0012359516,0.0006632228,0.00016491635,4.1887913e-7,0.0008090677,0.14188772],"genre_scores_gemma":[0.99228626,0.0000067200463,0.006477071,0.00018833808,0.000013314584,0.000008242469,8.925277e-7,0.0000053394433,0.0010137951],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9992254,0.00003379282,0.00017402717,0.0002950153,0.000085143976,0.00018662986],"domain_scores_gemma":[0.9994874,0.00003362846,0.000046908524,0.0003776954,0.00002103319,0.00003330959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014842459,0.00010327304,0.00010599075,0.00024807846,0.00004354124,0.00002932853,0.00049546245,0.00006521955,0.00018912961],"category_scores_gemma":[0.000021916581,0.00008564026,0.000033134605,0.0002570716,0.00003258123,0.00013942983,0.00013306065,0.0002716561,0.00018679851],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021306552,0.00068896037,0.023242379,0.0000064806854,0.000016584916,0.0000679055,0.0013135913,0.00003228124,0.0011651757,0.60095674,0.00067339384,0.3718152],"study_design_scores_gemma":[0.0015522729,0.0014798995,0.7136932,0.0005588375,0.000015035097,0.000046328256,0.0012010096,0.032456756,0.14824735,0.08009689,0.019118875,0.0015335347],"about_ca_topic_score_codex":0.00019847798,"about_ca_topic_score_gemma":0.00040010642,"teacher_disagreement_score":0.69045085,"about_ca_system_score_codex":0.000052606643,"about_ca_system_score_gemma":0.0000058068767,"threshold_uncertainty_score":0.34923086},"labels":[],"label_agreement":null},{"id":"W2054422927","doi":"10.1016/j.intcom.2011.02.008","title":"Activity recognition using eye-gaze movements and traditional interactions","year":2011,"lang":"en","type":"article","venue":"Interacting with Computers","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency","keywords":"Computer science; Gaze; Human–computer interaction; Task (project management); Hidden Markov model; Context (archaeology); Activity recognition; Interface (matter); Eye tracking; Eye movement; Artificial intelligence; Machine learning","score_opus":0.12695064124382444,"score_gpt":0.2776299254053302,"score_spread":0.15067928416150578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054422927","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5625603,0.0000028482903,0.4353887,0.00012653171,0.0009243881,0.00006534513,0.0000025538623,0.0002032453,0.0007260253],"genre_scores_gemma":[0.8205536,9.700794e-7,0.17924397,0.00012487263,0.00004295053,0.0000073387637,0.0000023416974,0.000009700169,0.000014248137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989779,0.000060840517,0.00016555321,0.0004175348,0.0001454328,0.00023273706],"domain_scores_gemma":[0.99910945,0.00030693642,0.00019320822,0.00022770202,0.00009158008,0.000071108174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011974148,0.00016717268,0.00015184075,0.0002139608,0.00019256627,0.000118281045,0.00028956,0.00004236287,0.000018092811],"category_scores_gemma":[0.000046659825,0.00015492969,0.000038198083,0.00020115254,0.00007900126,0.0010114176,0.00014295735,0.00031947673,0.000013633212],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025190186,0.0013522827,0.034072652,0.00006869091,0.0005349179,0.0002910786,0.006645674,0.00016244751,0.016693259,0.0038109506,0.0005619001,0.93555427],"study_design_scores_gemma":[0.0027243327,0.0014601673,0.3403543,0.0029151135,0.000092238384,0.0018175084,0.0007004418,0.5995035,0.03947838,0.008261034,0.00091930316,0.0017736327],"about_ca_topic_score_codex":0.00014583395,"about_ca_topic_score_gemma":0.000015514595,"teacher_disagreement_score":0.9337806,"about_ca_system_score_codex":0.00007762813,"about_ca_system_score_gemma":0.000027166916,"threshold_uncertainty_score":0.631785},"labels":[],"label_agreement":null},{"id":"W2055350558","doi":"10.3758/s13428-014-0550-3","title":"A comparison of scanpath comparison methods","year":2014,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Measure (data warehouse); Encoding (memory); Eye movement; Natural language processing; Machine learning; Pattern recognition (psychology); Data mining","score_opus":0.46569732647646395,"score_gpt":0.6720252201736696,"score_spread":0.20632789369720567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055350558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0633009,0.00029786857,0.9327527,0.0003428309,0.00029063743,0.00031848132,0.0000017749304,0.00030557154,0.0023891826],"genre_scores_gemma":[0.45052275,0.0000026833309,0.54923487,0.000007739843,0.000022676893,0.00008177718,0.0000011552377,0.00001284963,0.000113486],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9886192,0.0082335435,0.0007221451,0.00074664166,0.0008381904,0.0008402775],"domain_scores_gemma":[0.994393,0.0029155873,0.00024164097,0.0016217462,0.0006045227,0.00022351477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019739265,0.00021882427,0.00080601516,0.00077016366,0.000287107,0.00011851504,0.0025485083,0.00023557436,0.000034870463],"category_scores_gemma":[0.0022085216,0.00019693833,0.0001563843,0.0016963386,0.00062776817,0.0001769781,0.0010044898,0.0011560638,0.00003413675],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006948761,0.0004805375,0.04514718,0.000022271759,0.000009789221,0.0000023875627,0.0004398563,0.000007774563,0.08356711,0.038513493,0.00046643056,0.8313362],"study_design_scores_gemma":[0.0006301427,0.0012588652,0.18168785,0.000073804586,0.00003186066,0.000016732096,0.0004337027,0.0684175,0.7066609,0.009318478,0.031025333,0.00044478907],"about_ca_topic_score_codex":0.00014719587,"about_ca_topic_score_gemma":0.0000104956325,"teacher_disagreement_score":0.83089143,"about_ca_system_score_codex":0.00008931543,"about_ca_system_score_gemma":0.00012346856,"threshold_uncertainty_score":0.80309117},"labels":[],"label_agreement":null},{"id":"W2055569445","doi":"10.1109/tbme.2010.2085000","title":"An Automated Hirschberg Test for Infants","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kappa; Optical axis; Pupil; Optics; Limits of agreement; Mathematics; Physics; Medicine; Nuclear medicine; Geometry","score_opus":0.0071482646860813815,"score_gpt":0.25456211524980676,"score_spread":0.2474138505637254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055569445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035518844,0.0000023649231,0.9589756,0.0005531131,0.0014504618,0.0001043655,0.000020894193,0.003359042,0.000015273848],"genre_scores_gemma":[0.90467143,0.0000017590806,0.09512734,0.00005282748,0.000053060958,0.00005645052,0.0000023519958,0.000015426964,0.000019327601],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903566,0.0000052298965,0.00017708597,0.0003087949,0.00016392695,0.00030930937],"domain_scores_gemma":[0.99912417,0.00020859017,0.000025010217,0.00041502283,0.00004162803,0.00018558727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014857859,0.00014225516,0.00013638432,0.00028297267,0.00010832022,0.000050971314,0.0005420386,0.000198162,0.000012335261],"category_scores_gemma":[0.00004047115,0.00013362222,0.000060447433,0.00042594795,0.00006639412,0.00018232175,0.0000016713818,0.00040685583,0.000023865901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007938122,0.0007802087,0.00001800391,0.000033689153,0.00004325627,0.000019227118,0.00009842696,0.0063332864,0.7492336,0.0029337315,0.00033213355,0.24016652],"study_design_scores_gemma":[0.00038826428,0.0003030258,0.000540167,0.000016425018,0.000006729974,0.000028722305,0.0000021942915,0.9393452,0.054523554,0.0000552121,0.0046061566,0.00018438946],"about_ca_topic_score_codex":0.000006524672,"about_ca_topic_score_gemma":0.0000049953155,"teacher_disagreement_score":0.9330119,"about_ca_system_score_codex":0.000018922316,"about_ca_system_score_gemma":0.00003556149,"threshold_uncertainty_score":0.54489565},"labels":[],"label_agreement":null},{"id":"W2057047210","doi":"10.1145/1870076.1870077","title":"Modeling locomotor control","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Gaze; Computer science; Control (management); Artificial intelligence; Visual control; Computer vision; Human–computer interaction","score_opus":0.03883479809623689,"score_gpt":0.23740881267946065,"score_spread":0.19857401458322377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057047210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05298568,0.0000022013812,0.9435772,0.00028558366,0.00014589708,0.00018115828,0.0000027163078,0.0007142583,0.0021053404],"genre_scores_gemma":[0.9096383,0.00000780084,0.089909874,0.0002648398,0.000019106968,0.00009657354,0.0000011452316,0.000011205969,0.000051174433],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999065,0.000022603714,0.0001716013,0.00037137617,0.00014248422,0.000226969],"domain_scores_gemma":[0.9991075,0.000022245977,0.000030117131,0.00074343814,0.000038750968,0.00005799641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001242507,0.00014134824,0.00013271284,0.00017818926,0.00020505032,0.0000310169,0.0006760697,0.00012614671,0.00019586047],"category_scores_gemma":[0.0000036291228,0.00013738079,0.0000718192,0.00021006391,0.000044705987,0.00014765299,0.000007181972,0.00026349153,0.00053986255],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008686132,0.0005881965,0.000043545013,0.000011046891,0.00005203158,0.000004452529,0.002665826,0.009151873,0.02908483,0.01954299,0.000032006836,0.9387363],"study_design_scores_gemma":[0.0037770527,0.0006860229,0.01673402,0.000048271733,0.00011184483,0.000053972366,0.0013773313,0.9128778,0.007841675,0.054688398,0.0005647594,0.0012388405],"about_ca_topic_score_codex":0.00004733592,"about_ca_topic_score_gemma":0.000014130065,"teacher_disagreement_score":0.9374975,"about_ca_system_score_codex":0.000061484396,"about_ca_system_score_gemma":0.000017313767,"threshold_uncertainty_score":0.6939021},"labels":[],"label_agreement":null},{"id":"W2060175976","doi":"10.1145/506443.506572","title":"GAZE-2","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Gaze; Parallax; Computer vision; Videoconferencing; Artificial intelligence; Broadcasting (networking); Computer graphics (images); Multimedia","score_opus":0.025672960078200344,"score_gpt":0.20354137485463905,"score_spread":0.17786841477643872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060175976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006233708,0.00008293908,0.7623838,0.0058242395,0.00012599764,0.000019669587,7.2746545e-8,0.0010360086,0.22429354],"genre_scores_gemma":[0.95600796,0.0000043404348,0.038711134,0.00031099506,0.00000926552,0.0000014991833,2.8664395e-8,0.0000012881857,0.0049535185],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996883,0.000005558288,0.000041118914,0.00011533934,0.00004789324,0.00010178379],"domain_scores_gemma":[0.999714,0.000012885417,0.000008965416,0.00023393819,0.0000114786835,0.000018738048],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000028093576,0.000032192867,0.000035344667,0.000035535304,0.00002994975,0.000023720764,0.00035127925,0.00002170072,0.00019213317],"category_scores_gemma":[0.000009360879,0.000025738644,0.000014736913,0.00013465708,0.000017825574,0.00007503038,0.000058219986,0.00004384092,0.0010540718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.9545513e-8,0.000032180986,0.0009617779,6.719192e-7,0.0000023832401,0.000010829437,0.00003612547,0.0000012013116,0.000349321,0.7207303,0.023397366,0.25447777],"study_design_scores_gemma":[0.0009357308,0.00031930077,0.05647356,0.000020351396,0.0000057142906,0.00020576917,0.000036823323,0.309759,0.029539498,0.07610892,0.52575666,0.0008386752],"about_ca_topic_score_codex":0.000002502814,"about_ca_topic_score_gemma":8.931497e-7,"teacher_disagreement_score":0.9497742,"about_ca_system_score_codex":0.0000045274955,"about_ca_system_score_gemma":0.000001061866,"threshold_uncertainty_score":0.99972373},"labels":[],"label_agreement":null},{"id":"W2060876867","doi":"10.1016/j.visres.2010.09.008","title":"Item-specific location memory in visual search","year":2010,"lang":"en","type":"article","venue":"Vision Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual search; Random search; Eye movement; Perception; Computer science; Mathematics; Psychology; Artificial intelligence; Pattern recognition (psychology); Statistics; Algorithm; Neuroscience","score_opus":0.059814017294525165,"score_gpt":0.4195610432908558,"score_spread":0.3597470259963306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060876867","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9686333,0.000081937535,0.023378624,0.0038444656,0.0002878104,0.00021980074,3.5500832e-7,0.00019748053,0.0033562249],"genre_scores_gemma":[0.99544,0.000021537298,0.0037798556,0.000022680271,0.000063621585,0.000021379257,0.0000015368868,0.000008944653,0.000640441],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9976323,0.00027739388,0.00019608585,0.0005228051,0.0008347047,0.0005366835],"domain_scores_gemma":[0.99825084,0.00050973304,0.000019847652,0.0006849274,0.00043672812,0.00009791228],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0034111906,0.00008584387,0.00011364132,0.001038161,0.00019656803,0.00019648908,0.0011892321,0.00016077585,0.000079769714],"category_scores_gemma":[0.00031225063,0.000076786964,0.000023421397,0.0023325826,0.0002544535,0.000249952,0.00055246183,0.0014206283,0.0008059747],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002092468,0.00036405303,0.0074206972,0.000023149203,0.0000030706499,0.00009149025,0.0005219987,0.000029379924,0.18871656,0.09054743,0.0050421758,0.70721906],"study_design_scores_gemma":[0.0013915278,0.00089028163,0.70913833,0.00012876918,6.942037e-7,0.00004397856,0.0005930602,0.12826121,0.115023375,0.010767956,0.033257376,0.0005034317],"about_ca_topic_score_codex":0.000103836035,"about_ca_topic_score_gemma":0.00008512569,"teacher_disagreement_score":0.70671564,"about_ca_system_score_codex":0.000056224988,"about_ca_system_score_gemma":0.00013933571,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W2061458448","doi":"10.1186/1743-0003-7-22","title":"The design and testing of a novel mechanomyogram-driven switch controlled by small eyebrow movements","year":2010,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Holland Bloorview Kids Rehabilitation Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Eyebrow; Computer science; Cued speech; Binary number; Physical medicine and rehabilitation; Population; Artificial intelligence; Medicine; Psychology; Communication; Cognitive psychology; Mathematics","score_opus":0.011500281635830938,"score_gpt":0.21557751351781104,"score_spread":0.2040772318819801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061458448","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5651956,0.000047720394,0.43383056,0.00068717013,0.00013897746,0.000080652215,2.2503019e-7,0.00001646489,0.0000025954007],"genre_scores_gemma":[0.7955099,0.000011028339,0.20444483,0.000009241788,0.000011581789,0.000003518002,3.071778e-8,0.000004195535,0.000005708803],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935293,0.000033704397,0.00029762022,0.00010084607,0.00010742261,0.00010745205],"domain_scores_gemma":[0.99780434,0.0016365718,0.00023500927,0.00010979178,0.0001729108,0.00004136077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051793456,0.00007764182,0.00017104814,0.00009963245,0.000073362615,0.00004954949,0.00018220881,0.00003333571,6.29053e-8],"category_scores_gemma":[0.0011295847,0.000050996267,0.00003638299,0.00013646293,0.000060297127,0.00009669163,0.000035887526,0.00022288696,3.3190418e-8],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045237004,0.00009120941,0.0022653115,0.00003448716,0.00003556769,0.0000024322683,0.00023797352,0.0033020738,0.9598485,0.0020873488,0.000033207412,0.032016605],"study_design_scores_gemma":[0.0052967397,0.003111879,0.0601064,0.00016225656,0.00003756918,0.00017809453,0.00011674695,0.92183584,0.006368443,0.0021328316,0.00045250644,0.00020068456],"about_ca_topic_score_codex":0.0000025819847,"about_ca_topic_score_gemma":5.3876016e-7,"teacher_disagreement_score":0.9534801,"about_ca_system_score_codex":0.0000054393718,"about_ca_system_score_gemma":0.000016355512,"threshold_uncertainty_score":0.20795675},"labels":[],"label_agreement":null},{"id":"W2062581329","doi":"10.3791/2108","title":"Eye Movement Monitoring of Memory","year":2010,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Eye movement; Eye tracking; Eye tracking on the ISS; Computer science; Computer vision; BitTorrent tracker; Artificial intelligence; Movement (music)","score_opus":0.028816981434952312,"score_gpt":0.4157554375611044,"score_spread":0.3869384561261521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062581329","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97038764,0.0002114695,0.026996095,0.00014572513,0.0017847808,0.00004564273,3.1100328e-7,0.000034257788,0.00039410047],"genre_scores_gemma":[0.9262003,0.000012707291,0.07356447,0.000033975906,0.000108760745,0.000002136187,6.0027375e-8,0.0000067256783,0.0000708888],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988155,0.00003750778,0.0004722893,0.00012372718,0.00037892695,0.00017207334],"domain_scores_gemma":[0.99889576,0.000032063035,0.0005177733,0.0002879767,0.00018868456,0.00007771985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037667423,0.00010434508,0.0002584919,0.00020280704,0.00004460064,0.000031441374,0.0007652942,0.000070582544,0.000021252172],"category_scores_gemma":[0.00005874635,0.00008656428,0.0001093907,0.00016270894,0.000055686498,0.00024180744,0.00013557656,0.00026281018,0.0000053625386],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016773307,0.00027120073,0.008586765,0.0000054955967,0.0000516122,0.000021201324,0.00051316264,0.0000032769995,0.9789997,0.0033460236,0.00011705263,0.008067771],"study_design_scores_gemma":[0.00090316415,0.0002149109,0.012829021,0.000044054912,0.0000062741774,0.000011940048,0.00016658423,0.00010290145,0.9844047,0.000805859,0.00042862384,0.00008196549],"about_ca_topic_score_codex":0.000006581529,"about_ca_topic_score_gemma":1.2614215e-7,"teacher_disagreement_score":0.04656838,"about_ca_system_score_codex":0.000027884436,"about_ca_system_score_gemma":0.000050409177,"threshold_uncertainty_score":0.35299888},"labels":[],"label_agreement":null},{"id":"W2063073801","doi":"10.1080/09638280500052799","title":"Understanding and measuring powered wheelchair mobility and manoeuvrability. Part I. Reach in confined spaces","year":2005,"lang":"en","type":"article","venue":"Disability and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; Toronto Rehabilitation Institute; University of Toronto; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Wheelchair; Physical medicine and rehabilitation; Manual wheelchair; Psychology; Computer science; Human–computer interaction; Sociology; Medicine; World Wide Web","score_opus":0.05184563754281994,"score_gpt":0.2565878163902608,"score_spread":0.20474217884744084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063073801","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97590464,0.0002934514,0.007032331,0.015909184,0.000066273176,0.00039545915,0.000004584475,0.00016849284,0.00022560848],"genre_scores_gemma":[0.99328107,0.000034190816,0.0065801167,0.00003510503,0.000017945416,0.000033361328,0.0000020278876,0.0000058802557,0.000010298283],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998124,0.00027054685,0.0003856782,0.0007807363,0.00016958833,0.00026944643],"domain_scores_gemma":[0.99834335,0.0010003073,0.00007702076,0.00043613013,0.00004916775,0.00009405398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017325534,0.00018169526,0.0002856974,0.000097169315,0.00017625515,0.000115513016,0.00014085213,0.00014622435,0.0000046946784],"category_scores_gemma":[0.0007812375,0.00016805476,0.0000367625,0.00024512009,0.0014565088,0.00053163146,0.00014694175,0.00021498378,7.806196e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005157626,0.00041863226,0.8500422,0.00029796408,0.000011407958,8.261303e-7,0.005290127,0.00003339896,0.0006162634,0.105430245,0.000017117216,0.037790217],"study_design_scores_gemma":[0.00074308476,0.00023598755,0.9221728,0.00007976431,0.0000069109765,0.0000073486035,0.0023002257,0.0064970935,0.00010653857,0.06728646,0.0003321529,0.00023165716],"about_ca_topic_score_codex":0.00012900255,"about_ca_topic_score_gemma":0.0009085402,"teacher_disagreement_score":0.072130546,"about_ca_system_score_codex":0.00021759156,"about_ca_system_score_gemma":0.000021426738,"threshold_uncertainty_score":0.6853074},"labels":[],"label_agreement":null},{"id":"W2063236655","doi":"10.7224/1537-2073-3.4.34","title":"Wheelchair Selection and Configuration","year":2001,"lang":"en","type":"article","venue":"International Journal of MS Care","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medicine; Wheelchair; Selection (genetic algorithm); Physical medicine and rehabilitation; Human–computer interaction; Artificial intelligence; World Wide Web","score_opus":0.008695903868002432,"score_gpt":0.25877254294900115,"score_spread":0.2500766390809987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063236655","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61056256,0.00030573682,0.3824544,0.0041801026,0.0008711448,0.000022969976,7.767984e-7,0.00004376444,0.0015585345],"genre_scores_gemma":[0.9956073,0.000058383135,0.0040261145,0.000107524655,0.00015263139,4.834243e-7,7.135659e-7,0.0000020588636,0.00004480343],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99951017,0.00001630125,0.00014425568,0.0000727736,0.0002010765,0.000055423807],"domain_scores_gemma":[0.9991585,0.000022060482,0.00013796538,0.000042455133,0.00061358005,0.000025433977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000076387245,0.000043482723,0.00006037473,0.00015129308,0.000029074465,0.00007321183,0.000296664,0.000034133944,0.000011203748],"category_scores_gemma":[0.000039512153,0.000037892874,0.000029102976,0.00007117968,0.00001639995,0.00028231787,0.000030936942,0.00010474542,0.000003952665],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056693683,0.000070178954,0.080999374,0.0000065493064,0.00013694711,0.00028652925,0.0017858979,0.00029745637,0.023921262,0.07515155,0.001509238,0.8157783],"study_design_scores_gemma":[0.0041345186,0.0014124175,0.72340703,0.00034881424,0.000043228716,0.013203773,0.0017000921,0.012568399,0.07508163,0.021916933,0.14556019,0.0006229472],"about_ca_topic_score_codex":0.000008153379,"about_ca_topic_score_gemma":0.000014224161,"teacher_disagreement_score":0.8151554,"about_ca_system_score_codex":0.000052820415,"about_ca_system_score_gemma":0.000031923682,"threshold_uncertainty_score":0.15452266},"labels":[],"label_agreement":null},{"id":"W2063961776","doi":"10.1167/8.6.299","title":"Gaze strategies while grasping: What are you looking at?!","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Gaze; GRASP; Computer vision; Fixation (population genetics); Artificial intelligence; Eye movement; Kinematics; Computer science; Communication; Object (grammar); Frame of reference; Fixation point; Psychology; Physics; Biology","score_opus":0.01510274958172809,"score_gpt":0.27861849366243885,"score_spread":0.26351574408071077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063961776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.938634,0.0004871125,0.05461208,0.0038721762,0.0019005073,0.000032253516,1.7087308e-7,0.000081238926,0.00038047903],"genre_scores_gemma":[0.99026054,0.00008772657,0.009363334,0.00008074645,0.00014733095,3.5348236e-7,1.5436581e-7,0.0000065790073,0.00005320459],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990231,0.000030122195,0.00029501028,0.0001612539,0.00029824759,0.00019228311],"domain_scores_gemma":[0.99888426,0.000059344387,0.00047119672,0.00030043448,0.00020967747,0.000075110554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044222816,0.00010484094,0.000189271,0.00019798714,0.00013958955,0.00044889707,0.00072610006,0.000108484535,0.000019449808],"category_scores_gemma":[0.000063524996,0.00007983671,0.00009678251,0.00019183972,0.00006305483,0.0015851184,0.00018653335,0.0005327259,0.000024074101],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052192634,0.00041786963,0.015755437,0.00004786491,0.00006860561,0.0006992921,0.0011862925,0.00039964681,0.5265201,0.03913604,0.006128099,0.4095886],"study_design_scores_gemma":[0.0020127327,0.0019293943,0.83868843,0.001823973,0.000048288817,0.0027778475,0.0026468122,0.009349136,0.04641941,0.0475112,0.04598392,0.0008088331],"about_ca_topic_score_codex":0.0000019464348,"about_ca_topic_score_gemma":0.0000103582215,"teacher_disagreement_score":0.822933,"about_ca_system_score_codex":0.000031567037,"about_ca_system_score_gemma":0.000054128483,"threshold_uncertainty_score":0.43287247},"labels":[],"label_agreement":null},{"id":"W2064909625","doi":"10.1145/1141897.1141902","title":"An application of eyegaze tracking for designing radiologists' workstations","year":2006,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Computer vision; Computer science; Visual search; Artificial intelligence; Task (project management); Workstation; Tracking (education); Eye tracking; Psychology; Engineering","score_opus":0.02262451633808663,"score_gpt":0.2839811320870552,"score_spread":0.26135661574896857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064909625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04289774,0.000005161342,0.9556673,0.00028498514,0.0000661142,0.00042905222,0.000011129438,0.00046452283,0.00017398743],"genre_scores_gemma":[0.6584071,0.0000031285645,0.341213,0.000032047592,0.000028162234,0.0002636851,0.000029529669,0.00001044704,0.000012883819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988829,0.00003454375,0.0003001454,0.0004324913,0.00013250379,0.00021743838],"domain_scores_gemma":[0.9988537,0.00017995067,0.0001375196,0.0007004813,0.0000947661,0.00003359199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024654716,0.00014548596,0.00016991577,0.00024883242,0.00028930043,0.000049704602,0.0005782169,0.00015694187,0.000008920132],"category_scores_gemma":[0.000008817936,0.00015288952,0.00008063537,0.00040533722,0.00009513616,0.00023510374,0.0000037501416,0.00016586485,0.00001466127],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030709954,0.00032232655,0.00012799684,0.000014990379,0.000010942062,1.5875824e-7,0.00029609792,0.025090083,0.2908962,0.03684578,0.000022528196,0.64634216],"study_design_scores_gemma":[0.004266546,0.0015630649,0.21569322,0.00009488308,0.0002334005,0.000037701626,0.0024487923,0.30063546,0.21176992,0.2598271,0.0016441641,0.001785724],"about_ca_topic_score_codex":0.000049448478,"about_ca_topic_score_gemma":0.00003609665,"teacher_disagreement_score":0.64455646,"about_ca_system_score_codex":0.00007719268,"about_ca_system_score_gemma":0.00002317612,"threshold_uncertainty_score":0.62346536},"labels":[],"label_agreement":null},{"id":"W2065881050","doi":"10.3109/17483100903254561","title":"A multiple camera tongue switch for a child with severe spastic quadriplegic cerebral palsy","year":2009,"lang":"en","type":"review","venue":"Disability and Rehabilitation Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"","keywords":"Tongue; Modality (human–computer interaction); Cerebral palsy; Spastic cerebral palsy; Computer science; Spastic; Video camera; Physical medicine and rehabilitation; Computer vision; Medicine; Artificial intelligence; Psychology","score_opus":0.01758473464507068,"score_gpt":0.2884169781020713,"score_spread":0.27083224345700063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065881050","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018997283,0.85334015,0.12005641,0.013447853,0.00045984794,0.006961951,0.00035843014,0.0030805743,0.00039503732],"genre_scores_gemma":[0.23227438,0.46561456,0.29225457,0.00030014,0.00034335,0.008177696,0.0005034319,0.00029114055,0.00024073552],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99531937,0.0003473883,0.0010502345,0.0021921827,0.00028988093,0.00080092734],"domain_scores_gemma":[0.99341816,0.0037306817,0.0007197396,0.0016212455,0.00035304175,0.00015715163],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006366384,0.0009227963,0.0023959447,0.0006596289,0.00053281465,0.00012644047,0.0012124036,0.001238047,0.0000071911236],"category_scores_gemma":[0.0027281868,0.0006967281,0.00051501417,0.0017113362,0.0023210288,0.00026615788,0.000315345,0.0010905791,0.00001671875],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033158278,0.0005786013,0.0073450175,0.005176694,0.00015400184,0.0000084595185,0.00006773343,0.0000027178294,2.6524205e-7,0.03647525,0.000119002456,0.9500391],"study_design_scores_gemma":[0.004586476,0.008516257,0.08884074,0.01808403,0.0014452264,0.0017234655,0.0010160747,0.0010370723,0.000004196199,0.02291324,0.8476724,0.004160835],"about_ca_topic_score_codex":0.000036068555,"about_ca_topic_score_gemma":0.000331062,"teacher_disagreement_score":0.94587827,"about_ca_system_score_codex":0.00040577134,"about_ca_system_score_gemma":0.00029865152,"threshold_uncertainty_score":0.9995484},"labels":[],"label_agreement":null},{"id":"W2067291342","doi":"10.1109/bsc.2008.4563283","title":"Using infrared illumination to improve eye &amp;#x00026; face tracking in low quality video images","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; BitTorrent tracker; Pupil; Eye tracking; Face (sociological concept); Tracking (education); Image quality; Thresholding; Facial recognition system; Rotation (mathematics); Pattern recognition (psychology); Image (mathematics); Optics","score_opus":0.059282130004858345,"score_gpt":0.3411185868187079,"score_spread":0.28183645681384956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067291342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4821695,0.000016214093,0.5159225,0.00059119135,0.000117733194,0.00012022553,0.0000019839451,0.00028177575,0.0007788521],"genre_scores_gemma":[0.8733821,0.0000037707791,0.12545906,0.0002443184,0.000023616058,0.000010028925,0.0000016014441,0.000009718682,0.0008657993],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9982609,0.0001078023,0.000396288,0.0005738674,0.00026291338,0.000398232],"domain_scores_gemma":[0.9989523,0.000109227425,0.00011930086,0.0005812927,0.0001604158,0.00007744644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004943724,0.00017935527,0.00025023738,0.00033178017,0.000158975,0.000092909686,0.00065551064,0.00012777689,0.000013651424],"category_scores_gemma":[0.00040975143,0.0001761796,0.00005849502,0.00080887834,0.00010099155,0.00066814903,0.0002450017,0.0002290379,0.00006607031],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003461199,0.0006842144,0.093774185,0.00010643579,0.000036347796,0.00010696647,0.008154891,0.0028011338,0.6384915,0.01752454,0.001110627,0.23717453],"study_design_scores_gemma":[0.0009817465,0.00012291352,0.77581793,0.000117933305,0.000005541839,0.000038696395,0.00034818597,0.017019298,0.19995213,0.003355329,0.001363305,0.00087700033],"about_ca_topic_score_codex":0.00035997198,"about_ca_topic_score_gemma":0.00010695822,"teacher_disagreement_score":0.68204373,"about_ca_system_score_codex":0.00015355008,"about_ca_system_score_gemma":0.00006841692,"threshold_uncertainty_score":0.7184396},"labels":[],"label_agreement":null},{"id":"W2067456670","doi":"10.1037/a0015807","title":"On the control of attention.","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Psychology; Gaze; Inhibition of return; Closet; Attentional control; Cognitive psychology; Visual attention; Control (management); Set (abstract data type); Cognition; Variety (cybernetics); Task (project management); Visual search; Eye movement; Cognitive science; Neuroscience; Artificial intelligence; Psychoanalysis","score_opus":0.028757007850025707,"score_gpt":0.2865404214640348,"score_spread":0.2577834136140091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067456670","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9627131,0.0014650333,0.012291703,0.015586292,0.0016822491,0.00024126603,0.00001956386,0.00003735428,0.005963441],"genre_scores_gemma":[0.99106026,0.000008113976,0.0020797695,0.006657781,0.00009170463,0.000010045358,0.0000015318137,0.000014355498,0.000076463235],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9977352,0.00019172001,0.00063923624,0.0004381808,0.0001046769,0.0008910027],"domain_scores_gemma":[0.9976464,0.00014276379,0.0005151921,0.0009186944,0.00013308953,0.0006438715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008703931,0.00026830655,0.0004094066,0.00053808384,0.00021571892,0.00005217107,0.0022558863,0.00019258364,0.00013524796],"category_scores_gemma":[0.00015389563,0.00022039033,0.00025328266,0.00045868682,0.00043782342,0.00018752465,0.000019944093,0.0005248712,0.000028388895],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002309958,0.0011642036,0.010772049,0.00000773838,0.00032472098,0.0017917099,0.003664751,0.000073365925,0.46781027,0.4310518,0.0662693,0.016839093],"study_design_scores_gemma":[0.017138992,0.026383027,0.5842472,0.00082127145,0.0001568032,0.011944786,0.008536046,0.0006120713,0.17670105,0.15390095,0.016976401,0.002581418],"about_ca_topic_score_codex":0.00042362078,"about_ca_topic_score_gemma":0.0009774662,"teacher_disagreement_score":0.5734751,"about_ca_system_score_codex":0.00045168464,"about_ca_system_score_gemma":0.0002483873,"threshold_uncertainty_score":0.8987256},"labels":[],"label_agreement":null},{"id":"W2068152044","doi":"10.1145/1344471.1344486","title":"3D point-of-gaze estimation on a volumetric display","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Computer science; Computer vision; Artificial intelligence; Vergence (optics); Eye tracking; Point (geometry); Tracking (education); Computer graphics (images); Mathematics; Psychology","score_opus":0.018632877486652318,"score_gpt":0.24145199550850116,"score_spread":0.22281911802184884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068152044","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19869219,0.000015668573,0.7948888,0.00048075282,0.00010246046,0.000044621887,4.311887e-7,0.0002697857,0.005505281],"genre_scores_gemma":[0.86200136,0.000003884223,0.1373172,0.0000809058,0.0000065595978,0.0000033435754,6.4891475e-7,0.0000028980287,0.0005831821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936026,0.000017844788,0.00013565095,0.00019884773,0.00015259812,0.00013476974],"domain_scores_gemma":[0.9994291,0.000091069596,0.000061348794,0.00034612563,0.000044631575,0.00002772229],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009183872,0.000071378985,0.00010736978,0.00026573997,0.00006339184,0.000010483631,0.00037779674,0.00004853217,0.000015794265],"category_scores_gemma":[0.0001183235,0.000057793968,0.000034116325,0.00068699743,0.000062087856,0.00013503846,0.00006658743,0.00008406695,0.00015872037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010199807,0.0004661077,0.018799009,0.000019282726,0.000025383548,0.00006244759,0.00037445273,0.0010125831,0.0011424342,0.40296564,0.0061244178,0.56899804],"study_design_scores_gemma":[0.00058403815,0.0006539233,0.2652343,0.00003922157,0.000006281127,0.00011687221,0.000009463768,0.71257675,0.014459913,0.004857353,0.001170347,0.0002915666],"about_ca_topic_score_codex":0.000023195531,"about_ca_topic_score_gemma":0.0000018593365,"teacher_disagreement_score":0.7115641,"about_ca_system_score_codex":0.000018497532,"about_ca_system_score_gemma":0.000021572474,"threshold_uncertainty_score":0.23567696},"labels":[],"label_agreement":null},{"id":"W2069528928","doi":"10.1145/2168556.2168564","title":"A probabilistic approach for the estimation of angle kappa in infants","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Kappa; Calibration; Probabilistic logic; Visual angle; Range (aeronautics); Viewing angle; Estimation; Cohen's kappa; Statistics; Target range; Mathematics; Computer science; Artificial intelligence; Geometry; Engineering","score_opus":0.02987759176069757,"score_gpt":0.27079391062655284,"score_spread":0.24091631886585527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069528928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03723266,0.00005307064,0.9608052,0.00023174078,0.00004831,0.00023172646,5.629556e-7,0.000059449947,0.0013372937],"genre_scores_gemma":[0.81842405,3.653956e-7,0.18145578,0.000019275336,0.000006921191,0.000055880522,6.1658244e-7,0.0000015949654,0.000035509824],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995943,0.000013781072,0.00010545304,0.00008948386,0.000057545432,0.00013946762],"domain_scores_gemma":[0.9995471,0.0001533428,0.000040040002,0.00022422559,0.000023050015,0.000012194226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032658805,0.000041631967,0.00006872314,0.000048845046,0.00002550191,0.000009433493,0.0002979323,0.000030696156,0.0000013366423],"category_scores_gemma":[0.00013670058,0.00002546334,0.000018092387,0.0002088336,0.000043225748,0.0001229624,0.000054606007,0.0000417958,0.000001769342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066317657,0.00036821936,0.013559005,0.000057015448,0.00000988175,1.0267469e-7,0.00077155925,0.0046946346,0.00021583447,0.77279145,0.00034634303,0.20717932],"study_design_scores_gemma":[0.00021210677,0.00004025651,0.09071115,0.0000064900637,0.0000037976736,0.0000033484807,0.000038509614,0.90032077,0.0013047741,0.0071774987,0.000121480916,0.00005980092],"about_ca_topic_score_codex":0.00002045754,"about_ca_topic_score_gemma":0.0000035528087,"teacher_disagreement_score":0.8956261,"about_ca_system_score_codex":0.000011127647,"about_ca_system_score_gemma":0.000012288496,"threshold_uncertainty_score":0.103836484},"labels":[],"label_agreement":null},{"id":"W2069631400","doi":"10.1145/1344471.1344531","title":"Remote point-of-gaze estimation requiring a single-point calibration for applications with infants","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer science; Computer vision; Artificial intelligence; Calibration; Pupil; Point (geometry); Ranging; Eye tracking; Mathematics; Optics; Statistics; Physics","score_opus":0.030817362841584,"score_gpt":0.25571682726467043,"score_spread":0.22489946442308642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069631400","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021105058,0.000013305962,0.97612333,0.0010902804,0.000024434845,0.00033699052,0.0000013937436,0.0003658973,0.0009393248],"genre_scores_gemma":[0.5523059,0.0000010899187,0.4475389,0.000041233925,0.000008491144,0.000022539494,0.0000035698595,0.000004530934,0.00007376003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992553,0.000013065368,0.00021083724,0.00025699826,0.00011400352,0.00014980504],"domain_scores_gemma":[0.99924076,0.0000915741,0.00013548844,0.00038337614,0.00011728642,0.000031491443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011455746,0.00008739494,0.00012220208,0.00011997885,0.00012999594,0.000024502875,0.00027299207,0.000053955406,0.0000023949085],"category_scores_gemma":[0.000051017225,0.00007272629,0.00002944769,0.0003192708,0.00008899121,0.00040821236,0.00004781807,0.00005630631,0.0000042751803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006209369,0.0003733594,0.0021620013,0.00012030166,0.000059301878,0.000009046021,0.0012899372,0.0046737487,0.040872585,0.35632628,0.00096006575,0.5930913],"study_design_scores_gemma":[0.00064354896,0.00040458093,0.0022679241,0.000068000896,0.000010587348,0.00010777438,0.00002905794,0.8837809,0.09154946,0.020443821,0.00046622378,0.00022811668],"about_ca_topic_score_codex":0.000020413416,"about_ca_topic_score_gemma":0.000013582244,"teacher_disagreement_score":0.8791072,"about_ca_system_score_codex":0.000027343398,"about_ca_system_score_gemma":0.000046327932,"threshold_uncertainty_score":0.2965692},"labels":[],"label_agreement":null},{"id":"W2071663834","doi":"10.1080/17470210802168583","title":"Behaviour and Gaze Analyses During a Goal-Directed Locomotor Task","year":2008,"lang":"en","type":"article","venue":"Quarterly Journal of Experimental Psychology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaze; Psychology; Task (project management); Cognitive psychology; Communication","score_opus":0.03579178466287477,"score_gpt":0.3519487623079811,"score_spread":0.31615697764510636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071663834","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9895929,0.001791804,0.0073832297,0.00039713286,0.00043066658,0.00006600735,0.0000013287839,0.00012023877,0.00021665612],"genre_scores_gemma":[0.9884429,0.000034828274,0.011255973,0.00012633913,0.00007804012,0.000005068154,4.3837903e-7,0.000011259706,0.000045150307],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99858403,0.00012008048,0.00048583254,0.00031991198,0.00019146124,0.00029870335],"domain_scores_gemma":[0.9990619,0.00003572684,0.00034203767,0.0003249181,0.00009451294,0.00014094498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012516916,0.00018706969,0.00036264706,0.00035343648,0.00015717537,0.000036063127,0.0005856542,0.00011745266,0.000023974888],"category_scores_gemma":[0.000008968354,0.0001634151,0.00011787842,0.00025107144,0.00025303874,0.00032297327,0.000041771204,0.00030818643,0.00001438883],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000111503825,0.0007644348,0.020852208,0.00000381109,0.000110136454,0.0014186188,0.0031165238,8.8386713e-7,0.9679975,0.000301934,0.0010536369,0.0042687883],"study_design_scores_gemma":[0.00542893,0.0076178107,0.82097816,0.0000725312,0.000037287136,0.029403366,0.0010155204,0.00017912235,0.13377847,0.00061058666,0.00030803357,0.00057017064],"about_ca_topic_score_codex":0.00001121274,"about_ca_topic_score_gemma":0.0000011191295,"teacher_disagreement_score":0.83421904,"about_ca_system_score_codex":0.00004107493,"about_ca_system_score_gemma":0.000024646311,"threshold_uncertainty_score":0.6663874},"labels":[],"label_agreement":null},{"id":"W2074213764","doi":"10.1167/12.9.413","title":"Reading unsegmented text: The impact on fixation location and duration","year":2012,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fixation (population genetics); Word lists by frequency; Computer science; Psychology; Speech recognition; Mathematics; Natural language processing; Medicine; Population","score_opus":0.013543373629165037,"score_gpt":0.3068774427379788,"score_spread":0.29333406910881377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074213764","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71408576,0.00010543185,0.28318185,0.002294358,0.00018224213,0.00003891197,1.1292716e-7,0.000017939834,0.00009337856],"genre_scores_gemma":[0.99839157,0.000026137459,0.0014377653,0.00004932058,0.000081048056,3.586391e-7,3.264781e-7,0.0000022274698,0.000011229177],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994808,0.00005931741,0.00017236845,0.000051832,0.00014749252,0.000088228924],"domain_scores_gemma":[0.99937445,0.00009426299,0.00028250675,0.000110967616,0.00010484785,0.0000329724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065419334,0.00005111734,0.00006496077,0.000112155496,0.00009524873,0.000057092413,0.00013459101,0.00003414696,0.0000019183242],"category_scores_gemma":[0.00010625813,0.000026812106,0.000025225236,0.00018434097,0.000017770504,0.0005147594,0.000022021579,0.00012456458,0.0000061673295],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014135713,0.0004868376,0.036237415,0.000017547763,0.000062109444,0.0000065746426,0.0024126815,0.0011158213,0.11467712,0.04983678,0.005568425,0.78943735],"study_design_scores_gemma":[0.00021951954,0.0004782147,0.97719884,0.000088269415,0.000006280434,0.000106586245,0.000041682146,0.016118066,0.004418136,0.0011298698,0.0001486117,0.000045920442],"about_ca_topic_score_codex":0.0000030313606,"about_ca_topic_score_gemma":1.7312662e-7,"teacher_disagreement_score":0.9409614,"about_ca_system_score_codex":0.00005346504,"about_ca_system_score_gemma":0.000014553828,"threshold_uncertainty_score":0.1093366},"labels":[],"label_agreement":null},{"id":"W2074223177","doi":"10.1371/journal.pone.0083302","title":"Learning-Induced Changes in Attentional Allocation during Categorization: A Sizable Catalog of Attention Change as Measured by Eye Movements","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Eye movement; Cognitive psychology; Task (project management); Computer science; Psychology; Machine learning; Artificial intelligence","score_opus":0.04228019228537908,"score_gpt":0.23414119519746907,"score_spread":0.19186100291209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074223177","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99015945,0.00003206617,0.007945128,0.0013342819,0.000050030485,0.00020712288,0.0000018835686,0.00015062073,0.00011943627],"genre_scores_gemma":[0.9984984,0.000014580134,0.0007736418,0.000044093416,0.000038877006,0.00010074823,0.00006976969,0.000010041461,0.0004498739],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99880135,0.00009004869,0.00019703041,0.00032335965,0.000377292,0.00021091454],"domain_scores_gemma":[0.99936736,0.000019837775,0.00017711674,0.00023719389,0.00016404499,0.000034450393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029003687,0.00010855682,0.00018406578,0.00020506367,0.00008534939,0.00002695848,0.00030705935,0.000090855,0.00000894833],"category_scores_gemma":[0.000116237905,0.000121422236,0.000022547742,0.00041112877,0.000026148404,0.00023497269,0.00009606846,0.00013951138,0.000032945038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067085634,0.0008176459,0.103117414,0.00009679653,0.000043512566,0.0000010608384,0.00025522648,0.000010872699,0.8912877,0.0021138089,0.0000056552276,0.0022436017],"study_design_scores_gemma":[0.0011832003,0.0004511554,0.51465946,0.000300617,0.000025147901,0.0000013163284,0.000052856925,0.012085383,0.46988463,0.001039027,0.00003295819,0.0002842134],"about_ca_topic_score_codex":0.00019326356,"about_ca_topic_score_gemma":0.00009548801,"teacher_disagreement_score":0.42140305,"about_ca_system_score_codex":0.00006553167,"about_ca_system_score_gemma":0.00001469458,"threshold_uncertainty_score":0.49514553},"labels":[],"label_agreement":null},{"id":"W2074995870","doi":"10.3758/s13428-012-0294-x","title":"Capturing and evaluating blinks from video-based eyetrackers","year":2012,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Simon Fraser University","funders":"","keywords":"Computer science; Sensitivity (control systems); Computer vision; Artificial intelligence; Pupil; Workload; Contrast (vision); Plot (graphics); Psychology; Mathematics; Statistics","score_opus":0.4686376173035349,"score_gpt":0.6115095745346915,"score_spread":0.14287195723115653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074995870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6356411,0.00073139713,0.36221796,0.00055779406,0.00020865335,0.00021733159,0.0000032099351,0.0002412994,0.00018123395],"genre_scores_gemma":[0.5175104,0.0000032662656,0.4822763,0.000024870478,0.00005266449,0.0000822211,0.0000015552774,0.000010534906,0.00003819212],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99546766,0.002225669,0.00024434007,0.0005187496,0.0006726329,0.0008709218],"domain_scores_gemma":[0.9967314,0.0019597292,0.00006526714,0.0007569233,0.00021430678,0.00027238642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012050364,0.00015575695,0.00021917568,0.00041790673,0.00035130224,0.00019228499,0.00082542474,0.00018779044,0.000035228255],"category_scores_gemma":[0.001435305,0.00014254228,0.00005698402,0.00064633705,0.00029568627,0.00036612168,0.00051121996,0.00096994574,0.000034295266],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067695596,0.00014039855,0.07564526,0.000009066084,0.000008682816,0.000015318268,0.00048522349,0.0000026661648,0.08279234,0.0018847873,0.00007600883,0.83893347],"study_design_scores_gemma":[0.0008844443,0.0002654829,0.8027774,0.00008021138,0.000033904074,0.000017233142,0.00033750958,0.015074816,0.17460808,0.0034612,0.0020116721,0.00044804538],"about_ca_topic_score_codex":0.00033749867,"about_ca_topic_score_gemma":0.0000073279693,"teacher_disagreement_score":0.8384854,"about_ca_system_score_codex":0.00009656904,"about_ca_system_score_gemma":0.000100455996,"threshold_uncertainty_score":0.5812706},"labels":[],"label_agreement":null},{"id":"W2075804026","doi":"10.1145/1496984.1497033","title":"Experience in the design and development of a game based on head-tracking input","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Cockpit; Gaze; Head (geology); Tracking (education); Computer science; Eye tracking; Virtual reality; Computer vision; Human–computer interaction; Video game; Tracking system; Optical head-mounted display; Artificial intelligence; Multimedia; Engineering; Aeronautics; Psychology; Kalman filter","score_opus":0.11114725888389589,"score_gpt":0.30085558698351716,"score_spread":0.1897083280996213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075804026","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38608146,0.000009471015,0.61334807,0.00026494637,0.00001169753,0.000057315297,2.3330722e-8,0.00003945925,0.00018755482],"genre_scores_gemma":[0.8085379,0.0000013061372,0.19115986,0.00027652204,0.000001602644,0.000014340804,6.2619456e-8,0.0000015641951,0.000006822207],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999314,0.00006245551,0.00014719974,0.00019099932,0.00014870692,0.00013661364],"domain_scores_gemma":[0.9994462,0.00024450888,0.000037073976,0.00023566531,0.000021177513,0.000015378733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002944558,0.00006922789,0.00009042884,0.00010495326,0.000065783635,0.000014521789,0.00042103804,0.00003154267,0.0000024270612],"category_scores_gemma":[0.00004943117,0.000044799097,0.000009478363,0.00024699455,0.00009773658,0.00006899133,0.00004462429,0.00008502779,0.0000030761978],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008007553,0.0015299027,0.15502746,0.00005239179,0.000019610268,0.00039360707,0.11901595,0.0066896803,0.017416786,0.053774513,0.00028369646,0.6457163],"study_design_scores_gemma":[0.0010373758,0.0003996316,0.6893658,0.000121409736,0.0000013272655,0.0000646001,0.0006660458,0.15756118,0.14830223,0.00070648215,0.001399521,0.00037440006],"about_ca_topic_score_codex":0.000009491434,"about_ca_topic_score_gemma":0.000006884255,"teacher_disagreement_score":0.64534193,"about_ca_system_score_codex":0.000014083092,"about_ca_system_score_gemma":0.000068916524,"threshold_uncertainty_score":0.18268542},"labels":[],"label_agreement":null},{"id":"W2076842709","doi":"10.1145/2468356.2479609","title":"KINECT <sup>wheels</sup>","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wheelchair; Focus (optics); Computer science; Popularity; Human–computer interaction; Motion (physics); Multimedia; Artificial intelligence; World Wide Web","score_opus":0.01044981900431069,"score_gpt":0.21249288435552333,"score_spread":0.20204306535121264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076842709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1882542,0.000040618674,0.72540593,0.01089416,0.00013697108,0.00015841488,3.7609405e-7,0.001964752,0.073144585],"genre_scores_gemma":[0.9418838,0.0000018581728,0.05419461,0.00060517044,0.000027036402,0.00001892499,4.0889176e-7,0.00000482749,0.0032633666],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921834,0.000021915444,0.00011236894,0.00027706227,0.000111293884,0.0002589958],"domain_scores_gemma":[0.99931234,0.000052402153,0.000025509245,0.0004936397,0.000060587925,0.000055502936],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008060483,0.00009438903,0.00010306873,0.000088575805,0.00006276935,0.00010720803,0.00076525455,0.00006294851,0.00032312554],"category_scores_gemma":[0.000035937694,0.00007248917,0.00004052688,0.0002541285,0.000046214955,0.00027378337,0.000192348,0.00011826316,0.0028896278],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.5149e-7,0.00012148545,0.008784225,0.000008088756,0.000025246221,0.000019245837,0.00020377031,0.00013883832,0.002366436,0.575949,0.0744214,0.3379616],"study_design_scores_gemma":[0.0012168641,0.00048738747,0.27435753,0.000039987695,0.000012520615,0.0001614756,0.00016724136,0.45084032,0.024587672,0.15182194,0.0950963,0.0012107611],"about_ca_topic_score_codex":0.00013383456,"about_ca_topic_score_gemma":0.000002904016,"teacher_disagreement_score":0.75362957,"about_ca_system_score_codex":0.000014690586,"about_ca_system_score_gemma":0.000016164262,"threshold_uncertainty_score":0.9978867},"labels":[],"label_agreement":null},{"id":"W2077107286","doi":"10.1109/tbcas.2012.2227962","title":"A Wireless Magnetoresistive Sensing System for an Intraoral Tongue-Computer Interface","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Electrical engineering; Wireless; Chip; CMOS; Printed circuit board; System on a chip; Computer science; Interface (matter); Embedded system; Engineering; Transmitter; Computer hardware; Telecommunications","score_opus":0.02995816701455403,"score_gpt":0.267219377575142,"score_spread":0.237261210560588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077107286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084134474,0.00013058874,0.9111011,0.00012837732,0.0035870357,0.00035513533,0.000035200777,0.00047636064,0.000051746847],"genre_scores_gemma":[0.99765265,0.0000035865514,0.0018319308,0.000041985237,0.00034820996,0.000039839662,0.0000026374314,0.000019837926,0.00005932708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815214,0.00015053504,0.00037810754,0.00049203925,0.0002826362,0.00054456777],"domain_scores_gemma":[0.99886805,0.00018381886,0.00010317098,0.00037855608,0.00010101426,0.0003654083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054763374,0.00023889984,0.00037356018,0.00025505814,0.00030793095,0.00016737798,0.00034395175,0.00024435204,0.0000015136553],"category_scores_gemma":[0.0000036953961,0.00019915444,0.00008094736,0.0002921286,0.00019690275,0.0003336417,0.0000049985124,0.00025138396,0.000014420088],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003410393,0.00060277147,0.00005847505,0.00053889235,0.00016262196,0.000018646586,0.0019580992,0.00013895633,0.015484834,0.012820892,0.0004238483,0.9677579],"study_design_scores_gemma":[0.004508699,0.00497223,0.0022302356,0.0016221005,0.00023476293,0.0019915786,0.002739776,0.93819565,0.027773337,0.00014268678,0.013440871,0.0021480476],"about_ca_topic_score_codex":0.00009325926,"about_ca_topic_score_gemma":0.0000070844203,"teacher_disagreement_score":0.9656098,"about_ca_system_score_codex":0.00010851838,"about_ca_system_score_gemma":0.000037797392,"threshold_uncertainty_score":0.81212825},"labels":[],"label_agreement":null},{"id":"W2080435016","doi":"10.1145/1056808.1057041","title":"Media eyepliances","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Focus (optics); Computer science; Modality (human–computer interaction); Selection (genetic algorithm); Point (geometry); Human–computer interaction; Multimedia; Artificial intelligence","score_opus":0.016211664324056903,"score_gpt":0.24285425040120542,"score_spread":0.22664258607714852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080435016","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023568856,0.00016405017,0.84743863,0.021923501,0.00025385316,0.0000310851,2.7184063e-7,0.0014346972,0.10518508],"genre_scores_gemma":[0.84342587,0.0000044036324,0.15532246,0.00040355892,0.000046667286,0.0000019648025,1.0952763e-7,0.0000012757872,0.0007936966],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960744,0.000006141454,0.00005721362,0.00014097853,0.00006934818,0.00011886693],"domain_scores_gemma":[0.999685,0.000038104157,0.000014109846,0.00022246108,0.000015988708,0.000024340716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006376307,0.000039972267,0.00004955417,0.00003684848,0.00003161614,0.000026562466,0.00046723092,0.000025959052,0.00004988029],"category_scores_gemma":[0.000021152393,0.00003078504,0.000016435775,0.0001282401,0.000029303696,0.00014924165,0.00006565238,0.000049895567,0.00060200325],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.6219222e-7,0.0000151566555,0.000869502,4.2331922e-7,0.000001746046,0.0000019071452,0.000050119845,0.0000043040695,0.00021845607,0.52510023,0.0037112918,0.4700267],"study_design_scores_gemma":[0.00055019977,0.00008091157,0.11752925,0.000015554951,0.0000041059197,0.000043336186,0.000060843257,0.029647835,0.06716116,0.036750153,0.7476512,0.000505437],"about_ca_topic_score_codex":0.00000337878,"about_ca_topic_score_gemma":0.000020141928,"teacher_disagreement_score":0.819857,"about_ca_system_score_codex":0.000007796816,"about_ca_system_score_gemma":0.0000090562235,"threshold_uncertainty_score":0.77377343},"labels":[],"label_agreement":null},{"id":"W2084436125","doi":"10.1167/12.9.368","title":"Action video game players resist oculomotor capture, but only when told to do so","year":2012,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Distraction; Task (project management); Psychology; Action (physics); Cognitive psychology; Video game; Cognition; Computer science; Neuroscience; Multimedia","score_opus":0.021773012823706046,"score_gpt":0.2998209212389187,"score_spread":0.27804790841521265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084436125","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8035185,0.00050112157,0.18898162,0.0048292377,0.0015834501,0.00008653585,0.000001716074,0.000082320614,0.00041550078],"genre_scores_gemma":[0.9724507,0.000016530177,0.02650981,0.00036286455,0.00030576787,8.6915685e-7,2.2154386e-7,0.0000090709955,0.00034416324],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987082,0.000068898466,0.0003440049,0.00016465502,0.0004142976,0.00029997452],"domain_scores_gemma":[0.9989008,0.000081983475,0.000321709,0.00030838128,0.00017410067,0.00021305394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000651445,0.00012612045,0.000216032,0.0003182341,0.00010762218,0.00015579189,0.00061432185,0.0001213885,0.000019153138],"category_scores_gemma":[0.00015859133,0.00009757344,0.00011344998,0.00021299331,0.00003457647,0.0007661241,0.00013147769,0.00035715965,0.00008256729],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032842255,0.00081126904,0.015761023,0.00004717909,0.000118074015,0.00015619193,0.0032973876,0.00014138334,0.15936078,0.014308053,0.095501915,0.7101683],"study_design_scores_gemma":[0.0013838775,0.0018217682,0.6920232,0.0005393083,0.00005866262,0.0012778825,0.00040934674,0.0006893055,0.030148944,0.0029590826,0.26810217,0.0005864143],"about_ca_topic_score_codex":0.000027791923,"about_ca_topic_score_gemma":0.000008310208,"teacher_disagreement_score":0.7095819,"about_ca_system_score_codex":0.00013432042,"about_ca_system_score_gemma":0.000077087236,"threshold_uncertainty_score":0.39789292},"labels":[],"label_agreement":null},{"id":"W2085486672","doi":"10.1145/507072.507084","title":"What do the eyes behold for human-computer interaction?","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Human–computer interaction; Eye tracking; Usability; Desk; Eye movement; Cursor (databases); Computer vision; Artificial intelligence","score_opus":0.051265035472486876,"score_gpt":0.2979151869949846,"score_spread":0.24665015152249772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085486672","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029588107,0.00022433416,0.9470828,0.017728137,0.0017280625,0.00020286653,5.0439525e-7,0.000626949,0.0028181942],"genre_scores_gemma":[0.97814363,0.000024488618,0.01822744,0.0009842087,0.00013497917,0.000032828764,4.352384e-7,0.000005517586,0.0024464943],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99933785,0.000018395694,0.000121454344,0.00025686127,0.00008301131,0.00018241978],"domain_scores_gemma":[0.999306,0.0001352151,0.000045087847,0.0004432979,0.00004886843,0.000021505733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011272377,0.0000868328,0.00008345591,0.00006067859,0.00022027756,0.00047044182,0.0007475848,0.000045219913,0.00008086044],"category_scores_gemma":[0.0000072898406,0.00005411506,0.00006667411,0.00012651474,0.00005377337,0.0005543909,0.00014088537,0.00011467343,0.00010985815],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.067115e-7,0.00011265927,0.0004282786,0.000005162885,0.000026564514,0.00000352064,0.000494135,0.000037593018,0.00032286684,0.46565533,0.0434308,0.48948216],"study_design_scores_gemma":[0.0016570535,0.0010454775,0.015196369,0.00018173105,0.000038147195,0.0001673197,0.0010283242,0.2796673,0.016416216,0.10021485,0.5833146,0.0010726441],"about_ca_topic_score_codex":0.000005894626,"about_ca_topic_score_gemma":0.00001097107,"teacher_disagreement_score":0.9485555,"about_ca_system_score_codex":0.000014742478,"about_ca_system_score_gemma":0.000002193994,"threshold_uncertainty_score":0.45364815},"labels":[],"label_agreement":null},{"id":"W2085556084","doi":"10.1145/1056808.1056950","title":"eyeView","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Videoconferencing; Focus (optics); Multimedia; Real estate; Human–computer interaction; Space (punctuation)","score_opus":0.012467145697558957,"score_gpt":0.2479995717306686,"score_spread":0.23553242603310964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085556084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059075085,0.00032484994,0.86894125,0.0209152,0.00007542268,0.000030028095,6.426267e-8,0.00099298,0.10281269],"genre_scores_gemma":[0.86019444,0.0000117520185,0.13737942,0.00085213775,0.000020267442,0.0000018948693,5.2215142e-8,0.00000110625,0.0015389206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999707,0.000005971553,0.000049891616,0.00010552375,0.00004198663,0.000089642635],"domain_scores_gemma":[0.99973106,0.000010646386,0.000010363168,0.00021833323,0.000012232933,0.000017381159],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00005675428,0.000031150787,0.000041909523,0.000025837971,0.000023485003,0.000019015108,0.00035505643,0.00001736232,0.000048049864],"category_scores_gemma":[0.0000078669955,0.00002363866,0.00001653978,0.000107870335,0.000012865959,0.00010056684,0.0000631337,0.000038695656,0.000881246],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.0624523e-8,0.000009160544,0.00017120784,5.5724036e-7,8.6119104e-7,7.9215806e-7,0.0000061525516,0.0000013331805,0.000076920536,0.3701371,0.0024917335,0.62710416],"study_design_scores_gemma":[0.000118194264,0.000029115072,0.010046746,0.000007335945,0.0000013399043,0.000021633092,0.000002639099,0.0057051666,0.010115818,0.0066092527,0.9672145,0.00012826012],"about_ca_topic_score_codex":0.000001663772,"about_ca_topic_score_gemma":0.0000035542032,"teacher_disagreement_score":0.96472275,"about_ca_system_score_codex":0.0000069564267,"about_ca_system_score_gemma":0.000006422413,"threshold_uncertainty_score":0.9998967},"labels":[],"label_agreement":null},{"id":"W2085910247","doi":"10.1007/s00221-005-0260-2","title":"No automatic pilot for visually guided aiming based on colour","year":2005,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kinematics; Cued speech; Movement (music); Object (grammar); Artificial intelligence; Physical medicine and rehabilitation; Computer vision; Trajectory; Computer science; Psychology; Cognitive psychology; Medicine","score_opus":0.13694921618753067,"score_gpt":0.4515199645995642,"score_spread":0.3145707484120336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085910247","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68987465,0.00033937438,0.19008656,0.06733939,0.0008497019,0.0038025614,0.000017264196,0.0028832683,0.04480721],"genre_scores_gemma":[0.91583025,3.2176763e-7,0.0807052,0.001355023,0.00011108138,0.00032853903,0.0000037040243,0.000020517105,0.001645324],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763876,0.00022465525,0.00024393752,0.00055221183,0.00065366324,0.000686775],"domain_scores_gemma":[0.9979581,0.001105661,0.000046804107,0.0005927825,0.00017386055,0.00012275575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015513028,0.00015695307,0.00017614108,0.00036564417,0.00042153423,0.0001835145,0.0011731556,0.00007499204,0.00010841238],"category_scores_gemma":[0.00060100085,0.00014795846,0.000067331304,0.00041098273,0.00017479854,0.00016785384,0.00027839706,0.0002870348,0.0005237449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013455615,0.0023961097,0.00014351273,0.000050225568,0.00003066163,0.000038768838,0.00056229613,0.0002308224,0.7002508,0.08562993,0.16793908,0.042593203],"study_design_scores_gemma":[0.00162639,0.00360462,0.0006854704,0.000067741894,8.086309e-7,0.000006236679,0.00009609925,0.57547003,0.3964529,0.00035133708,0.02140134,0.00023701112],"about_ca_topic_score_codex":0.000016708824,"about_ca_topic_score_gemma":0.00000273586,"teacher_disagreement_score":0.57523924,"about_ca_system_score_codex":0.00031270256,"about_ca_system_score_gemma":0.00014951619,"threshold_uncertainty_score":0.67318565},"labels":[],"label_agreement":null},{"id":"W2086577692","doi":"10.1145/1344471.1344526","title":"Analysis of subject-dependent point-of-gaze estimation bias in the cross-ratios method","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Cross-ratio; Computer science; Artificial intelligence; Point (geometry); Computer vision; Property (philosophy); Pupil; Function (biology); Mathematics; Optics; Physics; Geometry","score_opus":0.06083445325362994,"score_gpt":0.3458306866881745,"score_spread":0.28499623343454455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086577692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40107712,0.000012308988,0.5978456,0.00027303267,0.000019712197,0.00004807627,9.882422e-7,0.00003836515,0.0006847637],"genre_scores_gemma":[0.8752759,0.0000036007002,0.124586396,0.000053733635,0.000002474119,0.000005737453,0.0000016962055,0.0000018224688,0.00006860421],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989216,0.00016198182,0.00033763456,0.00020889787,0.00024654873,0.00012336831],"domain_scores_gemma":[0.9988283,0.00039133508,0.00016666055,0.00050563394,0.0000953147,0.000012789402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009783307,0.000075599564,0.00023001985,0.0004579148,0.000047937934,0.000021251833,0.0006651452,0.000057452537,0.000017984716],"category_scores_gemma":[0.00019787948,0.000050336374,0.00009598273,0.0017504933,0.000090227426,0.00016686684,0.00007317853,0.000098316385,0.0000046707337],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015916512,0.00053793675,0.7068637,0.000025149562,0.00039300657,0.000044858785,0.007027353,0.05450014,0.0054078614,0.15635404,0.00015481873,0.068675205],"study_design_scores_gemma":[0.00017642014,0.000063816035,0.5811539,0.000004491863,0.000042375792,0.0000147767305,0.000068416666,0.39373252,0.022828642,0.0018356708,0.0000069208527,0.00007201036],"about_ca_topic_score_codex":0.00031146672,"about_ca_topic_score_gemma":0.00018992802,"teacher_disagreement_score":0.47419882,"about_ca_system_score_codex":0.000015838832,"about_ca_system_score_gemma":0.000036866095,"threshold_uncertainty_score":0.20526578},"labels":[],"label_agreement":null},{"id":"W2087054057","doi":"10.1167/11.11.502","title":"A new method for comparing scanpaths based on vectors and dimensions","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Similarity (geometry); Computer science; Eye tracking; Pattern recognition (psychology); Eye movement; Levenshtein distance; Computer vision","score_opus":0.04525017419375966,"score_gpt":0.3304397140461999,"score_spread":0.2851895398524402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087054057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06745167,0.000050831004,0.9315718,0.00046993775,0.00021282029,0.0000396541,1.828124e-7,0.000025299725,0.00017784676],"genre_scores_gemma":[0.5616031,0.0000017192708,0.43831334,0.00005644454,0.000016114098,2.3731916e-7,3.1239367e-8,0.0000023798573,0.000006624673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946433,0.000035961795,0.00017188529,0.00011016136,0.00011456757,0.00010307224],"domain_scores_gemma":[0.99937415,0.00016695284,0.0001677657,0.00013854996,0.00007247259,0.000080090016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004069469,0.000063296306,0.00015699772,0.00018612297,0.00006738002,0.000024430594,0.00023405267,0.000038879916,0.0000027350952],"category_scores_gemma":[0.00008054872,0.00004595988,0.000057053625,0.00010949765,0.000012804381,0.00010767077,0.000045528737,0.00012595174,0.0000012144985],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032285452,0.00048250292,0.017414836,0.000026669604,0.000056482942,0.00007631552,0.0010538863,0.0005330715,0.035451043,0.04436984,0.0070716524,0.89314085],"study_design_scores_gemma":[0.0030559471,0.0054618604,0.3895175,0.0005745817,0.000054807933,0.00016708137,0.000049316717,0.5504132,0.0251594,0.022378331,0.0028734466,0.00029455038],"about_ca_topic_score_codex":0.000013700936,"about_ca_topic_score_gemma":0.0000024015162,"teacher_disagreement_score":0.8928463,"about_ca_system_score_codex":0.000017288547,"about_ca_system_score_gemma":0.000044368553,"threshold_uncertainty_score":0.18741895},"labels":[],"label_agreement":null},{"id":"W2089782058","doi":"10.1002/acp.1742","title":"Is banner blindness genuine? Eye tracking internet text advertising","year":2010,"lang":"en","type":"article","venue":"Applied Cognitive Psychology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Psychology; Popularity; Eye tracking; Fixation (population genetics); The Internet; Advertising; Eye movement; Visual attention; Banner; Gaze; Internet privacy; Cognitive psychology; Cognition; Social psychology; World Wide Web; Computer science; Neuroscience","score_opus":0.02240629503432675,"score_gpt":0.32756710297246944,"score_spread":0.3051608079381427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089782058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57276225,0.000051009913,0.389944,0.0020666514,0.0011876891,0.00022751217,0.000005979017,0.0005553762,0.033199526],"genre_scores_gemma":[0.9872879,0.00000763438,0.006823202,0.0053474796,0.00016702747,0.0000786525,0.000008898591,0.000034014345,0.0002452294],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9975898,0.00005708961,0.00037374184,0.0011179148,0.00020185688,0.0006595899],"domain_scores_gemma":[0.9986267,0.00019112676,0.00019162208,0.00065758335,0.000209118,0.00012385663],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003382883,0.00032531936,0.00035088335,0.00033552735,0.0001443722,0.00011148229,0.0011886948,0.0004007867,0.00024373135],"category_scores_gemma":[0.00008295694,0.00032108757,0.000102297665,0.00056541036,0.00041099085,0.00019553614,0.00027673083,0.0010566702,0.0010082619],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008094251,0.00046593987,0.009549867,0.000012737826,0.0001294812,0.0000907094,0.0020022043,3.9144908e-7,0.08650995,0.10920458,0.0028569284,0.78909624],"study_design_scores_gemma":[0.0071843453,0.0006584147,0.6954718,0.00015358,0.00013704372,0.00050836336,0.00059256004,0.002263935,0.17566678,0.07202987,0.042907033,0.0024262671],"about_ca_topic_score_codex":0.000012378784,"about_ca_topic_score_gemma":0.000021344462,"teacher_disagreement_score":0.78666997,"about_ca_system_score_codex":0.000015698914,"about_ca_system_score_gemma":0.000032933964,"threshold_uncertainty_score":0.9999241},"labels":[],"label_agreement":null},{"id":"W2091289860","doi":"10.1145/2399016.2399140","title":"Demo of gaze controlled flying","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Computer science; Human–computer interaction; Computer graphics (images); Computer vision","score_opus":0.018150663414817317,"score_gpt":0.25152702154299533,"score_spread":0.23337635812817803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091289860","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050298505,0.0001871527,0.9202141,0.00079706335,0.00016760494,0.00007158779,1.15931975e-7,0.000278562,0.027985265],"genre_scores_gemma":[0.9617579,0.000001826236,0.037665684,0.00013322775,0.00001816102,0.0000047814046,8.0436784e-8,0.0000020611465,0.00041626117],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995023,0.000021694184,0.0001251318,0.000085286185,0.00007876393,0.00018684095],"domain_scores_gemma":[0.9995614,0.00008662142,0.00005301471,0.00023080887,0.000033433513,0.000034676144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023303293,0.000050752144,0.00015338074,0.000060889306,0.000035696343,0.000008768011,0.00031728452,0.000037273003,0.000029303572],"category_scores_gemma":[0.000053531505,0.000037277005,0.00004471952,0.00012756676,0.000030516672,0.00014099274,0.000074457086,0.000052746127,0.00004541095],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009785522,0.00014001221,0.037454758,0.000007347559,0.000034684293,0.0000013465999,0.0002023589,0.0000059079566,0.0068526766,0.8827252,0.0010946245,0.071471326],"study_design_scores_gemma":[0.023031445,0.0006762358,0.4730176,0.00014979926,0.00011325799,0.00016048178,0.0005135178,0.09423818,0.31504965,0.048180413,0.043204118,0.0016653034],"about_ca_topic_score_codex":0.00000928054,"about_ca_topic_score_gemma":6.278972e-7,"teacher_disagreement_score":0.9114594,"about_ca_system_score_codex":0.00000654547,"about_ca_system_score_gemma":0.000009357243,"threshold_uncertainty_score":0.15201122},"labels":[],"label_agreement":null},{"id":"W2095019168","doi":"10.1007/s11554-010-0178-1","title":"A real-time framework for eye detection and tracking","year":2010,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"BitTorrent tracker; Computer science; Robustness (evolution); Eye tracking; Artificial intelligence; Overtaking; Computer vision; Boosting (machine learning); Machine learning; Engineering","score_opus":0.010381625423986206,"score_gpt":0.28536001602240046,"score_spread":0.2749783905984143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095019168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34522414,0.0000741138,0.65264094,0.0010262942,0.00020826743,0.000079919344,0.0000011229042,0.00019057131,0.0005546274],"genre_scores_gemma":[0.4737312,0.00003690431,0.5258581,0.000023112945,0.00022374505,0.0000037786167,2.1076933e-7,0.000019793613,0.00010316763],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99857694,0.00004051834,0.00047728996,0.00030288406,0.00025137948,0.0003510051],"domain_scores_gemma":[0.9980431,0.00026170586,0.00075766747,0.00024420404,0.00056588097,0.0001274016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009422713,0.0001923145,0.00036271516,0.00031017856,0.00031568448,0.00050888356,0.0005321048,0.00021833919,0.000008813813],"category_scores_gemma":[0.0006311267,0.00016611045,0.00011187895,0.0003490199,0.00015014486,0.0011992266,0.00008493569,0.0006668266,0.000008745168],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023883866,0.000038950904,0.00015125086,0.000047581932,0.000012535334,0.000021078924,0.00017074941,0.00000154748,0.67265105,0.00024354855,0.000048091933,0.32658973],"study_design_scores_gemma":[0.0036974875,0.0021222115,0.04547641,0.0022575378,0.0003357185,0.004163018,0.000264266,0.19176915,0.6040878,0.14126876,0.0027773536,0.0017802889],"about_ca_topic_score_codex":0.000005171063,"about_ca_topic_score_gemma":0.0000011466484,"teacher_disagreement_score":0.32480943,"about_ca_system_score_codex":0.000036892005,"about_ca_system_score_gemma":0.00013451227,"threshold_uncertainty_score":0.6773787},"labels":[],"label_agreement":null},{"id":"W2096562421","doi":"10.1109/robot.2001.932832","title":"A study of natural eye movement detection and ocular implant movement control using processed EOG signals","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Eye movement; Computer vision; Orientation (vector space); Computer science; Controller (irrigation); Artificial intelligence; Movement control; Control system; SIGNAL (programming language); Machine vision; Engineering; Medicine; Physical medicine and rehabilitation; Electrical engineering","score_opus":0.01892987370079518,"score_gpt":0.2478871426477043,"score_spread":0.22895726894690913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096562421","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8307653,0.00011984251,0.16842693,0.0001452506,0.00006810172,0.00032962524,5.977898e-7,0.00010874297,0.00003561733],"genre_scores_gemma":[0.9980484,0.0000026679788,0.0016680862,0.00021759146,0.000008868242,0.000018328048,8.655994e-8,0.00000430298,0.000031673528],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901915,0.000041627787,0.00023222023,0.00030072566,0.00021492779,0.00019138027],"domain_scores_gemma":[0.99951816,0.000026450283,0.00011704842,0.00021850705,0.00008752736,0.00003228587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016393341,0.00011877159,0.00019012231,0.00012423594,0.00009199799,0.000054553893,0.0002199402,0.000031290267,0.0000067066026],"category_scores_gemma":[0.00001417756,0.00009394541,0.000024970737,0.00021325465,0.000028693617,0.0001622039,0.00008738225,0.00009197176,0.0000013543566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003514961,0.002022555,0.024290282,0.000072609226,0.00031130636,0.00009018184,0.0019428354,0.0009998716,0.87648654,0.0018110959,0.000033380198,0.091904216],"study_design_scores_gemma":[0.004298512,0.0015317942,0.035149336,0.000038498605,0.000052181662,0.000016464417,0.0010651456,0.7441534,0.21219938,0.0011248513,0.000027002514,0.00034347404],"about_ca_topic_score_codex":0.00016909173,"about_ca_topic_score_gemma":0.000060195816,"teacher_disagreement_score":0.7431535,"about_ca_system_score_codex":0.000031888285,"about_ca_system_score_gemma":0.0000067897336,"threshold_uncertainty_score":0.38309827},"labels":[],"label_agreement":null},{"id":"W2097313793","doi":"10.1109/tbme.2006.880503","title":"Erratum to &amp;#8220;General Theory of Remote Gaze Estimation Using the Pupil Center and Corneal Reflections&amp;#8221;","year":2006,"lang":"en","type":"erratum","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pupil; Gaze; Computer science; Center (category theory); Optics; Optometry; Computer vision; Physics; Medicine; Chemistry","score_opus":0.027782579090356068,"score_gpt":0.27992133065053776,"score_spread":0.2521387515601817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097313793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010493125,0.00012306268,0.98537844,0.00071416656,0.011736416,0.00023011361,0.00006646716,0.00042634754,0.00027569805],"genre_scores_gemma":[0.14167088,0.00024875684,0.82599986,0.00044673897,0.0013066806,0.00007101585,0.00016131398,0.00021346478,0.029881267],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977004,0.00009362186,0.0005592281,0.0006355568,0.00051523576,0.00049596064],"domain_scores_gemma":[0.9986139,0.00017026572,0.00015444866,0.0007743499,0.00010656526,0.00018048962],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044919888,0.0004295857,0.0004623019,0.00082977925,0.00025443005,0.00010086921,0.00068903895,0.00061453704,0.000009956537],"category_scores_gemma":[0.000051437783,0.00035584407,0.0001709936,0.0011330472,0.00024170955,0.00013169082,0.000019440857,0.0014624523,0.0000122718175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013608915,0.0011897641,0.0000037752748,0.0009267793,0.0008781065,0.000056901714,0.0016147578,0.28905633,0.04880429,0.004533398,0.21737126,0.43542856],"study_design_scores_gemma":[0.00081269356,0.0003388861,0.0001730848,0.0012816018,0.00020138714,0.00042973843,0.000019852268,0.6391189,0.0022362126,0.0014085023,0.35285053,0.0011285843],"about_ca_topic_score_codex":0.000121952435,"about_ca_topic_score_gemma":0.00006684268,"teacher_disagreement_score":0.43429998,"about_ca_system_score_codex":0.00018939869,"about_ca_system_score_gemma":0.0001378657,"threshold_uncertainty_score":0.9998894},"labels":[],"label_agreement":null},{"id":"W2097345068","doi":"10.2196/humanfactors.4062","title":"Using Eye Trackers for Usability Evaluation of Health Information Technology: A Systematic Literature Review","year":2015,"lang":"en","type":"review","venue":"JMIR Human Factors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Medical College of Wisconsin","keywords":"Usability; Eye tracking; Computer science; BitTorrent tracker; Web usability; Human–computer interaction; Cognitive walkthrough; Usability engineering; Pluralistic walkthrough; System usability scale; Usability goals; Artificial intelligence","score_opus":0.20409077803460493,"score_gpt":0.46369937566485364,"score_spread":0.2596085976302487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097345068","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005207763,0.98329103,0.0081603015,0.00006985435,0.00019511742,0.007863607,0.00006561027,0.00028301222,0.000019362513],"genre_scores_gemma":[0.00077475444,0.99348915,0.004044932,0.000032291257,0.000019790827,0.0012844385,0.0003193493,0.000026543861,0.000008763717],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99578846,0.00074613857,0.0019288826,0.0004456422,0.00075070275,0.00034018164],"domain_scores_gemma":[0.99451834,0.0001089696,0.0028018455,0.0012548459,0.0012472234,0.0000687748],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0056643025,0.00044687977,0.0028222364,0.0010211888,0.00016750208,0.00010375808,0.0012716482,0.00054734177,0.0000023869738],"category_scores_gemma":[0.0009133574,0.00032799135,0.00045779956,0.0017538199,0.00012148908,0.000637081,0.00015205778,0.00045317813,0.0000067812466],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.843728e-7,0.00004187603,0.000010905464,0.7065728,0.00005776236,9.671723e-8,0.0002592344,8.3245095e-7,1.3039897e-7,0.002365655,0.00024249901,0.29044804],"study_design_scores_gemma":[0.00033169892,0.00026285395,0.00002899942,0.9033496,0.000915509,0.000019882427,0.000090125446,0.00047742037,0.0000016588576,0.0023896422,0.09156564,0.0005669698],"about_ca_topic_score_codex":0.0000029474254,"about_ca_topic_score_gemma":0.000001514589,"teacher_disagreement_score":0.28988108,"about_ca_system_score_codex":0.0008707205,"about_ca_system_score_gemma":0.00085027434,"threshold_uncertainty_score":0.9999172},"labels":[],"label_agreement":null},{"id":"W2097493241","doi":"10.1109/tbme.2009.2015955","title":"Improving the Accuracy and Reliability of Remote System-Calibration-Free Eye-Gaze Tracking","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Computer science; Robustness (evolution); Artificial intelligence; Calibration; Eye tracking; Reflection (computer programming); Gaze; Displacement (psychology); Distortion (music); Optics; Mathematics; Physics","score_opus":0.007829556880879473,"score_gpt":0.22066866414872757,"score_spread":0.2128391072678481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097493241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033339925,0.000042535496,0.96321464,0.0024103138,0.00036316094,0.00012385446,0.000005475751,0.00048185483,0.00001822885],"genre_scores_gemma":[0.9576322,0.000013660379,0.042258006,0.000046507666,0.00003070091,0.0000040556924,3.6272522e-7,0.000007349719,0.0000071664626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988304,0.00003047053,0.00032843146,0.0003103416,0.00026818068,0.00023222278],"domain_scores_gemma":[0.9988507,0.00029244073,0.000078807585,0.00064418896,0.00004629981,0.00008755052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003767914,0.00014688524,0.00019085003,0.00016719874,0.00013450885,0.000053465603,0.0006168508,0.00012990614,0.0000017211794],"category_scores_gemma":[0.00013227943,0.00010936087,0.00007073215,0.00049670396,0.00010500268,0.00024938048,0.0000065407285,0.00036995747,0.0000012980547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013130224,0.00015274415,0.0000070584456,0.00016141849,0.000026022475,0.000013860753,0.00030855864,0.011881035,0.066152476,0.004040347,0.000028512854,0.9172148],"study_design_scores_gemma":[0.00035907922,0.00017739211,0.0013316239,0.0001562788,0.000017708888,0.000030834384,0.000033848875,0.96077245,0.036569737,0.00018558298,0.00020544803,0.00016001513],"about_ca_topic_score_codex":0.000029408835,"about_ca_topic_score_gemma":8.30844e-7,"teacher_disagreement_score":0.9488914,"about_ca_system_score_codex":0.000048568345,"about_ca_system_score_gemma":0.000032761112,"threshold_uncertainty_score":0.4459607},"labels":[],"label_agreement":null},{"id":"W2101038569","doi":"10.1109/tsmcb.2007.911378","title":"Fixation Precision in High-Speed Noncontact Eye-Gaze Tracking","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Eye tracking; Fixation (population genetics); Computer vision; Artificial intelligence; Computer science; Tracking (education); Medicine; Psychology","score_opus":0.026057646484355823,"score_gpt":0.24637501475280496,"score_spread":0.22031736826844914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101038569","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63680923,0.0003541746,0.3585785,0.000380449,0.0015481971,0.0005668773,0.000018308474,0.0004130862,0.0013311692],"genre_scores_gemma":[0.9951541,0.00053236645,0.0014112965,0.00007929511,0.000095334406,0.000054079544,0.000004773218,0.00004339354,0.0026253995],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99684477,0.00023981427,0.0008181017,0.0009230875,0.0005661721,0.0006080645],"domain_scores_gemma":[0.99829024,0.00025028668,0.00026813254,0.0008334576,0.00015734839,0.00020054833],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042761653,0.00043838113,0.0005643371,0.0005342312,0.00032292426,0.00021053704,0.00063987315,0.0003693153,0.00001991684],"category_scores_gemma":[0.000017039505,0.0004381309,0.00012264724,0.0006287805,0.0001979989,0.00032046015,0.000014080809,0.0006275477,0.00014473464],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008182188,0.00897359,0.0449733,0.00092481327,0.0009970908,0.0021398608,0.024919596,0.18923362,0.03436722,0.08893053,0.0074627018,0.5962595],"study_design_scores_gemma":[0.018602451,0.0055067535,0.40868643,0.003997438,0.00036715332,0.0018036566,0.0012910963,0.4044809,0.099674314,0.004085268,0.044677272,0.0068272627],"about_ca_topic_score_codex":0.00050571363,"about_ca_topic_score_gemma":0.00025972349,"teacher_disagreement_score":0.5894322,"about_ca_system_score_codex":0.00015887339,"about_ca_system_score_gemma":0.00006901226,"threshold_uncertainty_score":0.99980706},"labels":[],"label_agreement":null},{"id":"W2101686082","doi":"10.1109/i-society18435.2011.5978526","title":"A survey report on mobile eye-based Human-Computer Interaction","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Human–computer interaction; Wearable computer; Mobile computing; Mobile device; Mobile interaction; Human eye; Mobile technology; Multimedia; Artificial intelligence; Embedded system; Telecommunications; World Wide Web","score_opus":0.06736682228872161,"score_gpt":0.3207359297126608,"score_spread":0.2533691074239392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101686082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33619225,0.0000022625172,0.6523696,0.00007058792,0.00050143903,0.00009900616,6.224039e-7,0.0007425012,0.010021745],"genre_scores_gemma":[0.9758234,1.486173e-7,0.0232622,0.00023941226,0.000027422318,0.000022514507,0.0000060740504,0.0000069310877,0.00061189977],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99896795,0.0000776194,0.00021061033,0.00042723567,0.00012817683,0.00018838256],"domain_scores_gemma":[0.99901277,0.00006285665,0.000106353786,0.00068519457,0.00009101501,0.000041836174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004118105,0.00011649708,0.00012868014,0.00015856084,0.000083347855,0.000045595752,0.0004841597,0.00007224556,0.00009214964],"category_scores_gemma":[0.00002132114,0.00009676622,0.000052358246,0.000212286,0.00004087812,0.00014071546,0.00009021331,0.00016500021,0.00020210588],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008640412,0.0035711103,0.51986146,0.000034138728,0.00013932122,0.0019669526,0.00074990944,0.0004383051,0.0030193662,0.09400084,0.023456382,0.3526758],"study_design_scores_gemma":[0.00032127247,0.00084424554,0.957637,0.000024828094,0.0000032984983,0.000037440914,0.000006404041,0.015459879,0.022393802,0.0007388217,0.0022515205,0.0002814837],"about_ca_topic_score_codex":0.0003259761,"about_ca_topic_score_gemma":0.00007621819,"teacher_disagreement_score":0.63963115,"about_ca_system_score_codex":0.000034236717,"about_ca_system_score_gemma":0.000023960607,"threshold_uncertainty_score":0.3946012},"labels":[],"label_agreement":null},{"id":"W2101940840","doi":"10.1145/1054972.1054994","title":"EyeWindows","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Zoom; Window (computing); Focus (optics); Eye tracking; Key (lock); Task (project management); Selection (genetic algorithm); Computer vision; Artificial intelligence; Tracking (education); Computer graphics (images); Operating system; Engineering","score_opus":0.009194878013428345,"score_gpt":0.22903681444918642,"score_spread":0.21984193643575806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101940840","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015632128,0.00002973792,0.8750242,0.011312835,0.00007009527,0.000023260947,7.3930785e-8,0.0008766021,0.09703108],"genre_scores_gemma":[0.8605985,8.59616e-7,0.1368666,0.00055873377,0.000026522875,0.0000018550603,6.109035e-8,0.0000011935226,0.0019456709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99967074,0.000005342158,0.000048720212,0.000118424614,0.00004920355,0.000107573505],"domain_scores_gemma":[0.99971664,0.000012514498,0.000010293869,0.00022760748,0.0000130241615,0.000019930132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049167327,0.00003396449,0.00003748933,0.00003758031,0.000029262119,0.000024114403,0.00038192695,0.000023145769,0.000042071373],"category_scores_gemma":[0.0000075765547,0.000026978505,0.000015728869,0.00010603154,0.000015086084,0.00012048363,0.000071105525,0.000047157264,0.00069326407],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.2888319e-7,0.000018856237,0.0007717403,2.7869004e-7,0.0000017273861,0.0000018552297,0.000020224472,0.000008624066,0.000535686,0.5684276,0.0039666626,0.4262466],"study_design_scores_gemma":[0.00048082013,0.000085800755,0.048935927,0.000005481389,0.0000023748585,0.00005401686,0.000014189185,0.04113833,0.061462756,0.017197415,0.8302935,0.00032943074],"about_ca_topic_score_codex":0.0000027723645,"about_ca_topic_score_gemma":0.000005066991,"teacher_disagreement_score":0.84496635,"about_ca_system_score_codex":0.000008665515,"about_ca_system_score_gemma":0.0000074162167,"threshold_uncertainty_score":0.8910738},"labels":[],"label_agreement":null},{"id":"W2102806982","doi":"10.1109/tbme.2008.2005947","title":"Determining the Visual Angle of Objects in the Visual Field: An Extended Application of Eye Trackers","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Computer vision; BitTorrent tracker; Artificial intelligence; Computer science; Eye tracking; Gaze; Gaze-contingency paradigm; Peripheral vision; Fixation (population genetics); Visual field; Visual angle; Eye movement; Field of view; Computer graphics (images); Visual perception; Perception; Optics; Psychology; Physics; Neuroscience","score_opus":0.010450849010826553,"score_gpt":0.26600445192485167,"score_spread":0.2555536029140251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102806982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3687155,0.0000056669614,0.63087803,0.00017898866,0.00008580379,0.00007096841,0.0000010700631,0.00005699057,0.000007023427],"genre_scores_gemma":[0.9968523,0.000007958797,0.0030347714,0.000049831844,0.000016621432,0.000029646219,8.5759586e-7,0.0000060646603,0.0000019578972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991334,0.000035873094,0.00023786495,0.00017890707,0.00024811245,0.00016586087],"domain_scores_gemma":[0.99939966,0.0002337561,0.00005278547,0.00025010767,0.000027543108,0.000036147165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021339332,0.00009602603,0.00013606502,0.00020424621,0.000067899746,0.000007102964,0.0004796949,0.000096725926,0.0000018203785],"category_scores_gemma":[0.000017565648,0.00006555418,0.000055222976,0.00063810655,0.00011917023,0.000098806464,0.0000026890252,0.0002683991,0.0000013430664],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043973458,0.0029701306,0.00075198343,0.00009428455,0.00007336955,0.000037390193,0.006499185,0.017377293,0.11302993,0.0012803108,0.000028607501,0.85781354],"study_design_scores_gemma":[0.0008567443,0.0012813258,0.04838729,0.00006436732,0.000018048504,0.000039396975,0.00025142598,0.8231382,0.125523,0.000058639504,0.0001460358,0.00023558043],"about_ca_topic_score_codex":0.000024766206,"about_ca_topic_score_gemma":0.0000074173145,"teacher_disagreement_score":0.857578,"about_ca_system_score_codex":0.000017195302,"about_ca_system_score_gemma":0.000027986804,"threshold_uncertainty_score":0.26732218},"labels":[],"label_agreement":null},{"id":"W2103406235","doi":"10.1109/ccece.2011.6030667","title":"User-calibration-free remote eye-gaze tracking system with extended tracking range","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Gaze; Tracking (education); Pupil; Eye tracking; Calibration; Tracking system; Point (geometry); Range (aeronautics); Mathematics; Optics; Engineering; Physics; Kalman filter","score_opus":0.032343066017603186,"score_gpt":0.23551447368236672,"score_spread":0.20317140766476355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103406235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035181087,0.00006720148,0.9447056,0.0005587562,0.00025664535,0.00023599133,0.0000020360837,0.002520478,0.016472222],"genre_scores_gemma":[0.8256655,0.000002721019,0.17365228,0.000121626756,0.000049615046,0.000008865256,0.0000011707336,0.000025933981,0.0004723066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99790156,0.00010071648,0.00038513824,0.0007171317,0.0003713454,0.0005241109],"domain_scores_gemma":[0.99800956,0.00007457349,0.00019947742,0.0014262996,0.00017044411,0.000119638215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039572222,0.00029045308,0.00033442126,0.00023329195,0.0002452293,0.00022029036,0.0017942279,0.00017097917,0.00003614563],"category_scores_gemma":[0.0000584899,0.00022414782,0.00008848511,0.00060566264,0.00011927535,0.0010086071,0.00024078145,0.00029710005,0.000052191597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007463459,0.0002601623,0.015565744,0.00014959199,0.0001394872,0.0005154324,0.0022637143,0.00003512632,0.0019635786,0.8132084,0.0007199692,0.16510417],"study_design_scores_gemma":[0.0077083795,0.0017935422,0.61336046,0.0016388564,0.00018934924,0.0011967189,0.0027577856,0.24668281,0.10437524,0.014514977,0.0022168444,0.0035650458],"about_ca_topic_score_codex":0.00021422187,"about_ca_topic_score_gemma":0.00014875593,"teacher_disagreement_score":0.7986934,"about_ca_system_score_codex":0.00006833462,"about_ca_system_score_gemma":0.000065870656,"threshold_uncertainty_score":0.9140483},"labels":[],"label_agreement":null},{"id":"W2106424275","doi":"10.1109/iembs.2006.260774","title":"A Low Cost Human Computer Interface based on Eye Tracking","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Eye tracking; Computer vision; Gaze; Interface (matter); Eye tracking on the ISS; Artificial intelligence; Software; Eye movement; Point (geometry); Key (lock); Tracking (education)","score_opus":0.015452267677206137,"score_gpt":0.2763886149224978,"score_spread":0.26093634724529163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106424275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06790003,0.000003727202,0.92140335,0.0014273516,0.00018276784,0.000106180465,7.154635e-7,0.00089051144,0.008085361],"genre_scores_gemma":[0.9631081,7.647066e-8,0.035766754,0.0005687176,0.000073918025,0.000008510305,0.0000023628286,0.000009603359,0.0004619523],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988671,0.0000399678,0.0001900257,0.0004275024,0.00017223056,0.000303161],"domain_scores_gemma":[0.9992663,0.000064842636,0.0000625249,0.0005144847,0.000054467142,0.000037383954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014489438,0.00015684884,0.00015445492,0.00016546203,0.00014312,0.00016625413,0.0007363289,0.00008070583,0.000034005523],"category_scores_gemma":[0.0000055442065,0.00013529658,0.000063079,0.00025272052,0.0000692941,0.00012885791,0.000106830885,0.00020171868,0.0001499274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019404273,0.0020991953,0.027630817,0.00005309896,0.00003011053,0.00020485347,0.00016508455,0.03327831,0.019945007,0.48192057,0.03161598,0.40303758],"study_design_scores_gemma":[0.0012294461,0.00045479188,0.12515208,0.00014394459,0.000005823639,0.0000086148375,0.000008580664,0.73936063,0.123236105,0.0031715278,0.0066381916,0.0005902861],"about_ca_topic_score_codex":0.000055957193,"about_ca_topic_score_gemma":0.000038055223,"teacher_disagreement_score":0.89520806,"about_ca_system_score_codex":0.000050745122,"about_ca_system_score_gemma":0.000016148522,"threshold_uncertainty_score":0.5517234},"labels":[],"label_agreement":null},{"id":"W2106529685","doi":"10.1109/ccece.2011.6030581","title":"Design and development of fuzzy logic operated smart motorized wheelchair","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wheelchair; Manual wheelchair; Computer science; Architecture; Perception; Fuzzy logic; Control (management); Embedded system; Human–computer interaction; Simulation; Engineering; Psychology; Artificial intelligence","score_opus":0.0783716289060516,"score_gpt":0.24314021330453975,"score_spread":0.16476858439848815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106529685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05348215,0.000032054293,0.9438088,0.000075886455,0.000067203684,0.00010088608,7.987254e-8,0.00024290162,0.0021900488],"genre_scores_gemma":[0.50897086,0.0000018131202,0.49088943,0.000031372987,0.0000011229557,0.0000059451313,1.4228489e-7,0.0000018811214,0.000097429],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934196,0.000038032467,0.00017115955,0.00022173904,0.00007535486,0.00015173122],"domain_scores_gemma":[0.9996107,0.0000242584,0.000045309946,0.00020792942,0.000073699426,0.000038102164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245857,0.00008736928,0.00013531414,0.00008241108,0.000057509784,0.0000122935135,0.000347984,0.000058796817,0.00002344566],"category_scores_gemma":[0.000014937872,0.00006612024,0.00001005618,0.00015674387,0.000059838316,0.00008840545,0.00014911467,0.000057338577,0.000023399183],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008245587,0.00072271976,0.012724145,0.000056095178,0.00017622094,0.00006113088,0.009118857,0.000019365572,0.19158852,0.45256922,0.00062960386,0.33225167],"study_design_scores_gemma":[0.0021573014,0.00072588277,0.11964256,0.000053710744,0.000011742641,0.000050502083,0.0002183932,0.010603413,0.8402453,0.023508493,0.002026054,0.0007566639],"about_ca_topic_score_codex":0.000015976091,"about_ca_topic_score_gemma":0.0000021374296,"teacher_disagreement_score":0.6486567,"about_ca_system_score_codex":0.000011049857,"about_ca_system_score_gemma":0.000063568776,"threshold_uncertainty_score":0.26963052},"labels":[],"label_agreement":null},{"id":"W2109912146","doi":"10.1518/hfes.45.2.307.27235","title":"Gaze-Contingent Multiresolutional Displays: An Integrative Review","year":2003,"lang":"en","type":"review","venue":"Human Factors The Journal of the Human Factors and Ergonomics Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Gaze; Artificial intelligence; Teleconference; Virtual reality; Transmission (telecommunications); Teleoperation; Robot; Multimedia; Telecommunications","score_opus":0.06577764183850116,"score_gpt":0.3223613334193299,"score_spread":0.2565836915808287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109912146","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021245403,0.97649723,0.00075726304,0.00005597601,0.0007854932,0.00051459856,0.000044615463,0.00004937351,0.000050055638],"genre_scores_gemma":[0.007881924,0.99109834,0.0003539124,0.0001589065,0.00015081605,0.000006794265,0.0000185925,0.00005049543,0.00028019314],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964061,0.00081045897,0.0014167234,0.00047311297,0.00039093697,0.0005026818],"domain_scores_gemma":[0.99538094,0.0004059784,0.002747073,0.0010789755,0.00019481072,0.00019223368],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0014705946,0.0008345433,0.001978656,0.000106033,0.0015339062,0.00024108429,0.0034279064,0.00038509013,0.000022941256],"category_scores_gemma":[0.00008357079,0.00037897596,0.0019946275,0.00028483613,0.0006097774,0.00038849824,0.00061004667,0.002066919,0.0000025314794],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005205259,0.004656739,0.019562742,0.048621543,0.018933684,0.00003891295,0.05548026,0.00007965307,0.0003202582,0.19024763,0.21007359,0.45193294],"study_design_scores_gemma":[0.0005852232,0.00047166448,0.00747627,0.030082796,0.0026061065,0.00020332658,0.0008147843,0.0000482819,0.000027837712,0.0016927987,0.95449626,0.0014946352],"about_ca_topic_score_codex":0.00003918718,"about_ca_topic_score_gemma":0.000033993805,"teacher_disagreement_score":0.7444227,"about_ca_system_score_codex":0.00047307817,"about_ca_system_score_gemma":0.00026543395,"threshold_uncertainty_score":0.9998662},"labels":[],"label_agreement":null},{"id":"W2111036397","doi":"10.1109/tnsre.2009.2039593","title":"Augmentative Communication Based on Realtime Vocal Cord Vibration Detection","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"","keywords":"Computer science; Robustness (evolution); Accelerometer; Speech recognition; Vibration; Acoustics","score_opus":0.0073156605921303654,"score_gpt":0.22792607849325874,"score_spread":0.22061041790112837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111036397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1943754,0.000006667156,0.80337083,0.00074204704,0.0008423542,0.00025706697,0.0000037671698,0.00036643646,0.000035452154],"genre_scores_gemma":[0.9900465,0.0000022427262,0.009746264,0.00003187928,0.00001910159,0.00012046113,0.0000017035809,0.00001223923,0.00001959373],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990856,0.00008427898,0.00023991041,0.00028583812,0.00016211417,0.0001422784],"domain_scores_gemma":[0.9988716,0.00055527966,0.00007464757,0.000364257,0.00007364279,0.00006057607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023706253,0.00014370303,0.00013362276,0.00027768067,0.00022536796,0.00009092936,0.00015211436,0.00011383273,0.0000023248467],"category_scores_gemma":[0.000028845105,0.00013467718,0.000050887716,0.00028231458,0.00006210791,0.00028048444,0.0000016254835,0.0003997416,0.000006520511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008394998,0.00025817633,0.00006690691,0.00013352689,0.000026714088,0.0000020287096,0.0006642508,0.71142656,0.16484663,0.008666673,0.00001982537,0.113804765],"study_design_scores_gemma":[0.000333722,0.0006460836,0.004318796,0.000053020143,0.000006241965,0.000009297345,0.000055091623,0.9869742,0.0073172427,0.00005420341,0.00008901267,0.000143099],"about_ca_topic_score_codex":0.000066079454,"about_ca_topic_score_gemma":0.000028892748,"teacher_disagreement_score":0.7956711,"about_ca_system_score_codex":0.00005316378,"about_ca_system_score_gemma":0.0000116638475,"threshold_uncertainty_score":0.54919755},"labels":[],"label_agreement":null},{"id":"W2111400751","doi":"10.1109/tbme.2004.831523","title":"A New Methodology for Determining Point-of-Gaze in Head-Mounted Eye Tracking Systems","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer vision; Artificial intelligence; Eye tracking; Computer science; Focus (optics); Homography; Normalization (sociology); Mathematics; Optics; Physics","score_opus":0.039639615398917236,"score_gpt":0.32689717873664575,"score_spread":0.2872575633377285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111400751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02519888,0.000070473456,0.972367,0.00056685385,0.0012251241,0.00019707566,0.000004968322,0.00036376264,0.0000058892692],"genre_scores_gemma":[0.7471117,0.0000064787537,0.25276142,0.00002040491,0.000032567626,0.000040733583,7.0840605e-7,0.0000152535795,0.000010764853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985668,0.000034497,0.0004484964,0.0003653481,0.0001835741,0.0004012898],"domain_scores_gemma":[0.9990576,0.00041727093,0.00007183192,0.00028398322,0.000038641687,0.0001306858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046018744,0.00018264617,0.00037056426,0.0006939692,0.000047726335,0.000027547061,0.00044974816,0.0002254235,0.0000023230548],"category_scores_gemma":[0.000071585164,0.00018269145,0.000110094144,0.0007470709,0.000053379754,0.00015938575,0.0000036007423,0.0003510856,0.0000035032242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050449256,0.00036762297,0.000024246796,0.00027560323,0.00011810029,0.00008320238,0.0014200201,0.48609158,0.16680613,0.01123478,0.0000132267705,0.33351502],"study_design_scores_gemma":[0.005335476,0.0012654188,0.0009822943,0.0014388895,0.000046794285,0.00019310933,0.0001797397,0.8413054,0.14598155,0.001157297,0.0013472209,0.00076683547],"about_ca_topic_score_codex":0.00013927527,"about_ca_topic_score_gemma":0.000017244216,"teacher_disagreement_score":0.7219128,"about_ca_system_score_codex":0.0001308717,"about_ca_system_score_gemma":0.000107816864,"threshold_uncertainty_score":0.7449941},"labels":[],"label_agreement":null},{"id":"W2112146451","doi":"10.1109/tepra.2015.7219692","title":"Combination of eyetracking and computer vision for robotics control","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Joystick; Computer science; Artificial intelligence; Computer vision; Gaze; Robotics; Robot; Point (geometry); Simulation; Mathematics","score_opus":0.02416383046395967,"score_gpt":0.2760556568241382,"score_spread":0.25189182636017854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112146451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019549549,0.00002692172,0.9784838,0.0013764269,0.00015491115,0.00010559318,6.329019e-7,0.0001075469,0.00019461066],"genre_scores_gemma":[0.8113467,6.72961e-7,0.18853603,0.000079881065,0.000010653361,0.0000020637233,6.0499366e-7,0.0000022589493,0.00002115383],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954414,0.000016675735,0.00011989916,0.00014092377,0.00008472901,0.00009361057],"domain_scores_gemma":[0.9994745,0.00011348784,0.00006126685,0.00013798648,0.00017874566,0.00003396709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024981672,0.00005198498,0.000112842594,0.00006927772,0.000027476792,0.000031164058,0.00017467211,0.00004659487,2.0140921e-7],"category_scores_gemma":[0.000033693563,0.000043758577,0.000018021574,0.00008331203,0.000043432166,0.00013533946,0.00005668703,0.00003762706,0.0000012705297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009373094,0.00009845453,0.004135175,0.000015585614,0.00001117165,0.000001114165,0.00008628284,0.0004574039,0.00064612663,0.89274764,0.0009759797,0.10081572],"study_design_scores_gemma":[0.0017874697,0.00076024496,0.019957777,0.000017209952,0.0000062542495,0.000008041695,0.000012567301,0.94926715,0.0012502682,0.026519962,0.00032255217,0.00009053269],"about_ca_topic_score_codex":0.0000044088392,"about_ca_topic_score_gemma":0.0000017040985,"teacher_disagreement_score":0.94880974,"about_ca_system_score_codex":0.000010119727,"about_ca_system_score_gemma":0.000014029398,"threshold_uncertainty_score":0.17844228},"labels":[],"label_agreement":null},{"id":"W2113152693","doi":"10.1109/icsmc.2009.5346616","title":"Gaze tracking: A sclera recognition approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Gaze; Sclera; Computer science; Computer vision; Artificial intelligence; Eye tracking; Tracking (education); Medicine; Psychology; Ophthalmology","score_opus":0.04493307491066278,"score_gpt":0.2466582309438085,"score_spread":0.20172515603314572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113152693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023983095,0.00003193793,0.89248747,0.0025271575,0.0000754808,0.00007595148,3.945633e-7,0.0011802842,0.07963821],"genre_scores_gemma":[0.83847666,0.0000037921077,0.16060221,0.00065245136,0.000028029528,0.000003890801,0.00000232605,0.0000026811167,0.00022797428],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99919707,0.000024410469,0.00012392114,0.00031500007,0.00011887493,0.00022073115],"domain_scores_gemma":[0.9995233,0.000019241417,0.000038068996,0.00031632886,0.00005854774,0.00004452258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001514964,0.000093084775,0.00010249329,0.000111200185,0.00007592516,0.000099336816,0.00045816056,0.00007463177,0.000015977259],"category_scores_gemma":[0.000028343951,0.00008027555,0.000043970784,0.00034385896,0.000026668282,0.0002726747,0.00003212828,0.00014177889,0.000136504],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015187247,0.00015063454,0.000077962795,0.0000018639964,0.0000036266576,0.000005638224,0.000073962554,0.0000044469643,0.0016528094,0.07587678,0.0010766173,0.92107415],"study_design_scores_gemma":[0.0030035027,0.0016798198,0.35017675,0.00013420642,0.000036312238,0.00059804705,0.00025111358,0.115608834,0.09737957,0.4142761,0.014652233,0.0022035223],"about_ca_topic_score_codex":0.0000035152607,"about_ca_topic_score_gemma":4.4037097e-7,"teacher_disagreement_score":0.9188706,"about_ca_system_score_codex":0.00001711178,"about_ca_system_score_gemma":0.00001540673,"threshold_uncertainty_score":0.32735422},"labels":[],"label_agreement":null},{"id":"W2113165517","doi":"10.1109/tbme.2009.2039351","title":"An Automatic Personal Calibration Procedure for Advanced Gaze Estimation Systems","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer vision; Calibration; Computer science; Artificial intelligence; Point (geometry); Optical axis; Eye tracking; Optics; Mathematics; Physics; Geometry; Statistics","score_opus":0.006667132374520213,"score_gpt":0.23683469109234717,"score_spread":0.23016755871782696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113165517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07323787,0.0000059968397,0.9236927,0.00041120208,0.0013479824,0.00023632045,0.000011611392,0.0010536081,0.000002701582],"genre_scores_gemma":[0.9080934,0.0000010264733,0.09161507,0.000020583226,0.00004960925,0.00018168181,0.0000064365754,0.000014327497,0.000017863025],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907404,0.000009212095,0.00019654656,0.00028574478,0.00020717792,0.00022729572],"domain_scores_gemma":[0.9994738,0.00009054637,0.00004084817,0.00022521622,0.00003918864,0.00013038931],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014630507,0.00013668831,0.00013505772,0.0002257788,0.00011849549,0.00008063487,0.00028250107,0.00016931612,0.0000062789295],"category_scores_gemma":[0.000024492223,0.00012833752,0.000049626837,0.00030882435,0.000041968993,0.00036331243,8.999162e-7,0.00030382763,0.000005652829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012784566,0.0004292558,0.0000031710902,0.00034277476,0.00005076389,0.000007074523,0.00068924524,0.29470602,0.3688694,0.007222212,0.00008742808,0.3275799],"study_design_scores_gemma":[0.00031075787,0.00019052331,0.00005211581,0.000050164508,0.000007940599,0.000030156249,0.00001723013,0.988777,0.010171132,0.000046259796,0.00020552354,0.00014123651],"about_ca_topic_score_codex":0.0000033287058,"about_ca_topic_score_gemma":0.000002333158,"teacher_disagreement_score":0.83485556,"about_ca_system_score_codex":0.000033835327,"about_ca_system_score_gemma":0.000053369506,"threshold_uncertainty_score":0.5233452},"labels":[],"label_agreement":null},{"id":"W2114830306","doi":"10.1207/s15516709cog2501_2","title":"Comparative visual search: a difference that makes a difference","year":2001,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Visual search; Artificial intelligence; Cluster analysis; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Eye tracking; Mathematics; Computer vision","score_opus":0.11847549754040887,"score_gpt":0.3556952749898121,"score_spread":0.23721977744940326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114830306","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6490851,0.00007994488,0.34469727,0.0004295435,0.00017282275,0.00018072399,0.0000044116446,0.0003445223,0.0050056535],"genre_scores_gemma":[0.9962594,0.000027582502,0.0026707707,0.00034411595,0.00003064681,0.000038673494,0.0000016180202,0.0000068444306,0.0006203704],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9966745,0.00012797305,0.00020847189,0.001158066,0.00090227363,0.0009287147],"domain_scores_gemma":[0.9981519,0.00044493272,0.00011199242,0.00045266145,0.000578124,0.0002604154],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005777179,0.0002899784,0.00035160428,0.00047161788,0.0007193309,0.00046192616,0.0022336429,0.00007462285,0.000026893133],"category_scores_gemma":[0.00028156317,0.0002416058,0.000067617344,0.0024545933,0.0029012281,0.00062150764,0.00087291433,0.00041239028,0.00025338153],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009420637,0.0011196937,0.1932179,0.00002654008,0.000056678433,0.0004291162,0.0085804025,0.00000903755,0.110085584,0.09887896,0.000078234094,0.5874236],"study_design_scores_gemma":[0.00051523733,0.00039093065,0.90872234,0.00014515077,0.000009337123,0.00013013987,0.0010753984,0.020647371,0.06326999,0.004530065,0.00007831273,0.00048570667],"about_ca_topic_score_codex":0.00003115621,"about_ca_topic_score_gemma":0.000021333792,"teacher_disagreement_score":0.71550447,"about_ca_system_score_codex":0.00008685991,"about_ca_system_score_gemma":0.00032739277,"threshold_uncertainty_score":0.9998123},"labels":[],"label_agreement":null},{"id":"W2116311625","doi":"10.1109/pcthealth.2008.4571047","title":"Using eye contact and contextual speech recognition for hands-free surgical charting","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Hands free; Gaze; Speech recognition; Human–computer interaction; Eye contact; Software; Artificial intelligence; Psychology; Communication","score_opus":0.10404732914555435,"score_gpt":0.30488230889994516,"score_spread":0.2008349797543908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116311625","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80676115,0.000032980057,0.19123709,0.0005135865,0.000071186405,0.000115225834,0.000003193583,0.0002459787,0.0010196428],"genre_scores_gemma":[0.9355419,0.000007508975,0.06416166,0.000085685584,0.000052712585,0.0000064477363,0.0000021991557,0.0000053856425,0.00013645104],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992215,0.000022567203,0.00016179665,0.0002807818,0.00008696762,0.00022637482],"domain_scores_gemma":[0.9994442,0.0001559958,0.00006183327,0.00020184036,0.00008757504,0.000048585116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019568444,0.00009327102,0.0001556959,0.000072347706,0.00022100646,0.000042737058,0.00022748762,0.00007647423,0.000011268562],"category_scores_gemma":[0.000121849866,0.00008155814,0.000040052746,0.000105497755,0.000069808826,0.00024152211,0.000116780226,0.00008782972,0.0000070451506],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014261239,0.0002841212,0.055409934,0.000055336084,0.000086784166,0.0005305615,0.0006953852,0.0000027642075,0.013889373,0.06639513,0.0026978352,0.8598102],"study_design_scores_gemma":[0.04219598,0.0032266863,0.13002916,0.0003936916,0.00009217548,0.0066386443,0.000504084,0.3045921,0.44214854,0.054739296,0.012728802,0.0027108402],"about_ca_topic_score_codex":0.000038309656,"about_ca_topic_score_gemma":0.000009659328,"teacher_disagreement_score":0.8570993,"about_ca_system_score_codex":0.000013163427,"about_ca_system_score_gemma":0.000022957222,"threshold_uncertainty_score":0.33258444},"labels":[],"label_agreement":null},{"id":"W2116427785","doi":"10.1109/cvpr.2004.464","title":"Towards Automatic Retrieval of Blink-Based Lexicon for Persons Suffered from Brain-Stem Injury using Video Cameras","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Lexicon; Computer vision; Artificial intelligence; Face (sociological concept); Eye movement; Rehabilitation; Motion (physics); Human–computer interaction; Psychology","score_opus":0.040385561166672516,"score_gpt":0.298606547427636,"score_spread":0.25822098626096346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116427785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5191039,0.000035137018,0.47521555,0.0048676473,0.000108752945,0.00016004933,0.000026970622,0.00038078908,0.00010122671],"genre_scores_gemma":[0.763408,3.296254e-7,0.23590802,0.0005133712,0.000047671823,0.0000049356017,0.0000037987982,0.000011936974,0.000101950754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855494,0.00006637561,0.00037235787,0.0004367125,0.00023814668,0.000331492],"domain_scores_gemma":[0.99865544,0.00037328084,0.00019097033,0.0005789769,0.00012917815,0.00007216978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031624737,0.00018685606,0.00035389865,0.00023366683,0.00010810637,0.00006496106,0.0007131213,0.00016843193,0.000037213395],"category_scores_gemma":[0.00013200482,0.0001682493,0.00015048428,0.0004068144,0.00012132101,0.00019751446,0.00010301758,0.00014993321,0.000010233626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021123492,0.000865336,0.0049869986,0.0002305146,0.00030602212,0.000014712609,0.0019069908,0.0009007865,0.3578305,0.07361473,0.008480993,0.5506512],"study_design_scores_gemma":[0.00090690324,0.000266809,0.003017295,0.000073017494,0.000027107913,0.0000030296205,0.00006915803,0.73375726,0.2595671,0.0012442196,0.00081604486,0.00025200576],"about_ca_topic_score_codex":0.0001713601,"about_ca_topic_score_gemma":0.00003171642,"teacher_disagreement_score":0.7328565,"about_ca_system_score_codex":0.00011402394,"about_ca_system_score_gemma":0.00029706478,"threshold_uncertainty_score":0.6861008},"labels":[],"label_agreement":null},{"id":"W2117670894","doi":"10.1152/jn.01379.2007","title":"The Brain Stem Saccadic Burst Generator Encodes Gaze in Three-Dimensional Space","year":2008,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; William Osler Health System","funders":"","keywords":"Saccadic masking; Neuroscience; Gaze; Generator (circuit theory); Eye movement; Psychology; Communication; Space (punctuation); Computer science; Physics; Biology; Artificial intelligence","score_opus":0.024746726340957932,"score_gpt":0.23824359294556716,"score_spread":0.21349686660460923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117670894","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98744667,0.00027930996,0.0037717456,0.0076927254,0.00070317375,0.00004523226,7.7554574e-7,0.000033169414,0.000027211374],"genre_scores_gemma":[0.99685097,0.00008313375,0.002178179,0.00063230447,0.00015741035,0.0000016510098,8.264735e-8,0.00000922889,0.00008701695],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986153,0.00023165409,0.0004040066,0.00022760859,0.00020608888,0.00031534102],"domain_scores_gemma":[0.9985755,0.00056593557,0.00031249228,0.00034133226,0.00013693568,0.00006777583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015583799,0.00013983181,0.0002895535,0.00017642858,0.00021300725,0.00002274233,0.0010152498,0.00008439815,0.0000026604412],"category_scores_gemma":[0.00012581905,0.00008950183,0.00010481101,0.0003456697,0.00027912395,0.00014415699,0.00017651061,0.0005562442,0.000021585396],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001923164,0.00043089825,0.00607418,0.000012997847,0.0000968707,0.005297314,0.0002507496,0.011556763,0.89444065,0.040785916,0.0050932975,0.035768066],"study_design_scores_gemma":[0.001618442,0.002447683,0.9386094,0.00006476351,0.000010923354,0.0056682355,0.000023887877,0.021030782,0.008336147,0.011646872,0.0101384185,0.00040444327],"about_ca_topic_score_codex":0.0000066746716,"about_ca_topic_score_gemma":0.000009267098,"teacher_disagreement_score":0.93253523,"about_ca_system_score_codex":0.00003565836,"about_ca_system_score_gemma":0.00014991136,"threshold_uncertainty_score":0.36497787},"labels":[],"label_agreement":null},{"id":"W2120598715","doi":"10.1109/imtc.2011.5944101","title":"Driver drowsiness monitoring based on yawning detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":195,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gesture; Computer science; Eye movement; Computer vision; Physical medicine and rehabilitation; Artificial intelligence; Medicine","score_opus":0.03641979545558356,"score_gpt":0.22919197165877,"score_spread":0.19277217620318643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120598715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1631402,0.0000018616441,0.82541656,0.00009718542,0.0004413723,0.000029395684,4.8155677e-8,0.0006927437,0.0101806475],"genre_scores_gemma":[0.9571988,2.0166769e-7,0.042593233,0.000044960736,0.000026469123,0.0000060467632,5.766122e-8,0.0000046378104,0.00012556894],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942523,0.000017733255,0.000075845746,0.0002241566,0.00009958639,0.00015742256],"domain_scores_gemma":[0.9995763,0.000027411847,0.000031445587,0.00029738058,0.00003935422,0.000028145303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000090691115,0.000075745345,0.00006402664,0.0001346922,0.00009279752,0.000027944789,0.00033259185,0.000052980213,0.000014079738],"category_scores_gemma":[0.000018967137,0.000065414235,0.000026754004,0.00024110684,0.000024170955,0.00013597043,0.000046375044,0.000089366156,0.000048315527],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022605826,0.00029192172,0.11059939,0.000014295334,0.000019086547,0.00007289879,0.0005001857,0.0002652042,0.028035864,0.083041176,0.00007423552,0.77706313],"study_design_scores_gemma":[0.00029476514,0.00019579032,0.31095743,0.000028612176,0.0000038236467,0.0000057282577,0.000028446435,0.0736876,0.61249465,0.0017184197,0.00036753423,0.00021723963],"about_ca_topic_score_codex":0.000029397752,"about_ca_topic_score_gemma":0.0000034123211,"teacher_disagreement_score":0.7940586,"about_ca_system_score_codex":0.000024906229,"about_ca_system_score_gemma":0.000009603341,"threshold_uncertainty_score":0.2667515},"labels":[],"label_agreement":null},{"id":"W2120697543","doi":"","title":"Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Leverage (statistics); Eye movement; Eye tracking; Machine learning; Hidden Markov model; Visual search; Task (project management); Pattern recognition (psychology)","score_opus":0.016572749101109223,"score_gpt":0.2612844572372328,"score_spread":0.24471170813612358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120697543","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5140992,0.000008825174,0.48355925,0.0015440807,0.00013187535,0.00015652305,0.0001277805,0.00023356017,0.00013891989],"genre_scores_gemma":[0.8943555,0.0000066333137,0.10472426,0.0006123557,0.00005347356,0.000017033273,0.00006803654,0.000006856497,0.00015586895],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989854,0.00004218779,0.00013644311,0.00046112467,0.0001295288,0.000245306],"domain_scores_gemma":[0.9993343,0.00006217599,0.000044089324,0.0003765616,0.00005299064,0.00012990322],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00009499534,0.00012884397,0.00014971694,0.000110270135,0.000071150636,0.00023955342,0.00038059527,0.00006740594,0.00025493835],"category_scores_gemma":[0.000030086043,0.000111439585,0.000019212088,0.00015386358,0.00008553113,0.00062986097,0.00022168632,0.00014937855,0.0015640086],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004179486,0.00024423178,0.0035240978,0.000007693241,0.000059265614,0.00005910425,0.0003591016,0.00009149854,0.34279183,0.003912999,0.38828686,0.26062152],"study_design_scores_gemma":[0.004316197,0.0011303471,0.23305388,0.00004157309,0.00003750806,0.000056100165,0.00089864177,0.5313507,0.029262088,0.0017123666,0.19681022,0.0013303986],"about_ca_topic_score_codex":0.000902097,"about_ca_topic_score_gemma":0.00005665498,"teacher_disagreement_score":0.5312592,"about_ca_system_score_codex":0.000028777005,"about_ca_system_score_gemma":0.000016563825,"threshold_uncertainty_score":0.9992134},"labels":[],"label_agreement":null},{"id":"W2120875587","doi":"10.1109/tnsre.2004.842366","title":"Utilization of ultrasound sensors for anti-collision systems of powered wheelchairs","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; Sunnybrook Health Science Centre","funders":"","keywords":"Wheelchair; Collision; Computer science; Ultrasonic sensor; Object (grammar); Ultrasound; Simulation; Collision avoidance; Human–computer interaction; Real-time computing; Artificial intelligence; Acoustics; Computer security; Physics","score_opus":0.013151400368843172,"score_gpt":0.2374657410080832,"score_spread":0.22431434063924005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120875587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39840096,0.00012890257,0.6004956,0.000083131265,0.0004784774,0.0003008643,0.00001502533,0.00009287242,0.0000041878225],"genre_scores_gemma":[0.9944023,0.000020983895,0.005475284,0.0000014233017,0.000017882547,0.00004468782,0.0000010041865,0.000011358268,0.000025072783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990159,0.0000382004,0.0004229402,0.00022978237,0.00015847072,0.0001346759],"domain_scores_gemma":[0.9988506,0.00060128747,0.00013483426,0.00021414277,0.00016115153,0.000037982296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023624157,0.00012339423,0.00025513908,0.00028182156,0.00006388823,0.000028384762,0.00012548233,0.00009417818,3.5337007e-7],"category_scores_gemma":[0.000039090915,0.00011192654,0.00007863268,0.00028549874,0.00004734748,0.00022011688,0.000001530093,0.00007684327,4.0870782e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000747015,0.000061393424,0.000057413366,0.0003617489,0.000017688826,1.2397605e-7,0.0003616396,0.96199304,0.0310422,0.0036859706,0.000010910301,0.00240041],"study_design_scores_gemma":[0.00044469244,0.00039443062,0.0018003362,0.0002301958,0.000013852824,0.000017107053,0.00027494074,0.9822941,0.014169919,0.0000090586755,0.0002151103,0.0001362909],"about_ca_topic_score_codex":0.00003643887,"about_ca_topic_score_gemma":0.0000017769049,"teacher_disagreement_score":0.5960013,"about_ca_system_score_codex":0.00003434936,"about_ca_system_score_gemma":0.000010300784,"threshold_uncertainty_score":0.45642322},"labels":[],"label_agreement":null},{"id":"W2121103985","doi":"10.1145/506443.506526","title":"Designing attentive cell phone using wearable eyecontact sensors","year":2002,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Phone; Wearable computer; Computer science; Conversation; Human–computer interaction; Face (sociological concept); Wearable technology; Speech recognition; Embedded system; Communication; Psychology","score_opus":0.03858977769152086,"score_gpt":0.23351996315667772,"score_spread":0.19493018546515686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121103985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2967364,0.00015556766,0.6774294,0.00031362512,0.0001821328,0.00006868631,3.5332832e-7,0.0005428534,0.024571024],"genre_scores_gemma":[0.8411894,0.000009358538,0.15419906,0.00010172743,0.000018119297,0.0000017167465,6.979155e-8,0.000007643719,0.004472895],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898,0.00004673376,0.00014558515,0.00035392598,0.00012532255,0.00034844712],"domain_scores_gemma":[0.9994265,0.00006305428,0.00006883692,0.00033765103,0.000048809256,0.00005518344],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012744122,0.00012642793,0.00014933043,0.00012558093,0.00016633209,0.00008957975,0.00042009586,0.00007154776,0.00029069805],"category_scores_gemma":[0.000014093726,0.00011538066,0.00005465022,0.0003355729,0.00004134827,0.00027443477,0.00011379996,0.000162338,0.00080375385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008686137,0.00083916564,0.016544145,0.00005133862,0.00012280098,0.0005241977,0.0027579411,0.003063378,0.88969487,0.01170217,0.010946922,0.0637444],"study_design_scores_gemma":[0.0007336582,0.00011950657,0.00280596,0.00005119406,0.000016227164,0.00007890932,0.0002743499,0.48800504,0.5055134,0.00055357936,0.0013101753,0.0005379618],"about_ca_topic_score_codex":0.00005451514,"about_ca_topic_score_gemma":0.000002462889,"teacher_disagreement_score":0.544453,"about_ca_system_score_codex":0.000046607845,"about_ca_system_score_gemma":0.000009368469,"threshold_uncertainty_score":0.99997425},"labels":[],"label_agreement":null},{"id":"W2123215628","doi":"10.1145/1753846.1754139","title":"Input precision for gaze-based graphical passwords","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Password; Gaze; Computer science; Usability; Human–computer interaction; Eye tracking; Point (geometry); Cognitive password; Computer vision; Cued speech; Artificial intelligence; Computer security; Password strength; One-time password; Mathematics; Psychology","score_opus":0.012047513433780554,"score_gpt":0.26320846264009806,"score_spread":0.2511609492063175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123215628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07672725,0.0000040699397,0.9154477,0.005214453,0.0005063162,0.00012770238,0.000001304486,0.00059647934,0.0013747067],"genre_scores_gemma":[0.76728225,3.4453765e-7,0.23217204,0.0003258554,0.0000368904,0.000029432369,0.0000012518273,0.0000047114663,0.00014720485],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916786,0.000014469967,0.00013188254,0.0003257992,0.00013141181,0.00022855806],"domain_scores_gemma":[0.99905276,0.00023155533,0.00003654604,0.00052814605,0.000090249705,0.00006072128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002721403,0.00009337464,0.00010854507,0.00013912836,0.000095518546,0.00006617981,0.00072635,0.0001464248,0.000020353647],"category_scores_gemma":[0.00015317934,0.00007194797,0.00007700961,0.00027781713,0.00007679548,0.000093750685,0.000084918705,0.0002425343,0.000026175718],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010894256,0.00014742675,0.004874049,0.0000068721856,0.0000063663183,0.0000036067147,0.00001287886,0.0000073950664,0.01799869,0.71165943,0.005232777,0.2600396],"study_design_scores_gemma":[0.0028464592,0.0010038448,0.10881234,0.000039523857,0.000017410932,0.00002942749,0.000008409331,0.2712569,0.16755068,0.25867522,0.18887027,0.0008895461],"about_ca_topic_score_codex":0.0000056800054,"about_ca_topic_score_gemma":0.000042658394,"teacher_disagreement_score":0.69055504,"about_ca_system_score_codex":0.000005417964,"about_ca_system_score_gemma":0.000041788655,"threshold_uncertainty_score":0.2933953},"labels":[],"label_agreement":null},{"id":"W2123344987","doi":"10.5539/ibr.v8n10p25","title":"Associative Relevance Based Stimulus Shifts Focus in Eye Movements","year":2015,"lang":"en","type":"article","venue":"International Business Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eye movement; Portrait; Eye tracking; Stimulus (psychology); Perception; Cognitive psychology; Cognition; Psychology; Focus (optics); Computer science; Computer vision; Neuroscience; Visual arts; Art","score_opus":0.09221089898582482,"score_gpt":0.39731064630501994,"score_spread":0.3050997473191951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123344987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43470263,0.00023976801,0.45730263,0.06929565,0.0020006157,0.0005903209,0.000034186036,0.0004848557,0.035349347],"genre_scores_gemma":[0.9925713,0.0000062596073,0.006305623,0.00011649854,0.000060844737,0.00005396834,0.0000064318783,0.000008843809,0.00087021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99753267,0.00017391607,0.00021429348,0.0004146672,0.001271534,0.00039293486],"domain_scores_gemma":[0.997321,0.00038226013,0.00006823586,0.00031338836,0.0018400296,0.00007508073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013765262,0.00010266009,0.00013223248,0.0006476427,0.000061496896,0.00015555436,0.0015836437,0.00008663849,0.00001725039],"category_scores_gemma":[0.0029130217,0.000099277575,0.000023642338,0.0014696412,0.00011624663,0.00040363072,0.00048290545,0.00040387892,0.00018497928],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002718174,0.0019855134,0.7373971,0.000032120075,0.0001256386,0.0009497394,0.0012350499,0.005648626,0.0027697247,0.08065592,0.010270561,0.15865822],"study_design_scores_gemma":[0.0016148678,0.000069968984,0.88774735,0.00009865493,6.3390564e-7,0.000001130633,0.000053795447,0.058088012,0.0017660563,0.043860063,0.0065116244,0.0001878683],"about_ca_topic_score_codex":0.00049664394,"about_ca_topic_score_gemma":0.00014241386,"teacher_disagreement_score":0.55786866,"about_ca_system_score_codex":0.0006183787,"about_ca_system_score_gemma":0.00033542913,"threshold_uncertainty_score":0.4048422},"labels":[],"label_agreement":null},{"id":"W2124028049","doi":"10.1109/icra.2011.5980432","title":"A biologically inspired controller for fast eye movements","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canada Research Chairs","keywords":"Eye movement; Saccadic masking; Controller (irrigation); Overshoot (microwave communication); Computer science; Open-loop controller; Control theory (sociology); Movement (music); Artificial intelligence; Eye tracking on the ISS; Human eye; Computer vision; Neurophysiology; Simulation; Closed loop; Control engineering; Engineering; Neuroscience; Control (management); Psychology; Physics; Acoustics","score_opus":0.04765213884368066,"score_gpt":0.25651885798603474,"score_spread":0.20886671914235408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124028049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030547516,0.000009794786,0.9536964,0.00043625955,0.000116474366,0.00019742896,0.0000022413253,0.0005096302,0.0144842295],"genre_scores_gemma":[0.89149547,0.0000011153718,0.1064279,0.00092897593,0.000010743258,0.000059402973,6.441682e-7,0.0000030125439,0.0010727415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9993088,0.000014161483,0.00013494254,0.00026621443,0.00005330993,0.0002225204],"domain_scores_gemma":[0.9996013,0.000029606688,0.000048343456,0.00021961205,0.00006499667,0.000036122372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121494,0.000086151376,0.00013057144,0.00005558212,0.0000629131,0.000021013975,0.00060412835,0.000071544586,0.000029681833],"category_scores_gemma":[0.000041866835,0.00005871844,0.000055443823,0.00010379566,0.000050247865,0.0000769017,0.00011971415,0.00004775649,0.00005602252],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004221523,0.0003294529,0.020247614,0.0000050212134,0.00006346687,0.0000061900555,0.00019611964,8.089159e-7,0.009148264,0.7818726,0.0010505707,0.18703772],"study_design_scores_gemma":[0.007345759,0.0025112391,0.664109,0.00002698953,0.000017912393,0.0000058262353,0.00013085971,0.037454728,0.043993194,0.21506035,0.028388193,0.0009559684],"about_ca_topic_score_codex":0.000019426181,"about_ca_topic_score_gemma":0.0000038020664,"teacher_disagreement_score":0.86094797,"about_ca_system_score_codex":0.000009619296,"about_ca_system_score_gemma":0.000010481365,"threshold_uncertainty_score":0.23944685},"labels":[],"label_agreement":null},{"id":"W2124742892","doi":"10.5194/isprsarchives-xxxviii-5-w12-139-2011","title":"EVALUATION OF REAL-TIME HAND MOTION TRACKING USING A RANGE CAMERA AND THE MEAN-SHIFT ALGORITHM","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Tracking (education); Computer science; Kalman filter; Stereo camera; Match moving; Tracking system; Position (finance); Mean-shift; Trajectory; Motion (physics); Algorithm; Pattern recognition (psychology)","score_opus":0.029728108549500476,"score_gpt":0.2796866082747734,"score_spread":0.2499584997252729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124742892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03689378,0.000077992154,0.9554765,0.0028339562,0.0011868385,0.0006992446,0.000044268174,0.000045704528,0.0027416674],"genre_scores_gemma":[0.9893512,0.000121999496,0.010049406,0.0003226465,0.00011596048,5.388641e-7,0.000012170237,0.000008852918,0.000017221922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99463946,0.00073295034,0.0013135737,0.00036541137,0.0024415813,0.0005070257],"domain_scores_gemma":[0.99558514,0.001288757,0.0019239631,0.00053226366,0.0005476218,0.00012224192],"candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.005134362,0.00036900767,0.00046243914,0.00086001033,0.0014517854,0.0007506414,0.0016931049,0.00009861257,0.000004115719],"category_scores_gemma":[0.0009272392,0.000215796,0.0003021543,0.0009922573,0.004651721,0.00088711234,0.0009986964,0.00038894694,0.0000015082676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007038291,0.000022293349,0.00024838655,0.000022371332,0.00009425768,9.5480964e-8,0.007114174,0.0017655899,0.0022372801,0.000045852903,0.0000069592147,0.9883724],"study_design_scores_gemma":[0.001266179,0.00007866619,0.008257463,0.000260178,0.00010566117,0.0001372485,0.0012261594,0.9705879,0.0083871465,0.00920865,0.00025030045,0.00023445959],"about_ca_topic_score_codex":0.65073514,"about_ca_topic_score_gemma":0.043285728,"teacher_disagreement_score":0.9881379,"about_ca_system_score_codex":0.00006189136,"about_ca_system_score_gemma":0.00023093204,"threshold_uncertainty_score":0.9998482},"labels":[],"label_agreement":null},{"id":"W2125023753","doi":"10.1109/iembs.1989.95957","title":"An algebraic method for compensating for coil-placement errors in three-dimensional search-coil eye trackers","year":2003,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Position (finance); Electromagnetic coil; Basis (linear algebra); Laser tracker; Calibration; Computer science; Search coil; Eye tracking; BitTorrent tracker; Computer vision; Compensation (psychology); Artificial intelligence; Algorithm; Control theory (sociology); Mathematics; Optics; Physics; Engineering; Electrical engineering; Geometry","score_opus":0.04141169725674396,"score_gpt":0.3407857716793184,"score_spread":0.29937407442257447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125023753","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17845747,0.00002081639,0.81902045,0.0013696189,0.00015121822,0.00052834896,0.0000061709216,0.00021822528,0.00022768848],"genre_scores_gemma":[0.4795906,1.9772378e-7,0.51995105,0.0002752816,0.00000884316,0.00008409087,0.0000055685864,0.000010714417,0.000073651834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805623,0.00014476325,0.0003428596,0.00064386765,0.0002323631,0.00057991635],"domain_scores_gemma":[0.99868876,0.00055710913,0.000083035855,0.00043508364,0.0001355293,0.00010047193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018135259,0.00019661133,0.00028424864,0.00021654983,0.00020409422,0.00006490621,0.0005741776,0.000116339834,0.000023139894],"category_scores_gemma":[0.00014375166,0.00017771381,0.0000940152,0.00032979742,0.000078827674,0.00020898225,0.00006029739,0.0001822407,0.000008852766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067952606,0.0005901186,0.006758815,0.000067169734,0.00006196906,0.0000109512075,0.000497541,0.009803375,0.014209149,0.8900114,0.0007985504,0.07712298],"study_design_scores_gemma":[0.0028232944,0.00087509485,0.007257556,0.00004182226,0.000013367143,0.000014617994,0.00046447167,0.9178252,0.035291042,0.03356195,0.0013346816,0.00049685483],"about_ca_topic_score_codex":0.00009964978,"about_ca_topic_score_gemma":0.0006073041,"teacher_disagreement_score":0.90802187,"about_ca_system_score_codex":0.000085893815,"about_ca_system_score_gemma":0.00012889245,"threshold_uncertainty_score":0.7246959},"labels":[],"label_agreement":null},{"id":"W2125212540","doi":"10.1145/2578153.2578156","title":"The use of gaze to control drones","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Drone; Gaze; Computer science; Artificial intelligence; Computer vision; Human–computer interaction","score_opus":0.025512070218352702,"score_gpt":0.2273141262459865,"score_spread":0.2018020560276338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125212540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025128122,0.00000663083,0.96636117,0.0074826577,0.00009835251,0.000045666446,2.4766163e-7,0.00013305487,0.0007440893],"genre_scores_gemma":[0.97667325,9.0839916e-7,0.022128586,0.000513278,0.000009039724,0.0000040780233,2.6845825e-8,0.000001639763,0.000669219],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959373,0.000030965315,0.00008412902,0.000106409454,0.000070079186,0.00011468096],"domain_scores_gemma":[0.99921286,0.00031440204,0.000026413054,0.00037485667,0.00004797181,0.000023476749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014874291,0.000039196642,0.000068351124,0.000027658649,0.000055064975,0.000041392617,0.00045901383,0.000020243275,0.000002074501],"category_scores_gemma":[0.00015737358,0.000022343238,0.00002059483,0.00010811785,0.000044311015,0.000056172972,0.0000702191,0.000035875182,0.000042603377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023440252,0.000013857051,0.0018379444,9.089954e-7,0.0000061269,3.047939e-7,0.00002528654,0.00008182849,0.0016275,0.83287,0.004215802,0.15931809],"study_design_scores_gemma":[0.0008915642,0.00059284805,0.16613032,0.00002584859,0.000010127348,0.000010335669,0.00002242831,0.15105112,0.027318774,0.02665167,0.6269757,0.00031924702],"about_ca_topic_score_codex":0.00002765204,"about_ca_topic_score_gemma":0.000024502748,"teacher_disagreement_score":0.9515451,"about_ca_system_score_codex":0.0000034057373,"about_ca_system_score_gemma":0.000005961019,"threshold_uncertainty_score":0.09111308},"labels":[],"label_agreement":null},{"id":"W2125518445","doi":"10.1109/tbme.2008.2005943","title":"Noncontact Binocular Eye-Gaze Tracking for Point-of-Gaze Estimation in Three Dimensions","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Artificial intelligence; Gaze; Computer science; Augmented reality; Eye tracking; Tracking (education); Computer graphics (images)","score_opus":0.017365896056478217,"score_gpt":0.24456800576101553,"score_spread":0.2272021097045373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125518445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12231971,0.000042638774,0.87609416,0.0005158829,0.00050222373,0.00019578978,0.000008892572,0.0003130693,0.0000076185474],"genre_scores_gemma":[0.9000523,0.000021768736,0.0997993,0.000024102688,0.000015346506,0.00005908924,0.0000021912383,0.000016841403,0.000009048352],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986729,0.000014067009,0.0003859701,0.000338413,0.00025185625,0.00033677873],"domain_scores_gemma":[0.9992062,0.00024161463,0.00006192051,0.0003334058,0.000051382365,0.000105492814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021473067,0.00018111015,0.0002776276,0.0006008341,0.00010344517,0.0000138796495,0.00036105202,0.00017904694,0.0000054571474],"category_scores_gemma":[0.000046309615,0.00017770876,0.00013488652,0.00072995014,0.000089774134,0.00023653365,0.0000038051476,0.00032379586,0.000008115702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007238732,0.0017871327,0.00028915895,0.0002573841,0.00022522087,0.00022408165,0.0013171006,0.48624387,0.20184253,0.008474836,0.00007440047,0.2991919],"study_design_scores_gemma":[0.000928602,0.00025872272,0.0023116148,0.00019754255,0.000013865406,0.000041351246,0.000008142458,0.9517561,0.043756396,0.0002855722,0.00020987228,0.00023221812],"about_ca_topic_score_codex":0.000027506043,"about_ca_topic_score_gemma":0.000011015353,"teacher_disagreement_score":0.7777326,"about_ca_system_score_codex":0.00008578391,"about_ca_system_score_gemma":0.00005802153,"threshold_uncertainty_score":0.7246753},"labels":[],"label_agreement":null},{"id":"W2126333777","doi":"10.1145/1240624.1240802","title":"Dynamic shared visual spaces","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Tracking (education); Artificial intelligence; Motion (physics); Control (management); Tracking system; Human–computer interaction; Kalman filter; Psychology","score_opus":0.010006741190441353,"score_gpt":0.283438369949125,"score_spread":0.27343162875868365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126333777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15071137,0.000025239848,0.83684856,0.0011839984,0.000139129,0.000027628423,1.903844e-7,0.0007201709,0.010343704],"genre_scores_gemma":[0.9245105,9.78755e-7,0.07414336,0.00015329894,0.000007087201,8.624215e-7,5.542465e-7,0.0000028375514,0.0011805625],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99939024,0.000006269689,0.00008626802,0.00020087745,0.00009567625,0.0002206699],"domain_scores_gemma":[0.99966323,0.00004412441,0.00002524608,0.00020161616,0.000027670878,0.000038085258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019022757,0.00006236802,0.0000659103,0.000102671656,0.000051230087,0.00006115839,0.00042791248,0.000048664653,0.000029804072],"category_scores_gemma":[0.000019106948,0.00005157617,0.000024181918,0.00024713107,0.000035521232,0.00013008244,0.00011835855,0.000077869925,0.00018760581],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000063727666,0.00018733341,0.014145149,0.0000080673335,0.000024029141,0.0001374274,0.00023082651,0.0000074893046,0.015936712,0.46879667,0.0025464627,0.49797347],"study_design_scores_gemma":[0.00081637356,0.00045234733,0.8077103,0.000028998076,0.000007486729,0.00011085829,0.00026200502,0.11381524,0.032352444,0.019892965,0.023795351,0.0007556703],"about_ca_topic_score_codex":0.000014492441,"about_ca_topic_score_gemma":0.00005714024,"teacher_disagreement_score":0.7935651,"about_ca_system_score_codex":0.000019904774,"about_ca_system_score_gemma":0.000011451858,"threshold_uncertainty_score":0.24113557},"labels":[],"label_agreement":null},{"id":"W2127406376","doi":"10.1145/2470654.2470773","title":"High-precision pointing on large wall displays using small handheld devices","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Alberta","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Mobile device; Interaction technique; Task (project management); Orientation (vector space); Human–computer interaction; Isolation (microbiology); Computer vision; Artificial intelligence; Engineering","score_opus":0.039248795589275534,"score_gpt":0.27381221414409057,"score_spread":0.23456341855481505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127406376","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49805495,0.00007141053,0.49760038,0.0010248142,0.0009658871,0.0002199757,0.000004996397,0.00073662214,0.001320953],"genre_scores_gemma":[0.8005875,0.000011681303,0.19826481,0.0005010021,0.00010634396,0.00001912777,0.000010981954,0.000025745254,0.00047284906],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716586,0.00010926676,0.00048602352,0.0012450276,0.00033785554,0.0006559936],"domain_scores_gemma":[0.99759966,0.0002514855,0.00039020643,0.0014797885,0.00017218736,0.00010664844],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055091176,0.00046356287,0.0005301773,0.00037874523,0.00026257773,0.00057800935,0.002334121,0.0005792469,0.000070509835],"category_scores_gemma":[0.00011344368,0.0003752197,0.00017628755,0.00022672304,0.00004904579,0.00018547222,0.0036058447,0.0010792473,0.0003419407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030468049,0.0011758745,0.016509503,0.0005751014,0.00033999365,0.00019848505,0.00077242695,0.0073319743,0.005167381,0.76631737,0.0042479807,0.19733344],"study_design_scores_gemma":[0.001386014,0.0003816801,0.053441588,0.0029590959,0.000097717464,0.000048143145,0.00007991794,0.82321984,0.021690827,0.08887876,0.0052137603,0.0026026773],"about_ca_topic_score_codex":0.0009752006,"about_ca_topic_score_gemma":0.00017764048,"teacher_disagreement_score":0.81588787,"about_ca_system_score_codex":0.00011189467,"about_ca_system_score_gemma":0.00007782517,"threshold_uncertainty_score":0.99987},"labels":[],"label_agreement":null},{"id":"W2127623319","doi":"10.1145/765891.766113","title":"Hands on cooking","year":2003,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Human–computer interaction; Computer science; Gaze; User interface; Multimedia; User satisfaction; Natural (archaeology); User modeling; Computer user satisfaction; User experience design; User interface design; Artificial intelligence","score_opus":0.014538451397441605,"score_gpt":0.22922152432466172,"score_spread":0.2146830729272201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127623319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050433844,0.000008365489,0.62604624,0.0006584544,0.00014702395,0.00002396228,5.6884947e-8,0.0005422372,0.32213983],"genre_scores_gemma":[0.9870817,7.322919e-7,0.010904187,0.00041681612,0.0000046920604,0.0000017257148,6.1659236e-8,0.0000018454693,0.0015882119],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961525,0.000014782283,0.000046593785,0.00014351895,0.00006195415,0.0001178705],"domain_scores_gemma":[0.9996946,0.00002940988,0.00001286941,0.00023028621,0.00001365256,0.000019207726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008441257,0.00004322389,0.000045036617,0.00004982108,0.00005163436,0.000030585146,0.00022204041,0.00002794427,0.000025309388],"category_scores_gemma":[0.000032521668,0.000034121444,0.000015926304,0.00013830482,0.000016838534,0.000043691383,0.000019535546,0.00006271593,0.00016044083],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[1.83127e-7,0.000015780408,0.0010264942,3.5002682e-7,0.0000014774572,0.000004674803,0.000010285807,0.000004631048,0.000174453,0.9786795,0.0012957157,0.018786447],"study_design_scores_gemma":[0.002175383,0.0015927232,0.036017433,0.00005944173,0.0000068822355,0.0001320053,0.000057105663,0.008520882,0.5302501,0.16309954,0.25716096,0.0009275738],"about_ca_topic_score_codex":0.000002020965,"about_ca_topic_score_gemma":0.0000013177385,"teacher_disagreement_score":0.9366479,"about_ca_system_score_codex":0.000008950722,"about_ca_system_score_gemma":0.000009721922,"threshold_uncertainty_score":0.20621958},"labels":[],"label_agreement":null},{"id":"W2127867144","doi":"10.1186/1743-0003-10-90","title":"Evaluation of an intelligent wheelchair system for older adults with cognitive impairments","year":2013,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University Health Network; Toronto Rehabilitation Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Wheelchair; Usability; Cognition; Obstacle; Object (grammar); Human–computer interaction; Collision; Physical medicine and rehabilitation; Psychology; Categorization; Computer science; Applied psychology; Simulation; Artificial intelligence; Medicine; Computer security","score_opus":0.008438157333193799,"score_gpt":0.243676342966296,"score_spread":0.2352381856331022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127867144","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7844732,0.000050714287,0.21487822,0.00017598507,0.00011447536,0.000280171,6.312108e-7,0.00002343057,0.000003172189],"genre_scores_gemma":[0.978907,0.0000022783365,0.02103767,0.0000035468222,0.00001714708,0.000025250145,4.1387497e-7,0.000005616371,0.0000010981945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924064,0.00006710058,0.00022806443,0.000114141854,0.0002664704,0.00008360438],"domain_scores_gemma":[0.9982676,0.00021641373,0.00020046963,0.00008411783,0.001188406,0.000043029264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056162174,0.00006997778,0.00013059429,0.0001774596,0.000025369067,0.000022777504,0.0000958368,0.000025089052,4.170877e-7],"category_scores_gemma":[0.00021433819,0.00005090837,0.000033947752,0.00010752843,0.000027532706,0.0003360215,0.0000110590145,0.00006933497,2.481926e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020486303,0.0005989702,0.0038652082,0.001289667,0.00018221706,0.000008473893,0.010011466,0.028701419,0.0069263135,0.0014623168,0.000060260474,0.94668883],"study_design_scores_gemma":[0.0024102961,0.0066521554,0.3105701,0.0013248574,0.00009801257,0.00017922041,0.0031439094,0.6722591,0.0028640225,0.00036332675,0.0000053148137,0.00012971574],"about_ca_topic_score_codex":0.0000033345466,"about_ca_topic_score_gemma":4.005567e-7,"teacher_disagreement_score":0.94655913,"about_ca_system_score_codex":0.000037596503,"about_ca_system_score_gemma":0.000029388419,"threshold_uncertainty_score":0.2075983},"labels":[],"label_agreement":null},{"id":"W2127923372","doi":"10.1145/1026653.1026670","title":"Augmenting and sharing memory with eyeBlog","year":2004,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Computer vision; Face detection; Computer graphics (images); Artificial intelligence; Video tracking; Video processing; Multimedia; Facial recognition system; Feature extraction","score_opus":0.009102768899019526,"score_gpt":0.20901013033602237,"score_spread":0.19990736143700283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127923372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53865945,0.000032469346,0.44916636,0.0011841146,0.000028656803,0.00003165284,4.3586923e-8,0.00041635762,0.0104808835],"genre_scores_gemma":[0.9103059,0.0000012452653,0.08931086,0.00012247491,0.0000070761994,0.0000023122889,7.8751604e-8,0.00000259261,0.00024742883],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995192,0.0000025357538,0.000053642725,0.00022414482,0.000060077044,0.00014039398],"domain_scores_gemma":[0.9997387,0.000009046841,0.000020629157,0.00019106558,0.0000145421845,0.000026032403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007636992,0.0000548527,0.00005814994,0.00004809341,0.00007677579,0.00006191617,0.00024416033,0.000022730284,0.000003121378],"category_scores_gemma":[0.0000057630627,0.00004019941,0.0000073256683,0.00011678255,0.00004276605,0.00013909079,0.00016849331,0.000067839435,0.00001026936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000027850392,0.00006100755,0.031619005,0.000016531023,0.000026790058,0.000092163435,0.0004272233,0.00023600794,0.0035377957,0.8656141,0.000036892212,0.0983297],"study_design_scores_gemma":[0.007644908,0.0012248456,0.53812593,0.00045401824,0.000049225633,0.0011934203,0.00087856967,0.019751405,0.20845482,0.21842302,0.0018367734,0.0019630902],"about_ca_topic_score_codex":0.000036967536,"about_ca_topic_score_gemma":0.000019181394,"teacher_disagreement_score":0.64719105,"about_ca_system_score_codex":0.000011984434,"about_ca_system_score_gemma":0.000011448368,"threshold_uncertainty_score":0.16392843},"labels":[],"label_agreement":null},{"id":"W2133434946","doi":"10.1007/s12369-009-0032-4","title":"Development and Validation of a Robust Speech Interface for Improved Human-Robot Interaction","year":2009,"lang":"en","type":"article","venue":"International Journal of Social Robotics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Université de Montréal; Fonds Québécois de la Recherche sur la Nature et les Technologies; Polytechnique Montréal","keywords":"Robotics; Interface (matter); Computer science; Human–computer interaction; Robot; Artificial intelligence; Human–robot interaction; Task (project management); Context (archaeology); Domain (mathematical analysis); Rehabilitation robotics; Engineering; Systems engineering","score_opus":0.046224605409695294,"score_gpt":0.33854369465803763,"score_spread":0.2923190892483423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133434946","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12944557,0.000017832363,0.86468947,0.005124618,0.0005583767,0.00005903086,0.0000012723682,0.000018958835,0.00008487612],"genre_scores_gemma":[0.7512359,0.0000037416082,0.24855886,0.000046620444,0.00012167433,5.113925e-7,0.0000015817969,0.000003040728,0.000028037131],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991643,0.000017145732,0.00043190506,0.00010217591,0.00019250416,0.00009201069],"domain_scores_gemma":[0.99862415,0.00005158307,0.0005537059,0.00005008053,0.00069524563,0.0000252094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002728775,0.00007906602,0.00015989346,0.00018425328,0.00006517128,0.00007023097,0.00044064596,0.0000642069,0.0000016137164],"category_scores_gemma":[0.00008425725,0.00007621415,0.0000629663,0.00006986405,0.00003392539,0.00028105805,0.000056542423,0.00014128286,5.047356e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019562252,0.0007313021,0.00045797793,0.000027930246,0.00045344298,0.000019401967,0.0038048718,0.00906116,0.23179196,0.18456446,0.0006365095,0.56825536],"study_design_scores_gemma":[0.0058511435,0.002026707,0.035636388,0.000500306,0.0001307633,0.0005423041,0.0010466616,0.03632069,0.8491291,0.05996643,0.008024419,0.00082508364],"about_ca_topic_score_codex":0.0000015816053,"about_ca_topic_score_gemma":0.000002090403,"teacher_disagreement_score":0.62179035,"about_ca_system_score_codex":0.000110923065,"about_ca_system_score_gemma":0.000058342306,"threshold_uncertainty_score":0.31079227},"labels":[],"label_agreement":null},{"id":"W2133589160","doi":"10.1109/iembs.2011.6091711","title":"Mobility profile and wheelchair driving skills of powered wheelchair users: Sensor-based event recognition using a support vector machine classifier","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Université de Sherbrooke; Centre for Interdisciplinary Research in Rehabilitation; McGill University","funders":"","keywords":"Wheelchair; Accelerometer; Support vector machine; Classifier (UML); Computer science; Artificial intelligence; Event (particle physics); Computer vision; Simulation; Pattern recognition (psychology)","score_opus":0.03842638624248242,"score_gpt":0.25868810025814615,"score_spread":0.22026171401566372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133589160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6836018,0.00001592829,0.31478652,0.00016334158,0.00015457076,0.00028343115,0.000017952061,0.00032800526,0.0006484591],"genre_scores_gemma":[0.86694425,0.0000024726598,0.13284929,0.000079511105,0.00001136739,0.000014827997,0.000007361686,0.000013592277,0.00007730737],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982931,0.00012047857,0.00042304397,0.0005902025,0.00021817056,0.00035497313],"domain_scores_gemma":[0.99879014,0.00009283742,0.00023781133,0.0005801858,0.00019270796,0.000106295935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047146014,0.00022225085,0.00032203394,0.0002201789,0.00009739697,0.000031421536,0.00033866643,0.0001584676,0.00023333506],"category_scores_gemma":[0.000106130035,0.0001956248,0.000094506824,0.00031341432,0.00018623545,0.00029797154,0.00017898492,0.00020209036,0.000015535532],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017228445,0.005367516,0.68071914,0.00053718145,0.00029011435,0.00015595964,0.003939968,0.000047052974,0.13972603,0.005559854,0.00051669596,0.16296822],"study_design_scores_gemma":[0.0022377346,0.0013154715,0.6007739,0.00034483118,0.00007308045,0.000104174695,0.0004092858,0.147276,0.24277882,0.0034804926,0.00018199465,0.001024224],"about_ca_topic_score_codex":0.000234965,"about_ca_topic_score_gemma":0.000055651526,"teacher_disagreement_score":0.18334246,"about_ca_system_score_codex":0.000065669,"about_ca_system_score_gemma":0.000105561885,"threshold_uncertainty_score":0.7977348},"labels":[],"label_agreement":null},{"id":"W2134230037","doi":"10.1109/icra.2013.6630719","title":"SEPO: Selecting by pointing as an intuitive human-robot command interface","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interface (matter); Computer science; Robot; Position (finance); Human–computer interaction; Object (grammar); Computer vision; Point (geometry); Gesture; Human–robot interaction; Artificial intelligence; Mathematics","score_opus":0.0127428669217334,"score_gpt":0.28822190982765106,"score_spread":0.2754790429059177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134230037","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60821235,0.000021517193,0.37400734,0.0011467455,0.00008151407,0.000120848,2.9259087e-7,0.0008852407,0.01552416],"genre_scores_gemma":[0.97751933,7.02217e-7,0.020548826,0.0003955133,0.000023581972,0.000015681264,0.0000018063492,0.000012009087,0.0014825502],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987192,0.00007802296,0.00022287502,0.00046062484,0.00013303748,0.0003862514],"domain_scores_gemma":[0.99917156,0.0000904225,0.00010534372,0.000407769,0.00014057243,0.00008430907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023296113,0.00016346718,0.00017525401,0.0000983046,0.000302213,0.0003306903,0.0008427939,0.000091745525,0.0001284757],"category_scores_gemma":[0.000065214495,0.00014612914,0.000034861318,0.0002826681,0.00008284951,0.0007060738,0.0003631471,0.0003339268,0.00035016873],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040568407,0.00055189326,0.018598566,0.00003139348,0.00011953253,0.000020864154,0.0039044502,0.000058628764,0.50617886,0.17969589,0.03060559,0.26023024],"study_design_scores_gemma":[0.0012915963,0.0017137366,0.026777701,0.00015006198,0.000018562945,0.00022177964,0.0027653296,0.051556658,0.8444533,0.066626325,0.00293212,0.001492856],"about_ca_topic_score_codex":0.001020279,"about_ca_topic_score_gemma":0.000058623704,"teacher_disagreement_score":0.36930698,"about_ca_system_score_codex":0.00004201204,"about_ca_system_score_gemma":0.000016696915,"threshold_uncertainty_score":0.5958973},"labels":[],"label_agreement":null},{"id":"W2135069972","doi":"10.1177/1545968309341647","title":"Evaluation of Tooth-Click Triggering and Speech Recognition in Assistive Technology for Computer Access","year":2009,"lang":"en","type":"article","venue":"Neurorehabilitation and neural repair","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research","keywords":"Cursor (databases); Computer mouse; Tetraplegia; Computer science; Speech recognition; Human–computer interaction; Spinal cord; Artificial intelligence; Spinal cord injury; Psychology; Neuroscience","score_opus":0.06016464176018394,"score_gpt":0.34337594553624423,"score_spread":0.2832113037760603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135069972","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98996854,0.0000685631,0.0061057988,0.003021734,0.000090921356,0.00048187666,0.0000023146183,0.00021140778,0.000048836428],"genre_scores_gemma":[0.97664976,0.000008783664,0.023146935,0.00013592673,0.000011325634,0.00003939049,0.0000019708746,0.0000038864914,0.0000020365906],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99885774,0.00013528058,0.00027351137,0.00040849543,0.00018596405,0.0001389881],"domain_scores_gemma":[0.99902797,0.00033898003,0.0001127385,0.00017708351,0.00031690815,0.000026332775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007496495,0.000102212216,0.0001782418,0.00039345206,0.000051126066,0.00003210466,0.0001441468,0.000086954395,9.3377207e-7],"category_scores_gemma":[0.00067046325,0.000098477176,0.00003998692,0.00043095564,0.000095965435,0.0003605091,0.000055689517,0.000108377484,2.7986653e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001664371,0.0000670378,0.007195989,0.000022094826,0.0000032720914,0.0000010868679,0.00007377151,0.000033041404,0.0019288895,0.0010413664,0.00002296769,0.98959386],"study_design_scores_gemma":[0.0009544647,0.000984125,0.739888,0.000036305162,0.000016224523,0.000012529543,0.0000207808,0.23728743,0.003506156,0.017148653,0.000039481678,0.00010583964],"about_ca_topic_score_codex":0.0000044711296,"about_ca_topic_score_gemma":0.000010876582,"teacher_disagreement_score":0.989488,"about_ca_system_score_codex":0.000030157145,"about_ca_system_score_gemma":0.00002562948,"threshold_uncertainty_score":0.40157828},"labels":[],"label_agreement":null},{"id":"W2135491399","doi":"10.1109/tbme.2008.921161","title":"Tooth-Click Control of a Hands-Free Computer Interface","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Dalhousie University","keywords":"Computer science; Computer mouse; Cursor (databases); Dwell time; Interface (matter); Software; Accelerometer; Control system; Human–computer interaction; Computer hardware; Computer vision; Engineering; Medicine; Operating system","score_opus":0.009739944048415192,"score_gpt":0.2083349018221118,"score_spread":0.19859495777369662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135491399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036669027,0.000030339808,0.96143854,0.00066896214,0.0006199917,0.00007259565,0.000009150173,0.0004664517,0.000024934681],"genre_scores_gemma":[0.9715155,0.000016240208,0.028300555,0.00006333513,0.000037794216,0.000013182007,2.9565203e-7,0.000011125888,0.000042015643],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893856,0.000015807473,0.00026557894,0.0002677768,0.00025310682,0.0002591754],"domain_scores_gemma":[0.999181,0.00014003522,0.000043042743,0.00047930123,0.000041098832,0.00011555095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009646112,0.0001511046,0.00024409987,0.00029266853,0.00006775289,0.000011687672,0.00070293574,0.00012871803,0.000014463474],"category_scores_gemma":[0.00001123647,0.00013900663,0.000103555176,0.00043789446,0.0001545273,0.00010230488,0.0000062141785,0.00031130904,0.000022802766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020698397,0.0037319663,0.00014430005,0.0003340515,0.001031886,0.0005390332,0.002318167,0.43686786,0.13104796,0.009536069,0.0056595285,0.40858218],"study_design_scores_gemma":[0.0047839205,0.00081846956,0.00067401474,0.0001273577,0.000018034005,0.00021465124,0.0000043304544,0.91213286,0.07701345,0.000060867234,0.0038175967,0.00033445426],"about_ca_topic_score_codex":0.000011561954,"about_ca_topic_score_gemma":8.8587296e-7,"teacher_disagreement_score":0.9348464,"about_ca_system_score_codex":0.000035377845,"about_ca_system_score_gemma":0.000031577718,"threshold_uncertainty_score":0.56685257},"labels":[],"label_agreement":null},{"id":"W2135682029","doi":"10.1167/9.3.6","title":"Viewing task influences eye movement control during active scene perception","year":2009,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":383,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Army Research Office; Economic and Social Research Council; Natural Sciences and Engineering Research Council of Canada; Queen's University; National Science Foundation","keywords":"Eye movement; Fixation (population genetics); Gaze; Saccade; Memorization; Perception; Psychology; Task (project management); Cognitive psychology; Visual search; Computer science; Artificial intelligence; Computer vision","score_opus":0.008832606242384205,"score_gpt":0.28245674114037317,"score_spread":0.273624134897989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135682029","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92188895,0.000119718556,0.07485944,0.0028190995,0.0001679791,0.000043232732,3.2661563e-7,0.000038892562,0.00006234066],"genre_scores_gemma":[0.99197733,0.00005390923,0.0075009335,0.0003681752,0.00008472922,3.7575757e-7,7.968557e-8,0.0000024071321,0.000012047602],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99903786,0.000054254702,0.00030887235,0.00013661686,0.00030028104,0.00016214246],"domain_scores_gemma":[0.99927527,0.000025680412,0.0003370036,0.0001458334,0.00016166258,0.00005455244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032728596,0.00008978208,0.00018524064,0.00019785546,0.00012045396,0.00007213264,0.0004004219,0.000053732478,0.000005857758],"category_scores_gemma":[0.000049721406,0.000067836154,0.00008592645,0.00017252173,0.000023391216,0.000624794,0.0000348199,0.00023217332,0.000008911526],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000454573,0.0001320379,0.0025991285,0.000004819949,0.00001854783,0.00004696828,0.00054078165,0.00037156514,0.66751957,0.00023893277,0.00005368141,0.32842854],"study_design_scores_gemma":[0.000761025,0.0006872831,0.9880974,0.00018148634,0.00000986581,0.000028718692,0.000059391798,0.002432373,0.0057656555,0.0017405193,0.00015256323,0.000083693616],"about_ca_topic_score_codex":0.0000019037387,"about_ca_topic_score_gemma":1.8373996e-7,"teacher_disagreement_score":0.9854983,"about_ca_system_score_codex":0.000100822,"about_ca_system_score_gemma":0.000025378686,"threshold_uncertainty_score":0.2766278},"labels":[],"label_agreement":null},{"id":"W2137040619","doi":"10.3389/fnhum.2013.00361","title":"Neurophysiological constraints on the eye-mind link","year":2013,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Saccade; Eye movement; Cognition; Computer science; Cognitive psychology; Reading (process); Neurophysiology; Task (project management); Perception; Control (management); Motor control; Psychology; Word (group theory); Artificial intelligence; Neuroscience; Linguistics","score_opus":0.02791351155629083,"score_gpt":0.25274069996530696,"score_spread":0.22482718840901614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137040619","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77114016,0.000014996167,0.21327466,0.0083629135,0.002054692,0.00041938195,0.0000012558149,0.00023945463,0.0044925148],"genre_scores_gemma":[0.99353796,0.0000037818695,0.004005051,0.002144117,0.00002878257,0.00003189346,1.7147734e-7,0.000005197794,0.00024301487],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99832505,0.00014584522,0.0001993581,0.0006546142,0.0002432314,0.0004318728],"domain_scores_gemma":[0.9990708,0.000080953745,0.00008092311,0.0006719504,0.000031388867,0.00006393905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002031958,0.00015433536,0.00015710817,0.00015503347,0.00031122935,0.00019530387,0.0021965338,0.000066019355,0.000030335128],"category_scores_gemma":[0.00025141388,0.000100881014,0.000046360357,0.0005699613,0.0013752506,0.000223024,0.00028241632,0.00047346356,0.000035149995],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007819724,0.00054971006,0.03932487,0.000011026313,0.0000051681686,0.00029073114,0.0004222095,0.0012350498,0.2230341,0.28117564,0.026595078,0.42734858],"study_design_scores_gemma":[0.00032754947,0.00069680467,0.87706834,0.000034110162,0.0000020485395,0.000015774704,0.000047223297,0.07199917,0.0056934487,0.040003575,0.0036733176,0.00043865555],"about_ca_topic_score_codex":0.000003931708,"about_ca_topic_score_gemma":2.3221962e-7,"teacher_disagreement_score":0.83774346,"about_ca_system_score_codex":0.000024894829,"about_ca_system_score_gemma":0.000022922615,"threshold_uncertainty_score":0.50671685},"labels":[],"label_agreement":null},{"id":"W2139594594","doi":"10.1109/memea.2013.6549759","title":"The MouthPad: A tongue-computer interface","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Cursor (databases); Tongue; Touchpad; Interface (matter); Laptop; Tip of the tongue; Input device; Computer mouse; Computer graphics (images); Computer vision; Computer hardware","score_opus":0.006610121545758647,"score_gpt":0.2179834710553681,"score_spread":0.21137334950960945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139594594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034665503,0.00005654303,0.94314003,0.01261365,0.00039942216,0.000102114376,9.052581e-8,0.0006541585,0.008368505],"genre_scores_gemma":[0.9516496,0.0000028864013,0.043941613,0.00038052545,0.000036340443,0.000020809513,7.4982815e-8,0.0000039259567,0.0039642015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992967,0.000026814303,0.00011572477,0.00022050644,0.00009507862,0.00024517067],"domain_scores_gemma":[0.99921924,0.00011026908,0.000031314485,0.0005376214,0.00006291061,0.000038621147],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00011118668,0.00008315435,0.0000697334,0.0000329863,0.00015293037,0.00028688024,0.0011348038,0.00004115089,0.000038414226],"category_scores_gemma":[0.0000151123895,0.00004617898,0.00003541482,0.0001552952,0.00007559961,0.00018727161,0.0003499015,0.0001275231,0.0015061488],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[5.739753e-7,0.000030170244,0.00055579306,0.0000015510049,0.000017149441,0.0000024896542,0.00018401719,0.000020226567,0.0007598803,0.25003245,0.047978826,0.70041686],"study_design_scores_gemma":[0.0008963613,0.0005257718,0.11458381,0.00004284676,0.000009054976,0.00013497708,0.00032176523,0.545136,0.030095313,0.11204509,0.19518228,0.0010267415],"about_ca_topic_score_codex":0.000080535014,"about_ca_topic_score_gemma":0.000009766054,"teacher_disagreement_score":0.91698414,"about_ca_system_score_codex":0.000013512066,"about_ca_system_score_gemma":0.000011608908,"threshold_uncertainty_score":0.9992713},"labels":[],"label_agreement":null},{"id":"W2141462346","doi":"10.1109/tbme.2008.919722","title":"Investigation of the Cross-Ratios Method for Point-of-Gaze Estimation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cross-ratio; Gaze; Artificial intelligence; Computer science; Computer vision; Point (geometry); Plane (geometry); Function (biology); Image plane; Mathematics; Image (mathematics); Geometry","score_opus":0.022084524673722503,"score_gpt":0.2678226525835098,"score_spread":0.24573812790978727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141462346","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041219328,0.0000068342556,0.9574577,0.0005941434,0.0004489617,0.00013249187,0.000008795347,0.00012722859,0.000004471634],"genre_scores_gemma":[0.71523654,0.000002203863,0.28468865,0.000019338193,0.000008903254,0.000023446692,6.7864175e-7,0.000005026248,0.000015194314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925715,0.000018563262,0.0002590792,0.00015420148,0.0001912196,0.000119796015],"domain_scores_gemma":[0.99933803,0.00022438072,0.00007754708,0.000249875,0.000067886154,0.00004230099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020423783,0.00008338285,0.0001293453,0.00015773001,0.00008552179,0.000006899568,0.00031022623,0.000096727425,0.0000023730618],"category_scores_gemma":[0.00005224222,0.00006550098,0.00008826318,0.0004850662,0.00014396558,0.00012149984,0.0000024199715,0.00013869016,0.0000011710233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020174053,0.0001866546,0.0001319381,0.00024868516,0.000106573774,0.0000025076988,0.0013780901,0.44450358,0.42222887,0.011280253,0.00014600738,0.119766645],"study_design_scores_gemma":[0.00021272867,0.000072818555,0.0011388644,0.000040349296,0.0000062572185,0.00001315723,0.0000021486599,0.6579014,0.3401475,0.00035748817,0.00005332961,0.00005392515],"about_ca_topic_score_codex":0.000008281535,"about_ca_topic_score_gemma":6.414729e-7,"teacher_disagreement_score":0.67401725,"about_ca_system_score_codex":0.000025645102,"about_ca_system_score_gemma":0.00005193623,"threshold_uncertainty_score":0.26710522},"labels":[],"label_agreement":null},{"id":"W2141823827","doi":"10.1186/1743-0003-10-58","title":"Design and validation of an intelligent wheelchair towards a clinically-functional outcome","year":2013,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université Laval; Centre de réadaptation Lethbridge-Layton-Mackay; Université du Québec à Trois-Rivières; McGill University; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Wheelchair; Manual wheelchair; Test (biology); Software; Activities of daily living; Population; Robot; Protocol (science); Simulation; Computer science; Human–computer interaction; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Physical therapy","score_opus":0.026715914602211996,"score_gpt":0.27106328891888387,"score_spread":0.24434737431667186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141823827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5322587,0.000020050284,0.46666837,0.00086510263,0.00012460993,0.000044376215,7.351487e-8,0.000017128552,0.0000015993875],"genre_scores_gemma":[0.83649635,0.000014508106,0.16344796,0.00001411268,0.000016907465,0.000002257709,1.3594219e-7,0.0000036488464,0.0000041116273],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99915,0.00007381641,0.00045446714,0.00011337247,0.00013511603,0.000073245516],"domain_scores_gemma":[0.9990452,0.00038843893,0.00019333733,0.00010302334,0.00021265392,0.000057359193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004782942,0.00006813665,0.00015207926,0.00018800194,0.00002459999,0.00003516116,0.00010506565,0.000037759724,0.0000014525951],"category_scores_gemma":[0.00042684266,0.00005413207,0.000036714446,0.00010559162,0.000050896226,0.00038752,0.000027900822,0.00013809346,5.302481e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016775257,0.00080991944,0.07955513,0.00050903903,0.00013241837,0.000022957809,0.0041454714,0.1767664,0.11840981,0.010352905,0.00026955796,0.60885864],"study_design_scores_gemma":[0.000504934,0.0041458416,0.7839099,0.00007797971,0.00001746548,0.00016794796,0.00012757556,0.20100936,0.0031348318,0.0067067645,0.0000621371,0.00013524946],"about_ca_topic_score_codex":0.0000034690997,"about_ca_topic_score_gemma":3.899485e-8,"teacher_disagreement_score":0.70435476,"about_ca_system_score_codex":0.000012019845,"about_ca_system_score_gemma":0.000017142276,"threshold_uncertainty_score":0.22074418},"labels":[],"label_agreement":null},{"id":"W2142581410","doi":"10.1186/1743-0003-8-31","title":"Assessment of Joystick control during the performance of powered wheelchair driving tasks","year":2011,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health and Social Services Centre University Institute of Geriatrics of Sherbrooke; Université de Sherbrooke; Centre for Interdisciplinary Research in Rehabilitation; Jewish Rehabilitation Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Joystick; Wheelchair; Physical medicine and rehabilitation; Control (management); Neurology; Poison control; Psychology; Computer science; Simulation; Human–computer interaction; Medicine; Neuroscience; Medical emergency; Artificial intelligence","score_opus":0.005141139862499391,"score_gpt":0.20739942524523428,"score_spread":0.20225828538273488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142581410","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9325934,0.00005281855,0.06688213,0.00020776744,0.00017714554,0.00004788688,3.4624452e-7,0.000015877144,0.000022612527],"genre_scores_gemma":[0.98150456,0.000023090777,0.018447947,0.000003476339,0.000012203798,0.000001170447,1.9930692e-8,0.0000041458993,0.0000033821213],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992883,0.00004255396,0.00035590486,0.00007790166,0.0001451122,0.00009020133],"domain_scores_gemma":[0.9991157,0.0002516123,0.00029886025,0.00015318368,0.00015567546,0.000024954044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032146228,0.00006609868,0.00017183193,0.00015478457,0.000035656554,0.000007808065,0.00021755396,0.000023217306,7.1811894e-7],"category_scores_gemma":[0.00015265241,0.000044945693,0.000055810728,0.00013254964,0.000069758484,0.00016919537,0.00003025077,0.00016251077,6.1508295e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006115009,0.0002909693,0.56055486,0.0008011549,0.00013268749,0.000018362396,0.003865388,0.030503739,0.38569605,0.009158745,0.0000157486,0.00890113],"study_design_scores_gemma":[0.0003249614,0.0008393409,0.95190287,0.00012421596,0.000010236627,0.000048914688,0.000057826757,0.04468703,0.0018911604,0.000067142275,0.0000078899775,0.000038391183],"about_ca_topic_score_codex":0.0000018108899,"about_ca_topic_score_gemma":1.7323329e-7,"teacher_disagreement_score":0.39134803,"about_ca_system_score_codex":0.000014532385,"about_ca_system_score_gemma":0.00001961859,"threshold_uncertainty_score":0.18328322},"labels":[],"label_agreement":null},{"id":"W2142943459","doi":"10.1145/2207676.2207745","title":"Implanted user interfaces","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":75,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Autodesk (Canada)","funders":"","keywords":"User interface; Computer science; Human–computer interaction; Natural user interface; User interface design; Post-WIMP; User experience design; Operating system","score_opus":0.015674846636418868,"score_gpt":0.25177788014187025,"score_spread":0.23610303350545137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142943459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3662036,0.0000794157,0.61513627,0.0011234246,0.0004068058,0.000028837221,3.571085e-7,0.0008082856,0.016212987],"genre_scores_gemma":[0.9757765,0.0000012798268,0.023027694,0.00016993876,0.000020520982,0.000002162432,2.1421747e-7,0.0000019850288,0.0009997411],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995869,0.000011526728,0.00005825395,0.000092212525,0.000049121994,0.0002020194],"domain_scores_gemma":[0.99970096,0.000023966408,0.000016633478,0.00020836933,0.00001401243,0.000036028097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008492637,0.000048074726,0.000055024164,0.000045927034,0.000030501815,0.000025297984,0.00036957866,0.00003137688,0.00003922476],"category_scores_gemma":[0.000010665906,0.00003521317,0.000013403719,0.00011258218,0.000020700312,0.00024366143,0.00013086182,0.00005920559,0.0004103156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015689496,0.00010457488,0.07596316,0.0000035981493,0.000019498799,0.000003370507,0.00026631905,6.939158e-7,0.008707834,0.80724853,0.017624862,0.09005596],"study_design_scores_gemma":[0.00048248714,0.00013729022,0.4527407,0.00002279639,0.0000086003665,0.00026640712,0.00014934006,0.0020626716,0.3617026,0.004307139,0.17755023,0.00056975504],"about_ca_topic_score_codex":0.000010574698,"about_ca_topic_score_gemma":0.0000025807842,"teacher_disagreement_score":0.80294144,"about_ca_system_score_codex":0.0000070998753,"about_ca_system_score_gemma":0.00000405296,"threshold_uncertainty_score":0.5273914},"labels":[],"label_agreement":null},{"id":"W2144181652","doi":"10.1109/ccece.2006.277437","title":"Using Red-Eye to Improve Face Detection in Low Quality Video Images","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer vision; Thresholding; Computer science; Histogram; Face (sociological concept); Face detection; Facial recognition system; Image quality; Edge detection; Image processing; Histogram equalization; Image (mathematics); Pattern recognition (psychology)","score_opus":0.022283302235648176,"score_gpt":0.3044733680831745,"score_spread":0.2821900658475263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144181652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39882168,0.0000069920006,0.5992615,0.00067911064,0.00010735529,0.00006755594,5.898463e-7,0.0002447541,0.0008104413],"genre_scores_gemma":[0.9472162,3.0552837e-7,0.05235566,0.000090638045,0.000024587152,0.0000063154257,2.1349092e-7,0.0000044441476,0.00030163137],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990052,0.000053299555,0.00021168453,0.0003740497,0.00011358996,0.0002421901],"domain_scores_gemma":[0.99948007,0.0000369721,0.00005366812,0.00035578612,0.00004524579,0.000028243732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030952238,0.00009526234,0.0001242818,0.0001596691,0.000059204078,0.00007047494,0.00032833905,0.000072222094,0.0000032409455],"category_scores_gemma":[0.000060645027,0.000089181114,0.000031936917,0.0004767261,0.000028072234,0.00020615487,0.00014332359,0.0001227463,0.000026139442],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007428151,0.00010945778,0.00919755,0.000015294845,0.0000034435036,0.000012437267,0.00006136152,0.0013353822,0.8662337,0.007099943,0.00015467191,0.115769304],"study_design_scores_gemma":[0.00025737105,0.00006389532,0.22832932,0.00001896686,0.0000015502789,0.000003993473,0.00003864462,0.025607511,0.74017346,0.005060963,0.0002133219,0.00023101535],"about_ca_topic_score_codex":0.0020901628,"about_ca_topic_score_gemma":0.0005609265,"teacher_disagreement_score":0.5483945,"about_ca_system_score_codex":0.000094019975,"about_ca_system_score_gemma":0.00001757683,"threshold_uncertainty_score":0.36367002},"labels":[],"label_agreement":null},{"id":"W2152842511","doi":"10.1109/crv.2012.14","title":"Information Fusion in Visual-Task Inference","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Security token; Task (project management); Inference; Eye movement; Artificial intelligence; Process (computing); Probabilistic logic; Bayesian inference; Visual search; Modalities; Human–computer interaction; Machine learning; Natural language processing; Bayesian probability","score_opus":0.01304796934738521,"score_gpt":0.27578172544639035,"score_spread":0.2627337560990051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152842511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36039576,0.0000148744775,0.62848514,0.0004906939,0.0001473532,0.000044505887,1.3501408e-7,0.00027036588,0.010151179],"genre_scores_gemma":[0.99172837,0.000003693368,0.007988696,0.00022388545,0.0000092208265,0.0000048027027,0.0000011394116,9.018889e-7,0.000039284103],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99954623,0.000015432233,0.000117352756,0.000049949915,0.00008458274,0.00018645248],"domain_scores_gemma":[0.9997333,0.000033104774,0.000032814605,0.00014617876,0.000023573686,0.00003101943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017841802,0.000049306396,0.00005414995,0.00015305154,0.000026967547,0.000034066485,0.00023351618,0.000048515278,0.000015491221],"category_scores_gemma":[0.000054829,0.00004060477,0.000010644762,0.0002907707,0.000014809917,0.0013252454,0.00013859608,0.00008266498,0.00035342967],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014864478,0.000097174954,0.1682015,0.000006086891,0.0000015453671,5.5450096e-7,0.00123713,0.000011260224,0.0011762509,0.37849417,0.0004264652,0.45034638],"study_design_scores_gemma":[0.00031873633,0.00006820768,0.9558741,0.000019748582,0.0000010192697,0.0000072536595,0.00014108277,0.02124595,0.005908724,0.0030314878,0.013172964,0.00021071293],"about_ca_topic_score_codex":0.000032360076,"about_ca_topic_score_gemma":0.000009236195,"teacher_disagreement_score":0.7876726,"about_ca_system_score_codex":0.000020422432,"about_ca_system_score_gemma":0.000012818917,"threshold_uncertainty_score":0.45427412},"labels":[],"label_agreement":null},{"id":"W2154187357","doi":"10.1109/ccece.2011.6030604","title":"Remote point-of-gaze estimation with single-point personal calibration based on the pupil boundary and corneal reflections","year":2011,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Calibration; Pupil; Computer vision; Computer science; Artificial intelligence; Point (geometry); Boundary (topology); Optics; Computer graphics (images); Mathematics; Physics; Statistics","score_opus":0.05005436825906656,"score_gpt":0.24597674487841326,"score_spread":0.1959223766193467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154187357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054823615,0.0000054820757,0.9341715,0.003514911,0.000056943205,0.00010330169,0.0000011647456,0.00020234488,0.0071207266],"genre_scores_gemma":[0.819471,4.5039778e-7,0.1801427,0.0002997973,0.0000069696134,0.0000029701466,0.0000015360826,0.000004638703,0.00006993406],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993252,0.000058508434,0.00012606049,0.00022835903,0.00013972896,0.0001221308],"domain_scores_gemma":[0.99945474,0.000109699715,0.00008748736,0.00025419597,0.00006393599,0.00002995779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020348607,0.000092822615,0.000086779546,0.000096614815,0.00018945723,0.00006384447,0.00017225428,0.000049533566,0.000030082538],"category_scores_gemma":[0.000049606362,0.00005766428,0.000021829295,0.00024320645,0.00020301333,0.00021533138,0.00003409718,0.00012898355,0.0000039938463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004972987,0.0013288348,0.0067242635,0.00009391278,0.00012674072,0.00005985808,0.010452872,0.0006790489,0.020145413,0.52860844,0.0026675125,0.42861584],"study_design_scores_gemma":[0.00026663474,0.0009270566,0.009226966,0.000050906398,0.000010093589,0.000041609142,0.0001124777,0.9660222,0.014683701,0.008480284,0.000059588427,0.00011847023],"about_ca_topic_score_codex":0.0000976263,"about_ca_topic_score_gemma":0.00008456629,"teacher_disagreement_score":0.9653432,"about_ca_system_score_codex":0.000026802809,"about_ca_system_score_gemma":0.00006500518,"threshold_uncertainty_score":0.23514812},"labels":[],"label_agreement":null},{"id":"W2154244113","doi":"10.1167/11.1.9","title":"The eye dominates in guiding attention during simultaneous eye and hand movements","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Eye Institute; Canadian Institutes of Health Research","keywords":"Eye movement; Optometry; Psychology; Cognitive psychology; Neuroscience; Medicine","score_opus":0.01672131166159492,"score_gpt":0.2695074650745708,"score_spread":0.2527861534129759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154244113","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923347,0.0002896693,0.006825003,0.00025775275,0.00016799151,0.000029783176,7.586411e-8,0.000009929627,0.000085091386],"genre_scores_gemma":[0.99691445,0.00010368387,0.0028669732,0.0000097511065,0.000016128299,3.3157167e-7,2.3581435e-8,0.000002791056,0.00008585388],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99936795,0.000037316393,0.0002446715,0.00009312827,0.000133082,0.00012387919],"domain_scores_gemma":[0.99951285,0.0000751387,0.00020431337,0.00010793602,0.00007291206,0.000026821766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044792905,0.000057379963,0.000093153794,0.00011535307,0.0001625144,0.000082486644,0.00029005352,0.000036003512,0.0000012463593],"category_scores_gemma":[0.00011572446,0.000036348425,0.000028101025,0.00009830485,0.000047587408,0.0002193233,0.00011641495,0.000107854015,0.00000204935],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018599711,0.00031488805,0.14968796,0.000034836237,0.000053660086,0.00075661927,0.0022995942,0.00010738416,0.4983873,0.0025057062,0.000036690202,0.34562933],"study_design_scores_gemma":[0.0009577987,0.00037882352,0.96493083,0.00026997432,0.0000054024204,0.00005963179,0.00017365067,0.008334735,0.020845158,0.003738,0.0002122429,0.000093769566],"about_ca_topic_score_codex":0.0000067291203,"about_ca_topic_score_gemma":0.000003849278,"teacher_disagreement_score":0.8152428,"about_ca_system_score_codex":0.000028597437,"about_ca_system_score_gemma":0.0000073764554,"threshold_uncertainty_score":0.14822458},"labels":[],"label_agreement":null},{"id":"W2154923718","doi":"10.1109/innovations.2006.301968","title":"Computer Interface by Gesture and Voice for Users with Special Needs","year":2006,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Gesture; Computer science; Gesture recognition; Human–computer interaction; Cursor (databases); Interface (matter); User interface; Input device; Head (geology); Voice command device; Speech recognition; Artificial intelligence; Computer hardware","score_opus":0.005702559564093316,"score_gpt":0.2075642004623923,"score_spread":0.20186164089829897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154923718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.108218275,0.000035965113,0.8869809,0.0026857334,0.0001301713,0.00010177703,0.0000024368821,0.00023539581,0.0016093542],"genre_scores_gemma":[0.8581064,0.0000010483385,0.13975017,0.00026359464,0.0002567847,0.0000069551206,0.0000019908912,0.0000070319834,0.0016060746],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994317,0.000008484535,0.00008172192,0.00022661388,0.0000693317,0.00018208944],"domain_scores_gemma":[0.9996779,0.000052457435,0.00003292585,0.00017015569,0.0000399836,0.000026585989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051795763,0.00010204518,0.00010708774,0.00006379293,0.00006731851,0.000098147975,0.00028535177,0.00005982034,0.0000029680318],"category_scores_gemma":[0.00000204432,0.000074202566,0.000016225282,0.00015975763,0.00007601049,0.00012643052,0.000083761624,0.00008267084,0.000006918696],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004973016,0.00021773217,0.020888258,0.00003244806,0.000053135507,0.000011175043,0.0004136622,0.0001924553,0.0026849208,0.22925995,0.65885943,0.0873371],"study_design_scores_gemma":[0.004685659,0.0024272886,0.097467504,0.00008328542,0.000044064152,0.00019791305,0.00022961898,0.056336284,0.031007146,0.009679649,0.79646385,0.0013777106],"about_ca_topic_score_codex":0.00004274672,"about_ca_topic_score_gemma":0.000063260646,"teacher_disagreement_score":0.74988806,"about_ca_system_score_codex":0.000012287732,"about_ca_system_score_gemma":0.000009546543,"threshold_uncertainty_score":0.30258927},"labels":[],"label_agreement":null},{"id":"W2155532786","doi":"10.1145/1743666.1743668","title":"An eye on input","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Eye movement; Eye tracking on the ISS; Eye tracking; Jitter; Fixation (population genetics); Computer vision; Human–computer interaction; Human eye; Point (geometry); Artificial intelligence; Selection (genetic algorithm); Input device; Controller (irrigation); Computer hardware","score_opus":0.009544705580754476,"score_gpt":0.2706448780799387,"score_spread":0.2611001724991842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155532786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7235413,8.422908e-7,0.24158742,0.0031496529,0.0004387753,0.000028165654,2.1130873e-7,0.000995994,0.030257618],"genre_scores_gemma":[0.9557806,1.4413622e-7,0.0433046,0.00049742154,0.000028822318,0.0000019230688,2.1906098e-7,0.0000022198224,0.0003840332],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995835,0.000008141243,0.000045877892,0.00018371991,0.00006467961,0.00011412384],"domain_scores_gemma":[0.99940324,0.00002173508,0.000013131294,0.000505102,0.00001922023,0.00003758345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007971563,0.000046621364,0.000043457138,0.00005561138,0.000044635577,0.00004482269,0.000603895,0.000051881427,0.00003432045],"category_scores_gemma":[0.000021684615,0.000035622557,0.000013199232,0.0001033913,0.000032177722,0.00010363346,0.000042729167,0.00017639884,0.00028110298],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.6028174e-7,0.000063216634,0.0033432576,3.56415e-7,0.0000011835076,0.0000050504677,0.00002187195,0.000002498598,0.02870199,0.8660862,0.0003872836,0.10138662],"study_design_scores_gemma":[0.0006163444,0.0010838285,0.5202852,0.000008781599,0.0000033742567,0.000018533106,0.000023587363,0.05143213,0.27617753,0.07175366,0.07795176,0.0006452726],"about_ca_topic_score_codex":0.000009454763,"about_ca_topic_score_gemma":0.000022077375,"teacher_disagreement_score":0.79433256,"about_ca_system_score_codex":0.0000026848188,"about_ca_system_score_gemma":0.0000118256485,"threshold_uncertainty_score":0.3613104},"labels":[],"label_agreement":null},{"id":"W2155898164","doi":"10.1109/crv.2012.53","title":"Design and Evaluation of a Flexible Interface for Spatial Navigation","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Interface (matter); Human–computer interaction; Robot; Graphical user interface; Point (geometry); Modalities; Variety (cybernetics); Wheelchair; Selection (genetic algorithm); User interface; Mode (computer interface); Embedded system; Simulation; Artificial intelligence","score_opus":0.08421361700953336,"score_gpt":0.34928068003106705,"score_spread":0.2650670630215337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155898164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14305225,0.000091634116,0.85624725,0.00012444239,0.00009532235,0.00016993954,1.6595143e-7,0.00005870395,0.00016027756],"genre_scores_gemma":[0.8352169,2.982085e-7,0.16470876,0.0000058790706,0.000009373454,0.000023685297,4.2441377e-7,0.0000014294922,0.000033269684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962693,0.00003917425,0.000072496136,0.00008258517,0.00009265393,0.00008618517],"domain_scores_gemma":[0.9996649,0.00006436393,0.00004131405,0.00010304529,0.000110703906,0.000015677126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080468465,0.00003356738,0.00004822059,0.00003499547,0.000022679445,0.000009591745,0.00009065349,0.00002838644,0.0000051848456],"category_scores_gemma":[0.000029868343,0.0000285749,0.000008729377,0.00006072192,0.000020926018,0.00016145714,0.000031426378,0.000020145442,0.000002581907],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012760887,0.00009114714,0.0020308862,0.00001618223,0.000016685968,2.839722e-8,0.0009265522,0.00039831342,0.019397147,0.10563301,0.0002706035,0.8712067],"study_design_scores_gemma":[0.00047414968,0.00017775908,0.015765548,0.000016508373,0.000019373747,0.00000470068,0.000039937655,0.59896916,0.37340194,0.010965905,0.00008864691,0.00007635946],"about_ca_topic_score_codex":0.000014447252,"about_ca_topic_score_gemma":4.334145e-7,"teacher_disagreement_score":0.87113035,"about_ca_system_score_codex":0.000012633325,"about_ca_system_score_gemma":0.000015217185,"threshold_uncertainty_score":0.11652506},"labels":[],"label_agreement":null},{"id":"W2156548034","doi":"10.1016/j.visres.2014.08.014","title":"An inverse Yarbus process: Predicting observers’ task from eye movement patterns","year":2014,"lang":"en","type":"article","venue":"Vision Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Hidden Markov model; Eye movement; Task (project management); Inference; Probabilistic logic; Maximum a posteriori estimation; Pattern recognition (psychology); Bayesian inference; Bayesian probability; Process (computing); Computer vision; Machine learning; Mathematics; Maximum likelihood; Statistics","score_opus":0.05068540195553831,"score_gpt":0.38274732296479574,"score_spread":0.3320619210092574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156548034","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90920496,0.0000141350765,0.08805582,0.0015858245,0.00014755387,0.0001611886,0.000008806773,0.00038354285,0.00043817802],"genre_scores_gemma":[0.99464595,0.000008443076,0.0048820814,0.00021603564,0.000094065224,0.000030646253,0.000010847561,0.000012943712,0.00009900168],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971849,0.00037625377,0.00021855083,0.0007194703,0.0009480602,0.0005528218],"domain_scores_gemma":[0.9982066,0.00023434473,0.000063380074,0.0010145842,0.00029904104,0.00018204025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016631895,0.00012905583,0.0001528127,0.00028748554,0.0003293121,0.00025932357,0.0017264568,0.00012308813,0.000051697523],"category_scores_gemma":[0.00029251655,0.000111961366,0.000037563314,0.00059628277,0.00010901138,0.00041177945,0.00052583165,0.0005485743,0.00016116198],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031719297,0.0007434415,0.6807639,0.00007543766,0.000039691888,0.000071286704,0.0024446335,0.00037295624,0.038174115,0.007726155,0.0029664356,0.26659024],"study_design_scores_gemma":[0.00082669064,0.0012057173,0.47668582,0.00020008853,0.000003816625,7.4306155e-7,0.0006923865,0.48637104,0.014896738,0.016292622,0.002528604,0.00029572786],"about_ca_topic_score_codex":0.00066876516,"about_ca_topic_score_gemma":0.00011955822,"teacher_disagreement_score":0.4859981,"about_ca_system_score_codex":0.00006469188,"about_ca_system_score_gemma":0.000065835935,"threshold_uncertainty_score":0.4565652},"labels":[],"label_agreement":null},{"id":"W2158189696","doi":"10.5539/cis.v8n4p77","title":"Fuzzy Logic Based Eye-Brain Controlled Web Access System","year":2015,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Pittsburgh","keywords":"Computer science; Span (engineering); Attention span; Class (philosophy); Fuzzy logic; Artificial intelligence; Cognition; Medicine","score_opus":0.03157256090345187,"score_gpt":0.28773839224600495,"score_spread":0.2561658313425531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158189696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02066727,0.000025412872,0.96115094,0.0034674704,0.00072117825,0.00022325713,0.0000016850518,0.0005480329,0.013194778],"genre_scores_gemma":[0.97017485,0.0000011719296,0.027376384,0.0023856517,0.000032142212,0.000015533573,0.000001555144,0.0000015906321,0.000011123435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986404,0.000043260217,0.000330587,0.0002427089,0.00045651948,0.00028648204],"domain_scores_gemma":[0.9987186,0.000078318,0.0001796097,0.0003970884,0.00044409762,0.00018228522],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0014916974,0.00012628126,0.00022289428,0.0005008336,0.00025111553,0.0011735104,0.0015309021,0.00005199132,7.8209547e-7],"category_scores_gemma":[0.00013143916,0.00009562469,0.000032798675,0.0010681888,0.0003006592,0.0075339954,0.00050615816,0.00010369738,0.000071575974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003582228,0.000041787618,0.0021438927,0.00006117876,0.0000083477535,0.000009174842,0.0007250066,0.0026516395,0.00014774306,0.89185905,0.005495294,0.096821085],"study_design_scores_gemma":[0.0023940383,0.00012483339,0.006430646,0.000028687558,0.0000022721586,0.00002668711,0.00004811141,0.9839975,0.00018706628,0.000905064,0.005688157,0.00016688388],"about_ca_topic_score_codex":0.0000043067967,"about_ca_topic_score_gemma":2.809318e-7,"teacher_disagreement_score":0.9813459,"about_ca_system_score_codex":0.000075990974,"about_ca_system_score_gemma":0.00033867217,"threshold_uncertainty_score":0.9998634},"labels":[],"label_agreement":null},{"id":"W2158363907","doi":"10.1109/tbme.2005.863952","title":"General Theory of Remote Gaze Estimation Using the Pupil Center and Corneal Reflections","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":686,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Pupil; Computer vision; Gaze; Calibration; Computer science; Artificial intelligence; Point source; Optics; Point (geometry); Eye tracking; Mathematics; Physics; Geometry; Statistics","score_opus":0.017145376162604024,"score_gpt":0.2525094646764512,"score_spread":0.23536408851384716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158363907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05722774,0.000023737615,0.94176286,0.00032647743,0.00038058124,0.000054743567,0.000005500466,0.00018272537,0.00003561368],"genre_scores_gemma":[0.87825453,0.000007813653,0.12164074,0.000022373113,0.000031799962,0.000002618566,8.4827695e-7,0.0000072495186,0.00003200377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993054,0.000025237932,0.00018297548,0.00017391266,0.00014700714,0.00016542836],"domain_scores_gemma":[0.99960566,0.00008823736,0.00003776654,0.00020418964,0.000025037238,0.000039125385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016843117,0.000097828386,0.00010370355,0.00018357606,0.00011672676,0.000023968083,0.0001788338,0.000080673635,0.0000036117026],"category_scores_gemma":[0.0000070409274,0.00007550187,0.000044536162,0.00038441873,0.00012961873,0.00008401225,0.0000032897526,0.00021327731,0.0000014616796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023209559,0.00037888964,0.000015199961,0.00006279877,0.00009781019,0.00001841793,0.00030666322,0.36161184,0.17368256,0.029042443,0.000074386015,0.43468577],"study_design_scores_gemma":[0.00022683048,0.000055938268,0.00037009007,0.000040874696,0.000013461244,0.00008200046,0.000007020936,0.9809208,0.016840953,0.0009970336,0.00035633723,0.00008869614],"about_ca_topic_score_codex":0.00004444475,"about_ca_topic_score_gemma":0.0000042809515,"teacher_disagreement_score":0.8210268,"about_ca_system_score_codex":0.00003584148,"about_ca_system_score_gemma":0.00001806821,"threshold_uncertainty_score":0.30788767},"labels":[],"label_agreement":null},{"id":"W2158863665","doi":"10.1109/iembs.2007.4353353","title":"Remote Point-of-Gaze Estimation with Free Head Movements Requiring a Single-Point Calibration","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer science; Head (geology); Single point; Point (geometry); Calibration; Computer vision; Artificial intelligence; Mathematics; Geology; Statistics","score_opus":0.034146561746073056,"score_gpt":0.2637909812326183,"score_spread":0.22964441948654524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158863665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27582285,0.000012531475,0.72004086,0.0008056688,0.000060356513,0.00014455222,6.864915e-7,0.00029928132,0.0028131797],"genre_scores_gemma":[0.79789543,0.0000021663243,0.2019303,0.00008082209,0.00001762134,0.000003346531,0.0000016650181,0.000009452999,0.000059179925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854875,0.000005682961,0.0003544863,0.00043048387,0.00031489288,0.00034572536],"domain_scores_gemma":[0.99888456,0.00004085196,0.00030509787,0.0002866509,0.0004057825,0.00007707204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048171508,0.00017639177,0.0002061812,0.00022762852,0.0000967426,0.00015699868,0.00074211316,0.00010471825,0.0000063834864],"category_scores_gemma":[0.00022960063,0.00015721547,0.00003079204,0.00048277315,0.00013961412,0.0010138347,0.00022064873,0.00018488546,0.0000053305357],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000107544765,0.0002464734,0.011332092,0.00022869583,0.000046338188,0.000022437973,0.0047014775,0.000033698256,0.23775329,0.22432658,0.0002871487,0.52091426],"study_design_scores_gemma":[0.0015787588,0.0013269775,0.018167421,0.0011660221,0.000022555705,0.00006610358,0.00068112183,0.46468455,0.4200491,0.09143222,0.00017696376,0.0006482101],"about_ca_topic_score_codex":0.00004220639,"about_ca_topic_score_gemma":0.000022811777,"teacher_disagreement_score":0.5220726,"about_ca_system_score_codex":0.00007252828,"about_ca_system_score_gemma":0.00006038261,"threshold_uncertainty_score":0.6411061},"labels":[],"label_agreement":null},{"id":"W2159571890","doi":"10.1109/crv.2008.23","title":"Active Vision for Door Localization and Door Opening using Playbot: A Computer Controlled Wheelchair for People with Mobility Impairments","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wheelchair; Computer science; Computer vision; Context (archaeology); Frame (networking); Artificial intelligence; Robot; Human–computer interaction; Simulation; Telecommunications","score_opus":0.016492584062762484,"score_gpt":0.2713697595980618,"score_spread":0.2548771755352993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159571890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33967716,0.000008308613,0.6587644,0.00017529792,0.000070264,0.0011373735,0.000006489099,0.0001468096,0.000013857671],"genre_scores_gemma":[0.7652672,0.000001579787,0.23439199,0.00012117984,0.000024255183,0.00013300261,0.0000064694386,0.000010115543,0.000044192773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875885,0.00003961265,0.00023454468,0.0005331412,0.00013668645,0.0002971681],"domain_scores_gemma":[0.9990712,0.0002357794,0.0001471143,0.00025080715,0.00022941739,0.000065691806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024658194,0.00018054618,0.00040510547,0.00010671954,0.00044671734,0.000081400285,0.00024799962,0.00008926587,0.0000023916039],"category_scores_gemma":[0.000034110213,0.00013081008,0.00006423225,0.00020117046,0.00009119165,0.00043273898,0.00013645455,0.00006749774,7.0376115e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.034626454,0.010504078,0.32969728,0.001867839,0.0029112003,0.00018647203,0.030730998,0.0658593,0.031245137,0.076207384,0.010607195,0.40555665],"study_design_scores_gemma":[0.00839229,0.0011663294,0.014008497,0.00004254528,0.000020571444,0.00006750441,0.000051454117,0.97411984,0.0013501343,0.00043789242,0.00014533533,0.00019758413],"about_ca_topic_score_codex":0.00004371996,"about_ca_topic_score_gemma":0.00011370352,"teacher_disagreement_score":0.9082606,"about_ca_system_score_codex":0.000053074473,"about_ca_system_score_gemma":0.00006682391,"threshold_uncertainty_score":0.533428},"labels":[],"label_agreement":null},{"id":"W2160991763","doi":"10.1177/0539018408092574","title":"Attentive user interfaces: the surveillance and sousveillance of gaze-aware objects","year":2008,"lang":"en","type":"article","venue":"Social Science Information","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Human–computer interaction; Computer science; Gaze; Bridge (graph theory); User interface; Key (lock); User experience design; Computer security; Artificial intelligence","score_opus":0.010113165491729753,"score_gpt":0.2272529277670074,"score_spread":0.21713976227527765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160991763","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94531494,0.000026149975,0.048096333,0.0014812042,0.00023061335,0.00014514536,0.000005876044,0.0001412597,0.0045584743],"genre_scores_gemma":[0.9991866,0.00001734344,0.0006314856,0.00012872343,0.00001335901,0.0000048817883,7.7329315e-7,0.0000011555126,0.000015719757],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991547,0.000025483354,0.00018309346,0.00012160768,0.0003137503,0.00020137774],"domain_scores_gemma":[0.99928665,0.00004819467,0.00018852815,0.00021911717,0.00023396117,0.000023539402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045803355,0.00007015203,0.00010016859,0.000092857816,0.0006193082,0.00007527134,0.00090534484,0.000040503255,0.000001391573],"category_scores_gemma":[0.00014909211,0.00004958191,0.000021025835,0.0007887601,0.0011321632,0.0014582094,0.00029265467,0.0000998635,0.000012914601],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036256897,0.00010482568,0.25122985,0.000115351744,0.000043973167,0.0000059117638,0.1838315,0.0001391699,0.008816627,0.27272746,0.003750984,0.27919808],"study_design_scores_gemma":[0.00037129025,0.00011129058,0.97624356,0.000026064057,0.0000020189539,0.000041380496,0.0033414604,0.0070649404,0.007945478,0.0026915579,0.0019003025,0.00026068112],"about_ca_topic_score_codex":0.000031855925,"about_ca_topic_score_gemma":0.00000971646,"teacher_disagreement_score":0.7250137,"about_ca_system_score_codex":0.000036856713,"about_ca_system_score_gemma":0.000098320925,"threshold_uncertainty_score":0.47632807},"labels":[],"label_agreement":null},{"id":"W2161771389","doi":"10.1109/iembs.2009.5334183","title":"An automatic calibration procedure for remote eye-gaze tracking systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Gaze; Eye tracking; Computer vision; Calibration; Artificial intelligence; Tracking (education); Tracking system; Kalman filter; Mathematics; Psychology","score_opus":0.02025456427360126,"score_gpt":0.28827088829481406,"score_spread":0.2680163240212128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161771389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04753626,0.00004357915,0.94752085,0.0022854998,0.00016296408,0.0003266537,8.9326085e-7,0.0016833255,0.00043999264],"genre_scores_gemma":[0.88231975,8.048952e-7,0.11715219,0.00024567984,0.000055312834,0.000007699339,0.0000034813027,0.0000061162514,0.00020898262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901867,0.000031782307,0.00021820082,0.00034945118,0.00013177906,0.0002501143],"domain_scores_gemma":[0.99932647,0.00003409178,0.000089753885,0.0004183387,0.0000752354,0.000056127246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022737727,0.00011935758,0.00015597194,0.00010463929,0.00013340914,0.00026101706,0.00054537493,0.00010674742,0.0000018870321],"category_scores_gemma":[0.00005172687,0.000099773206,0.00003941772,0.00024034604,0.000017537,0.00058768457,0.000014616461,0.00008311059,0.000006623194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000048564534,0.00018303857,0.00033876285,0.000114518094,0.0000152265,0.000009799948,0.0004815736,0.0009506915,0.018107032,0.3505596,0.0015452624,0.62768966],"study_design_scores_gemma":[0.00018920461,0.00028721182,0.006130473,0.00004926862,0.0000049276314,0.00001718611,0.000040536197,0.98297614,0.0036945788,0.0061730435,0.00028834178,0.00014906592],"about_ca_topic_score_codex":0.000010600297,"about_ca_topic_score_gemma":0.0000047360304,"teacher_disagreement_score":0.98202544,"about_ca_system_score_codex":0.000029411909,"about_ca_system_score_gemma":0.000045574332,"threshold_uncertainty_score":0.40686333},"labels":[],"label_agreement":null},{"id":"W2161861544","doi":"10.5430/air.v3n3p35","title":"Predicting reading comprehension scores from eye movements using artificial neural networks and fuzzy output error","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Task (project management); Eye tracking; Mean squared error; Gaze; Machine learning; Reading (process); Fuzzy logic; Word error rate; Eye movement; Feature (linguistics); Pattern recognition (psychology); Speech recognition; Statistics; Mathematics; Engineering","score_opus":0.19902949490260544,"score_gpt":0.3988840134134479,"score_spread":0.1998545185108425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161861544","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58553207,0.00007378441,0.4131676,0.00039753848,0.00036681874,0.00016445843,0.0000019313948,0.00017905183,0.00011677518],"genre_scores_gemma":[0.9921013,0.00001275239,0.0073465295,0.00007151809,0.00040835238,0.00001235683,0.000005103517,0.000022815002,0.000019256022],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623543,0.00054169336,0.00057482906,0.00093420886,0.0007311207,0.0009827255],"domain_scores_gemma":[0.99776465,0.0008865212,0.00013422208,0.0006805373,0.00033054606,0.00020351686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001928475,0.00023468243,0.00030766986,0.0004128104,0.0009280442,0.0005495932,0.0009945704,0.00020045319,0.000009463484],"category_scores_gemma":[0.0006333257,0.00022674761,0.000063097184,0.0008861461,0.00054108235,0.00041037294,0.0009664128,0.0008835967,0.000044563018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007326279,0.00018472415,0.03268442,0.00001858738,0.00003780615,0.000038732833,0.00072759256,0.022423504,0.034013033,0.11208966,0.00002849656,0.7976802],"study_design_scores_gemma":[0.000027212187,0.00016139205,0.003932844,0.000085203195,0.000004863326,0.0000032275705,0.0003602152,0.8918392,0.01657545,0.086785235,0.000031887383,0.00019326039],"about_ca_topic_score_codex":0.001518408,"about_ca_topic_score_gemma":0.00020275133,"teacher_disagreement_score":0.8694157,"about_ca_system_score_codex":0.00008548344,"about_ca_system_score_gemma":0.000042352818,"threshold_uncertainty_score":0.9246499},"labels":[],"label_agreement":null},{"id":"W2167584018","doi":"10.1109/tnsre.2007.891385","title":"An Intelligent Powered Wheelchair to Enable Mobility of Cognitively Impaired Older Adults: An Anticollision System","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Wheelchair; Physical medicine and rehabilitation; Computer science; Cognition; Object (grammar); Simulation; Human–computer interaction; Psychology; Medicine; Artificial intelligence; Neuroscience","score_opus":0.007369073360531972,"score_gpt":0.23626637168782874,"score_spread":0.22889729832729677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167584018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4884909,0.000021181933,0.51039505,0.000036835263,0.00040761018,0.00038039812,0.000008552071,0.00025623222,0.000003242269],"genre_scores_gemma":[0.99266726,0.0000018034792,0.007227319,0.000006859621,0.000023098028,0.00005093965,0.0000011282812,0.000015875317,0.0000057253446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984429,0.00007998151,0.0005066379,0.0004786087,0.00022206479,0.00026981792],"domain_scores_gemma":[0.99877584,0.00029973342,0.00008839087,0.00040407458,0.0002425533,0.00018943303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006536275,0.00019046455,0.0002825683,0.00040805343,0.00011609385,0.000056412155,0.0002211821,0.000117638774,8.7909535e-7],"category_scores_gemma":[0.000020570556,0.00017190498,0.00006990474,0.0004935826,0.00004672891,0.00041764847,0.0000030621077,0.00015555279,0.0000019558581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037060145,0.0020760456,0.0010350968,0.00263236,0.00009749346,0.00002544297,0.013885966,0.7171902,0.13188198,0.006313355,0.000009955007,0.1244815],"study_design_scores_gemma":[0.00075939717,0.0030494246,0.039279696,0.0010586765,0.000019030625,0.000048314338,0.0056414735,0.91832703,0.031354245,0.000010383438,0.000032232012,0.0004200874],"about_ca_topic_score_codex":0.00019921541,"about_ca_topic_score_gemma":0.000013384294,"teacher_disagreement_score":0.5041763,"about_ca_system_score_codex":0.000104554514,"about_ca_system_score_gemma":0.000014618072,"threshold_uncertainty_score":0.70100814},"labels":[],"label_agreement":null},{"id":"W2167732087","doi":"10.1109/icorr.2005.1501078","title":"The Laser Line Object Detection Method in an Anti-Collision System for Powered Wheelchair","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute","funders":"","keywords":"Wheelchair; Collision; Line (geometry); Computer science; Object (grammar); Object detection; Computer vision; Simulation; Artificial intelligence; Pattern recognition (psychology); Computer security; Mathematics","score_opus":0.017453064835164865,"score_gpt":0.2975543212558434,"score_spread":0.28010125642067857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167732087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1471619,0.00003522937,0.85036093,0.0012540815,0.00024720936,0.00023293389,0.0000010007604,0.00047838895,0.00022831897],"genre_scores_gemma":[0.8774982,0.000003195143,0.12218305,0.00005216866,0.000054612025,0.00003802268,6.244858e-7,0.0000065214053,0.00016358482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899405,0.00011296486,0.00021463864,0.00032304562,0.00010873979,0.00024656963],"domain_scores_gemma":[0.99915737,0.00021840887,0.00006818694,0.00043842194,0.00008475204,0.000032837215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008940768,0.00010115993,0.00013842108,0.00012522495,0.0001988938,0.000104899555,0.00051531737,0.00009721552,6.811017e-7],"category_scores_gemma":[0.000056769684,0.0000654669,0.000047353125,0.00034509113,0.000020805479,0.0002457212,0.00007301524,0.000117428455,0.000012639159],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035374745,0.00014086507,0.0003918845,0.000020211664,0.00001257981,0.000005165922,0.00019406785,0.0028679671,0.024212435,0.032417417,0.000107779415,0.93959427],"study_design_scores_gemma":[0.0006684282,0.00031269144,0.0053466395,0.000025560794,0.0000040609307,0.000021819154,0.00019093425,0.64434284,0.34327772,0.00061266095,0.005037383,0.0001592751],"about_ca_topic_score_codex":0.000040247032,"about_ca_topic_score_gemma":0.0009388004,"teacher_disagreement_score":0.939435,"about_ca_system_score_codex":0.00008385946,"about_ca_system_score_gemma":0.00002237761,"threshold_uncertainty_score":0.26696628},"labels":[],"label_agreement":null},{"id":"W2169856901","doi":"10.1109/iembs.2008.4649261","title":"Unused information: Detecting and applying eye contact data in computerized healthcare systems","year":2008,"lang":"en","type":"review","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Gaze; Computer science; Human–computer interaction; User interface; Eye contact; Artificial intelligence","score_opus":0.10749048533215036,"score_gpt":0.3477652022926445,"score_spread":0.24027471696049416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169856901","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000044422163,0.8243221,0.173733,0.00008260772,0.00035898248,0.00086241553,0.000018697685,0.0004821831,0.00013558564],"genre_scores_gemma":[0.0007928613,0.9852986,0.013590129,0.00005725066,0.00004280784,0.000104409824,0.00009189077,0.000012802118,0.000009263249],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977474,0.00021686305,0.0009007447,0.00056697946,0.00020817017,0.00035979165],"domain_scores_gemma":[0.9977127,0.00028755327,0.0004992327,0.0013644558,0.000058024103,0.00007806917],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043967136,0.000343947,0.0013729652,0.0005275295,0.00016173293,0.0003006551,0.0019251755,0.0003227041,5.189408e-7],"category_scores_gemma":[0.00007086371,0.00028271467,0.00006123152,0.0007085321,0.00003871143,0.00086318166,0.0012834596,0.00059539784,0.000024513634],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.1803963e-7,0.000005878221,0.00002922712,0.004288752,0.000017557677,0.000021402857,0.00004290782,5.0237935e-7,5.4988444e-8,0.003155574,0.00004529152,0.9923924],"study_design_scores_gemma":[0.0003278925,0.00003591373,0.000044022185,0.0068817353,0.000013236921,0.00038684602,0.000030736934,0.008508586,1.7683094e-7,0.000011079303,0.9833913,0.00036851654],"about_ca_topic_score_codex":0.00038344195,"about_ca_topic_score_gemma":0.000029480687,"teacher_disagreement_score":0.99202394,"about_ca_system_score_codex":0.00010787203,"about_ca_system_score_gemma":0.00023973777,"threshold_uncertainty_score":0.9999625},"labels":[],"label_agreement":null},{"id":"W2171866551","doi":"10.1145/985921.986005","title":"Attentive display","year":2004,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Gaze; Visualization; Human–computer interaction; Point (geometry); Display device; Computer graphics (images); Point of interest; Computer vision; Artificial intelligence","score_opus":0.009593702746351442,"score_gpt":0.23016979708676513,"score_spread":0.2205760943404137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171866551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07893082,0.000010036419,0.9033805,0.00399588,0.00010947057,0.000023439536,1.7456082e-7,0.00045088536,0.0130988015],"genre_scores_gemma":[0.9483811,8.4372175e-7,0.05091448,0.00023459207,0.000008231402,0.0000025341496,1.9226442e-7,0.0000016003886,0.0004564074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996126,0.00000434925,0.00004858742,0.00015477018,0.000059880018,0.00011981283],"domain_scores_gemma":[0.9997214,0.000009440377,0.000013775643,0.00021425623,0.000018901543,0.000022224865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003732584,0.00004350143,0.000045691457,0.000037446054,0.000043166296,0.000027024604,0.00036196024,0.000025058358,0.0000071848654],"category_scores_gemma":[0.0000086726,0.000033299828,0.000021799671,0.0001470189,0.00003270813,0.00010774095,0.00009112311,0.000054768283,0.0003260914],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[2.0881339e-7,0.000025884723,0.000806496,5.1902606e-7,0.0000032857215,0.000011990134,0.00004522599,0.000014547,0.000748502,0.98779356,0.00013075255,0.010419037],"study_design_scores_gemma":[0.002010897,0.0003775004,0.41938743,0.00004592424,0.000009895144,0.00015021881,0.000113631824,0.002270917,0.10204575,0.45408523,0.018747514,0.00075509376],"about_ca_topic_score_codex":0.000018877427,"about_ca_topic_score_gemma":0.000007791375,"teacher_disagreement_score":0.8694503,"about_ca_system_score_codex":0.000017318845,"about_ca_system_score_gemma":0.000014079086,"threshold_uncertainty_score":0.4191354},"labels":[],"label_agreement":null},{"id":"W2192411834","doi":"10.1609/aaai.v29i1.9485","title":"Constructing Models of User and Task Characteristics from Eye Gaze Data for User-Adaptive Information Highlighting","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Visualization; Gaze; Human–computer interaction; Classifier (UML); Task (project management); Machine learning; User interface; Interface (matter); Eye tracking; Artificial intelligence; Data visualization; Information visualization","score_opus":0.1471941926282617,"score_gpt":0.30105281607366446,"score_spread":0.15385862344540277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2192411834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4184115,0.00001336991,0.5779237,0.0016379246,0.00033784469,0.00038288432,0.00019652877,0.000099979305,0.0009962807],"genre_scores_gemma":[0.9546844,0.000009528632,0.045191616,0.000045803794,0.00003228303,0.000010310793,0.000007828883,0.0000059838976,0.000012241841],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858505,0.000011571329,0.00057216745,0.00034211765,0.00027332333,0.00021579377],"domain_scores_gemma":[0.9976372,0.00015468881,0.0007363734,0.0003778983,0.0010316615,0.00006217312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052918197,0.00016256746,0.0002887139,0.00010677583,0.00009741621,0.00016211705,0.0017065426,0.00009992903,0.0000016090635],"category_scores_gemma":[0.000890148,0.00012929131,0.000034252836,0.00024944302,0.0003065123,0.0013213024,0.00074750034,0.00018446393,0.0000043025857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055867065,0.000032816937,0.0008584879,0.000026395337,0.000018232065,7.996574e-8,0.0010854078,0.000011822515,0.004370692,0.91828233,0.00006398706,0.075193875],"study_design_scores_gemma":[0.000094758165,0.00023481232,0.0005406504,0.000363656,0.000032515494,0.000002580729,0.0029955483,0.58641887,0.1670093,0.24187791,0.00017708598,0.0002523026],"about_ca_topic_score_codex":0.000082244085,"about_ca_topic_score_gemma":0.0000069260864,"teacher_disagreement_score":0.6764044,"about_ca_system_score_codex":0.000030452045,"about_ca_system_score_gemma":0.00009973025,"threshold_uncertainty_score":0.5272346},"labels":[],"label_agreement":null},{"id":"W2205448886","doi":"10.20380/gi2015.35","title":"Palpebrae superioris: exploring the design space of eyelid gestures","year":2015,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gesture; Computer science; Eyelid; Perception; Modality (human–computer interaction); Human–computer interaction; Space (punctuation); Computer vision; Artificial intelligence; Psychology; Medicine; Neuroscience","score_opus":0.15743620817536452,"score_gpt":0.2791819382583795,"score_spread":0.12174573008301498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205448886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033111673,0.0015091888,0.94375265,0.019916723,0.0005465646,0.00031423775,0.0000047888307,0.0003641516,0.00048003427],"genre_scores_gemma":[0.70928746,0.000058424575,0.29005116,0.0004260862,0.000048907757,0.00005435912,0.0000039711626,0.000011434238,0.00005819339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853504,0.0002721234,0.00030607634,0.0002744145,0.0003221891,0.0002901434],"domain_scores_gemma":[0.9960067,0.00037322196,0.00015128267,0.0030811368,0.00028645006,0.000101182806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006067178,0.00017550404,0.00022901277,0.00003756743,0.0005915302,0.00009697058,0.0046727494,0.000062072264,0.0000013338878],"category_scores_gemma":[0.00003161432,0.00014213216,0.00011002549,0.0004506828,0.0003606869,0.0002258955,0.0012827982,0.00041183992,0.000002571417],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004544463,0.00034011714,0.004109753,0.00005856546,0.00046691071,0.000011995086,0.031286225,0.005546797,0.0011851075,0.4879522,0.42637008,0.042667698],"study_design_scores_gemma":[0.0029836192,0.0006146897,0.08842828,0.0003787685,0.00017974772,0.00012569997,0.010605482,0.26227573,0.013058547,0.027238255,0.5915635,0.0025476608],"about_ca_topic_score_codex":0.038966812,"about_ca_topic_score_gemma":0.018512364,"teacher_disagreement_score":0.6761758,"about_ca_system_score_codex":0.00022424267,"about_ca_system_score_gemma":0.0006767232,"threshold_uncertainty_score":0.9993972},"labels":[],"label_agreement":null},{"id":"W2206769161","doi":"10.1109/iccke.2015.7365830","title":"Designing a pervasive eye movement-based system for ALS and paralyzed patients","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Simon Fraser University","keywords":"Computer science; Gesture; Human–computer interaction; Interface (matter); Eye movement; User interface; Movement (music); Amyotrophic lateral sclerosis; Computer vision; Artificial intelligence; Medicine","score_opus":0.03154423190298828,"score_gpt":0.25700387949476133,"score_spread":0.22545964759177306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2206769161","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17541131,0.000023935241,0.8231017,0.00035851428,0.00011027823,0.00026151707,0.0000023175928,0.00033806972,0.0003923236],"genre_scores_gemma":[0.8351231,1.4168052e-7,0.16451935,0.00021151212,0.000007848069,0.00005820879,0.0000021303786,0.0000048670777,0.00007280248],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992157,0.000035141424,0.00014035341,0.00027698037,0.00012417448,0.00020759873],"domain_scores_gemma":[0.9993689,0.00008138909,0.00007041945,0.00021969926,0.0001781274,0.00008144184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023609535,0.000097790304,0.00014471135,0.000076029675,0.000073541545,0.000071256894,0.0002713365,0.000049870494,5.3594096e-7],"category_scores_gemma":[0.00007627132,0.00007941969,0.000028875444,0.00009414382,0.000030282745,0.000119766795,0.00007281135,0.00003844636,0.0000082101415],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017209699,0.0007853196,0.5749397,0.0004646861,0.00022213103,0.000038131577,0.0023015,0.00054584094,0.009281223,0.3026775,0.009238207,0.09933364],"study_design_scores_gemma":[0.027271228,0.0043084905,0.10109223,0.0005371061,0.000121354824,0.000006488017,0.002193566,0.48240608,0.35965338,0.016944282,0.003382504,0.002083265],"about_ca_topic_score_codex":0.000020525993,"about_ca_topic_score_gemma":0.0000024698322,"teacher_disagreement_score":0.65971184,"about_ca_system_score_codex":0.0000482344,"about_ca_system_score_gemma":0.000044225275,"threshold_uncertainty_score":0.3238641},"labels":[],"label_agreement":null},{"id":"W2216719796","doi":"10.1109/iros.2015.7354082","title":"Automatically characterizing driving activities onboard smart wheelchairs from accelerometer data","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wheelchair; Accelerometer; Computer science; Classifier (UML); Random forest; Machine learning; Artificial intelligence; Human–computer interaction; World Wide Web","score_opus":0.09207690936223566,"score_gpt":0.2856834849862595,"score_spread":0.19360657562402384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2216719796","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5761102,0.000018341465,0.41443655,0.0036726615,0.00054300675,0.000078148216,0.000010884803,0.0015752405,0.0035549887],"genre_scores_gemma":[0.78781766,0.0000022864667,0.21142507,0.00038238257,0.00006615143,0.0000070964757,0.000021499149,0.000012123125,0.00026574737],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983147,0.00006975611,0.0002555472,0.0006529372,0.0003206406,0.00038645783],"domain_scores_gemma":[0.9977113,0.00020521783,0.00010106382,0.0017773808,0.00005063366,0.00015440948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039062227,0.00019094191,0.00027425928,0.00014889071,0.00008941809,0.00038157206,0.002684913,0.00010982152,0.00004539621],"category_scores_gemma":[0.00018745371,0.00016345644,0.000037604532,0.00023743983,0.000092045266,0.0015388604,0.0019946946,0.00023535496,0.00020153994],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026336884,0.0007376743,0.117342696,0.00004298363,0.00048081548,0.0002459664,0.003957441,0.000010602572,0.10480825,0.06942212,0.031798612,0.6711265],"study_design_scores_gemma":[0.0022651607,0.00051191804,0.46652886,0.00033157424,0.00008039086,0.0000868458,0.0011557785,0.4065532,0.053865038,0.023453658,0.042829603,0.0023379484],"about_ca_topic_score_codex":0.00009614818,"about_ca_topic_score_gemma":0.00004441187,"teacher_disagreement_score":0.66878855,"about_ca_system_score_codex":0.00005052749,"about_ca_system_score_gemma":0.00009057408,"threshold_uncertainty_score":0.66655606},"labels":[],"label_agreement":null},{"id":"W2217359636","doi":"10.1109/icvr.2015.7358615","title":"Using a 3D hand motion controller in a virtual power wheelchair simulator for navigation-reaching","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Jewish Rehabilitation Hospital","funders":"","keywords":"Joystick; Wheelchair; Virtual reality; Computer science; Simulation; Task (project management); Driving simulator; Motion (physics); Controller (irrigation); Interface (matter); Human–computer interaction; Artificial intelligence; Engineering","score_opus":0.05961640283056261,"score_gpt":0.3093779158927465,"score_spread":0.2497615130621839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2217359636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3441161,0.00001512355,0.65509707,0.00024735907,0.00013274848,0.00014355501,9.135532e-7,0.00011800341,0.0001291609],"genre_scores_gemma":[0.9542973,6.120987e-8,0.045454588,0.00012217656,0.000022895938,0.000012868753,0.000001444065,0.0000069973266,0.00008164756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913615,0.000040357627,0.00019229247,0.00027880436,0.00013267688,0.00021971833],"domain_scores_gemma":[0.99947184,0.00010354366,0.000065220236,0.00018259347,0.0001189863,0.00005779839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004433941,0.000102531536,0.00016336136,0.00014247274,0.0000842302,0.00010638345,0.00024443705,0.00008848762,0.0000015931716],"category_scores_gemma":[0.00015341493,0.00008933429,0.000038279766,0.00019772237,0.000043161755,0.00032242038,0.00007048995,0.00011119646,0.000008073903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021822439,0.00084482506,0.024020126,0.000029421662,0.00010420377,0.000046006582,0.008855572,0.19569676,0.035808396,0.5832089,0.00068018475,0.15048738],"study_design_scores_gemma":[0.0020705962,0.00013307194,0.001004056,0.000027958473,0.000003190399,0.000007644931,0.00011529358,0.9901639,0.00087045453,0.005112307,0.00035951624,0.0001319973],"about_ca_topic_score_codex":0.00007837284,"about_ca_topic_score_gemma":0.000018638173,"teacher_disagreement_score":0.79446715,"about_ca_system_score_codex":0.00010091529,"about_ca_system_score_gemma":0.00005325234,"threshold_uncertainty_score":0.36429465},"labels":[],"label_agreement":null},{"id":"W2220206302","doi":"10.1007/s00221-015-4413-7","title":"Anticipatory gaze strategies when grasping moving objects","year":2015,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Gaze; GRASP; Computer vision; Fixation (population genetics); Artificial intelligence; Kinematics; Fixation point; Computer science; Movement (music); Eye movement; Psychology; Index finger; Communication; Physics","score_opus":0.2076491630485377,"score_gpt":0.43981856820215537,"score_spread":0.23216940515361767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2220206302","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91165555,0.0028929096,0.039665535,0.004653596,0.0005968561,0.00042123164,0.0000010609491,0.0010503705,0.039062876],"genre_scores_gemma":[0.99009526,0.0000019505596,0.009139429,0.00016487055,0.00008154485,0.000053436874,0.000001459601,0.000016442827,0.00044562644],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973343,0.0003643975,0.00018990623,0.00054462923,0.00081798394,0.0007488096],"domain_scores_gemma":[0.99870086,0.0002488687,0.000038869028,0.00062283815,0.00016498468,0.00022358405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016698798,0.00015164551,0.0001715546,0.00036179935,0.00028047385,0.00043514575,0.0012817649,0.00010132291,0.000022812646],"category_scores_gemma":[0.00030084542,0.00014609209,0.000046173245,0.00049356587,0.00035947358,0.0006462549,0.00091776147,0.00048789906,0.00022197641],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037753834,0.00060903025,0.0025859298,0.000034634755,0.000054057902,0.00053669536,0.025538456,0.00007714882,0.52074796,0.39355436,0.044432398,0.0117915915],"study_design_scores_gemma":[0.0021499163,0.0018689111,0.0072961147,0.00022092005,0.0000024340654,0.00010630199,0.060414508,0.01811201,0.8411879,0.060280498,0.007389958,0.00097055937],"about_ca_topic_score_codex":0.0002106463,"about_ca_topic_score_gemma":0.000011003456,"teacher_disagreement_score":0.33327386,"about_ca_system_score_codex":0.00022039228,"about_ca_system_score_gemma":0.00035081973,"threshold_uncertainty_score":0.5957462},"labels":[],"label_agreement":null},{"id":"W2223614028","doi":"10.5753/cbie.wcbie.2015.390","title":"Aplicação para reconhecimento dinâmico de emoções em ambientes virtuais de aprendizagem","year":2015,"lang":"pt","type":"article","venue":"Anais ... Workshops do Congresso Brasileiro de Informática na Educação","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Bureau for International Education","keywords":"Humanities; Computer science; Philosophy","score_opus":0.05693513672404247,"score_gpt":0.3196182597092658,"score_spread":0.2626831229852233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2223614028","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8700921,0.004729322,0.10769724,0.0054450915,0.003729435,0.0012398772,0.00014853531,0.0018432916,0.0050751045],"genre_scores_gemma":[0.97947603,0.00051891734,0.011063617,0.003221175,0.0005525307,0.00027130375,0.00005896454,0.0001739417,0.004663506],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99011,0.00061283814,0.0019197534,0.0019913036,0.0012279027,0.004138234],"domain_scores_gemma":[0.9910111,0.0013930526,0.0011394041,0.0031367461,0.00090056006,0.00241913],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0028502347,0.0015725424,0.0016136161,0.0012519587,0.00087517686,0.0028570658,0.005005392,0.0015470731,0.00038100372],"category_scores_gemma":[0.002793938,0.001661482,0.000697846,0.0026223576,0.0010376188,0.0020889116,0.0017596663,0.002443876,0.0010577317],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038937584,0.0022456085,0.42425537,0.0003122051,0.0011266848,0.0007491318,0.030567097,0.0004539414,0.0005323673,0.01708329,0.1599595,0.36232543],"study_design_scores_gemma":[0.016798278,0.004688013,0.6115201,0.0069661094,0.0018694703,0.0044813445,0.06954713,0.15073822,0.021903204,0.02282271,0.075956106,0.012709317],"about_ca_topic_score_codex":0.00037529255,"about_ca_topic_score_gemma":0.00023268852,"teacher_disagreement_score":0.3496161,"about_ca_system_score_codex":0.0016703103,"about_ca_system_score_gemma":0.0024529868,"threshold_uncertainty_score":0.99985754},"labels":[],"label_agreement":null},{"id":"W2223947898","doi":"","title":"Regression Based Gaze Estimation with Natural Head Movement","year":2015,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Gaze; Computer vision; Artificial intelligence; Computer science; Eye tracking; Tracking (education); Pupil; Movement (music); Head (geology); Tracking system; Software; Eye tracking on the ISS; Kalman filter","score_opus":0.027559826370393475,"score_gpt":0.30250569305004393,"score_spread":0.2749458666796505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2223947898","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90640974,0.00039022282,0.0125343315,0.0017424677,0.0018701889,0.0009957786,0.000007600008,0.00109364,0.07495603],"genre_scores_gemma":[0.96641046,0.000014270111,0.002350567,0.000013896144,0.000090476315,0.0000073554743,0.000121683006,0.00003406205,0.030957213],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9961044,0.00047338573,0.00023845187,0.0010382202,0.0013878043,0.00075771764],"domain_scores_gemma":[0.99742377,0.00016803251,0.0002798428,0.0011264136,0.0007273627,0.00027459863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005892931,0.000376718,0.0004076262,0.0016809815,0.00066310464,0.00027370287,0.0017879589,0.00037050003,0.000007562456],"category_scores_gemma":[0.00008150525,0.0003367359,0.00012014251,0.001760861,0.00023172179,0.0005116291,0.00021274012,0.0015626361,0.000038348044],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.014504861,0.0046807984,0.10282996,0.0036346086,0.0024762766,0.04682661,0.0063970843,0.0045761513,0.08660556,0.24495013,0.07094766,0.4115703],"study_design_scores_gemma":[0.010819579,0.00874654,0.34038362,0.0059640445,0.00031709488,0.00017392763,0.0050554136,0.24327926,0.31169808,0.020405207,0.048239645,0.004917583],"about_ca_topic_score_codex":0.0026560298,"about_ca_topic_score_gemma":0.007364686,"teacher_disagreement_score":0.40665272,"about_ca_system_score_codex":0.0012198791,"about_ca_system_score_gemma":0.0018947794,"threshold_uncertainty_score":0.99990845},"labels":[],"label_agreement":null},{"id":"W2256387306","doi":"","title":"Development of a low-cost, portable, tablet-based eye tracking system for children with impairments","year":2015,"lang":"en","type":"article","venue":"International Convention on Rehabilitation Engineering & Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital","funders":"","keywords":"Eye tracking; Computer science; Tracking system; Computer vision; Robustness (evolution); Eye tracking on the ISS; Artificial intelligence; Gaze; Eye movement; Video tracking; Tracking (education); Human–computer interaction; Video processing","score_opus":0.012007786212487057,"score_gpt":0.26314918231519535,"score_spread":0.2511413961027083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256387306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29597393,0.000010160438,0.701813,0.00064671773,0.00032526997,0.0004849473,0.00001679751,0.00064726814,0.00008192516],"genre_scores_gemma":[0.7784882,1.1253722e-7,0.22099273,0.0000120270515,0.000014305376,0.00039774968,0.00004614612,0.000017899105,0.00003080191],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998285,0.000024536417,0.00053992565,0.000491947,0.00039471398,0.00026387416],"domain_scores_gemma":[0.9981946,0.0001507783,0.00036879812,0.00033843375,0.0008817874,0.00006564514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005289203,0.00022292203,0.0002902192,0.0010121261,0.000067825444,0.00003903422,0.0006095694,0.00015978902,0.0000029610758],"category_scores_gemma":[0.00041994968,0.00021609078,0.000083636405,0.00058245304,0.00009750028,0.0002068869,0.00006226303,0.00017575672,0.000008989564],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005442807,0.0021357026,0.21138644,0.0006014647,0.0011247551,0.000031568292,0.0006109386,0.06701935,0.01025887,0.59344316,0.00036493075,0.11247853],"study_design_scores_gemma":[0.011342142,0.0029624212,0.68304366,0.0022933835,0.000075063836,0.0000705971,0.00092956354,0.19748586,0.090122156,0.00097539247,0.009330456,0.0013692809],"about_ca_topic_score_codex":0.0000038674416,"about_ca_topic_score_gemma":0.0000026088003,"teacher_disagreement_score":0.5924678,"about_ca_system_score_codex":0.0005210894,"about_ca_system_score_gemma":0.00019191575,"threshold_uncertainty_score":0.88119256},"labels":[],"label_agreement":null},{"id":"W2262976093","doi":"","title":"Designing Intelligent Wheelchairs: Reintegrating AI","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Wheelchair; Engineering management; Human–computer interaction; Artificial intelligence; Knowledge management; Engineering; World Wide Web","score_opus":0.11807837501570881,"score_gpt":0.3369628519752738,"score_spread":0.218884476959565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2262976093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023479438,0.000022638626,0.97296536,0.011361908,0.00034891212,0.00025824754,0.0000022259833,0.0004567522,0.01223603],"genre_scores_gemma":[0.95358974,0.000009178008,0.044656444,0.0013096802,0.0000894806,0.00009541669,0.000004263245,0.000011757128,0.00023402103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975433,0.00011354232,0.00054579094,0.0006544469,0.00070933875,0.00043359917],"domain_scores_gemma":[0.9976944,0.00032819336,0.0001726989,0.0003765396,0.0013038862,0.0001243243],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005449907,0.0002547578,0.00021939928,0.00035147942,0.00028389329,0.00055969745,0.0012156869,0.00015204106,0.00045780404],"category_scores_gemma":[0.00076734973,0.00023214522,0.00008688012,0.00061780616,0.0001738738,0.00060386554,0.00016076175,0.00056428905,0.0029554265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003056568,0.00007128224,0.000030550236,0.0000026944294,0.000007992218,0.0000030440824,0.00015817287,0.00046903183,0.007888692,0.76792747,0.0004458818,0.22299215],"study_design_scores_gemma":[0.000015938494,0.00015857324,0.00022583865,0.00007646313,0.0000019884278,0.000008436513,0.0002752177,0.25470352,0.18872206,0.55522645,0.000304529,0.00028099035],"about_ca_topic_score_codex":0.00007763071,"about_ca_topic_score_gemma":0.00002956388,"teacher_disagreement_score":0.9512418,"about_ca_system_score_codex":0.00014840359,"about_ca_system_score_gemma":0.00024745354,"threshold_uncertainty_score":0.9978209},"labels":[],"label_agreement":null},{"id":"W2274218579","doi":"","title":"An Eye-Gaze Tracking and Human Computer Interface System for People with ALS and other Locked-in Diseases","year":2012,"lang":"en","type":"article","venue":"Journal of Medical and Biological Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Eye tracking; Computer science; Computer vision; Eye tracking on the ISS; Artificial intelligence; Eye movement; Gaze; Tracking system; Tracking (education); User interface; Interface (matter); Graphical user interface; Kalman filter","score_opus":0.016986884728596056,"score_gpt":0.26863370852586355,"score_spread":0.2516468237972675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274218579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6731202,0.0007034747,0.325814,0.00024638372,0.00005288455,0.000031762254,0.0000010064341,0.00002923517,0.0000010617816],"genre_scores_gemma":[0.99404263,0.000018213575,0.00575177,0.000038126458,0.00014300809,0.0000019566457,1.7778706e-7,0.0000036050965,5.266582e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99935776,0.00002101167,0.00019480442,0.00012076005,0.00012182515,0.00018384871],"domain_scores_gemma":[0.99952525,0.00013044069,0.00006527016,0.000052935877,0.00002131052,0.00020481733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042007948,0.00009305934,0.00025010592,0.000067127374,0.0000338937,0.000054199965,0.00017727005,0.000097605414,0.0000011739169],"category_scores_gemma":[0.00003919517,0.00005111262,0.000018885285,0.000060728547,0.000056248846,0.00015264425,0.000065166605,0.0001747221,8.191213e-8],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006954531,0.00038842292,0.8521162,0.0003906598,0.00008692977,0.000091935544,0.000717772,0.0002752469,0.0042033563,0.027298125,0.000023093846,0.11433868],"study_design_scores_gemma":[0.0020067233,0.0021777933,0.90374684,0.0011872622,0.000025297773,0.0012450737,0.00027792162,0.08711414,0.00054814277,0.000098421435,0.0012166201,0.0003557475],"about_ca_topic_score_codex":0.00000276207,"about_ca_topic_score_gemma":0.0000010670473,"teacher_disagreement_score":0.32092243,"about_ca_system_score_codex":0.000009933048,"about_ca_system_score_gemma":0.0000062348645,"threshold_uncertainty_score":0.20843123},"labels":[],"label_agreement":null},{"id":"W2278250372","doi":"","title":"An Intelligent Powered Wheelchair for Users with Dementia: Case Studies with NOAH (Navigation and Obstacle Avoidance Help).","year":2012,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Wheelchair; Obstacle avoidance; Obstacle; Dementia; Cognitive impairment; Computer science; Human–computer interaction; Quality of life (healthcare); Navigation system; Physical medicine and rehabilitation; Cognition; Collision avoidance; Psychology; Computer security; Artificial intelligence; Medicine; World Wide Web; Psychiatry; Mobile robot","score_opus":0.029435311648040426,"score_gpt":0.2952973514709236,"score_spread":0.2658620398228832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2278250372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5516912,0.0004716606,0.44709548,0.00031059605,0.00005737178,0.00017258618,0.0000015711166,0.00017365954,0.000025898891],"genre_scores_gemma":[0.89319706,0.000014029985,0.10656692,0.000091220696,0.000019324027,0.000048230555,0.0000019702761,0.000008865543,0.000052362928],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99912703,0.000026487423,0.00012448285,0.000307494,0.000108695756,0.00030578583],"domain_scores_gemma":[0.99930257,0.00007316882,0.00007726293,0.00031487134,0.00014504035,0.00008710921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021667859,0.00014283843,0.00015117544,0.00006015896,0.00019962506,0.000059764112,0.00017704778,0.000040211016,0.0000014372742],"category_scores_gemma":[0.000013197421,0.00009532314,0.000014044082,0.00016333186,0.00013801,0.0005875645,0.00006068484,0.000077534816,0.0000026803912],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031895068,0.0014592387,0.46619815,0.00043517284,0.0014565395,0.0006615886,0.019242259,0.00046767225,0.0053553707,0.27917916,0.0013716744,0.22385424],"study_design_scores_gemma":[0.015840989,0.0274766,0.17372009,0.0015284825,0.0012313875,0.026040776,0.087463535,0.094855875,0.5331857,0.016369635,0.014924288,0.0073626405],"about_ca_topic_score_codex":0.000029138584,"about_ca_topic_score_gemma":0.00013120852,"teacher_disagreement_score":0.5278303,"about_ca_system_score_codex":0.000030643165,"about_ca_system_score_gemma":0.00001660376,"threshold_uncertainty_score":0.3887165},"labels":[],"label_agreement":null},{"id":"W2279000668","doi":"10.1007/978-3-642-39094-4_46","title":"Multi-distributions Particle Filter for Eye Tracking Inside a Vehicle","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Particle filter; Computer vision; Artificial intelligence; Autoregressive model; Rotation (mathematics); Eye tracking; Face (sociological concept); Tracking (education); Similarity (geometry); Filter (signal processing); Image (mathematics); Mathematics","score_opus":0.03568826234325436,"score_gpt":0.2814927893074319,"score_spread":0.2458045269641775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279000668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019046126,0.00019261935,0.99406093,0.0020213844,0.00075383665,0.00050416845,0.000016418184,0.00038078145,0.00016527451],"genre_scores_gemma":[0.56321216,0.0000050781377,0.43585053,0.000492179,0.00012429085,0.000042279982,0.0000052703544,0.000020293533,0.00024793754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970539,0.000022720726,0.00044900816,0.0012915815,0.00039060842,0.000792177],"domain_scores_gemma":[0.9977331,0.00041530113,0.00022089135,0.0011316417,0.00035651983,0.00014255862],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047851086,0.0004053585,0.00042457372,0.0003314539,0.00038592666,0.0005082124,0.002295349,0.00033027,0.000020090063],"category_scores_gemma":[0.00021119571,0.0003726401,0.0001545331,0.00043665268,0.000680024,0.0005450043,0.0007375109,0.00062721386,0.00009821045],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050800054,0.00012723649,0.00059080415,0.000046945235,0.000025024612,0.000047278118,0.0005763923,0.0052861418,0.0059303087,0.04961414,0.00012078081,0.9376299],"study_design_scores_gemma":[0.00055518525,0.0001904539,0.003619802,0.00022623225,0.000011718452,0.000023534769,2.7951774e-7,0.88965034,0.015628101,0.08710911,0.0023284554,0.00065679784],"about_ca_topic_score_codex":0.000021979497,"about_ca_topic_score_gemma":0.0000575601,"teacher_disagreement_score":0.9369731,"about_ca_system_score_codex":0.00022129432,"about_ca_system_score_gemma":0.00023864886,"threshold_uncertainty_score":0.99987257},"labels":[],"label_agreement":null},{"id":"W2281196487","doi":"","title":"Impact of crowding in letter recognition for age-related macular degeneration people","year":2008,"lang":"en","type":"article","venue":"The HKU Scholars Hub (University of Hong Kong)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Macular degeneration; Optometry; Medicine; Ophthalmology","score_opus":0.029187367529366806,"score_gpt":0.2302730718673576,"score_spread":0.20108570433799078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281196487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.950314,0.000029051505,0.04875133,0.000560181,0.00005392893,0.0001485937,0.000006816327,0.000056306795,0.00007979758],"genre_scores_gemma":[0.98995495,0.000013364271,0.009913967,0.00001857028,0.000007143999,4.2302074e-7,0.000010942506,0.000004905739,0.00007575006],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99930954,0.00008651965,0.00012207482,0.00019409365,0.00012340098,0.00016436279],"domain_scores_gemma":[0.9993804,0.00006843978,0.00014714489,0.0002700851,0.000110705194,0.000023200466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003393234,0.000085397885,0.00017339153,0.00022758321,0.00023264094,0.000011456548,0.00052029424,0.000092622104,0.0000115251605],"category_scores_gemma":[0.000044600696,0.00008188502,0.00013498997,0.00047957434,0.00013025358,0.0005045487,0.0000988835,0.0001952638,0.0000082422675],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028843392,0.0007889621,0.49045685,0.00011106494,0.0004539181,0.0003126418,0.015758395,0.006084161,0.4308631,0.0067201513,0.004099334,0.044062965],"study_design_scores_gemma":[0.0012383617,0.00020062758,0.9788087,0.000056364297,0.000028225162,0.00003227304,0.00015090726,0.009093434,0.007815637,0.0023723345,0.000029011166,0.00017412181],"about_ca_topic_score_codex":0.000245932,"about_ca_topic_score_gemma":0.00011106678,"teacher_disagreement_score":0.48835185,"about_ca_system_score_codex":0.000050372426,"about_ca_system_score_gemma":0.00004065655,"threshold_uncertainty_score":0.3339174},"labels":[],"label_agreement":null},{"id":"W2287624579","doi":"10.1186/s12984-016-0112-2","title":"Powered wheelchair simulator development: implementing combined navigation-reaching tasks with a 3D hand motion controller","year":2016,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University; Jewish Rehabilitation Hospital","funders":"Centre for Interdisciplinary Research in Rehabilitation; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Virtual reality; Task (project management); Wheelchair; Desk; Computer science; Simulation; Kinematics; Motion (physics); Rehabilitation; Interface (matter); Physical medicine and rehabilitation; Human–computer interaction; Artificial intelligence; Physical therapy; Medicine; Engineering","score_opus":0.005647517094289892,"score_gpt":0.2123047712176928,"score_spread":0.2066572541234029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287624579","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5606082,0.000025388832,0.43831387,0.0008775365,0.000078076024,0.00005358297,2.728823e-7,0.00003944233,0.0000035934945],"genre_scores_gemma":[0.95657504,0.0000026203145,0.043359634,0.00001367717,0.000023597928,0.0000030466324,3.3395983e-7,0.00000901281,0.0000130606295],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999063,0.00004590016,0.0003675445,0.00016045,0.00018701568,0.00017610026],"domain_scores_gemma":[0.9989975,0.000399367,0.0002474112,0.00010109925,0.00019604177,0.00005859078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039256847,0.000112996735,0.00018092107,0.0002090806,0.00013832326,0.00007439078,0.0001321514,0.000032837757,5.7275173e-7],"category_scores_gemma":[0.00022012256,0.0000708124,0.00003565752,0.00014066402,0.000046095887,0.0004149007,0.000032850607,0.00012708319,5.9774453e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032971098,0.00032185603,0.05426849,0.00028878398,0.0002737301,0.00008167648,0.007313043,0.015424605,0.3129413,0.010206918,0.000069819565,0.59848005],"study_design_scores_gemma":[0.016999347,0.006011424,0.80424637,0.0025892155,0.00008578542,0.0006048845,0.00038079958,0.15002204,0.009775964,0.0023549048,0.005991782,0.0009375037],"about_ca_topic_score_codex":0.0000010559739,"about_ca_topic_score_gemma":5.5058905e-7,"teacher_disagreement_score":0.7499778,"about_ca_system_score_codex":0.000048442223,"about_ca_system_score_gemma":0.0000278466,"threshold_uncertainty_score":0.28876457},"labels":[],"label_agreement":null},{"id":"W2296634298","doi":"10.1109/embc.2015.7319370","title":"Intuitive wireless control of a robotic arm for people living with an upper body disability","year":2015,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Controller (irrigation); Joystick; Microcontroller; Computer science; Robotic arm; Interface (matter); Wireless; Embedded system; User interface; Human–computer interaction; Simulation; Artificial intelligence; Operating system","score_opus":0.017828979997627132,"score_gpt":0.25901518157150594,"score_spread":0.2411862015738788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296634298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37429062,0.000004251592,0.6247281,0.00043531918,0.000042177497,0.0001606344,0.0000016556339,0.00014024485,0.00019704358],"genre_scores_gemma":[0.97407746,1.9127421e-7,0.025758335,0.000057910307,0.000016355607,0.000043485114,7.83204e-7,0.0000067656415,0.00003873215],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99909014,0.00005917042,0.00015729482,0.00033044678,0.00013694001,0.00022602892],"domain_scores_gemma":[0.9988504,0.00030814158,0.000079564044,0.00043891743,0.0002336947,0.00008924839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036571966,0.00010867873,0.00026878825,0.00003792004,0.000044288816,0.00003329457,0.00046015438,0.000053413583,0.000004392037],"category_scores_gemma":[0.00014036974,0.00007780587,0.000040165487,0.00018527592,0.00016731853,0.00024755148,0.000078219724,0.00007931048,0.0000027179578],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000988444,0.0016966849,0.58768344,0.00006490143,0.00008167794,0.000002088485,0.0024202897,0.00056789047,0.0006439083,0.3865281,0.00008428806,0.020127866],"study_design_scores_gemma":[0.002498569,0.004755959,0.53519183,0.0001031639,0.000047076577,0.000023622308,0.0018970377,0.43831325,0.0032161085,0.013465337,0.000028364333,0.00045965862],"about_ca_topic_score_codex":0.00013745012,"about_ca_topic_score_gemma":0.0002584511,"teacher_disagreement_score":0.5997868,"about_ca_system_score_codex":0.00004382166,"about_ca_system_score_gemma":0.00007265629,"threshold_uncertainty_score":0.31728312},"labels":[],"label_agreement":null},{"id":"W2312189361","doi":"10.7210/jrsj.32.550","title":"Saddle Type Human Body Motion Interface for Personal Mobility Vehicle","year":2014,"lang":"en","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Interface (matter); Personal mobility; Simulation; Usability; Motion (physics); Saddle; Computer science; Engineering; Human–computer interaction; Computer vision; Mechanical engineering; Telecommunications","score_opus":0.024439565603367724,"score_gpt":0.2786586575125865,"score_spread":0.25421909190921876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312189361","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5086701,0.0000412205,0.4872559,0.0035678146,0.00035485154,0.000061999264,8.017519e-7,0.000018803628,0.000028517132],"genre_scores_gemma":[0.9655804,0.0000030437666,0.034136873,0.00008093355,0.00008118876,4.047433e-7,1.5333605e-7,0.000005601364,0.00011137518],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992041,0.000055484394,0.00028484652,0.00011364314,0.00019590564,0.00014600903],"domain_scores_gemma":[0.99889326,0.000104612336,0.00042172734,0.00022904272,0.00031528308,0.00003607883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078460714,0.00008048133,0.0001964037,0.000020714304,0.00015209409,0.000027525926,0.0007308321,0.00008113448,0.0000020907703],"category_scores_gemma":[0.00010333994,0.000057293622,0.00032162052,0.00013512003,0.0001373999,0.000111158704,0.00013430699,0.00022234836,9.600168e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001280595,0.0032900106,0.116916314,0.00076103455,0.0011369807,5.088221e-7,0.018712528,0.053981286,0.63593006,0.060781557,0.05172201,0.056639645],"study_design_scores_gemma":[0.0018428048,0.0020429376,0.142907,0.00024032354,0.00013121298,0.00005342322,0.0007433687,0.7685462,0.051609203,0.030185338,0.0013617356,0.00033645338],"about_ca_topic_score_codex":0.0000043015148,"about_ca_topic_score_gemma":0.0000016805769,"teacher_disagreement_score":0.7145649,"about_ca_system_score_codex":0.00006604046,"about_ca_system_score_gemma":0.00003729532,"threshold_uncertainty_score":0.23363662},"labels":[],"label_agreement":null},{"id":"W2319343938","doi":"10.1123/jab.2014-0292","title":"To What Extent Can the Use of a Mobility Assistance Dog Reduce Upper Limb Efforts When Manual Wheelchair Users Ascend a Ramp?","year":2015,"lang":"en","type":"article","venue":"Journal of Applied Biomechanics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Université de Sherbrooke; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Wheelchair; Manual wheelchair; Physical medicine and rehabilitation; Computer science; Medicine; World Wide Web","score_opus":0.049032920662645835,"score_gpt":0.27688015785097236,"score_spread":0.22784723718832653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319343938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7754321,0.00031189376,0.21411563,0.008159216,0.001349246,0.00048578484,0.000012042768,0.00009347518,0.000040621475],"genre_scores_gemma":[0.9406546,0.000059277743,0.05859507,0.0005383954,0.00007571963,0.000013495522,6.766218e-7,0.000016773525,0.00004599432],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99763894,0.00007265652,0.00071610475,0.0003921702,0.0007638555,0.00041624333],"domain_scores_gemma":[0.9974638,0.00013413522,0.0007203233,0.00090869487,0.0004872744,0.00028577246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014029085,0.00024590045,0.00046215559,0.00034001266,0.00009554049,0.00028006782,0.0014759157,0.00016188135,0.0000033552947],"category_scores_gemma":[0.000110861394,0.00016747086,0.00018142918,0.0006689481,0.000108165405,0.00045026137,0.00041336915,0.00042374557,0.000006712454],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016451092,0.0026376862,0.0003937693,0.00016744943,0.0007098285,0.00026397023,0.0128239235,0.0018984971,0.22834502,0.120656714,0.02974903,0.600709],"study_design_scores_gemma":[0.0064419922,0.0067617134,0.004826391,0.0012336159,0.0003715851,0.001078716,0.010515252,0.024444323,0.5998894,0.14083931,0.201209,0.0023887113],"about_ca_topic_score_codex":0.00002835729,"about_ca_topic_score_gemma":0.00003165203,"teacher_disagreement_score":0.5983203,"about_ca_system_score_codex":0.00030870517,"about_ca_system_score_gemma":0.0002878483,"threshold_uncertainty_score":0.68292636},"labels":[],"label_agreement":null},{"id":"W2327899200","doi":"10.5539/ijms.v8n2p74","title":"Eye Gazes Based on Associative Relevance Assist in Decision Making Processes during Scene Perception","year":2016,"lang":"en","type":"article","venue":"International Journal of Marketing Studies","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perception; Relevance (law); Eye tracking; Eye movement; Cognitive psychology; Cognition; Associative property; Psychology; Computer science; Artificial intelligence; Neuroscience; Political science","score_opus":0.017434503160298567,"score_gpt":0.33198635067324705,"score_spread":0.3145518475129485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327899200","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92421615,0.0003198375,0.06724876,0.00703267,0.00081429083,0.000044743923,0.0000026211596,0.00005451966,0.00026638675],"genre_scores_gemma":[0.9758434,0.00031973448,0.02355327,0.00009076049,0.0001331215,0.0000038845183,7.6191e-8,0.0000068420177,0.00004886997],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99831206,0.00021377047,0.00044847277,0.00022336532,0.000627638,0.0001746718],"domain_scores_gemma":[0.99441534,0.0037809229,0.0006040832,0.00010652222,0.0010744891,0.000018637913],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001983402,0.00012660817,0.00022290806,0.00043596487,0.00009365518,0.000061820865,0.00067327835,0.000046304227,0.000005605238],"category_scores_gemma":[0.017625792,0.00008470513,0.00006820806,0.0002232575,0.000071144714,0.00034076857,0.0001541722,0.00018082497,0.0000049451314],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011409291,0.00026285136,0.5242891,0.000057992398,0.00022365904,0.00027709865,0.0006940528,0.00036043706,0.0047222325,0.0002160912,0.00037926462,0.46737632],"study_design_scores_gemma":[0.000915278,0.00008939021,0.9901604,0.005282847,0.000005580111,0.00001791341,0.00021728786,0.0007457396,0.0007787355,0.0015713946,0.000091268805,0.00012415597],"about_ca_topic_score_codex":7.859822e-7,"about_ca_topic_score_gemma":0.000013368746,"teacher_disagreement_score":0.46725217,"about_ca_system_score_codex":0.0005555565,"about_ca_system_score_gemma":0.00006612811,"threshold_uncertainty_score":0.99064916},"labels":[],"label_agreement":null},{"id":"W2340249237","doi":"10.1177/1473871615609787","title":"Eye tracking evaluation of visual analytics","year":2015,"lang":"en","type":"article","venue":"Information Visualization","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Visual analytics; Computer science; Eye tracking; Visualization; Cultural analytics; Analytics; Human–computer interaction; Tracking (education); Data science; Cognition; Field (mathematics); Process (computing); Data visualization; Artificial intelligence; Semantic analytics; Psychology","score_opus":0.06827886543160647,"score_gpt":0.3785115235552849,"score_spread":0.3102326581236784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340249237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13410144,0.000015879357,0.8631635,0.00010641874,0.00020029422,0.00013215678,9.71582e-7,0.00019551827,0.0020838443],"genre_scores_gemma":[0.9969345,0.0000019635938,0.002903999,0.000076807424,0.00001707978,0.000008093886,0.00004761522,0.0000031330187,0.000006863935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985714,0.000090223206,0.00041237418,0.00009091066,0.00072142825,0.000113654576],"domain_scores_gemma":[0.99778277,0.00002032861,0.00033855872,0.00019554167,0.0016176607,0.000045144934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013928638,0.000079367775,0.000108144166,0.00035398098,0.00004521228,0.000089396075,0.00024226253,0.00007975782,0.0000064954856],"category_scores_gemma":[0.00056001794,0.000079453916,0.00002782514,0.0007503879,0.00003061018,0.0023441997,0.00005379444,0.00005006052,0.00004720539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016347654,0.00022660365,0.019510856,0.00006129997,0.000048729577,4.5822804e-7,0.009405313,0.0510086,0.00063441665,0.48280293,0.0015323422,0.4347521],"study_design_scores_gemma":[0.00057203125,0.00009299261,0.013705558,0.000017217777,0.000018628383,0.0000019050557,0.00029839962,0.974771,0.006223449,0.0031241379,0.0010753146,0.00009932866],"about_ca_topic_score_codex":0.000009762013,"about_ca_topic_score_gemma":0.0000019242186,"teacher_disagreement_score":0.92376244,"about_ca_system_score_codex":0.00010324309,"about_ca_system_score_gemma":0.00016809774,"threshold_uncertainty_score":0.32400367},"labels":[],"label_agreement":null},{"id":"W2340496736","doi":"10.2298/tsci151005038p","title":"Efficient feature for classification of eye movements using electrooculography signals","year":2016,"lang":"en","type":"article","venue":"Thermal Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Electronics and Computer Technology Center; Prince of Songkla University","keywords":"Electrooculography; Artificial intelligence; Feature (linguistics); Eye movement; Computer science; SIGNAL (programming language); Pattern recognition (psychology); Computer vision; Feature extraction; Interface (matter)","score_opus":0.025595830929020617,"score_gpt":0.2917408251079121,"score_spread":0.2661449941788915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2340496736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5917819,0.000021701542,0.4073909,0.00039713213,0.00006276777,0.00010462481,0.0000021367941,0.00005779733,0.00018106427],"genre_scores_gemma":[0.9837461,8.847402e-7,0.016147338,0.000040540282,0.000013155459,0.000010062314,1.0927665e-7,0.000003376512,0.000038402024],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99887997,0.000021985163,0.00012887338,0.00034868863,0.00028173436,0.00033875887],"domain_scores_gemma":[0.9992229,0.000056066547,0.0001450604,0.00035223833,0.0001782428,0.000045513923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006060922,0.000083435334,0.000102678874,0.00019668459,0.00016677853,0.000029529567,0.0010519666,0.000042197273,0.000003572836],"category_scores_gemma":[0.00005966389,0.000052500265,0.000051170377,0.0008795483,0.00044969827,0.00013806659,0.00010235144,0.000040886687,0.0000026942223],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004771109,0.000036133562,0.002111176,0.0000026831474,0.0000030170618,1.5734095e-7,0.00003749018,0.00009076408,0.95134914,0.015740704,0.000005283353,0.030618683],"study_design_scores_gemma":[0.00038610699,0.00019096301,0.26589113,0.000043231325,0.0000051012375,0.0000013797369,0.000013395997,0.07281384,0.65781534,0.0025703788,0.000106335734,0.00016277167],"about_ca_topic_score_codex":0.0000039357824,"about_ca_topic_score_gemma":4.0954248e-7,"teacher_disagreement_score":0.39196426,"about_ca_system_score_codex":0.000050061284,"about_ca_system_score_gemma":0.00008409581,"threshold_uncertainty_score":0.21408987},"labels":[],"label_agreement":null},{"id":"W2344297280","doi":"10.5555/2906831.2907023","title":"Optimal Gaze-Based Robot Selection in Multi-Human Multi-Robot Interaction","year":2016,"lang":"en","type":"article","venue":"Human-Robot Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robot; Computer science; Gaze; Artificial intelligence; Human–robot interaction; Selection (genetic algorithm); Computer vision; Population; Robot kinematics; Human–computer interaction; Mobile robot","score_opus":0.09996377699074672,"score_gpt":0.36644496578272867,"score_spread":0.26648118879198196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344297280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21153077,0.000018565734,0.78518444,0.00080634863,0.0010516697,0.00031825047,0.0000020952837,0.0009084914,0.00017935922],"genre_scores_gemma":[0.95042694,0.0000061486276,0.04808619,0.00013221127,0.00013839443,0.000112565496,0.000016181411,0.000047240144,0.0010341597],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9968378,0.00028463072,0.0007785259,0.0011058896,0.0003207464,0.0006724343],"domain_scores_gemma":[0.9983475,0.00019969682,0.0004947105,0.00057985086,0.00025788473,0.000120330405],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004418748,0.0004606425,0.000423971,0.0010790168,0.00041419227,0.00025335004,0.0007700337,0.00031735963,0.0001844697],"category_scores_gemma":[0.00013845322,0.0004020518,0.00020030355,0.00061093445,0.00012877172,0.0017406357,0.00017875254,0.00077009335,0.00039729613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086435786,0.0015628597,0.013633268,0.000041670824,0.000062699655,0.000045882316,0.00042449264,0.023409825,0.9202792,0.0021942873,0.00047158528,0.037787773],"study_design_scores_gemma":[0.0084958365,0.0013165233,0.41956297,0.0014786385,0.000061761566,0.0002356354,0.0002717509,0.2839852,0.27873883,0.00035217922,0.003661403,0.0018392736],"about_ca_topic_score_codex":0.0005445515,"about_ca_topic_score_gemma":0.0016525526,"teacher_disagreement_score":0.73889613,"about_ca_system_score_codex":0.00094025803,"about_ca_system_score_gemma":0.00005958045,"threshold_uncertainty_score":0.9998431},"labels":[],"label_agreement":null},{"id":"W2354230116","doi":"","title":"An Eye Movement Study on Performance of Brand Recognition","year":2007,"lang":"en","type":"article","venue":"Chinese Journal of Ergonomics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Advertising; Brand awareness; Brand equity; Brand image; Brand management; Psychology; Brand extension; Business","score_opus":0.01669465188667072,"score_gpt":0.28310168043557,"score_spread":0.26640702854889925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2354230116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9782366,0.000016844317,0.021134041,0.00005301464,0.00034878144,0.000060932223,8.4463454e-7,0.000016740432,0.00013220755],"genre_scores_gemma":[0.9946717,0.000013021305,0.0051498315,0.000063539905,0.000090302594,3.923567e-7,3.5046435e-7,0.0000051511056,0.0000056866484],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99917966,0.000024989211,0.00042725864,0.00011420915,0.00013081428,0.00012305005],"domain_scores_gemma":[0.99909085,0.000055191907,0.00041088313,0.00024092307,0.0001466692,0.00005547301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009380489,0.00009778113,0.00020641572,0.00024683174,0.00004476457,0.000022161854,0.00049216,0.00003868123,0.000002021653],"category_scores_gemma":[0.00003719542,0.000070225346,0.000055143915,0.0001756731,0.000027917433,0.0002921971,0.000037145903,0.00018906104,0.000003461422],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073844433,0.0026395775,0.76772857,0.000020009684,0.000110251465,0.00004244804,0.001671864,0.0015150298,0.015296427,0.00047608465,0.000034817756,0.20972648],"study_design_scores_gemma":[0.0008541088,0.002565752,0.9858702,0.000029989074,0.00000819577,0.000011363537,0.00010060684,0.0023458698,0.0073890574,0.00072687527,0.00001398182,0.00008401416],"about_ca_topic_score_codex":0.0000025147904,"about_ca_topic_score_gemma":0.00000646897,"teacher_disagreement_score":0.21814162,"about_ca_system_score_codex":0.000047022277,"about_ca_system_score_gemma":0.00003661681,"threshold_uncertainty_score":0.28637064},"labels":[],"label_agreement":null},{"id":"W2361172000","doi":"","title":"An Eye Movement Study on Visual Search of Web Page Layout","year":2008,"lang":"en","type":"article","venue":"Chinese Journal of Ergonomics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fixation (population genetics); Web page; Visual search; Page layout; Computer science; Eye movement; Information retrieval; World Wide Web; Artificial intelligence; Medicine; Art; Visual arts","score_opus":0.024252166132310193,"score_gpt":0.31527554621487325,"score_spread":0.29102338008256307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2361172000","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9934862,0.00002861674,0.005823957,0.00016005855,0.00028310146,0.00007693947,0.0000018796683,0.000027556682,0.00011165723],"genre_scores_gemma":[0.9970668,0.000018801575,0.002697939,0.000073300966,0.00010915384,8.4576067e-7,2.9230634e-7,0.000009418158,0.000023457691],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99883205,0.00008863474,0.0004663686,0.00017888592,0.0002536345,0.00018044676],"domain_scores_gemma":[0.99898976,0.00006742283,0.00030195387,0.00037276634,0.00017149496,0.00009659982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005684585,0.00014247783,0.00033659593,0.00031572452,0.00008563551,0.00002656956,0.00094422273,0.000050968807,0.000004142534],"category_scores_gemma":[0.000047497022,0.0001012673,0.00010161144,0.00024762968,0.00007293392,0.00023381197,0.00012501047,0.00032435078,0.000006674893],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002568386,0.0047829426,0.9457885,0.000011695653,0.00017560295,0.0003217212,0.0032612905,0.00502136,0.032406613,0.0018673796,0.00016457077,0.0059414525],"study_design_scores_gemma":[0.001259023,0.003380108,0.9751591,0.000019225334,0.000008432749,0.000041225903,0.000260012,0.01687748,0.0024659585,0.00036748112,0.000023689967,0.00013822834],"about_ca_topic_score_codex":0.000007910191,"about_ca_topic_score_gemma":0.000006005249,"teacher_disagreement_score":0.029940655,"about_ca_system_score_codex":0.000066675624,"about_ca_system_score_gemma":0.00017051729,"threshold_uncertainty_score":0.4129561},"labels":[],"label_agreement":null},{"id":"W2395804249","doi":"","title":"NOAH for Wheelchair Users with Cognitive Impairment: Navigation and Obstacle Avoidance Help.","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"","keywords":"Wheelchair; Obstacle; Cognitive impairment; Obstacle avoidance; Cognition; Computer science; Physical medicine and rehabilitation; Human–computer interaction; Collision avoidance; Psychology; Artificial intelligence; Computer security; Mobile robot; Robot; Medicine; Collision; Psychiatry; World Wide Web","score_opus":0.01651897847081276,"score_gpt":0.23471688254779166,"score_spread":0.2181979040769789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395804249","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.566041,0.000039743663,0.432744,0.00052274286,0.000028783952,0.0001717692,0.0000032599514,0.00022669909,0.00022200304],"genre_scores_gemma":[0.962187,0.000008198147,0.037191678,0.00016529953,0.000012700825,0.000047503476,0.000003850435,0.0000065420877,0.00037726387],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99927443,0.000014170909,0.00009307003,0.0003112469,0.0001012014,0.0002058507],"domain_scores_gemma":[0.9995369,0.00010217574,0.000053485717,0.00014806913,0.00011186572,0.000047506317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007612902,0.00010216539,0.00010824125,0.000050117378,0.0002063548,0.000030391591,0.00016415631,0.000045678094,0.0000017900545],"category_scores_gemma":[0.00001645235,0.00008074562,0.000020150883,0.00017207155,0.00015061304,0.00026606058,0.000048623337,0.00007617577,0.000008094141],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005797722,0.0015074912,0.4688383,0.00033827458,0.0003882328,0.00039548476,0.010432819,0.00017653298,0.008404594,0.27526116,0.0069331033,0.22674425],"study_design_scores_gemma":[0.01044866,0.0055659562,0.7691921,0.00055949145,0.000055950117,0.0014037895,0.0018096708,0.057834927,0.13864681,0.010253236,0.0025784315,0.0016509647],"about_ca_topic_score_codex":0.000021051852,"about_ca_topic_score_gemma":0.000010504695,"teacher_disagreement_score":0.39614594,"about_ca_system_score_codex":0.000019453719,"about_ca_system_score_gemma":0.000031551237,"threshold_uncertainty_score":0.3292711},"labels":[],"label_agreement":null},{"id":"W2401510503","doi":"","title":"Seeing how you're Looking - Using Real-Time Eye Gaze Data for User-Adaptive Visualization.","year":2013,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Computer science; Visualization; Eye tracking; Human–computer interaction; Task (project management); Perception; Data visualization; Cognition; Eye movement; User interface; Artificial intelligence; Psychology","score_opus":0.05724177166995813,"score_gpt":0.31014698893438297,"score_spread":0.25290521726442483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401510503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024632636,0.000027600174,0.97187036,0.0011342686,0.00015067539,0.00035270661,0.000012540773,0.0007922352,0.0010269731],"genre_scores_gemma":[0.6221949,0.000005872497,0.37577298,0.00014206152,0.00008241819,0.00002068417,0.000033231157,0.000025328247,0.0017225017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984195,0.000051835996,0.00020306812,0.00070487696,0.00019297024,0.00042772826],"domain_scores_gemma":[0.99820423,0.0001433734,0.0001509289,0.0012036109,0.00022809481,0.00006975827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029839372,0.00019080803,0.0002284269,0.00016424715,0.00027445995,0.00040198158,0.0015760531,0.00012046676,0.000047538608],"category_scores_gemma":[0.00013589773,0.0001722746,0.0000437527,0.00040017872,0.00007176965,0.0014674981,0.0008198232,0.00009972666,0.000061375125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002386982,0.00051021995,0.01634,0.00016488071,0.00041642511,0.000042979857,0.0013574981,0.0014177939,0.17607322,0.6255073,0.06360297,0.11454286],"study_design_scores_gemma":[0.00029782107,0.000076778444,0.0017134555,0.000061982,0.0000181339,0.000006130104,0.00017636779,0.9889235,0.0037705824,0.0021595678,0.002484526,0.00031116098],"about_ca_topic_score_codex":0.00021863436,"about_ca_topic_score_gemma":0.000012337795,"teacher_disagreement_score":0.9875057,"about_ca_system_score_codex":0.000059276532,"about_ca_system_score_gemma":0.000064814834,"threshold_uncertainty_score":0.7025154},"labels":[],"label_agreement":null},{"id":"W2405289190","doi":"10.1117/12.2228782","title":"Electronic eyebox for weapon sights","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Raytheon Technologies (Canada)","funders":"","keywords":"Sight; Computer science; Computer security; Astronomy; Physics","score_opus":0.009705850990764596,"score_gpt":0.22551082665591193,"score_spread":0.21580497566514734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405289190","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9800045,0.000081374674,0.008031662,0.00998957,0.00025733304,0.00048550637,0.000022402868,0.00025329986,0.00087435276],"genre_scores_gemma":[0.8268907,0.0000707646,0.17187688,0.00010470997,0.00029467064,0.00025444166,0.0000018776178,0.000044682994,0.00046124856],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978746,9.935287e-9,0.0005263607,0.0005171341,0.00047899192,0.000602895],"domain_scores_gemma":[0.99778485,0.0002813741,0.000340391,0.00009474899,0.001405859,0.000092796196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006023109,0.00028904292,0.0003638702,0.00012623095,0.00010202782,0.000100754805,0.0020041217,0.00020954096,0.00000435505],"category_scores_gemma":[0.00056321075,0.00019484674,0.0005915817,0.00030944,0.00021856649,0.00063114055,0.00023345494,0.00021898007,0.0000029582447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002570344,0.000055302797,0.00009308918,0.00008472474,0.00014696481,2.8998112e-8,0.0000375467,0.000003213694,0.38063207,0.61542195,0.0016915849,0.0018078417],"study_design_scores_gemma":[0.0031157904,0.001451925,0.001527531,0.00057837967,0.00015297622,0.000034528475,0.00025236927,0.0253997,0.8501175,0.07793182,0.03858789,0.00084956066],"about_ca_topic_score_codex":0.0000026882105,"about_ca_topic_score_gemma":1.5635912e-7,"teacher_disagreement_score":0.53749007,"about_ca_system_score_codex":0.00020544359,"about_ca_system_score_gemma":0.000054254706,"threshold_uncertainty_score":0.794562},"labels":[],"label_agreement":null},{"id":"W2472796880","doi":"10.3389/fnhum.2016.00344","title":"The Role of Cognitive and Perceptual Loads in Inattentional Deafness","year":2016,"lang":"en","type":"article","venue":"Frontiers in Human Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inattentional blindness; Cognitive load; Perception; Cognition; Psychology; Stimulus (psychology); Audiology; Cognitive psychology; Medicine; Neuroscience","score_opus":0.01036036659431371,"score_gpt":0.23945103942423962,"score_spread":0.2290906728299259,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2472796880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8899722,0.00012859594,0.10890022,0.00034513645,0.00028631455,0.00007881478,0.0000017821326,0.000027089427,0.00025982628],"genre_scores_gemma":[0.9992126,0.000022939603,0.0006245961,0.000040092385,0.0000046852483,0.0000107780415,8.0546315e-8,0.0000026271462,0.00008154857],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99903065,0.00007213447,0.00016283839,0.00033606042,0.00017054661,0.00022774383],"domain_scores_gemma":[0.99958336,0.000120889534,0.00006360312,0.00017209775,0.00003394877,0.000026079855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028953713,0.00007364159,0.00010256857,0.00018571249,0.00013562062,0.000036449906,0.0006787627,0.00003410235,7.748045e-7],"category_scores_gemma":[0.00020991922,0.00004695714,0.000016772228,0.00035867444,0.001065399,0.00023716308,0.00022172306,0.00010478512,3.2332363e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008487719,0.00008316066,0.7531552,0.0000026947769,9.84772e-7,0.000012613979,0.00040415578,0.0000037026687,0.033519287,0.06715038,0.000070972914,0.14558835],"study_design_scores_gemma":[0.00038981833,0.00009943526,0.96325654,0.000066696324,9.3854686e-7,0.000007953533,0.0002553368,0.0023488018,0.0037690974,0.029519614,0.00018984478,0.00009589913],"about_ca_topic_score_codex":0.000008840034,"about_ca_topic_score_gemma":0.000013616156,"teacher_disagreement_score":0.21010137,"about_ca_system_score_codex":0.000023627064,"about_ca_system_score_gemma":0.000026438465,"threshold_uncertainty_score":0.3925507},"labels":[],"label_agreement":null},{"id":"W2481670370","doi":"10.1145/2945292.2945298","title":"Interactive gaze driven animation of the eye region","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Animation; Computer science; Gaze; Computer vision; Computer facial animation; Eye tracking; Computer graphics (images); Artificial intelligence; Eye movement; Skeletal animation; Computer animation; Character animation; Movement (music); Art","score_opus":0.013908868290989715,"score_gpt":0.2442713682169028,"score_spread":0.23036249992591307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2481670370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12998565,0.0000033009273,0.8544186,0.0081203235,0.00014922867,0.000055003464,3.3888338e-7,0.00013504934,0.007132555],"genre_scores_gemma":[0.99526125,0.0000018204012,0.003654275,0.000051867133,0.0000069258986,0.000002409089,3.5696385e-8,0.0000015789805,0.00101983],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99963945,0.000029633213,0.00008129587,0.00011511428,0.00006565506,0.00006885526],"domain_scores_gemma":[0.99952084,0.00005590452,0.00007835538,0.0002949918,0.000041186686,0.000008721698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035413952,0.000038553622,0.000053064112,0.000033681463,0.000026379072,0.000007674398,0.0004358184,0.000027153239,0.0000044801],"category_scores_gemma":[0.000044935918,0.000017409156,0.000029940367,0.0001250865,0.00005802559,0.00016375288,0.00013210838,0.00003564702,0.000016166598],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000072349894,0.000086826316,0.042833477,0.0000060133507,0.000024129567,0.0000027562635,0.00036827798,0.0000043100026,0.18138184,0.54725134,0.0031115946,0.22492222],"study_design_scores_gemma":[0.00040500634,0.00014325119,0.5305874,0.00017163031,0.000005378251,0.000013188538,0.000040064915,0.0039820154,0.4417747,0.019664185,0.0030721463,0.00014108368],"about_ca_topic_score_codex":0.0000049554615,"about_ca_topic_score_gemma":0.00000543433,"teacher_disagreement_score":0.8652756,"about_ca_system_score_codex":0.000014942578,"about_ca_system_score_gemma":0.000010951585,"threshold_uncertainty_score":0.08098663},"labels":[],"label_agreement":null},{"id":"W2482180150","doi":"10.1007/978-1-4471-4784-8","title":"Eye Gaze in Intelligent User Interfaces","year":2013,"lang":"en","type":"book","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Human–computer interaction; Computer science; Eye tracking; Psychology; Artificial intelligence","score_opus":0.016569030587324302,"score_gpt":0.2483545628377028,"score_spread":0.23178553225037848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2482180150","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005333595,0.00057830184,0.20142376,0.0015270344,0.0008085892,0.00031416025,0.0000018337089,0.00086771994,0.79394525],"genre_scores_gemma":[0.013047122,0.000080144026,0.011518256,0.00024092017,0.00004453025,0.000027412942,0.000004017765,0.000020989179,0.9750166],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985142,0.000029460769,0.0003406674,0.000600953,0.00018298067,0.000331734],"domain_scores_gemma":[0.9989111,0.00007450815,0.000120053934,0.0007737614,0.00006805385,0.000052535644],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00014688283,0.00028680472,0.00035899415,0.00041171577,0.00002452095,0.00013244516,0.0017886474,0.00038874595,0.00033715263],"category_scores_gemma":[0.000024014487,0.00023623409,0.000072002724,0.00013796097,0.00010425362,0.00016730768,0.0006621189,0.0006253029,0.0026669705],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016809402,0.00008985444,0.0005236972,0.00004952656,0.00004521053,0.000059923597,0.00019967735,0.00001343785,0.000051874085,0.57798296,0.25292343,0.16805871],"study_design_scores_gemma":[0.00018853955,0.00016024751,0.0014746418,0.00045587742,0.000008057276,0.0000107944425,0.000023854223,0.0017416049,0.0026264582,0.065391935,0.9271378,0.00078018534],"about_ca_topic_score_codex":0.000055670935,"about_ca_topic_score_gemma":0.00008817474,"teacher_disagreement_score":0.67421436,"about_ca_system_score_codex":0.0001800014,"about_ca_system_score_gemma":0.00013341528,"threshold_uncertainty_score":0.9981096},"labels":[],"label_agreement":null},{"id":"W2485949287","doi":"10.1152/jn.00605.2015","title":"Modeling eye-head gaze shifts in multiple contexts without motor planning","year":2016,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Canadian Institutes of Health Research","keywords":"Gaze; Motor planning; Head (geology); Psychology; Eye movement; Cognitive psychology; Communication; Computer science; Neuroscience; Computer vision; Geology","score_opus":0.041058143619926046,"score_gpt":0.2921540970556028,"score_spread":0.25109595343567676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485949287","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86882067,0.000053346197,0.12953573,0.0010230782,0.00045242868,0.00004343968,8.6257586e-7,0.000046896457,0.000023543902],"genre_scores_gemma":[0.9946061,0.000022054883,0.004997707,0.00022181284,0.00011164029,0.0000019499844,6.900282e-8,0.000011677228,0.00002698748],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99865323,0.00015456164,0.00046632974,0.00026203957,0.00013910196,0.00032473286],"domain_scores_gemma":[0.99906254,0.00022071884,0.00023966745,0.00027880317,0.0001250875,0.00007316573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011066,0.00014433867,0.000377299,0.00031098878,0.000049952592,0.000018482213,0.0007971419,0.000098173216,0.0000025481106],"category_scores_gemma":[0.00032430634,0.00009350135,0.0000919226,0.00015931416,0.000084010426,0.00028487275,0.00015027385,0.000348017,0.000018798399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015897602,0.000114389535,0.0086182775,0.0000071755167,0.000019151834,0.0004686865,0.00013965869,0.006998219,0.9623217,0.0009666718,0.00001824563,0.020168856],"study_design_scores_gemma":[0.0047112363,0.0024571167,0.70752317,0.00058765284,0.000013205883,0.00025891216,0.000029678784,0.26948532,0.002854648,0.0110345455,0.0005675919,0.0004769173],"about_ca_topic_score_codex":0.0000073011865,"about_ca_topic_score_gemma":0.0000021790772,"teacher_disagreement_score":0.95946705,"about_ca_system_score_codex":0.000037852846,"about_ca_system_score_gemma":0.000053918633,"threshold_uncertainty_score":0.38128746},"labels":[],"label_agreement":null},{"id":"W2490278295","doi":"10.1016/b978-008043642-5/50008-5","title":"Saccadic Inhibition and Gaze Contingent Research Paradigms","year":2000,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Saccadic masking; Psychology; Cognitive psychology; Neuroscience; Eye movement; Psychoanalysis","score_opus":0.04051785162436017,"score_gpt":0.2864832347026702,"score_spread":0.24596538307831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2490278295","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006327821,0.0020877542,0.00017813953,0.00047984158,0.00015723742,0.00038281997,0.0000075475155,0.00033290684,0.99574095],"genre_scores_gemma":[0.058438987,0.0005556858,0.0015508367,0.0002204308,0.00019510531,0.000037339174,0.000008101863,0.000054025575,0.9389395],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977046,0.000076519456,0.00036960765,0.00084326643,0.0004977365,0.00050822954],"domain_scores_gemma":[0.99864066,0.00013782886,0.00013874777,0.0008155655,0.00012729576,0.00013990542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007320296,0.0003312053,0.00042652868,0.00047646466,0.0002736115,0.00019135472,0.0005713584,0.00045935146,0.000057413545],"category_scores_gemma":[0.000021082431,0.00031853965,0.00010287794,0.00004161624,0.0005012641,0.00007701357,0.00034835242,0.0011484674,0.00032367554],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031075033,0.0000059386844,0.000004686304,0.00001901674,0.000020345191,0.000078260884,0.00011143671,8.3823394e-8,0.000037110058,0.19816871,0.00020755063,0.80134374],"study_design_scores_gemma":[0.00021426551,0.00012020854,0.00012392763,0.00045249925,0.0000144459755,0.00007718484,0.0000027399947,0.00003567482,0.0001904132,0.18393101,0.81453013,0.0003075012],"about_ca_topic_score_codex":0.0000013152137,"about_ca_topic_score_gemma":0.000010482125,"teacher_disagreement_score":0.8143226,"about_ca_system_score_codex":0.00008975861,"about_ca_system_score_gemma":0.00010243145,"threshold_uncertainty_score":0.9999267},"labels":[],"label_agreement":null},{"id":"W2505251359","doi":"10.4018/978-1-61350-098-9.ch015","title":"Evaluating Eye Tracking Systems for Computer Input","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"BitTorrent tracker; Eye tracking; Computer science; Eye tracking on the ISS; Human–computer interaction; Artificial intelligence; Tracking (education); Function (biology); Channel (broadcasting); Control (management); Input device; Computer vision; Computer hardware; Psychology","score_opus":0.06630937653546166,"score_gpt":0.32716849530508424,"score_spread":0.2608591187696226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505251359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025732667,0.0017852848,0.58399636,0.0001248419,0.003389659,0.0010023928,0.00006911663,0.0012518952,0.40812314],"genre_scores_gemma":[0.79049987,0.00001029581,0.16255404,0.00071877596,0.0035111331,0.00018162982,0.000016803591,0.00018496014,0.04232247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972295,0.00004250841,0.0006067472,0.00088361703,0.0005088533,0.00072875974],"domain_scores_gemma":[0.99781454,0.00014400575,0.00052326074,0.0010161668,0.00034062105,0.00016139194],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006198452,0.0005674767,0.00071133487,0.00013861389,0.00023784714,0.00033460255,0.0014324128,0.0006813821,0.0000032458533],"category_scores_gemma":[0.00003056898,0.0005558552,0.00030940995,0.000032369735,0.000117125,0.00013338393,0.00047827695,0.00043075145,0.00013706756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004948547,0.000009724269,0.000025271413,0.00007994281,0.00008121996,0.000011933264,0.00005725832,0.000065226784,0.000022160073,0.8588758,0.00077342114,0.13999309],"study_design_scores_gemma":[0.0028214385,0.001632507,0.00081683596,0.0030345968,0.0005379128,0.0004766678,0.000023741579,0.1045882,0.00023821404,0.71775967,0.16392103,0.0041491813],"about_ca_topic_score_codex":0.000023645054,"about_ca_topic_score_gemma":0.0000051790557,"teacher_disagreement_score":0.79024255,"about_ca_system_score_codex":0.00031592703,"about_ca_system_score_gemma":0.00016661428,"threshold_uncertainty_score":0.9996893},"labels":[],"label_agreement":null},{"id":"W2505462564","doi":"10.1007/978-3-319-41267-2_72","title":"Comparison of Two Methods to Control the Mouse Using a Keypad","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Keypad; Computer science; Software; Human–computer interaction; Throughput; Pointing device; Control (management); Component (thermodynamics); Input device; Computer hardware; Artificial intelligence; Operating system; Wireless","score_opus":0.04873087243238862,"score_gpt":0.37025836938311696,"score_spread":0.32152749695072835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505462564","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046064996,0.0002569685,0.99551445,0.0017342613,0.0007989637,0.00039224475,0.0000066638686,0.00015862827,0.0006771901],"genre_scores_gemma":[0.37952667,0.0000018268661,0.61956906,0.0006573765,0.000119617136,0.0000051547536,1.9145529e-7,0.000017665852,0.000102442405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968246,0.00013528911,0.0006287919,0.0011469481,0.00064955495,0.00061478873],"domain_scores_gemma":[0.9960905,0.0012801279,0.00045814284,0.0017322532,0.00032014525,0.0001188444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016520955,0.00043474953,0.00080498424,0.00074750045,0.0002444676,0.00018152354,0.00467642,0.00023310604,0.000008010142],"category_scores_gemma":[0.00022038395,0.00027472,0.00014864774,0.0006285687,0.0011325296,0.00019680781,0.00125485,0.00067793514,0.000018390305],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007565396,0.000033926633,0.00032843114,0.00001422637,0.0000235841,0.0000105325835,0.0005719456,0.02091693,0.013386389,0.08005543,0.000014194565,0.8846368],"study_design_scores_gemma":[0.0007701304,0.0003992055,0.00024937707,0.00047946605,0.00003152647,0.000058104404,4.810848e-7,0.74577886,0.0718704,0.17781916,0.0016777966,0.0008654774],"about_ca_topic_score_codex":0.000025554185,"about_ca_topic_score_gemma":0.000033265278,"teacher_disagreement_score":0.88377136,"about_ca_system_score_codex":0.00020546278,"about_ca_system_score_gemma":0.000315869,"threshold_uncertainty_score":0.9999705},"labels":[],"label_agreement":null},{"id":"W2508850332","doi":"10.1167/16.12.142","title":"Adapted use of audiovisual information for person and object recognition in people with one eye","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Psychology; Identity (music); Modality (human–computer interaction); Object (grammar); Cognitive neuroscience of visual object recognition; Communication; Face (sociological concept); Facial recognition system; Eye movement; Cognitive psychology; Computer science; Artificial intelligence; Linguistics; Pattern recognition (psychology); Neuroscience","score_opus":0.032243013516231545,"score_gpt":0.2682929611536174,"score_spread":0.23604994763738585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508850332","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.73573816,0.000011577294,0.263452,0.0007021666,0.00002929885,0.000050096296,0.0000018701534,0.000007916959,0.000006903064],"genre_scores_gemma":[0.96257335,0.000026891543,0.037365038,0.000016997239,0.000009556184,9.862359e-7,4.7194604e-7,0.0000018292669,0.000004867312],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99952084,0.000022937826,0.00019977282,0.0000536484,0.00013022528,0.00007257643],"domain_scores_gemma":[0.9992688,0.00012211478,0.00030365153,0.00006329702,0.0002207734,0.000021364285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002721382,0.000044900426,0.00012936801,0.000251657,0.000020795402,0.000024198993,0.00008325549,0.00004111686,0.0000012097207],"category_scores_gemma":[0.00014865215,0.00002709616,0.000022771526,0.00014310826,0.000021003174,0.0012032446,0.000016525077,0.000056672274,7.9824554e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004152747,0.00013492034,0.015810587,0.000039499137,0.000021315373,0.0000027482142,0.0008916385,0.00001328523,0.033068612,0.00046469568,0.00016054415,0.9489769],"study_design_scores_gemma":[0.0023591318,0.0031914911,0.98083913,0.00078863214,0.000013419986,0.000044542216,0.000109343826,0.002914987,0.008885245,0.0005119729,0.000250446,0.000091626716],"about_ca_topic_score_codex":0.000008704696,"about_ca_topic_score_gemma":0.000015877175,"teacher_disagreement_score":0.9650286,"about_ca_system_score_codex":0.000028256844,"about_ca_system_score_gemma":0.000032376054,"threshold_uncertainty_score":0.110494934},"labels":[],"label_agreement":null},{"id":"W2509053041","doi":"10.3389/fnsys.2016.00073","title":"Construction and Operation of a High-Speed, High-Precision Eye Tracker for Tight Stimulus Synchronization and Real-Time Gaze Monitoring in Human and Animal Subjects","year":2016,"lang":"en","type":"article","venue":"Frontiers in Systems Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; McGill University Health Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer science; Stimulus (psychology); Computer vision; Synchronization (alternating current); Artificial intelligence; Eye movement; Psychology; Cognitive psychology","score_opus":0.010585782726533521,"score_gpt":0.24360853287770226,"score_spread":0.23302275015116874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2509053041","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7339027,0.00010850941,0.26495084,0.000087257315,0.0006385268,0.00026432256,0.000003826092,0.00003947369,0.000004555485],"genre_scores_gemma":[0.9844254,0.000104082195,0.015389571,0.0000025077757,0.000024833493,0.000015313777,4.762853e-7,0.0000070457018,0.00003076589],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998681,0.000095904456,0.0003055837,0.0005458588,0.00016414648,0.00020751038],"domain_scores_gemma":[0.99949116,0.0000747794,0.00014352582,0.00018729467,0.000058947822,0.00004431652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040300877,0.00012226812,0.00024826787,0.00032725904,0.00011989416,0.00010244747,0.00019786185,0.00009139919,1.357021e-7],"category_scores_gemma":[0.00012283307,0.00009954907,0.000008542738,0.00032146374,0.00030048657,0.0006355812,0.0000910175,0.00006008622,1.3925941e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032308093,0.00003322439,0.27161807,0.000069710055,0.0000016744038,0.0000073761144,0.00023501873,0.00017990246,0.6944686,0.0052322377,0.00003186741,0.028089967],"study_design_scores_gemma":[0.0021781847,0.0007135707,0.8379665,0.0006025485,0.000007658869,0.00004763198,0.000099650824,0.11933426,0.03756014,0.001158056,0.000025240286,0.000306556],"about_ca_topic_score_codex":0.00011270865,"about_ca_topic_score_gemma":0.0000039385336,"teacher_disagreement_score":0.6569085,"about_ca_system_score_codex":0.00006619939,"about_ca_system_score_gemma":0.000025982486,"threshold_uncertainty_score":0.40594932},"labels":[],"label_agreement":null},{"id":"W2516431700","doi":"","title":"Remote, Non-contact Gaze Estimation with Minimal Subject Cooperation","year":2010,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gaze; Subject (documents); Computer science; Eye contact; Artificial intelligence; Computer vision; Human–computer interaction; Geography; Psychology; Communication; World Wide Web","score_opus":0.002369015200894241,"score_gpt":0.15515808305458423,"score_spread":0.15278906785368998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516431700","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69661677,0.00020600412,0.03207003,0.0036688314,0.0012333622,0.0006656553,0.00012641428,0.00013520088,0.26527774],"genre_scores_gemma":[0.9670093,0.000030469413,0.025332315,0.00019532119,0.00003525086,0.0000070271567,0.00008403494,0.000022419625,0.0072838324],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979784,0.00003349384,0.00027619977,0.00040846606,0.0010436842,0.00025973722],"domain_scores_gemma":[0.9990945,0.00014351639,0.00029140542,0.0003398202,0.0000022748832,0.00012849207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001289837,0.00026847573,0.00030828148,0.000054719138,0.00018626449,0.0000700572,0.00047685762,0.00010192621,0.000007620959],"category_scores_gemma":[0.000005890199,0.00024048275,0.000023239225,0.00013187119,0.00003804025,0.0004650018,0.000055648066,0.00038218845,5.223751e-9],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017199921,0.00013710787,0.007297533,0.0011321483,0.00035851318,0.0005427831,0.00082807615,0.0003245337,0.19190472,0.24553667,0.0017090547,0.5485089],"study_design_scores_gemma":[0.0010988223,0.0006803518,0.27233136,0.0009046841,0.00008189747,0.00005270162,0.0011825806,0.15587531,0.5605239,0.0016990975,0.004385855,0.0011834403],"about_ca_topic_score_codex":0.0026426858,"about_ca_topic_score_gemma":0.13430028,"teacher_disagreement_score":0.54732543,"about_ca_system_score_codex":0.000007633427,"about_ca_system_score_gemma":0.0023797792,"threshold_uncertainty_score":0.9806602},"labels":[],"label_agreement":null},{"id":"W2541233814","doi":"10.1109/ichr.2006.321309","title":"Implementation of a neurophysiological model of saccadic eye movements on an anthropomorphic robotic head","year":2006,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Saccadic masking; Computer vision; Saccadic suppression of image displacement; Saccade; Computer science; Artificial intelligence; Kinematics; Superior colliculus; Neurophysiology; Eye movement; Supplementary eye field; Humanoid robot; Robot kinematics; Robot; Mobile robot; Psychology; Neuroscience; Physics","score_opus":0.05877027514004549,"score_gpt":0.3486781823190164,"score_spread":0.2899079071789709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2541233814","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61938816,0.000007607772,0.3798257,0.00015520627,0.00011199441,0.00018525917,0.000015605061,0.00013219901,0.00017824056],"genre_scores_gemma":[0.9678819,0.000010710287,0.03190612,0.00009054653,0.000014218778,0.000017453047,0.000032565924,0.000010893717,0.000035543777],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99811333,0.0001082131,0.0005382092,0.000678876,0.00031098217,0.00025039137],"domain_scores_gemma":[0.99846536,0.000029446004,0.0004337038,0.00090100366,0.00012827483,0.000042199703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000138296,0.00024772633,0.00047909154,0.00023785903,0.000044010176,0.000022558319,0.0011002995,0.00020024892,0.00001852228],"category_scores_gemma":[0.00000856335,0.00020949767,0.00011847278,0.000172415,0.00018415561,0.000080742466,0.0007037348,0.00034542172,0.0000042261895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003729849,0.002296133,0.015381958,0.0002802063,0.000109241475,0.000020606254,0.00016042861,0.69364035,0.122234076,0.11706293,0.00021799706,0.04855877],"study_design_scores_gemma":[0.00054115575,0.0011309664,0.21728158,0.000083907405,0.000020977624,9.709906e-7,0.000025536307,0.7141934,0.031640742,0.034751598,0.0000014306582,0.0003277092],"about_ca_topic_score_codex":0.000887282,"about_ca_topic_score_gemma":0.000056346293,"teacher_disagreement_score":0.34849375,"about_ca_system_score_codex":0.000041179348,"about_ca_system_score_gemma":0.00009637009,"threshold_uncertainty_score":0.8543067},"labels":[],"label_agreement":null},{"id":"W2545566750","doi":"10.1109/tic-sth.2009.5444437","title":"Covert monitoring of the point-of-gaze","year":2009,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer vision; Artificial intelligence; Computer science; Calibration; Point (geometry); Covert; Vanishing point; Mathematics; Image (mathematics); Statistics; Geometry","score_opus":0.013266095640869908,"score_gpt":0.24217360521045955,"score_spread":0.22890750956958963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545566750","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76269746,0.000090532696,0.21528172,0.004530075,0.00045767368,0.00007184115,4.472645e-7,0.00020365303,0.01666661],"genre_scores_gemma":[0.9830743,0.0000024919457,0.01664631,0.000039346152,0.000009499801,3.3263802e-7,1.4635057e-8,9.5126586e-7,0.00022676708],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961364,0.000011970854,0.00010258638,0.000091747745,0.00009639491,0.0000836751],"domain_scores_gemma":[0.99951553,0.000027987624,0.000054737648,0.00035389268,0.000037841157,0.000010018222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007657364,0.00003943765,0.00007546089,0.000029583813,0.000022263072,0.000006224907,0.0006156172,0.00002801141,0.0000025834097],"category_scores_gemma":[0.000029114473,0.000024424577,0.000039543043,0.00020332975,0.00003596532,0.00006261966,0.00007412958,0.000057650836,0.0000031593152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000030017477,0.00014378822,0.11191093,0.000008743816,0.000013918964,0.0000023519983,0.0002274463,0.000045936507,0.0934922,0.5951302,0.0008795596,0.19814192],"study_design_scores_gemma":[0.00010743962,0.00007905351,0.4634325,0.000031027383,0.0000021503508,0.0000039923666,0.000019777573,0.0005028472,0.52239037,0.013097603,0.0002823749,0.000050872433],"about_ca_topic_score_codex":0.000010872026,"about_ca_topic_score_gemma":3.4209057e-7,"teacher_disagreement_score":0.5820326,"about_ca_system_score_codex":0.0000064792994,"about_ca_system_score_gemma":0.000014192595,"threshold_uncertainty_score":0.114398025},"labels":[],"label_agreement":null},{"id":"W2548946944","doi":"10.1109/ccece.2016.7726745","title":"Wearable system-on-module for prosopagnosia rehabilitation","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Eyewear; Computer science; Raspberry pi; Wearable computer; Computer hardware; Embedded system; Facial recognition system; Artificial intelligence; Computer vision; Feature extraction; Internet of Things","score_opus":0.011282172300268534,"score_gpt":0.2425007784163219,"score_spread":0.23121860611605335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548946944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02679434,0.0000129053315,0.95538163,0.012166451,0.00025586304,0.0003256083,0.0000022523348,0.00092319335,0.0041377638],"genre_scores_gemma":[0.9181163,6.015563e-7,0.080316804,0.00006390526,0.000023631063,0.00015624262,1.3423664e-7,0.000004915304,0.0013174638],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999292,0.000021737244,0.00011871356,0.00029716003,0.00008929085,0.00018106806],"domain_scores_gemma":[0.99906343,0.00039567356,0.000039858944,0.00038995175,0.00008300673,0.00002806757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022695187,0.00006984787,0.0000918593,0.00007209635,0.000078464225,0.000029452476,0.00033653592,0.00004913887,0.0000038116038],"category_scores_gemma":[0.00016978546,0.000040891904,0.000040673847,0.000115269526,0.00003924475,0.00015218642,0.000037664246,0.000026412701,0.00016450458],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050585923,0.00004327476,0.0013035965,0.00002410852,0.0000036536721,5.782918e-7,0.000014508627,0.0000023835594,0.002492783,0.9423749,0.0038516195,0.049883563],"study_design_scores_gemma":[0.009234612,0.0123140905,0.16793802,0.0027549425,0.00003540885,0.00007641365,0.00032046568,0.046296347,0.33037293,0.31459218,0.1139594,0.0021052046],"about_ca_topic_score_codex":0.0000036665633,"about_ca_topic_score_gemma":0.0000016789114,"teacher_disagreement_score":0.89132196,"about_ca_system_score_codex":0.000057085745,"about_ca_system_score_gemma":0.000018700537,"threshold_uncertainty_score":0.21144284},"labels":[],"label_agreement":null},{"id":"W2563817626","doi":"","title":"Investigating Memory for Spatial and Temporal Relations with Eye Movement Monitoring","year":2012,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Movement (music); Eye movement; Cartography; Cognitive psychology; Computer science; Geography; Psychology; Artificial intelligence; Art","score_opus":0.005851364214656708,"score_gpt":0.17390260241424013,"score_spread":0.16805123819958342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563817626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9311,0.0010571968,0.009722526,0.0040402003,0.0010969633,0.00077479763,0.00016683288,0.00009439356,0.051947102],"genre_scores_gemma":[0.97307485,0.000013621589,0.020918002,0.00009692299,0.00007069466,0.000034108576,0.000027725466,0.000018558187,0.0057455003],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99848646,0.000024019782,0.00023087552,0.00028656068,0.0007196897,0.00025242244],"domain_scores_gemma":[0.999239,0.00014939533,0.00026819308,0.00018418005,0.0000013979087,0.00015780762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000013982931,0.00020063226,0.00022764661,0.000036773992,0.00024620755,0.0000352284,0.00024191319,0.000050860857,0.0000017579619],"category_scores_gemma":[0.0000045445095,0.00018622841,0.000016501011,0.000060801045,0.000050773735,0.00032673456,0.00007752119,0.00017704429,7.111027e-10],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016493782,0.000053329575,0.72381085,0.0008845853,0.0002549048,0.000025868165,0.00087144336,0.00006465167,0.018585404,0.14573875,0.0003260144,0.10921925],"study_design_scores_gemma":[0.00050989457,0.00016241964,0.88154113,0.00054422696,0.000054286418,0.0000022978493,0.0028442529,0.0030751773,0.10548104,0.0036732503,0.0016238502,0.00048813678],"about_ca_topic_score_codex":0.0037768357,"about_ca_topic_score_gemma":0.032484964,"teacher_disagreement_score":0.15773031,"about_ca_system_score_codex":0.000008332623,"about_ca_system_score_gemma":0.0009871916,"threshold_uncertainty_score":0.98516965},"labels":[],"label_agreement":null},{"id":"W2564892412","doi":"","title":"On the Use of Modular Software and Hardware for Designing Wheelchair Robots.","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia; McGill University","funders":"","keywords":"Wheelchair; Modular design; Robot; Embedded system; Software; Computer science; Self-reconfiguring modular robot; Work (physics); Computer architecture; Software engineering; Mobile robot; Engineering; Operating system; Robot control; Artificial intelligence; World Wide Web","score_opus":0.27038643741897544,"score_gpt":0.32832668050513986,"score_spread":0.05794024308616441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564892412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00948704,0.000007739012,0.9831509,0.0068809227,0.00010148355,0.00019321957,0.000025234967,0.000087214445,0.00006624232],"genre_scores_gemma":[0.9585341,0.000008067841,0.040987175,0.00031702907,0.000019659577,0.000040700583,0.0000011429603,0.0000058492305,0.000086276596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988615,0.00005888994,0.00023569957,0.00034363993,0.00032558836,0.00017466153],"domain_scores_gemma":[0.9971056,0.0018742906,0.00012446532,0.00023869959,0.0006210729,0.000035892783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034665593,0.00012060622,0.00012898214,0.00012678611,0.00016130216,0.00008768266,0.00047597967,0.00007536562,0.00002116405],"category_scores_gemma":[0.0022202188,0.0000731763,0.000044867338,0.00016461503,0.00021039453,0.00019311518,0.00007034101,0.000100957026,0.000025252815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017513557,0.000033049997,0.000034882734,0.0000032424916,0.0000066775206,5.0960074e-7,0.000043158863,0.0006339039,0.0045030406,0.8147307,0.00013357631,0.17985974],"study_design_scores_gemma":[0.000042368913,0.0003063069,0.0009876387,0.00017040233,0.0000035103646,0.0000021167164,0.000026255282,0.07008303,0.14716758,0.7806357,0.00040242207,0.00017263797],"about_ca_topic_score_codex":0.000006053702,"about_ca_topic_score_gemma":0.000008999121,"teacher_disagreement_score":0.949047,"about_ca_system_score_codex":0.000035294266,"about_ca_system_score_gemma":0.00009840338,"threshold_uncertainty_score":0.2984043},"labels":[],"label_agreement":null},{"id":"W2567604508","doi":"10.1109/iros.2016.7759745","title":"Optimal robot selection by gaze direction in multi-human multi-robot interaction","year":2016,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer science; Robot; Selection (genetic algorithm); Artificial intelligence; Human–robot interaction; Computer vision; Human–computer interaction","score_opus":0.03542150826037834,"score_gpt":0.30720476434642646,"score_spread":0.27178325608604814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567604508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10519842,0.000013863658,0.89253175,0.00082767097,0.00033413694,0.00010859415,6.6161647e-7,0.0007579063,0.00022698936],"genre_scores_gemma":[0.90813047,0.0000087519375,0.08879112,0.00004173286,0.000021288068,0.000030170524,0.0000016497752,0.000010382988,0.0029644188],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987492,0.00008188041,0.0002492032,0.0005036926,0.00011545207,0.0003005815],"domain_scores_gemma":[0.9994937,0.000055139615,0.00009721315,0.00023528557,0.00007017388,0.00004846177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020189142,0.00015731825,0.00015251763,0.00027199183,0.00011723372,0.000063288164,0.00032098044,0.00013592836,0.000038120477],"category_scores_gemma":[0.000053161166,0.0001178767,0.00004746114,0.00038537802,0.00004824051,0.0006415823,0.00009410396,0.00019619535,0.00010844811],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009937065,0.00061031367,0.027335878,0.0000058317514,0.000018392873,0.0000052748346,0.00013241616,0.00066851143,0.82816285,0.0019088654,0.0012338181,0.1399079],"study_design_scores_gemma":[0.003746526,0.00043111003,0.4547186,0.00016767319,0.000010652406,0.00008647888,0.000081636565,0.20643042,0.32882208,0.0001524889,0.004555022,0.000797312],"about_ca_topic_score_codex":0.00039314106,"about_ca_topic_score_gemma":0.0006020024,"teacher_disagreement_score":0.8037406,"about_ca_system_score_codex":0.00022387756,"about_ca_system_score_gemma":0.00001454963,"threshold_uncertainty_score":0.4806872},"labels":[],"label_agreement":null},{"id":"W2568686031","doi":"","title":"Attentive Headphones: Augmenting Conversational Attention with a Real World TiVo ®","year":2005,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Headset; Headphones; Computer science; Human–computer interaction; Gaze; Context (archaeology); Noise (video); Utterance; Interface (matter); Filter (signal processing); Speech recognition; Artificial intelligence; Computer vision; Engineering","score_opus":0.013330724052339737,"score_gpt":0.24017281926095582,"score_spread":0.2268420952086161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568686031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37480837,0.000010884679,0.5888811,0.0077307285,0.00013493572,0.00014043978,0.0000012767575,0.00081345515,0.027478838],"genre_scores_gemma":[0.92686784,0.0000016605791,0.06740912,0.00022519978,0.000054909844,0.0000127790845,0.0000042092142,0.000005656533,0.0054185996],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9990229,0.00002652892,0.00015516939,0.00034311006,0.00021310555,0.00023920192],"domain_scores_gemma":[0.9994967,0.00004638012,0.00008752223,0.00023268693,0.00009489641,0.000041835337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015460886,0.00010927322,0.000112381815,0.00017545087,0.00012996352,0.00007423228,0.00029695593,0.000033494656,0.00007435578],"category_scores_gemma":[0.000005877182,0.00008784931,0.000037468177,0.00041740722,0.00007941814,0.0003877739,0.00008857926,0.000113579306,0.00022311414],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003497437,0.00040468323,0.30282524,0.000018339158,0.00015959567,0.000035673398,0.0006738444,0.00017293023,0.006641888,0.58265716,0.0046563805,0.10171927],"study_design_scores_gemma":[0.0022012556,0.0002982709,0.9288457,0.00010925089,0.000030905972,0.00005687172,0.0003560697,0.047643997,0.0075839246,0.0018301519,0.010386155,0.000657437],"about_ca_topic_score_codex":0.00008320489,"about_ca_topic_score_gemma":0.00041717486,"teacher_disagreement_score":0.62602043,"about_ca_system_score_codex":0.00007864291,"about_ca_system_score_gemma":0.000041383584,"threshold_uncertainty_score":0.35823908},"labels":[],"label_agreement":null},{"id":"W2568766316","doi":"10.1167/16.12.350","title":"Distinct roles of eye movements during memory encoding and retrieval","year":2016,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Eye movement; Dissociation (chemistry); Psychology; Encoding (memory); Recognition memory; Memoria; Cognitive psychology; Neuroscience; Cognition","score_opus":0.010534290705035245,"score_gpt":0.26823797166384045,"score_spread":0.2577036809588052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568766316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9814948,0.00015244975,0.017578539,0.00053282466,0.0001324512,0.000015697884,7.3623835e-7,0.000013819604,0.00007868658],"genre_scores_gemma":[0.99714756,0.00004945381,0.0026979079,0.00000693406,0.00003379253,5.4094954e-8,1.8122671e-8,0.000002668544,0.00006163928],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9993218,0.000026999362,0.00025559883,0.00009457771,0.00020449537,0.00009652905],"domain_scores_gemma":[0.9993688,0.00006423157,0.0003146067,0.00012311715,0.00008994288,0.000039319715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031954926,0.000055711393,0.00013861508,0.0001317797,0.00004481085,0.000017320628,0.0002753072,0.000035490706,0.0000036193342],"category_scores_gemma":[0.00012310277,0.00003303669,0.000039749037,0.000091698195,0.000047473048,0.00025520963,0.000107868254,0.000078233235,0.0000012062164],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046929334,0.000055269687,0.028258326,0.000017332919,0.000017152384,0.000040823546,0.000118796255,0.0000014516796,0.88550097,0.0004903617,0.00004457588,0.085408],"study_design_scores_gemma":[0.00076525926,0.0003327692,0.8269853,0.00043290076,0.0000052763335,0.000032411994,0.000025384561,0.00010258486,0.16958666,0.0015720038,0.00009608149,0.000063365944],"about_ca_topic_score_codex":6.7924e-7,"about_ca_topic_score_gemma":5.21926e-7,"teacher_disagreement_score":0.798727,"about_ca_system_score_codex":0.000025318035,"about_ca_system_score_gemma":0.000014264948,"threshold_uncertainty_score":0.13471971},"labels":[],"label_agreement":null},{"id":"W2581843808","doi":"10.1145/3418057","title":"A Comparison of Touchscreen and Mouse for Real-World and Abstract Tasks with Older Adults","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Accessible Computing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Touchscreen; Task (project management); Fitts's law; Cognition; Computer science; Elementary cognitive task; Mobile device; Human–computer interaction; Input device; Test (biology); Psychology; Cognitive psychology; Engineering; Computer hardware","score_opus":0.04544839170909691,"score_gpt":0.3322827295687928,"score_spread":0.28683433785969586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2581843808","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39295083,0.000041287072,0.60523635,0.001287319,0.00002176542,0.00018202463,0.0000067931614,0.00018910457,0.0000845257],"genre_scores_gemma":[0.89238846,0.00001065397,0.1074329,0.000116085925,0.000015794587,0.0000091904385,0.0000016975308,0.00001245132,0.000012745567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886364,0.000018889372,0.00029370544,0.00047061406,0.00013156801,0.00022160252],"domain_scores_gemma":[0.99895334,0.00033813287,0.00018371346,0.00034381635,0.00008332527,0.00009765908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010259843,0.00016275178,0.00030607023,0.00016122965,0.00022383331,0.000098098666,0.0005337116,0.00006238976,0.000003237086],"category_scores_gemma":[0.000023854456,0.00014203838,0.000036804402,0.00042747436,0.00009296428,0.000234853,0.00004140754,0.00023876516,7.4994006e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021223267,0.00033204159,0.03374261,0.00042742555,0.00010612195,0.0000054390794,0.0022724208,0.002559767,0.0012011415,0.0016036261,0.0001041914,0.957433],"study_design_scores_gemma":[0.0053638634,0.0017838198,0.3948809,0.00090746826,0.0001115207,0.0000170301,0.0011279251,0.5080203,0.08609636,0.0005285683,0.00027655246,0.00088571315],"about_ca_topic_score_codex":0.0001238805,"about_ca_topic_score_gemma":0.0001006149,"teacher_disagreement_score":0.95654726,"about_ca_system_score_codex":0.000011773664,"about_ca_system_score_gemma":0.000029674664,"threshold_uncertainty_score":0.5792157},"labels":[],"label_agreement":null},{"id":"W2590354927","doi":"10.7287/peerj.preprints.2718v1","title":"Assessment of accuracy for target detection in 3D-space using eye tracking and computer vision","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Workspace; Computer vision; Computer science; Artificial intelligence; Eye tracking; Tracking (education); Tracking system; Adaptation (eye); Protocol (science); Point (geometry); Gaze; Position (finance); Robot; Kalman filter","score_opus":0.03285648312048868,"score_gpt":0.3655551700261314,"score_spread":0.33269868690564275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590354927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41542745,0.0000066521056,0.5840822,0.00019119636,0.00010743186,0.00008634134,3.4127942e-7,0.00003633568,0.0000620564],"genre_scores_gemma":[0.64721197,0.0000018643663,0.35274887,0.000011533461,0.000013985528,0.0000027785181,1.6320921e-7,0.0000030001631,0.000005865159],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993064,0.000022791484,0.00016041982,0.00026909207,0.00008527351,0.00015605458],"domain_scores_gemma":[0.99925596,0.00010320166,0.00018943388,0.00036366045,0.000066124776,0.000021600155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032571057,0.00008317809,0.00015758257,0.00012203078,0.00019922922,0.00015332976,0.00035535046,0.00006689559,0.0000010566416],"category_scores_gemma":[0.000058263056,0.00007397674,0.000027318853,0.000059624515,0.00006133599,0.0005077074,0.00021164972,0.00009171121,2.1905964e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001011449,0.00018038772,0.12807934,0.00008797183,0.00002028152,0.000009213838,0.00020016346,0.0010311686,0.13732664,0.032835297,0.000014191509,0.7002052],"study_design_scores_gemma":[0.0002704422,0.00008487944,0.422745,0.00003156689,0.0000017887896,0.0000028780607,0.0000060983907,0.5633871,0.012441102,0.0009073251,0.00006215232,0.000059691105],"about_ca_topic_score_codex":0.00008904111,"about_ca_topic_score_gemma":0.000046453915,"teacher_disagreement_score":0.70014554,"about_ca_system_score_codex":0.000032349693,"about_ca_system_score_gemma":0.00002186799,"threshold_uncertainty_score":0.3016684},"labels":[],"label_agreement":null},{"id":"W2608656908","doi":"10.1109/thms.2017.2706727","title":"A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future","year":2017,"lang":"en","type":"review","venue":"IEEE Transactions on Human-Machine Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":245,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Kent; Université de Lorraine; National Research Council Canada; Universidade de Aveiro","keywords":"Wheelchair; Assistive technology; Computer science; Data science; Engineering ethics; Human–computer interaction; Engineering; World Wide Web","score_opus":0.11132362243359879,"score_gpt":0.378502560642799,"score_spread":0.26717893820920025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608656908","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[2.2978567e-7,0.9039372,0.091715194,0.00031293064,0.0017389505,0.0013605057,0.00019065276,0.00029126488,0.00045303872],"genre_scores_gemma":[0.00021903077,0.9980916,0.0002982515,0.000028751007,0.0004874199,0.00032397042,0.00001647466,0.000054078162,0.00048043288],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9965611,0.0005329032,0.0011509216,0.00094117015,0.00042998517,0.00038389556],"domain_scores_gemma":[0.9959128,0.0002498178,0.001221023,0.0022468725,0.00023237696,0.00013710871],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00040105352,0.00071548676,0.0029425183,0.00052342704,0.00051820616,0.00015398992,0.0017210303,0.0004330459,0.000012961372],"category_scores_gemma":[0.000003445014,0.0005597289,0.0006745326,0.00033047108,0.00024052143,0.00021783682,0.000021805314,0.0010346529,0.000034383036],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014747288,0.00011867349,2.8564787e-7,0.13142508,0.00029979672,0.000022930057,0.00003186419,0.000004743232,6.350467e-7,0.0007799182,0.0012608741,0.8660537],"study_design_scores_gemma":[0.00019743516,0.00021602302,0.0000022103868,0.083986506,0.0003886286,0.0002324567,0.000008219978,0.000086933345,0.0000019569602,0.000016755943,0.91445273,0.00041015912],"about_ca_topic_score_codex":0.00018265094,"about_ca_topic_score_gemma":0.000011669615,"teacher_disagreement_score":0.91319185,"about_ca_system_score_codex":0.00007664648,"about_ca_system_score_gemma":0.00011212036,"threshold_uncertainty_score":0.9996854},"labels":[],"label_agreement":null},{"id":"W2610038789","doi":"10.71781/10679","title":"Mise en correspondance active et passive pour la vision par ordinateur multivue","year":2007,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Art; Philosophy","score_opus":0.0063828301381390565,"score_gpt":0.2210625564561607,"score_spread":0.21467972631802165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610038789","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8888493,0.0054708626,0.054653782,0.0032100638,0.0032415837,0.00071685424,0.0001413184,0.0006014856,0.043114707],"genre_scores_gemma":[0.91595185,0.0003923237,0.008264782,0.00017306315,0.00013409079,0.000030205885,0.00018307907,0.00004822909,0.07482236],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99567986,0.0007464675,0.00054510473,0.0014006712,0.00083186396,0.0007960302],"domain_scores_gemma":[0.99616295,0.0010773892,0.00080869935,0.00072254805,0.0008407141,0.00038771945],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005677225,0.0007632872,0.00065171596,0.00070055376,0.0051048896,0.00017063439,0.0013449719,0.0011993048,0.00002585475],"category_scores_gemma":[0.00037483915,0.0008806966,0.0004239647,0.0007976742,0.00060512905,0.0009265144,0.0004968891,0.0014906564,0.00022028048],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0040528197,0.0020402642,0.011792307,0.00037513298,0.0010098282,0.035587892,0.13696267,0.0057964474,0.11840135,0.34274033,0.0028865943,0.33835438],"study_design_scores_gemma":[0.004088225,0.0005732442,0.6305306,0.0018657873,0.00046481576,0.0036455644,0.037029445,0.0270505,0.10642927,0.001802749,0.18435124,0.002168537],"about_ca_topic_score_codex":0.01462862,"about_ca_topic_score_gemma":0.004972902,"teacher_disagreement_score":0.6187383,"about_ca_system_score_codex":0.0050671203,"about_ca_system_score_gemma":0.002784155,"threshold_uncertainty_score":0.9993644},"labels":[],"label_agreement":null},{"id":"W2611058793","doi":"10.16910/jemr.6.3.1","title":"Abstracts of the 17th European Conference on Eye Movements 2013","year":2013,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Exhibition; Eye movement; Event (particle physics); Library science; Sociology; Computer science; Visual arts; Artificial intelligence","score_opus":0.08032802910222597,"score_gpt":0.3522569531909326,"score_spread":0.2719289240887066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611058793","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96284825,0.000059277496,0.0022628033,0.014476017,0.00030936048,0.0002800458,0.0000015197098,0.000019090168,0.01974365],"genre_scores_gemma":[0.9963293,0.000057897552,0.0011818132,0.00029000954,0.000056774028,0.000003979471,1.4361012e-7,0.000008302677,0.0020717431],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9970753,0.0003985567,0.0005583536,0.00019972758,0.0013599915,0.00040806725],"domain_scores_gemma":[0.99766105,0.000121447694,0.00044358923,0.00062944007,0.001034601,0.00010987552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026292102,0.00011706622,0.00019278128,0.0003070834,0.00014533816,0.00015305741,0.0023632376,0.00004483855,0.00011737933],"category_scores_gemma":[0.00019495805,0.00007082228,0.000104144456,0.00040508146,0.00019773895,0.00026848525,0.0005813521,0.0007997192,0.00015375628],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084240884,0.0038800433,0.09348015,0.000150428,0.00049843476,0.00021914515,0.0017419661,0.0010392664,0.4506397,0.13738185,0.10682107,0.20406371],"study_design_scores_gemma":[0.0006886903,0.0010244728,0.92855155,0.00028264566,0.000003664783,0.0000021477626,0.00018791661,0.00083046086,0.04682161,0.019029507,0.002453722,0.00012363728],"about_ca_topic_score_codex":0.00013041396,"about_ca_topic_score_gemma":0.0000065768045,"teacher_disagreement_score":0.8350714,"about_ca_system_score_codex":0.00008526132,"about_ca_system_score_gemma":0.00012019279,"threshold_uncertainty_score":0.43915227},"labels":[],"label_agreement":null},{"id":"W2611492058","doi":"10.1145/3027063.3053174","title":"Enhancing Zoom and Pan in Ultrasound Machines with a Multimodal Gaze-based Interface","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"B.C. Women's Hospital & Health Centre; University of British Columbia","funders":"","keywords":"Gaze; Computer science; Zoom; Interface (matter); Human–computer interaction; Field (mathematics); Eye tracking; Computer vision; Artificial intelligence; Interface design; Engineering","score_opus":0.010030552263081575,"score_gpt":0.2603126454599687,"score_spread":0.2502820931968871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611492058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6241367,0.000020819054,0.3742241,0.000742273,0.000047435748,0.000052522217,4.3213657e-7,0.00012565144,0.00065004657],"genre_scores_gemma":[0.9275398,0.0000015653991,0.07224062,0.000060095434,0.000009689785,0.0000064774918,2.2877127e-7,0.0000051675984,0.00013630914],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99926305,0.000021268852,0.00010543147,0.00032593936,0.00008116996,0.00020312179],"domain_scores_gemma":[0.9992213,0.000107357155,0.00007134751,0.0005384481,0.000025852518,0.000035663743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015527055,0.00011548058,0.0001384049,0.00008693593,0.0001669673,0.00024004612,0.00059264386,0.000050022358,0.0000051980273],"category_scores_gemma":[0.00009567352,0.000083330175,0.000013361105,0.000059227365,0.00014555585,0.0002341838,0.00012298925,0.00014984279,0.000007640912],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041549916,0.00018349514,0.8365594,0.0000394446,0.00002167804,0.00008495294,0.00060253486,0.000104512794,0.053246565,0.009838531,0.000044710465,0.099232584],"study_design_scores_gemma":[0.0011985955,0.00020570244,0.8910317,0.00012825006,0.0000037580774,0.000037567308,0.000047464015,0.04513331,0.060990483,0.0008550553,0.000098540746,0.00026960936],"about_ca_topic_score_codex":0.00070078525,"about_ca_topic_score_gemma":0.0028547798,"teacher_disagreement_score":0.30340314,"about_ca_system_score_codex":0.000016724449,"about_ca_system_score_gemma":0.000027520327,"threshold_uncertainty_score":0.33981058},"labels":[],"label_agreement":null},{"id":"W2613918921","doi":"10.1108/ijpcc-02-2017-0011","title":"A mobile platform for controlling and interacting with a do-it-yourself smart eyewear","year":2017,"lang":"en","type":"article","venue":"International Journal of Pervasive Computing and Communications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Eyewear; Computer science; Human–computer interaction; Augmented reality; Usability; Smartwatch; Laptop; Modalities; Headset; Multimedia; Wearable computer; Embedded system","score_opus":0.04104493499271526,"score_gpt":0.353005856824583,"score_spread":0.31196092183186774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613918921","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41933268,0.0010748456,0.56639546,0.011838686,0.00047755885,0.00018063193,0.0000071364902,0.000046535657,0.0006464916],"genre_scores_gemma":[0.8924247,0.00021209677,0.10714038,0.00012276458,0.00007014953,0.0000059516,0.0000010991163,0.0000060268317,0.000016840015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924076,0.000031303134,0.00030088314,0.00014461642,0.00015579414,0.00012665612],"domain_scores_gemma":[0.99707943,0.00076153915,0.00072182744,0.00053095265,0.0008496345,0.000056610916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004886177,0.000100718265,0.00019152738,0.00014014292,0.00068909355,0.0006162338,0.00199063,0.000039806648,6.755896e-7],"category_scores_gemma":[0.00043012828,0.00008272552,0.000058624297,0.000031166048,0.00021132689,0.00045094325,0.0006388026,0.0002820632,5.3401044e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001921496,0.00037925495,0.057560526,0.000031475403,0.0009847315,0.000052610638,0.01140205,0.0003744425,0.0013818236,0.10225166,0.00037067302,0.8250186],"study_design_scores_gemma":[0.014549411,0.0035890897,0.08413434,0.004582462,0.00030182124,0.005896814,0.012578031,0.78704697,0.0013705547,0.027201917,0.057466794,0.0012818034],"about_ca_topic_score_codex":0.000021854607,"about_ca_topic_score_gemma":0.000021711438,"teacher_disagreement_score":0.8237368,"about_ca_system_score_codex":0.000032216507,"about_ca_system_score_gemma":0.000056804885,"threshold_uncertainty_score":0.59423566},"labels":[],"label_agreement":null},{"id":"W2614236470","doi":"10.1016/s1474-6670(17)33958-7","title":"Using Biomedical Signal to Detect Natural Eye Movement and to Control Artificial Ocular Implant","year":2002,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Dalhousie University","funders":"","keywords":"Eye movement; SIGNAL (programming language); Movement control; Computer vision; Artificial intelligence; Computer science; Control signal; Signal processing; Engineering; Control system; Simulation; Computer hardware; Digital signal processing; Physical medicine and rehabilitation; Electrical engineering","score_opus":0.0196172610843274,"score_gpt":0.2524555464761013,"score_spread":0.2328382853917739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2614236470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8209325,0.00015859622,0.17353934,0.0044574835,0.00025238327,0.0002852921,0.000006019376,0.00032540725,0.000042978063],"genre_scores_gemma":[0.9493739,0.000001602756,0.049280588,0.0011422387,0.00011510236,0.000022325645,2.9407184e-7,0.000011155291,0.000052821095],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982979,0.000008609523,0.00025866888,0.000564131,0.00034614303,0.00052451174],"domain_scores_gemma":[0.99942446,0.000024373776,0.00006785388,0.00012489887,0.00011930398,0.00023908273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022670189,0.00020139398,0.00025749355,0.0002877983,0.0002078611,0.00024615455,0.000493546,0.00010008765,0.000014499622],"category_scores_gemma":[0.00009852889,0.00018128181,0.00004946421,0.0004830997,0.00008742782,0.00020009931,0.00025286636,0.00020167828,0.000050870138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003773109,0.00012234802,0.0061564613,0.00003993717,0.00008538724,0.000082304534,0.0011991885,0.000022845566,0.5914317,0.007510752,0.0057032434,0.38760814],"study_design_scores_gemma":[0.0022607083,0.0027046648,0.035286784,0.00033304488,0.000106892134,0.0003684596,0.00051255454,0.7709726,0.1529222,0.011993961,0.020410255,0.002127883],"about_ca_topic_score_codex":0.000031637923,"about_ca_topic_score_gemma":0.0000032116977,"teacher_disagreement_score":0.7709498,"about_ca_system_score_codex":0.00007308086,"about_ca_system_score_gemma":0.000014267114,"threshold_uncertainty_score":0.7392458},"labels":[],"label_agreement":null},{"id":"W2617597639","doi":"10.11159/icbes17.131","title":"Analysis of the Spatio-Temporal Parameters of an Amputee Patient vs. a Healthy Patient","year":2017,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Medicine; Computer vision","score_opus":0.00935249083938408,"score_gpt":0.22096859598381088,"score_spread":0.2116161051444268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2617597639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9963784,0.000064120955,0.0024553456,0.00028860237,0.00058335403,0.0001727464,0.0000021484234,0.000030421272,0.000024829664],"genre_scores_gemma":[0.9978021,0.0000042516867,0.002131933,0.000030962445,0.000010872161,0.000007975514,6.728562e-8,0.0000036985798,0.000008147833],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858177,0.000011395677,0.00035498748,0.0003763773,0.000431969,0.00024351096],"domain_scores_gemma":[0.9986044,0.00006867244,0.0005840863,0.00042796446,0.00023193064,0.00008291249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036412358,0.00013739259,0.00037181747,0.00037851965,0.00029005404,0.0001905871,0.0014214467,0.000036241294,5.4502618e-8],"category_scores_gemma":[0.00007823134,0.00008284791,0.0000799759,0.0012020124,0.00038190561,0.0002269091,0.00046047595,0.00016076058,2.8216153e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015316848,0.0007050552,0.2972503,0.0006017904,0.00059355784,0.0000037286986,0.0019658615,0.039295703,0.0046101557,0.28011948,0.00024352167,0.3744577],"study_design_scores_gemma":[0.00013092649,0.00059259986,0.09375017,0.00015932831,0.00003189824,0.0000058946544,0.000006423205,0.89946806,0.005629233,0.000044590237,0.00007444212,0.00010644457],"about_ca_topic_score_codex":0.00012267192,"about_ca_topic_score_gemma":0.000005973573,"teacher_disagreement_score":0.86017233,"about_ca_system_score_codex":0.000025848527,"about_ca_system_score_gemma":0.000036552112,"threshold_uncertainty_score":0.33784395},"labels":[],"label_agreement":null},{"id":"W2619326953","doi":"10.1109/ivs.2017.7995781","title":"Detection and recognition of traffic signs inside the attentional visual field of drivers","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer vision; Gaze; Artificial intelligence; Computer science; Advanced driver assistance systems; Traffic sign recognition; Histogram; Support vector machine; Histogram of oriented gradients; Feature extraction; Classifier (UML); Visual field; Feature (linguistics); Traffic sign; Pattern recognition (psychology); Sign (mathematics); Mathematics; Psychology","score_opus":0.026634903951820416,"score_gpt":0.277146594244267,"score_spread":0.2505116902924466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619326953","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9037776,0.000004193144,0.09495544,0.00064476184,0.00007064223,0.000033674365,4.8359936e-7,0.000027417254,0.00048577087],"genre_scores_gemma":[0.9982917,0.0000047215863,0.0016401756,0.000023618779,0.000007310753,0.0000015880823,2.0212431e-7,9.828309e-7,0.000029748247],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999694,0.000015359148,0.00008056917,0.000094537536,0.000066180815,0.000049385584],"domain_scores_gemma":[0.999607,0.00007519289,0.00010383426,0.00015410478,0.000051054878,0.000008796813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010048148,0.000032914228,0.00005521564,0.000039938663,0.00011901984,0.000019893563,0.00019531499,0.00003701112,0.000006827323],"category_scores_gemma":[0.000063523185,0.000023354924,0.000023917813,0.000032844317,0.00011218858,0.000117783544,0.0000660433,0.000053321204,0.0000017963368],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008605144,0.00005632751,0.0030540335,0.000011142147,0.000020611895,0.0000011853195,0.00012632502,0.000015921421,0.049655125,0.003150286,0.0000636431,0.9438368],"study_design_scores_gemma":[0.00074398465,0.0011194607,0.4977025,0.000058229456,0.00002576455,0.000027066502,0.00023720875,0.033247154,0.45737705,0.00917077,0.00011335551,0.00017744907],"about_ca_topic_score_codex":0.0000344154,"about_ca_topic_score_gemma":0.000068383604,"teacher_disagreement_score":0.94365937,"about_ca_system_score_codex":0.0000030974466,"about_ca_system_score_gemma":0.000008090653,"threshold_uncertainty_score":0.09523862},"labels":[],"label_agreement":null},{"id":"W2620685652","doi":"10.1002/sdtp.11840","title":"75‐3: Reducing Glare from Reflected Highlights in Mobile and Automotive Displays","year":2017,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Oralys (Canada)","funders":"","keywords":"GLARE; Automotive industry; Computer science; Computer vision; Front (military); Computer graphics (images); Artificial intelligence; Engineering; Materials science; Mechanical engineering; Nanotechnology","score_opus":0.009551343725858972,"score_gpt":0.260270971516328,"score_spread":0.250719627790469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620685652","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9078236,0.0002429847,0.00009330404,0.0056178058,0.00029848702,0.00037676358,0.000025742685,0.000644946,0.084876336],"genre_scores_gemma":[0.99816275,0.00012979454,0.0015027744,0.000040495474,0.000027003849,0.00004105363,0.0000052049727,0.0000141634,0.000076778786],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9983337,0.000053168544,0.0003506138,0.00069093745,0.00022232658,0.00034924995],"domain_scores_gemma":[0.9981613,0.00017832157,0.00026608832,0.0012284264,0.00006999221,0.000095832416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017526752,0.00021576248,0.0003987281,0.00013931941,0.00024682775,0.00011202986,0.0013989399,0.0002578609,0.0000049757214],"category_scores_gemma":[0.00021316136,0.0001891888,0.00007344336,0.00017599357,0.00045603572,0.00030365874,0.0006230887,0.00034390946,0.000005843504],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016051805,0.00014429104,0.009175274,0.0000136368835,0.000017945327,0.000055281103,0.00021340084,0.000030725903,0.9777977,0.01159742,0.00005799006,0.0008802752],"study_design_scores_gemma":[0.00072193856,0.00036155473,0.86024255,0.0003051708,0.000018643774,0.000015489431,0.00003204609,0.00007029855,0.13594206,0.000924067,0.0010012325,0.0003649675],"about_ca_topic_score_codex":0.00046541955,"about_ca_topic_score_gemma":0.00017521906,"teacher_disagreement_score":0.85106725,"about_ca_system_score_codex":0.00008995668,"about_ca_system_score_gemma":0.000039504732,"threshold_uncertainty_score":0.7714895},"labels":[],"label_agreement":null},{"id":"W2639089507","doi":"10.1299/jsmermd.2014._1p1-c02_1","title":"1P1-C02 Development of a passive-type wheelchair using a powder brake : 4th report: Control of wheelchair braking by slope in environment(Welfare Robotics and Mechatronics (2))","year":2014,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nurses Association","funders":"","keywords":"Wheelchair; Mechatronics; Robotics; Brake; Tilt (camera); Computer science; Automotive engineering; Simulation; Point (geometry); Artificial intelligence; Engineering; Robot; Control engineering; Mechanical engineering; Mathematics","score_opus":0.015127550233031929,"score_gpt":0.22879028574988694,"score_spread":0.21366273551685502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2639089507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44673917,0.000797339,0.54517055,0.0056024096,0.00022913389,0.00094261894,0.00003751659,0.00012447176,0.00035676168],"genre_scores_gemma":[0.95140386,0.00018715233,0.048226885,0.000069654816,0.000014838122,0.000010373982,0.000005685425,0.000033213397,0.000048325244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968561,0.000047740035,0.0010826773,0.0007624593,0.0005786334,0.0006723866],"domain_scores_gemma":[0.99749345,0.00012269888,0.001225428,0.00042759438,0.0005880625,0.00014278828],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011476903,0.00046518544,0.00092352706,0.00033348697,0.00022800057,0.00009096593,0.0009311136,0.00029240802,0.000004827546],"category_scores_gemma":[0.00017496795,0.00039014328,0.000087361695,0.00039478223,0.00037130105,0.0003284827,0.00061668764,0.0005694232,9.969481e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010792245,0.0003866214,0.0019957426,0.00033453072,0.00019254987,0.0000042771258,0.0035891086,0.011442136,0.038921427,0.9209577,0.00003257488,0.02203545],"study_design_scores_gemma":[0.010581491,0.0061814003,0.0067019365,0.0032468485,0.00060657563,0.00035261433,0.015107448,0.7804725,0.0798779,0.090248376,0.0028186091,0.0038043063],"about_ca_topic_score_codex":0.000043560667,"about_ca_topic_score_gemma":0.000016646647,"teacher_disagreement_score":0.8307093,"about_ca_system_score_codex":0.000104992265,"about_ca_system_score_gemma":0.00023983396,"threshold_uncertainty_score":0.99985504},"labels":[],"label_agreement":null},{"id":"W2715337786","doi":"10.1299/jsmermd.2006._1a1-e01_1","title":"1A1-E01 Experiment of intelligent wheelchair \"TAO Aicle\" at the EXPO 2005 AICHI JAPAN","year":2006,"lang":"en","type":"article","venue":"The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Compass; Wheelchair; Robot; Global Positioning System; Computer science; Position (finance); Simulation; Real-time computing; Embedded system; Artificial intelligence; Telecommunications; Geography; World Wide Web; Business; Cartography","score_opus":0.016815752669375315,"score_gpt":0.23856174760949456,"score_spread":0.22174599494011923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2715337786","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9101311,0.0018542407,0.054722223,0.025667777,0.00046789518,0.0010383531,0.000040351308,0.00031103048,0.005767009],"genre_scores_gemma":[0.99412316,0.0003407934,0.004252549,0.00012405559,0.00004804883,0.000027745918,0.000003067199,0.000020118281,0.0010604439],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99780285,0.000022355476,0.0005564759,0.0005269461,0.00049424166,0.0005971219],"domain_scores_gemma":[0.9983345,0.000099541765,0.0004824745,0.0004485727,0.0005436655,0.000091225265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006920253,0.000333264,0.00043742036,0.00015950688,0.00029431368,0.00011893093,0.0014877612,0.00015101489,0.0000189483],"category_scores_gemma":[0.000039803344,0.00021143838,0.00012792704,0.00029596552,0.00046425365,0.0002069616,0.0008228088,0.00037198205,0.000013417416],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034880017,0.00020318662,0.00025860942,0.000036583606,0.000045054683,4.919382e-7,0.0016887073,0.0013133119,0.016731374,0.9747453,0.0014157855,0.0035267211],"study_design_scores_gemma":[0.002395689,0.0041769617,0.003208488,0.0007020634,0.00022113035,0.00009417757,0.02081016,0.07407494,0.66188824,0.22271825,0.007954506,0.0017553995],"about_ca_topic_score_codex":0.000098798315,"about_ca_topic_score_gemma":0.000034644414,"teacher_disagreement_score":0.75202703,"about_ca_system_score_codex":0.00008653973,"about_ca_system_score_gemma":0.00009786416,"threshold_uncertainty_score":0.8622207},"labels":[],"label_agreement":null},{"id":"W2726881550","doi":"10.1093/oxfordhb/9780199539789.013.0016","title":"Eye-head gaze shifts","year":2011,"lang":"en","type":"book","venue":"Oxford University Press eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Gaze; Head (geology); Optometry; Psychology; Geography; Computer science; Computer vision; Geology; Medicine; Paleontology","score_opus":0.03237533505239026,"score_gpt":0.21837147279529703,"score_spread":0.18599613774290677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2726881550","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006960384,0.00005818298,0.035476215,0.000037794063,0.00037157087,0.00020344894,0.000027583981,0.00097388605,0.9627817],"genre_scores_gemma":[0.001451188,0.000053485608,0.0056595034,0.000057832116,0.000062576866,8.241047e-7,0.000011214896,0.000028759088,0.9926746],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982452,0.00006405056,0.000171505,0.0007865574,0.00026221425,0.00047051662],"domain_scores_gemma":[0.9981844,0.000052546122,0.00024580045,0.0012504747,0.00011970024,0.0001470872],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000111678666,0.00039199326,0.00042362677,0.00031405344,0.00021418331,0.00006709812,0.0029243845,0.0006469006,0.000013063266],"category_scores_gemma":[0.000007994764,0.0004452832,0.00020947924,0.000032074437,0.00027867936,0.0001717694,0.0012675315,0.0007640411,0.000011869152],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014913163,0.000025476991,0.00001323193,0.000039845712,0.00008211843,0.0003046382,0.00021843266,6.2680454e-7,0.000009157935,0.9577794,0.013572367,0.027939787],"study_design_scores_gemma":[0.0003388059,0.0000952912,0.00022649157,0.00012828034,0.00005433196,0.0000068101504,0.000006774121,0.00007776829,0.00013188574,0.0056455624,0.9928278,0.00046021424],"about_ca_topic_score_codex":0.000042301483,"about_ca_topic_score_gemma":0.000019183206,"teacher_disagreement_score":0.97925544,"about_ca_system_score_codex":0.0002131747,"about_ca_system_score_gemma":0.00027986936,"threshold_uncertainty_score":0.9997999},"labels":[],"label_agreement":null},{"id":"W2737686350","doi":"10.1109/icorr.2017.8009393","title":"Cheap or Robust? The practical realization of self-driving wheelchair technology","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Wheelchair; Desk; Focus (optics); Tree traversal; Realization (probability); Obstacle; Software; Global Positioning System","score_opus":0.04220394715218849,"score_gpt":0.31614898991808754,"score_spread":0.27394504276589904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2737686350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014706476,0.000011462006,0.9466979,0.03185547,0.00017660316,0.000114135815,2.5395556e-7,0.0007878397,0.005649837],"genre_scores_gemma":[0.8919932,0.00001650026,0.10753449,0.000062996936,0.000019993407,0.000009554911,1.8931703e-7,0.000005162814,0.0003578807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999186,0.000033333363,0.00017853892,0.00025953935,0.00014022033,0.00020236098],"domain_scores_gemma":[0.99816287,0.00011290716,0.00024723585,0.0013237603,0.00013018162,0.0000230253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003250244,0.00008946799,0.00014126446,0.000105290586,0.00037659446,0.0000997549,0.001385389,0.00014618176,0.000013642392],"category_scores_gemma":[0.0007371598,0.000051607673,0.000029468465,0.00021068563,0.00024079994,0.00030419117,0.00053497514,0.00019140926,0.000016383896],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000024343533,0.00007966781,0.011393768,0.0000074779387,0.000019135654,0.000012516042,0.00007566672,0.000016834903,0.0005365661,0.9700371,0.0011075054,0.016711336],"study_design_scores_gemma":[0.0022247785,0.0013927271,0.36316225,0.00028895005,0.000120774755,0.0010183095,0.00089044444,0.3378834,0.10282548,0.13820149,0.050741915,0.0012494688],"about_ca_topic_score_codex":0.00002980196,"about_ca_topic_score_gemma":0.00008325392,"teacher_disagreement_score":0.87728673,"about_ca_system_score_codex":0.000019840347,"about_ca_system_score_gemma":0.0000844591,"threshold_uncertainty_score":0.2896498},"labels":[],"label_agreement":null},{"id":"W2744592570","doi":"10.1145/3131275","title":"BubbleView","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Computer-Human Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Kwanjeong Educational Foundation","keywords":"Eye tracking; Visualization; Image (mathematics); Focus (optics); Rank (graph theory); Measure (data warehouse); Image processing; Eye movement","score_opus":0.055401545961989394,"score_gpt":0.3393102857871984,"score_spread":0.28390873982520903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2744592570","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016580349,0.0000144878295,0.97508276,0.0035269177,0.0024688684,0.0001399272,0.000002540666,0.0006961864,0.0014879453],"genre_scores_gemma":[0.9546454,0.000028700828,0.044434395,0.0002672343,0.00014088681,0.000033995235,0.0000022026493,0.000015646398,0.00043151568],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985809,0.000063121915,0.00029423955,0.00057060044,0.00020429284,0.0002868804],"domain_scores_gemma":[0.9967703,0.00013986551,0.00026313087,0.002637474,0.000106570755,0.00008261175],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00017306134,0.00022468816,0.00023695738,0.00024986645,0.001574368,0.0006682939,0.002533273,0.000112783346,0.00008321594],"category_scores_gemma":[0.000031697116,0.0002220619,0.0001722124,0.00010423182,0.0001030583,0.0010991036,0.00007369804,0.0005137146,0.00035624797],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000766467,0.0002907242,0.00009649489,0.000018165942,0.000058901136,0.000023568788,0.000093173985,0.00029192807,0.00057912106,0.010177781,0.0007655156,0.987597],"study_design_scores_gemma":[0.008039592,0.0070810267,0.16639307,0.0030793867,0.00034195656,0.0017005686,0.00015633408,0.22138228,0.08411192,0.13254565,0.3697319,0.0054363147],"about_ca_topic_score_codex":0.00009720512,"about_ca_topic_score_gemma":0.00006220881,"teacher_disagreement_score":0.9821606,"about_ca_system_score_codex":0.0001070784,"about_ca_system_score_gemma":0.00002127203,"threshold_uncertainty_score":0.99972546},"labels":[],"label_agreement":null},{"id":"W2749038514","doi":"10.1109/tpami.2017.2737423","title":"Photorealistic Monocular Gaze Redirection Using Machine Learning","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Gaze; Computer science; Monocular; Artificial intelligence; Computer vision; Deep learning; Machine learning","score_opus":0.03594395738738556,"score_gpt":0.29296352742325904,"score_spread":0.2570195700358735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749038514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020187462,0.00010558112,0.9787851,0.00026688242,0.00026038755,0.00007650909,0.000018570081,0.0002084317,0.00009105079],"genre_scores_gemma":[0.99550754,0.00025981266,0.003938299,0.00007109839,0.00002070023,0.000010763779,0.0000035339608,0.000013440139,0.00017482894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848133,0.00009414958,0.00030853032,0.0006195009,0.00022403199,0.00027246535],"domain_scores_gemma":[0.9986419,0.00007682857,0.0002430799,0.0008506226,0.00008191167,0.00010566383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003086668,0.00024131447,0.0003495443,0.0005213361,0.0011013922,0.00034840696,0.0006835485,0.00010233383,0.00005058097],"category_scores_gemma":[0.000024101513,0.00022067463,0.00022868313,0.00041802507,0.000145221,0.00027927163,0.000014630539,0.00047268995,0.000014115513],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001056933,0.00012961887,0.009649087,0.000014433998,0.000499945,0.000038175694,0.00013249667,0.021772599,0.0024532618,0.000098299555,0.0000014037905,0.9652001],"study_design_scores_gemma":[0.00008704346,0.000094429284,0.0048284987,0.000029411296,0.00037571107,0.000027759257,0.000013178193,0.86873186,0.12520473,0.0002772527,0.00008294923,0.00024717677],"about_ca_topic_score_codex":0.00935137,"about_ca_topic_score_gemma":0.0021025972,"teacher_disagreement_score":0.97532004,"about_ca_system_score_codex":0.000051971056,"about_ca_system_score_gemma":0.000014934149,"threshold_uncertainty_score":0.99724543},"labels":[],"label_agreement":null},{"id":"W2749457793","doi":"10.1007/s11042-017-5091-1","title":"Physiological heatmaps: a tool for visualizing users’ emotional reactions","year":2017,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Social Sciences and Humanities Research Council of Canada","keywords":"Computer science; Gaze; Human–computer interaction; Interface (matter); Cognition; Artificial intelligence; Psychology","score_opus":0.08002373470876753,"score_gpt":0.3425787505858175,"score_spread":0.26255501587705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2749457793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03081147,0.000036218466,0.96356297,0.0042241905,0.000094949246,0.00056882354,0.000073304764,0.000298042,0.00033004239],"genre_scores_gemma":[0.8502473,0.000029290564,0.14823478,0.00011153766,0.00017390221,0.0009787873,0.000030414347,0.000006432964,0.00018753295],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99922174,0.000011421133,0.00014978721,0.0003585455,0.00007118873,0.00018729694],"domain_scores_gemma":[0.9990052,0.0001928316,0.000111152396,0.0005614438,0.00007426255,0.00005511693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116525465,0.00009928187,0.0001267147,0.00003646996,0.0010452117,0.00027898912,0.00050189556,0.000082349834,0.0000052025584],"category_scores_gemma":[0.00016110422,0.00008672923,0.000055428834,0.00005223065,0.00016780371,0.00030159927,0.00014961988,0.00010071389,0.000025686759],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004819098,0.00028120316,0.0035344714,0.000026414149,0.000035738703,0.0000010746858,0.000090527996,0.0000142312165,0.027501661,0.45220414,0.0018969637,0.51440877],"study_design_scores_gemma":[0.0010948561,0.00012466263,0.7619317,0.00004276884,0.00002303857,0.00001987568,0.000039407496,0.054141212,0.0024301435,0.03709303,0.14261869,0.00044063063],"about_ca_topic_score_codex":0.000011485143,"about_ca_topic_score_gemma":0.0000026195378,"teacher_disagreement_score":0.81943583,"about_ca_system_score_codex":0.000015481379,"about_ca_system_score_gemma":0.000022193784,"threshold_uncertainty_score":0.8039029},"labels":[],"label_agreement":null},{"id":"W2750460922","doi":"10.1109/hri.2016.7451898","title":"Optimal gaze-based robot selection in multi-human multi-robot interaction","year":2016,"lang":"en","type":"article","venue":"2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gaze; Computer science; Human–robot interaction; Robot; Artificial intelligence; Selection (genetic algorithm); Computer vision; Human–computer interaction; Robot control; Mobile robot","score_opus":0.250824737054364,"score_gpt":0.428233806082312,"score_spread":0.177409069027948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750460922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10748651,0.000016064581,0.8782435,0.0065691015,0.004442894,0.00060234516,0.000028213622,0.001030742,0.0015806291],"genre_scores_gemma":[0.97180283,0.00003009532,0.023266083,0.00050327653,0.0003012709,0.00020116803,0.00004816149,0.00006250486,0.003784638],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995333,0.0003497234,0.0012059011,0.0015616649,0.0007604729,0.0007891943],"domain_scores_gemma":[0.9964936,0.00040086094,0.00089439703,0.0009151716,0.0010828739,0.00021309374],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005857674,0.00070818246,0.00059433124,0.001685019,0.00040133178,0.0005181135,0.0024337065,0.00041426482,0.0011299426],"category_scores_gemma":[0.00044569542,0.0006046377,0.00026960543,0.00052817195,0.00021091304,0.0020457187,0.00032695974,0.0011200832,0.0011812324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005417474,0.0058083837,0.008114212,0.000063359395,0.00037894023,0.0001374901,0.0008514384,0.017300645,0.8616153,0.04035507,0.0058805505,0.05895289],"study_design_scores_gemma":[0.01934832,0.0029971644,0.11997311,0.0058884397,0.00009965204,0.00036129315,0.00076744414,0.50608903,0.32589746,0.0025031627,0.011785871,0.004289049],"about_ca_topic_score_codex":0.00048408125,"about_ca_topic_score_gemma":0.0012490319,"teacher_disagreement_score":0.8643163,"about_ca_system_score_codex":0.001353793,"about_ca_system_score_gemma":0.00023061814,"threshold_uncertainty_score":0.99978316},"labels":[],"label_agreement":null},{"id":"W2750971774","doi":"10.1145/3123021.3123043","title":"An empirical study of foot gestures for hands-occupied mobile interaction","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gesture; Foot (prosody); Computer science; Human–computer interaction; Gesture recognition; Mobile device; Artificial intelligence; World Wide Web","score_opus":0.06137172486234391,"score_gpt":0.4071128959932463,"score_spread":0.3457411711309024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750971774","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8794016,0.0000025921465,0.11920534,0.00021547983,0.0001793729,0.00024755188,5.615968e-7,0.00015155826,0.0005958979],"genre_scores_gemma":[0.992478,3.9904833e-7,0.0072881775,0.000029597779,0.000027788414,0.000070095506,5.7071145e-7,0.0000040303244,0.000101299425],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993574,0.000023129776,0.00013775601,0.0002686851,0.00008926833,0.00012376985],"domain_scores_gemma":[0.998844,0.000063502666,0.0001300486,0.00084291765,0.00008876007,0.0000307268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015077299,0.000075018674,0.0001410581,0.00007055526,0.00021884764,0.00012034332,0.00092668843,0.000051866937,0.000004958479],"category_scores_gemma":[0.000057465935,0.00005750198,0.000034777033,0.000037249392,0.000045798835,0.00031563247,0.00013014075,0.00007745047,0.0000029706591],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015252724,0.0058300197,0.5304981,0.000029198476,0.00012887699,0.0000129826685,0.00404828,0.00016794074,0.008385257,0.010752203,0.006226972,0.43376768],"study_design_scores_gemma":[0.0019721542,0.0068388092,0.9486819,0.000011396221,0.000017398921,0.0000066261528,0.00094082835,0.018532211,0.018960372,0.0022146886,0.0016240971,0.00019952944],"about_ca_topic_score_codex":0.000088190514,"about_ca_topic_score_gemma":0.00014369986,"teacher_disagreement_score":0.43356815,"about_ca_system_score_codex":0.000010331953,"about_ca_system_score_gemma":0.000013322861,"threshold_uncertainty_score":0.23448627},"labels":[],"label_agreement":null},{"id":"W2751205969","doi":"10.1167/17.10.709","title":"Does uncertainty about the terrain explain gaze behavior during visually guided walking?","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Gaze; Terrain; Computer science; Psychology; Computer vision; Human–computer interaction; Cognitive psychology; Physical medicine and rehabilitation; Artificial intelligence; Geography; Cartography; Medicine","score_opus":0.02015625667743757,"score_gpt":0.3320625262475179,"score_spread":0.31190626957008033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751205969","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98393816,0.00006689327,0.009138497,0.005528655,0.0010133862,0.00008042655,8.9229087e-7,0.00005257907,0.0001805041],"genre_scores_gemma":[0.9950832,0.000036965703,0.004402534,0.00008832521,0.0001927637,0.000003039406,1.2891513e-7,0.000008336991,0.00018471811],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986138,0.00009715638,0.00044106354,0.00019851726,0.00039680532,0.00025266086],"domain_scores_gemma":[0.9979917,0.00008828445,0.00084458623,0.00080174056,0.00020200541,0.00007170807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010929328,0.00013679772,0.0002227564,0.00013404292,0.00077063363,0.0004674422,0.002306373,0.000087527864,0.000012823925],"category_scores_gemma":[0.0003599966,0.00006319001,0.00015001839,0.00007095283,0.00014404488,0.00055236183,0.00041096087,0.00039584303,0.000008796816],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108264016,0.0006469757,0.047935702,0.000044292687,0.00010593503,0.0013651615,0.0032192436,0.00017424005,0.25312963,0.0064890333,0.0042543663,0.6825272],"study_design_scores_gemma":[0.00075464754,0.00027283575,0.98540753,0.00029747305,0.00002160923,0.00029697322,0.00010674004,0.0011204603,0.0074949204,0.0018357106,0.0022332044,0.00015787366],"about_ca_topic_score_codex":0.000023848399,"about_ca_topic_score_gemma":0.000018659184,"teacher_disagreement_score":0.93747187,"about_ca_system_score_codex":0.00006864079,"about_ca_system_score_gemma":0.000052599727,"threshold_uncertainty_score":0.5927169},"labels":[],"label_agreement":null},{"id":"W2752356709","doi":"10.1145/3098279.3098561","title":"Designing a gaze gesture guiding system","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Gesture; Gaze; Computer science; Task (project management); Modality (human–computer interaction); Human–computer interaction; Vocabulary; Computer vision; Presentation (obstetrics); Artificial intelligence; Engineering","score_opus":0.06747349202781526,"score_gpt":0.2816483371140398,"score_spread":0.21417484508622453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752356709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016000024,0.00037730281,0.9578641,0.0012444907,0.0015267595,0.00018879822,0.0000017264061,0.0027319319,0.03446489],"genre_scores_gemma":[0.7676626,0.000009282956,0.23119776,0.000040325514,0.00011921101,0.000035090863,0.0000017828862,0.000014677239,0.00091926515],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981377,0.00007359911,0.00027191933,0.0008809387,0.0002454974,0.00039030734],"domain_scores_gemma":[0.997048,0.00006938979,0.0003641566,0.0023072883,0.00013636235,0.000074797776],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055339024,0.00030785965,0.00042249277,0.00019480317,0.00035462866,0.00072072505,0.00385091,0.00053985394,0.0000032452156],"category_scores_gemma":[0.00011753586,0.00026534256,0.00014229999,0.000067553265,0.0000666427,0.0001312559,0.0032637464,0.00059269473,0.000105288396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000044617805,0.00007933487,0.0037406671,0.001010181,0.0002734805,0.00083796977,0.00069398625,0.00067456113,0.0044017737,0.8711379,0.016618708,0.100526944],"study_design_scores_gemma":[0.0034374674,0.0006600944,0.052885294,0.02774966,0.00056154863,0.0023398492,0.0015344255,0.5610442,0.14717235,0.13776885,0.05277424,0.012072006],"about_ca_topic_score_codex":0.00009218057,"about_ca_topic_score_gemma":0.000005954681,"teacher_disagreement_score":0.7660626,"about_ca_system_score_codex":0.00013876228,"about_ca_system_score_gemma":0.00015096577,"threshold_uncertainty_score":0.99997985},"labels":[],"label_agreement":null},{"id":"W2753412330","doi":"10.1167/17.10.686","title":"Where is your attention?: Estimating the frequency of gaze following in the cuing task using a trial-by trial analysis.","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gaze; Psychology; Task (project management); Facilitation; Cognitive psychology; Audiology; Medicine; Neuroscience","score_opus":0.04374863279637308,"score_gpt":0.36512042677088935,"score_spread":0.32137179397451626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753412330","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.80861515,0.0003074092,0.18801862,0.002311298,0.0005740052,0.000120326775,7.4449065e-7,0.0000074524232,0.000045008474],"genre_scores_gemma":[0.97453004,0.000009358395,0.025278589,0.000037076064,0.00013148788,0.0000011745035,1.5516588e-7,0.0000044120015,0.000007711574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982121,0.00023842457,0.00070693856,0.00016527451,0.000501311,0.00017595319],"domain_scores_gemma":[0.9974533,0.00021611265,0.0015699558,0.0006240477,0.00011179425,0.000024811094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031601586,0.00010744477,0.00037087675,0.00024117838,0.0004507857,0.0003314906,0.0016940879,0.00007664064,0.0000030963422],"category_scores_gemma":[0.0005848628,0.000058978807,0.0004391515,0.0004245885,0.0000625927,0.00056099944,0.00013470877,0.00038509272,8.2385935e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.015882855,0.0043463744,0.1981758,0.00022138243,0.004988369,0.0010974397,0.021209287,0.012238001,0.32775912,0.0074787964,0.0059130993,0.40068948],"study_design_scores_gemma":[0.18482241,0.0037874365,0.21630755,0.002976093,0.0020641251,0.0001615859,0.0016047554,0.5664811,0.0009381541,0.019818429,0.0002684071,0.00076995854],"about_ca_topic_score_codex":0.00011813246,"about_ca_topic_score_gemma":0.000014671073,"teacher_disagreement_score":0.5542431,"about_ca_system_score_codex":0.000043245596,"about_ca_system_score_gemma":0.000061537314,"threshold_uncertainty_score":0.34671247},"labels":[],"label_agreement":null},{"id":"W2758776636","doi":"10.1109/iscas.2017.8050660","title":"Pupil localization for gaze estimation using unsupervised graph-based model","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pupil; Ellipse; Computer vision; Computer science; Artificial intelligence; Gaze; Graph; Pattern recognition (psychology); Mathematics; Optics; Theoretical computer science; Geometry; Physics","score_opus":0.0729298553334527,"score_gpt":0.3207433755960155,"score_spread":0.24781352026256281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2758776636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0119110625,0.0000055462406,0.98624927,0.0009499977,0.00009967715,0.00015130451,0.0000019374693,0.00033286947,0.0002983552],"genre_scores_gemma":[0.6279026,3.716127e-7,0.37191826,0.00011788167,0.0000064055153,0.000010094227,0.0000030974375,0.0000048364695,0.00003644266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993229,0.000010557096,0.00012811611,0.00026096625,0.00010391989,0.00017356574],"domain_scores_gemma":[0.9990583,0.000028344355,0.00010437053,0.00065034104,0.00012582449,0.000032802018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001558155,0.000091595066,0.000100739395,0.00010507078,0.00051223824,0.00022752145,0.00065300596,0.00007546479,0.0000020262298],"category_scores_gemma":[0.00010104837,0.000083930085,0.000048786613,0.000078309495,0.0000719098,0.00039912816,0.000062877145,0.00004308297,0.000003991065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069431867,0.00006783182,0.0012960609,0.00002873837,0.000008171194,0.0000011160496,0.000043158958,0.77646464,0.002198756,0.18147655,0.00024691387,0.03816115],"study_design_scores_gemma":[0.0004052444,0.000027318592,0.0004919395,0.00001790861,0.0000062939125,8.231456e-7,0.0000021526564,0.9576271,0.006165448,0.035123143,0.00002758066,0.000105016414],"about_ca_topic_score_codex":0.00002801676,"about_ca_topic_score_gemma":0.000018971848,"teacher_disagreement_score":0.61599153,"about_ca_system_score_codex":0.00002575186,"about_ca_system_score_gemma":0.0000611115,"threshold_uncertainty_score":0.39397743},"labels":[],"label_agreement":null},{"id":"W2762022909","doi":"10.1145/3131277.3134365","title":"Head vs. eye-based selection in virtual reality","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Eye tracking; Selection (genetic algorithm); Virtual reality; Head (geology); Gaze; Optical head-mounted display; Task (project management); Computer vision; Reciprocal; Artificial intelligence; Human–computer interaction; Computer graphics (images); Engineering","score_opus":0.03599796259681985,"score_gpt":0.32794171979527736,"score_spread":0.2919437571984575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762022909","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2819351,0.0000021809064,0.7007574,0.010450073,0.00014201213,0.000056089473,4.590589e-7,0.0004064585,0.0062502725],"genre_scores_gemma":[0.99389184,5.1450843e-7,0.0054961178,0.00018236872,0.000015966409,0.000005704353,3.6185656e-7,0.0000024564624,0.00040464787],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993505,0.000032688025,0.00010188101,0.00025490057,0.000089000336,0.0001710561],"domain_scores_gemma":[0.9994203,0.00002358319,0.000060731218,0.00043690912,0.00003222231,0.000026270538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025706424,0.0000650517,0.00009453908,0.00008124142,0.00018252218,0.00011867787,0.00067985355,0.000069290894,0.000008734564],"category_scores_gemma":[0.00008760239,0.000058059217,0.000023508686,0.00009616205,0.00006785919,0.00019986306,0.00009451448,0.00013322064,0.00004091189],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028917093,0.00030792755,0.41553757,0.000008953679,0.000008178472,0.000028667582,0.00006453711,0.00042991922,0.0044510495,0.35223818,0.002402089,0.22449403],"study_design_scores_gemma":[0.0003763025,0.00012613287,0.91215944,0.000010674229,7.8028705e-7,0.0000015379219,0.000002534175,0.07460087,0.008590381,0.0025134664,0.0015176312,0.00010023376],"about_ca_topic_score_codex":0.0007294783,"about_ca_topic_score_gemma":0.0013253326,"teacher_disagreement_score":0.71195674,"about_ca_system_score_codex":0.000041947384,"about_ca_system_score_gemma":0.000045429566,"threshold_uncertainty_score":0.23675862},"labels":[],"label_agreement":null},{"id":"W2762185336","doi":"10.1145/3131277.3132182","title":"The eyes don't have it","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Selection (genetic algorithm); Eye tracking; Computer science; Head (geology); Gaze; Artificial intelligence; Task (project management); Computer vision; Optical head-mounted display; Reciprocal; Virtual reality; Engineering","score_opus":0.02775006737072484,"score_gpt":0.2956864600594404,"score_spread":0.26793639268871555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762185336","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033569727,0.000094538715,0.37468156,0.1944035,0.00094157975,0.00010545184,5.303648e-7,0.000760962,0.39544216],"genre_scores_gemma":[0.9832234,0.000010837308,0.0057711997,0.00023453345,0.00002380601,0.000003875032,3.7785004e-8,0.0000017801067,0.01073057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995603,0.00000983591,0.000057618603,0.00014462059,0.00007553051,0.00015209166],"domain_scores_gemma":[0.9987962,0.00006135429,0.000050350933,0.0010422483,0.000029916679,0.000019878486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015960642,0.000046989924,0.000046139823,0.000013439818,0.0008882874,0.00044136043,0.0018995422,0.000030238489,0.000008103486],"category_scores_gemma":[0.000096237534,0.000025927106,0.000025141175,0.00001542763,0.00015529853,0.00013675167,0.00034759843,0.000077992045,0.00011800059],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.892419e-7,0.000011940183,0.012455652,7.1482685e-7,0.000008010409,0.000009185855,0.0000492148,5.4702497e-7,0.00015263825,0.72888947,0.020169722,0.23825224],"study_design_scores_gemma":[0.00033273193,0.00008812444,0.32388583,0.000014506907,0.0000042576507,0.000022901524,0.00017280759,0.0118550975,0.011034671,0.08496407,0.5673547,0.0002703377],"about_ca_topic_score_codex":0.000062426894,"about_ca_topic_score_gemma":0.00018494108,"teacher_disagreement_score":0.9496536,"about_ca_system_score_codex":0.000005840255,"about_ca_system_score_gemma":0.000012055201,"threshold_uncertainty_score":0.6832078},"labels":[],"label_agreement":null},{"id":"W2765232187","doi":"10.1007/s12369-017-0434-7","title":"A Decision-Theoretic Approach for the Collaborative Control of a Smart Wheelchair","year":2017,"lang":"en","type":"article","venue":"International Journal of Social Robotics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"National Institute of Biomedical Imaging and Bioengineering; Natural Sciences and Engineering Research Council of Canada","keywords":"Wheelchair; Human–computer interaction; USable; Usability; Computer science; Partially observable Markov decision process; Robot; Control (management); Process (computing); Markov decision process; Controller (irrigation); Artificial intelligence; Human–robot interaction; Markov process; Machine learning; Markov chain; Multimedia; Markov model; World Wide Web","score_opus":0.01833688487344144,"score_gpt":0.30652422217939035,"score_spread":0.2881873373059489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765232187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013924715,0.00007772171,0.98450327,0.012633332,0.0010065554,0.00009899374,0.00001320729,0.000008282829,0.00026614135],"genre_scores_gemma":[0.910029,0.000016410604,0.08959905,0.000095043935,0.00022208542,0.0000036908916,2.4512602e-7,0.0000046217438,0.000029881567],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99904156,0.000032387212,0.0003269016,0.00009153521,0.00040202044,0.00010560617],"domain_scores_gemma":[0.99666494,0.0005441873,0.0008979613,0.00018265139,0.0016851033,0.000025139865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005755952,0.00007558919,0.00019939482,0.00008195934,0.00026838126,0.00016802308,0.002272195,0.00006437847,0.0000016990899],"category_scores_gemma":[0.00089936604,0.00005106063,0.00015081145,0.000055676624,0.00031474468,0.0001849156,0.00012487081,0.00015710489,7.733808e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018831779,0.00015651825,0.0012478933,0.0000046961914,0.00038954022,0.000011245016,0.00071675185,0.0036837053,0.00018564232,0.9554518,0.0009467504,0.037017167],"study_design_scores_gemma":[0.012281401,0.0014359818,0.06617872,0.00022955815,0.00032241302,0.00025177226,0.0014219028,0.4021859,0.0023673146,0.505112,0.0076393764,0.0005736633],"about_ca_topic_score_codex":0.0000024012356,"about_ca_topic_score_gemma":0.0000025766715,"teacher_disagreement_score":0.9086365,"about_ca_system_score_codex":0.000047977,"about_ca_system_score_gemma":0.0001519702,"threshold_uncertainty_score":0.42223415},"labels":[],"label_agreement":null},{"id":"W2787013146","doi":"10.1109/ssci.2017.8285207","title":"Using machine learning based on eye gaze to predict targets: An exploratory study","year":2017,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Haptic technology; Computer science; Perceptron; Fixation (population genetics); Human–computer interaction; Artificial intelligence; Robot; Task (project management); Gaze; Task analysis; Machine learning; Artificial neural network; Engineering","score_opus":0.05985587390656106,"score_gpt":0.3228912375985728,"score_spread":0.26303536369201175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787013146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49485505,0.000007813514,0.5022209,0.0006339579,0.00023751598,0.00020223159,0.0000012725986,0.00074683124,0.0010944105],"genre_scores_gemma":[0.9663799,2.5861914e-7,0.033212796,0.00020307727,0.000040907657,0.00001376931,8.634459e-7,0.000013165776,0.00013525833],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986742,0.00012683566,0.00014920987,0.0005229606,0.00024243876,0.0002843179],"domain_scores_gemma":[0.99843025,0.0000329577,0.000100348436,0.001249906,0.00006511175,0.0001214064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005225727,0.00015833382,0.00017427735,0.00017922418,0.00070841395,0.00028247412,0.0013624321,0.000052988642,0.000018532819],"category_scores_gemma":[0.0001905531,0.00013463177,0.00003198426,0.000110940906,0.000046643887,0.0004099183,0.0003384872,0.00026095504,0.000052188367],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058241087,0.0019980033,0.9224786,0.000012978696,0.000048000726,0.00026193672,0.0018525377,0.02151613,0.0064006364,0.004671292,0.00020508724,0.040496565],"study_design_scores_gemma":[0.0007427831,0.0018685702,0.1189176,0.00003385024,0.000008736219,0.0000012756165,0.0003379875,0.87365717,0.0030583283,0.0001427919,0.0009210549,0.00030987003],"about_ca_topic_score_codex":0.000102025886,"about_ca_topic_score_gemma":0.00014176186,"teacher_disagreement_score":0.852141,"about_ca_system_score_codex":0.000043521115,"about_ca_system_score_gemma":0.000042267857,"threshold_uncertainty_score":0.5490124},"labels":[],"label_agreement":null},{"id":"W2788543801","doi":"10.3758/s13428-018-1015-x","title":"iTemplate: A template-based eye movement data analysis approach","year":2018,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Foundation for the National Institutes of Health","keywords":"Computer science; Eye movement; Consistency (knowledge bases); Eye tracking; Artificial intelligence; Software; Process (computing); Set (abstract data type); Data set; Data mining; Computer vision","score_opus":0.4956475261834326,"score_gpt":0.6034427315023565,"score_spread":0.10779520531892389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2788543801","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025606014,0.000068708876,0.9714334,0.0005918435,0.0001839147,0.00041830825,0.000032078835,0.00041158882,0.0012541414],"genre_scores_gemma":[0.2519224,0.0000028543886,0.74726814,0.000068767135,0.000097479715,0.00017179048,0.000052398824,0.000016340193,0.00039983224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940512,0.0021200285,0.00039741737,0.001464784,0.0010222408,0.00094430626],"domain_scores_gemma":[0.99395275,0.0004992178,0.00010296451,0.004665268,0.00055072736,0.00022907052],"candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01301762,0.00022134712,0.00042843798,0.0015105132,0.0005482114,0.00034989303,0.0053991447,0.00019447168,0.00009468153],"category_scores_gemma":[0.0005386366,0.00019213953,0.00012764984,0.005505883,0.00067850645,0.00033668522,0.0023470132,0.00071271334,0.00009200901],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006298469,0.0024882269,0.09286949,0.000057188998,0.0006013591,0.0002204119,0.00044742232,0.000056238492,0.038465705,0.018268576,0.009764396,0.836698],"study_design_scores_gemma":[0.001683604,0.0013362814,0.35059342,0.00004600036,0.00055835216,0.000015609241,0.00029442142,0.5027153,0.085918345,0.004395461,0.051118735,0.001324453],"about_ca_topic_score_codex":0.00037989215,"about_ca_topic_score_gemma":0.000034784207,"teacher_disagreement_score":0.8353735,"about_ca_system_score_codex":0.00012103225,"about_ca_system_score_gemma":0.00022000833,"threshold_uncertainty_score":0.9999821},"labels":[],"label_agreement":null},{"id":"W2791219751","doi":"10.1109/lra.2018.2809512","title":"Free Head Movement Eye Gaze Contingent Ultrasound Interfaces for the da Vinci Surgical System","year":2018,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Modality (human–computer interaction); Computer vision; Eye tracking; Eye movement; Computer science; Artificial intelligence; Eye tracking on the ISS; Pupil; Psychology","score_opus":0.01946166577728276,"score_gpt":0.26577884498804233,"score_spread":0.24631717921075957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2791219751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23711015,0.00005965429,0.7462034,0.015310489,0.00079094735,0.00020824987,0.000006337914,0.0002736111,0.0000371619],"genre_scores_gemma":[0.98353773,0.000005243184,0.0154206855,0.00080558774,0.00016381781,0.00002448671,0.000001459818,0.000007469048,0.000033507513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990789,0.000037731555,0.00023264777,0.00026275165,0.0001610629,0.0002268711],"domain_scores_gemma":[0.99909604,0.00027986703,0.00012895085,0.00038243906,0.00007644985,0.00003623146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032730115,0.00012469545,0.00014341151,0.00005622115,0.00028664203,0.00026036927,0.0005061419,0.000050242503,0.0000014621426],"category_scores_gemma":[0.000031122785,0.000088032015,0.000042738906,0.000109225344,0.00016582993,0.000112569476,0.000079810765,0.00008044434,0.00000801735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006440514,0.0003695044,0.008370919,0.0005675205,0.00070525485,0.000058538757,0.0037852726,0.037381854,0.14246875,0.6568862,0.052102875,0.09723887],"study_design_scores_gemma":[0.0017162007,0.00035224875,0.016338386,0.00025360828,0.000060588663,0.000048249603,0.00023473319,0.9464119,0.024886638,0.00097530894,0.008243031,0.0004791212],"about_ca_topic_score_codex":0.000022186125,"about_ca_topic_score_gemma":0.00001313513,"teacher_disagreement_score":0.90903,"about_ca_system_score_codex":0.000052060746,"about_ca_system_score_gemma":0.000011465304,"threshold_uncertainty_score":0.35898414},"labels":[],"label_agreement":null},{"id":"W2793897905","doi":"10.21037/aes.2018.ab055","title":"AB055. Eye movements in the dark: saccades to non-visual targets","year":2018,"lang":"en","type":"article","venue":"Annals of Eye Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Saccade; Saccadic masking; Eye movement; Fixation (population genetics); Saccadic suppression of image displacement; Proprioception; Psychology; Computer vision; Sensory system; Computer science; Cognitive psychology; Neuroscience; Medicine","score_opus":0.04356942246156628,"score_gpt":0.38231729550263427,"score_spread":0.338747873041068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793897905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9669143,0.000032306587,0.021635571,0.006662202,0.0002590563,0.00015479048,0.0000017804489,0.000051949417,0.004288035],"genre_scores_gemma":[0.9918969,0.000005039154,0.0049552624,0.0029979127,0.000036314934,0.000009360674,1.6234289e-7,0.0000030721092,0.000095925265],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9981352,0.000040476923,0.00023550232,0.00048581304,0.0005691054,0.0005339161],"domain_scores_gemma":[0.9988942,0.00005314526,0.000095922536,0.00060228456,0.0002760445,0.00007840789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001894438,0.00011497791,0.00014640018,0.00035515058,0.00022836991,0.00011244109,0.0032037636,0.000040434013,0.000006637791],"category_scores_gemma":[0.00029810713,0.00008163044,0.00003660132,0.0021791896,0.0008360472,0.00049016206,0.0005001128,0.00012474123,0.00009085151],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047463378,0.0014700139,0.16641462,0.000040827574,0.000032758788,0.0000696886,0.023937037,0.00011869802,0.41584945,0.0803829,0.018312173,0.29332435],"study_design_scores_gemma":[0.00012948476,0.00060094177,0.80537647,0.00005052779,7.526317e-7,0.0000011857644,0.00020244069,0.0023680513,0.18176968,0.0072164726,0.002102214,0.00018179193],"about_ca_topic_score_codex":0.000087821434,"about_ca_topic_score_gemma":0.000024014884,"teacher_disagreement_score":0.63896185,"about_ca_system_score_codex":0.000012033837,"about_ca_system_score_gemma":0.00011464055,"threshold_uncertainty_score":0.5953443},"labels":[],"label_agreement":null},{"id":"W2795957134","doi":"","title":"Remote Point-Of-Gaze Estimation for Studies of Selective Attention and Mood Disorders","year":2007,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Point (geometry); Mood; Computer science; Psychology; Cognitive psychology; Artificial intelligence; Social psychology; Mathematics","score_opus":0.01895406751978312,"score_gpt":0.2994427055085698,"score_spread":0.28048863798878665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795957134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6275021,0.00012650578,0.3713781,0.00035407583,0.00004864236,0.00015136706,5.741747e-7,0.00007549269,0.00036314462],"genre_scores_gemma":[0.8985641,0.000031797084,0.10136176,0.0000061169967,0.000008022998,0.0000036694373,3.9589395e-7,0.000004284831,0.000019825282],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993822,0.000002035276,0.00019024876,0.0002014705,0.000085648746,0.00013839899],"domain_scores_gemma":[0.99932754,0.00008748595,0.0001761867,0.00005493119,0.00033702742,0.000016851858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037358937,0.00007727953,0.00015629632,0.00013682175,0.00006263185,0.000011249975,0.00014238464,0.00005084141,9.190659e-8],"category_scores_gemma":[0.00031960665,0.00007090719,0.000032434247,0.0002648106,0.00013970133,0.00022989546,0.000065658714,0.000052709634,2.9467213e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009090432,0.00017229482,0.053441323,0.0009863342,0.0001930545,2.8478635e-7,0.0058675087,0.000011627689,0.09624778,0.22358862,0.0005011069,0.61889917],"study_design_scores_gemma":[0.0017656618,0.0017785529,0.39470375,0.00053369364,0.000093543575,0.000022913975,0.0032777938,0.07585933,0.23650524,0.28491545,0.00010420302,0.00043988496],"about_ca_topic_score_codex":0.0000055593164,"about_ca_topic_score_gemma":0.000008292473,"teacher_disagreement_score":0.6184593,"about_ca_system_score_codex":0.000019098736,"about_ca_system_score_gemma":0.000007845657,"threshold_uncertainty_score":0.28915113},"labels":[],"label_agreement":null},{"id":"W2796090248","doi":"10.1088/1757-899x/342/1/012088","title":"The comparison respond of braking torque control between PID and SMC controller for electric powered wheelchair descending on slope condition","year":2018,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Council Canada; Universiti Malaysia Pahang; Ministry of Higher Education, Malaysia","keywords":"Control theory (sociology); PID controller; Threshold braking; Torque; Skid (aerodynamics); Dynamic braking; Controller (irrigation); Wheelchair; Engine braking; Braking system; Computer science; Automotive engineering; Engineering; Retarder; Brake; Control (management); Control engineering; Structural engineering; Physics","score_opus":0.017315641856122856,"score_gpt":0.2555990547596342,"score_spread":0.23828341290351132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796090248","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8894837,0.00006483369,0.10900397,0.00059301185,0.00036714075,0.00029064837,0.000014459827,0.00011990136,0.000062352316],"genre_scores_gemma":[0.9981797,0.00002185697,0.0016339936,0.00003515785,0.00008018344,0.000029866962,8.7359825e-7,0.000007982956,0.000010432351],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860346,0.00003581763,0.00032764542,0.00037312828,0.00022406116,0.0004358858],"domain_scores_gemma":[0.9988842,0.00031718146,0.00017424858,0.00022713358,0.0003266337,0.00007061961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012389241,0.00017161897,0.00034642353,0.00020935471,0.0005258579,0.0004740478,0.0005221275,0.00007069737,0.0000022908634],"category_scores_gemma":[0.00048519313,0.00013112841,0.000018189552,0.00033633295,0.0005475209,0.0005116529,0.00010453462,0.00008341167,0.000001256812],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007277207,0.0000058402065,0.0002050478,0.000023926126,0.000011494496,4.5380887e-7,0.00022163098,0.000011508796,0.917539,0.07315466,0.000012163382,0.008741483],"study_design_scores_gemma":[0.0007806094,0.00093253196,0.0333935,0.00014196339,0.000014051656,0.0000118798425,0.000095264426,0.014452693,0.94783425,0.0013974608,0.00070555846,0.00024026357],"about_ca_topic_score_codex":0.000012631745,"about_ca_topic_score_gemma":0.000004182115,"teacher_disagreement_score":0.10869597,"about_ca_system_score_codex":0.00003885114,"about_ca_system_score_gemma":0.000087286026,"threshold_uncertainty_score":0.5347262},"labels":[],"label_agreement":null},{"id":"W2800374499","doi":"","title":"Simplifying the Cross-Ratios Method of Point-Of-Gaze Estimation","year":2017,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Estimation; Point (geometry); Computer science; Artificial intelligence; Cross-ratio; Mathematics; Computer vision; Statistics; Engineering; Geometry","score_opus":0.03181074227650112,"score_gpt":0.35357978639729687,"score_spread":0.32176904412079577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800374499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3722944,0.000034106826,0.6219031,0.0015838154,0.00014453538,0.00013190626,0.0000012225439,0.0001291248,0.0037777605],"genre_scores_gemma":[0.8508992,0.0000029532882,0.14897642,0.000029288576,0.000020388388,0.000009426475,2.0333582e-7,0.000005089922,0.000057042053],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992067,0.0000066029866,0.00023888718,0.00022462015,0.00016590778,0.00015727492],"domain_scores_gemma":[0.9987132,0.0000864918,0.00047967042,0.00038837292,0.00031019573,0.000022063654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006209219,0.000093457405,0.00016670409,0.000064010965,0.00036154067,0.00022698792,0.0014227125,0.00007150052,0.0000026588302],"category_scores_gemma":[0.00079121976,0.00006688377,0.00005441142,0.000115178715,0.00027115148,0.0006436031,0.00032046417,0.00012936081,0.000005740621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011278892,0.00006978531,0.064337455,0.00019580337,0.00004126919,0.0000014377772,0.0023088527,0.00008798231,0.07738617,0.6489488,0.000679354,0.20593181],"study_design_scores_gemma":[0.00048834673,0.00018773368,0.27648523,0.00019264314,0.000027131513,0.000045795132,0.00018739577,0.168039,0.4790781,0.07453938,0.00047094366,0.00025827502],"about_ca_topic_score_codex":0.000021094536,"about_ca_topic_score_gemma":0.0000010446398,"teacher_disagreement_score":0.5744094,"about_ca_system_score_codex":0.0000122250185,"about_ca_system_score_gemma":0.000025455345,"threshold_uncertainty_score":0.27807152},"labels":[],"label_agreement":null},{"id":"W2803334930","doi":"","title":"LAIF: A Logging and Interaction Framework for Gaze- Based Interfaces in Virtual Entertainment Environments.","year":2010,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Gaze; Computer science; Human–computer interaction; Entertainment; Eye tracking; Entertainment industry; Video game development; Video game; Multimedia; Artificial intelligence; Game design","score_opus":0.012483272593034158,"score_gpt":0.26755644266832235,"score_spread":0.2550731700752882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803334930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37241355,0.000005296687,0.6259772,0.001156647,0.00021634405,0.000089398156,5.967787e-7,0.00006332164,0.00007761116],"genre_scores_gemma":[0.90891826,0.0000023921634,0.090680994,0.0002813442,0.000013513817,0.000038130587,9.57628e-7,0.000004936505,0.000059473045],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927557,0.000019003506,0.00014194076,0.0003091862,0.00007119061,0.0001831169],"domain_scores_gemma":[0.9994623,0.00021904052,0.000050168583,0.00022975216,0.0000044644244,0.000034258777],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017947686,0.00009960733,0.00010429472,0.00013087316,0.000046122917,0.00006430343,0.000261203,0.00008985188,0.000021221247],"category_scores_gemma":[0.00006124264,0.00008807834,0.000022098402,0.00005415049,0.000058396014,0.00017069929,0.00011561694,0.00027344126,0.00000923913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008220528,0.00061962823,0.038668353,0.00003204414,0.000032423606,0.000016279491,0.0009396287,0.0002102356,0.11141201,0.42706442,0.0001856194,0.42073718],"study_design_scores_gemma":[0.002979385,0.0013497933,0.061226662,0.00029143045,0.000017673512,0.000034754317,0.0010880091,0.5590087,0.2984254,0.05114508,0.023513801,0.00091928313],"about_ca_topic_score_codex":0.000014628275,"about_ca_topic_score_gemma":0.00004653882,"teacher_disagreement_score":0.5587985,"about_ca_system_score_codex":0.00002808336,"about_ca_system_score_gemma":0.0000072522016,"threshold_uncertainty_score":0.35917306},"labels":[],"label_agreement":null},{"id":"W2805359754","doi":"10.1145/3204493.3204583","title":"Sensitivity to natural 3D image transformations during eye movements","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 2018 ACM Symposium on Eye Tracking Research &amp; Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saccade; Saccadic masking; Computer vision; Saccadic suppression of image displacement; Computer science; Artificial intelligence; Sensitivity (control systems); Eye movement; Transformation (genetics); Object (grammar); Virtual image; Engineering","score_opus":0.03640482931694007,"score_gpt":0.35284863311433656,"score_spread":0.3164438037973965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805359754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9409643,0.000015451664,0.03011521,0.022118041,0.00018599974,0.0016859694,0.000039061477,0.0005293366,0.0043466273],"genre_scores_gemma":[0.95953506,0.000014662662,0.038568433,0.0001365559,0.00018109799,0.0004150048,0.000004041605,0.00003053187,0.0011145943],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9968038,0.00005756551,0.00046243818,0.00076935656,0.0010630527,0.0008437739],"domain_scores_gemma":[0.996366,0.00023836082,0.00020598642,0.0012609083,0.0017487943,0.00017990464],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0017217477,0.0002643488,0.00026534183,0.00059548335,0.0014788018,0.00033545846,0.003185004,0.00013715812,0.000008137712],"category_scores_gemma":[0.0006432396,0.00021773914,0.00013495685,0.0022213536,0.0007997168,0.00071101607,0.0010691888,0.00081928296,0.00045110277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028334522,0.00029747418,0.002416922,0.00008863348,0.00003904778,2.2734518e-7,0.0021076407,0.0000090990325,0.9774684,0.013061348,0.0015563753,0.0029265499],"study_design_scores_gemma":[0.00077452103,0.0002568596,0.14780335,0.00039995322,0.00002967435,0.000019556579,0.00022660533,0.0010384837,0.80703455,0.014784,0.026922664,0.0007097538],"about_ca_topic_score_codex":0.000060552786,"about_ca_topic_score_gemma":0.00004528411,"teacher_disagreement_score":0.17043377,"about_ca_system_score_codex":0.00024923251,"about_ca_system_score_gemma":0.00007519483,"threshold_uncertainty_score":0.9998211},"labels":[],"label_agreement":null},{"id":"W2806389585","doi":"10.1145/3204493.3204577","title":"Pupil size as an indicator of visual-motor workload and expertise in microsurgical training tasks","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 2018 ACM Symposium on Eye Tracking Research &amp; Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Workload; Computer science; Training (meteorology); Pupil; Human–computer interaction; Artificial intelligence; Aeronautics; Psychology; Engineering; Operating system","score_opus":0.06508891089450225,"score_gpt":0.3899228394063512,"score_spread":0.32483392851184895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806389585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9920439,0.000107733096,0.0009118804,0.0049137687,0.000048790516,0.0007884825,0.000007727228,0.0001421817,0.0010355094],"genre_scores_gemma":[0.98763007,0.00006863724,0.011489831,0.000071127615,0.00011334846,0.00038435907,0.000001072451,0.000028415257,0.00021311331],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970517,0.000073950905,0.00056382135,0.0008643473,0.00077256886,0.0006736223],"domain_scores_gemma":[0.9973912,0.0005531266,0.00033744256,0.0008961966,0.00062515464,0.00019686541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002146542,0.00023347583,0.00036817187,0.0005312034,0.00046543867,0.00019076536,0.0030796304,0.00024578103,0.000014404756],"category_scores_gemma":[0.0011603914,0.00019025919,0.00008684086,0.0015405881,0.0016470093,0.0003827561,0.0008841836,0.0007223294,0.000045417324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009813468,0.00081452576,0.021650322,0.000081279955,0.000026305443,4.244294e-7,0.0057899724,7.4327687e-7,0.92411566,0.016043961,0.00028378246,0.031094884],"study_design_scores_gemma":[0.0026573425,0.0024972935,0.34207714,0.0015708251,0.00004575226,0.00007257414,0.0016598334,0.0012558231,0.5829748,0.05024528,0.0137461135,0.0011972489],"about_ca_topic_score_codex":0.000086027525,"about_ca_topic_score_gemma":0.000027395352,"teacher_disagreement_score":0.34114087,"about_ca_system_score_codex":0.00011911394,"about_ca_system_score_gemma":0.000146464,"threshold_uncertainty_score":0.77585447},"labels":[],"label_agreement":null},{"id":"W2884790594","doi":"10.1167/18.6.18","title":"Using synchronized eye and motion tracking to determine high-precision eye-movement patterns during object-interaction tasks","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glenrose Rehabilitation Hospital; Alberta Health Services; Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Eye movement; Computer vision; Fixation (population genetics); Object (grammar); Artificial intelligence; Eye tracking; Computer science; Visual search; Visual field; Task (project management); Motion (physics); Video tracking; Psychology; Communication; Neuroscience; Medicine","score_opus":0.02526213307195909,"score_gpt":0.32594430003543606,"score_spread":0.300682166963477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884790594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.624797,0.000025071959,0.37407482,0.00040089537,0.00060383603,0.000059599253,5.8600517e-7,0.000030444378,0.0000077807945],"genre_scores_gemma":[0.969171,0.000019428287,0.030417755,0.0000771223,0.00029050128,7.295505e-7,3.011602e-7,0.000011080381,0.000012086583],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9985612,0.00008310294,0.0004912655,0.00027895506,0.00035582157,0.00022963596],"domain_scores_gemma":[0.9988608,0.000052450847,0.0004402518,0.00025269683,0.00028867903,0.00010513001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005074484,0.00014981517,0.00024965836,0.0004560625,0.00021184748,0.00018967515,0.00033049475,0.00008485786,0.000015028486],"category_scores_gemma":[0.00010784143,0.00012482941,0.00006709324,0.00022844218,0.00002962203,0.00078134454,0.00022674675,0.00023644391,0.000007689397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000095861535,0.000108677574,0.0061383815,0.000020894166,0.000020709604,0.0000636926,0.00033639022,0.00027558228,0.61059743,0.000044595938,0.000026088734,0.3822717],"study_design_scores_gemma":[0.0014742676,0.0016521008,0.72302014,0.0011863126,0.000031084328,0.00018952254,0.000082687824,0.040709216,0.23069891,0.00058016425,0.00012456665,0.00025102487],"about_ca_topic_score_codex":0.000035038498,"about_ca_topic_score_gemma":0.000013464203,"teacher_disagreement_score":0.71688175,"about_ca_system_score_codex":0.00022425265,"about_ca_system_score_gemma":0.000020237785,"threshold_uncertainty_score":0.5090396},"labels":[],"label_agreement":null},{"id":"W2886975378","doi":"10.16910/jemr.10.5.1","title":"Eye tracking and visualization. Introduction to the Special Thematic Issue","year":2018,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Movement Disorders","funders":"Deutsche Forschungsgemeinschaft","keywords":"Visualization; Computer science; Eye tracking; Human–computer interaction; Usability; Thematic map; Visual analytics; Perception; Creative visualization; Data science; Data visualization; Artificial intelligence","score_opus":0.046809308634746485,"score_gpt":0.3996600125789497,"score_spread":0.3528507039442032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886975378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5237416,0.0001801702,0.33117983,0.14174077,0.0014567957,0.00037208264,4.939783e-7,0.000040691488,0.001287566],"genre_scores_gemma":[0.982187,0.000039928345,0.0057548806,0.00040298753,0.010617138,0.0000050183085,1.587204e-7,0.000008176446,0.000984714],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99835926,0.00019352067,0.0002935858,0.0001782502,0.00071654434,0.00025885177],"domain_scores_gemma":[0.9986228,0.00009922576,0.00012566421,0.0002899085,0.00079288735,0.00006952993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033137896,0.000070396316,0.00012707485,0.00038409303,0.00032548112,0.00025552718,0.00070614595,0.00003580114,0.0000897007],"category_scores_gemma":[0.0005322544,0.000044001365,0.000030077548,0.0007174041,0.00015374577,0.0002456858,0.00026233276,0.00029957888,0.00005588267],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009815594,0.00054984353,0.008391261,0.000066108696,0.0001556217,0.00004701253,0.011741962,0.000094924115,0.04077603,0.23056693,0.2878249,0.41968724],"study_design_scores_gemma":[0.0017553273,0.0074392506,0.17527388,0.00044749692,0.000037624643,0.00008441079,0.0047521624,0.011233048,0.13809875,0.090048544,0.5702703,0.0005591941],"about_ca_topic_score_codex":0.000006764639,"about_ca_topic_score_gemma":0.0000128818465,"teacher_disagreement_score":0.4584454,"about_ca_system_score_codex":0.00007715828,"about_ca_system_score_gemma":0.000051277053,"threshold_uncertainty_score":0.25033703},"labels":[],"label_agreement":null},{"id":"W2888319272","doi":"10.1109/percom.2018.8444580","title":"Activity Classification in Independent Living Environment with JINS MEME Eyewear","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Eyewear; Wearable computer; Activities of daily living; Computer science; Independent living; Population; Assisted living; Wearable technology; Medicine; Human–computer interaction; Physical medicine and rehabilitation; Gerontology; Embedded system; Physical therapy; Environmental health","score_opus":0.020978716866877096,"score_gpt":0.2345711653978763,"score_spread":0.2135924485309992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888319272","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5800545,0.000003015339,0.41397846,0.0008636516,0.000039346083,0.00006910352,2.48119e-7,0.0001594646,0.004832179],"genre_scores_gemma":[0.98603034,0.000003232292,0.013491871,0.000052894862,0.00001882918,0.000011237753,2.0603919e-7,0.0000043400896,0.00038702277],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99917406,0.00004111573,0.000087559194,0.00033513448,0.00016820419,0.00019389922],"domain_scores_gemma":[0.99945086,0.000040109953,0.000050371393,0.0004100614,0.000016242557,0.000032373995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021602913,0.00008339391,0.00008469749,0.00010704192,0.00006404556,0.00003753071,0.00036903526,0.000059330527,0.000046740806],"category_scores_gemma":[0.000013776619,0.00006539693,0.0000113314245,0.0001945631,0.000099863806,0.00020757697,0.00015062463,0.00013518771,0.00014828106],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003566187,0.0016106794,0.44312194,0.00001870599,0.00005613326,0.000045802426,0.002227103,0.000072501905,0.10341275,0.14071742,0.000594913,0.3080864],"study_design_scores_gemma":[0.00013085802,0.00019533251,0.9759109,0.00001755373,0.0000016519543,0.000006049974,0.000047539168,0.013860546,0.008449528,0.00029930242,0.00096007215,0.00012063641],"about_ca_topic_score_codex":0.000064494,"about_ca_topic_score_gemma":0.00024861845,"teacher_disagreement_score":0.532789,"about_ca_system_score_codex":0.000086829816,"about_ca_system_score_gemma":0.000026268897,"threshold_uncertainty_score":0.26668096},"labels":[],"label_agreement":null},{"id":"W2888761084","doi":"10.3390/vision2030035","title":"Accurate Model-Based Point of Gaze Estimation on Mobile Devices","year":2018,"lang":"en","type":"article","venue":"Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer vision; Computer science; Offset (computer science); Artificial intelligence; Eye tracking; Mobile device; Point (geometry); Tracking (education); Mathematics; Geometry","score_opus":0.017540946348225648,"score_gpt":0.3095980436289964,"score_spread":0.2920570972807707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888761084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29810554,0.000008406271,0.70084625,0.0002681976,0.00006947708,0.000058592774,9.54594e-7,0.00013869813,0.00050390826],"genre_scores_gemma":[0.9310742,7.790175e-7,0.068770714,0.000114503506,0.000010676472,0.000005940519,0.0000013838362,0.000003833001,0.000017955143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993831,0.000022578697,0.00013442486,0.00021403072,0.00013532027,0.000110555644],"domain_scores_gemma":[0.9993569,0.00005146449,0.00009667606,0.00037604175,0.00009609341,0.000022781855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016185398,0.00007126323,0.000090618574,0.00012057737,0.00005937582,0.00002522441,0.00033832475,0.00005259676,0.0000066871858],"category_scores_gemma":[0.000033058866,0.000057261324,0.000027470844,0.00021186759,0.000066582725,0.00014870113,0.000060669372,0.000057301313,0.0000829532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044516182,0.00038386713,0.00036058624,0.000046198766,0.000008988518,0.000004049411,0.00037153016,0.14369549,0.023983637,0.034319557,0.0012739503,0.7955076],"study_design_scores_gemma":[0.00014496596,0.00069794775,0.0023192086,0.00006580862,0.000001768303,5.357861e-7,0.0000033018264,0.93006253,0.06367576,0.0028726396,0.000096700154,0.000058805086],"about_ca_topic_score_codex":0.000004614508,"about_ca_topic_score_gemma":0.0000026267805,"teacher_disagreement_score":0.79544884,"about_ca_system_score_codex":0.000016632255,"about_ca_system_score_gemma":0.00002627407,"threshold_uncertainty_score":0.2335049},"labels":[],"label_agreement":null},{"id":"W2889195092","doi":"10.1109/icce-china.2018.8448581","title":"Reading Behavior Analysis with Gaze Tracking Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gaze; Computer science; Reading (process); Human–computer interaction; Eye tracking; Tracking (education); Artificial intelligence; Computer vision","score_opus":0.05416828771077315,"score_gpt":0.3106362742262125,"score_spread":0.25646798651543934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889195092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093654305,0.000007242379,0.90026724,0.00041136134,0.00006238833,0.000049283964,0.0000029570356,0.00060474477,0.0049404562],"genre_scores_gemma":[0.8672076,9.776148e-7,0.13231222,0.000087825545,0.000037515976,0.000004134391,0.0000075494654,0.0000050020963,0.00033720766],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99887085,0.000021891537,0.0001297805,0.0005604922,0.00016707815,0.00024990374],"domain_scores_gemma":[0.99807096,0.00003795707,0.00006123626,0.0016818127,0.000099540375,0.00004852728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002442953,0.000102520644,0.00016163869,0.00026212874,0.00014070571,0.0001451721,0.0017507785,0.000048974947,0.000053879965],"category_scores_gemma":[0.000025187665,0.00007496115,0.00002864404,0.0014226919,0.00013045249,0.0004727388,0.0004143485,0.00010341192,0.00006893407],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010798456,0.00030037828,0.44420293,0.000006588976,0.00065770507,0.00017939835,0.00042443746,0.000014465616,0.0038088688,0.11630709,0.0022422716,0.43184507],"study_design_scores_gemma":[0.00060306856,0.00055237196,0.8540154,0.000040403942,0.001050912,0.00012384451,0.00016223923,0.1120408,0.023154149,0.0005743592,0.006834105,0.00084834447],"about_ca_topic_score_codex":0.00008349101,"about_ca_topic_score_gemma":0.00019160788,"teacher_disagreement_score":0.77355325,"about_ca_system_score_codex":0.000015737949,"about_ca_system_score_gemma":0.00002427114,"threshold_uncertainty_score":0.3253411},"labels":[],"label_agreement":null},{"id":"W2891392316","doi":"10.1186/s40561-018-0065-y","title":"Static and dynamic eye movement metrics for students’ performance assessment","year":2018,"lang":"en","type":"article","venue":"Smart Learning Environments","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Eye tracking; Eye movement; Computer science; Tracking (education); Multimedia; Artificial intelligence; Psychology; Pedagogy","score_opus":0.010396112682672327,"score_gpt":0.29631188113411605,"score_spread":0.28591576845144373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891392316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62902975,0.000017087761,0.37023476,0.00022674947,0.00013605232,0.00015970076,0.0000010025839,0.00007654741,0.00011837386],"genre_scores_gemma":[0.9565952,0.00005399065,0.041427244,0.00019386405,0.000014625176,0.000049928833,0.0000047079043,0.000012925327,0.0016475493],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99876684,0.000045438203,0.00016772139,0.00040107546,0.00031583686,0.00030310993],"domain_scores_gemma":[0.9994825,0.00006054216,0.000107930915,0.00028147208,0.000011798785,0.000055761924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042296603,0.00013625462,0.00014094927,0.00012686862,0.0002838244,0.00008005642,0.0004448054,0.000050357063,0.00000878782],"category_scores_gemma":[0.00003343357,0.0001305397,0.000025459807,0.00014292065,0.00012045283,0.00015294016,0.0003618039,0.00017137648,0.000041307838],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000069071557,0.00020227561,0.86522365,0.000031178974,0.0000727469,0.0000026395198,0.00028014978,0.00020873082,0.0023560226,0.0007852278,0.00010019503,0.1307303],"study_design_scores_gemma":[0.000557389,0.000733463,0.8939657,0.00001666441,0.000012802492,8.3291275e-7,0.000038760998,0.08930895,0.00046094574,0.00029042925,0.014451464,0.00016255271],"about_ca_topic_score_codex":0.0000031656634,"about_ca_topic_score_gemma":0.000001279559,"teacher_disagreement_score":0.3288075,"about_ca_system_score_codex":0.00012647074,"about_ca_system_score_gemma":0.000011915806,"threshold_uncertainty_score":0.53232545},"labels":[],"label_agreement":null},{"id":"W2892346178","doi":"10.1167/18.9.13","title":"A nonvisual eye tracker calibration method for video-based tracking","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Calibration; Computer vision; Computer science; Artificial intelligence; Eye tracking; Saccade; Eye–hand coordination; Eye movement; Mathematics; Statistics","score_opus":0.028085274865110015,"score_gpt":0.3719578639363304,"score_spread":0.34387258907122037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892346178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05948492,0.00003747256,0.9371314,0.002704673,0.00047699953,0.000074013165,9.887527e-7,0.000053204792,0.000036322268],"genre_scores_gemma":[0.61245954,6.3869265e-7,0.38701916,0.00029349484,0.00020639252,0.000001232925,3.0799623e-7,0.00000621087,0.000012993649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890536,0.000092177645,0.00039155618,0.0001791581,0.00025030662,0.00018143398],"domain_scores_gemma":[0.99867994,0.00022945477,0.00041813552,0.00019506665,0.00041611525,0.0000612733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115108,0.00009997346,0.00020266918,0.0002472067,0.00013321587,0.000119425305,0.00046115345,0.000085209154,0.000008787097],"category_scores_gemma":[0.00022633768,0.00007724514,0.0001288965,0.00025071512,0.000048928134,0.00054855255,0.000033560893,0.0001701711,0.0000052707023],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027259596,0.00050759583,0.0016313168,0.00004054626,0.000052717776,0.00004830525,0.00049680553,0.0002803188,0.21284044,0.01061373,0.006642758,0.7665729],"study_design_scores_gemma":[0.0023868787,0.004826182,0.038201917,0.00026931596,0.000044883203,0.000112217574,0.0000412677,0.742912,0.19006963,0.009544021,0.011301892,0.0002897877],"about_ca_topic_score_codex":0.0000024823764,"about_ca_topic_score_gemma":0.0000029419227,"teacher_disagreement_score":0.7662831,"about_ca_system_score_codex":0.00003997372,"about_ca_system_score_gemma":0.00009663852,"threshold_uncertainty_score":0.31499654},"labels":[],"label_agreement":null},{"id":"W2893496800","doi":"10.1167/18.10.596","title":"Pursuit eye movements enhance decision making and hitting accuracy in a go/no-go manual interception task","year":2018,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Interception; Fixation (population genetics); Eye movement; Smooth pursuit; Computer science; Task (project management); Artificial intelligence; Computer vision; Trajectory; Medicine; Engineering; Physics; Population","score_opus":0.012881561365509775,"score_gpt":0.3497163467392962,"score_spread":0.3368347853737864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893496800","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7772264,0.000082412385,0.22171383,0.00027289218,0.00046051684,0.000046716472,2.7922354e-7,0.0000228675,0.00017408484],"genre_scores_gemma":[0.9450959,0.000052515432,0.05448976,0.0001634704,0.0001425231,7.2054144e-7,1.2778332e-7,0.000006173161,0.000048822825],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99861336,0.00006812865,0.0005055834,0.00024193992,0.00035499968,0.00021597116],"domain_scores_gemma":[0.9988503,0.00017602982,0.0004972067,0.0002073572,0.00022227479,0.00004684066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009849517,0.00011602045,0.00020027324,0.0003568367,0.00009340828,0.00015943896,0.00059131096,0.0000875814,0.000012014837],"category_scores_gemma":[0.00050355715,0.00009499192,0.000048328337,0.00026241332,0.000063887135,0.000774466,0.00037066624,0.00031109178,0.00005255818],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006902503,0.000086552725,0.006310356,0.000008548382,0.000006774088,0.000039312883,0.0004455269,0.000004420956,0.055719506,0.00015567051,0.0004580647,0.93669623],"study_design_scores_gemma":[0.0013397548,0.002410438,0.95185506,0.00325288,0.000009741326,0.00014541794,0.00015926863,0.02162515,0.0058643445,0.009666771,0.0033694573,0.00030168836],"about_ca_topic_score_codex":0.000002838336,"about_ca_topic_score_gemma":0.000007664408,"teacher_disagreement_score":0.9455447,"about_ca_system_score_codex":0.00009608185,"about_ca_system_score_gemma":0.000022871034,"threshold_uncertainty_score":0.3873658},"labels":[],"label_agreement":null},{"id":"W2895405984","doi":"10.1145/3242969.3243008","title":"Path Word","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Password; Computer science; Gaze; Modality (human–computer interaction); Human–computer interaction; Gesture; Path (computing); Modalities; Authentication (law); Input method; Word (group theory); Eye tracking; Artificial intelligence; Computer vision; Computer security","score_opus":0.02519365924313425,"score_gpt":0.2647805521108021,"score_spread":0.23958689286766785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895405984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013335708,0.00006856868,0.94234866,0.002260223,0.0013901612,0.00008840328,0.0000018049444,0.001795444,0.038711037],"genre_scores_gemma":[0.7784989,0.000010915491,0.2190392,0.00027334868,0.00012987563,0.000016696102,0.0000025066965,0.000007806923,0.002020795],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989184,0.000025601124,0.00014083846,0.0005608734,0.00013411115,0.00022017816],"domain_scores_gemma":[0.99859333,0.00002465154,0.00008234743,0.0011755413,0.00008232847,0.000041798194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015095565,0.00015563596,0.00017606904,0.00010790759,0.000048253656,0.0001264401,0.0018332264,0.00025906888,0.000055379645],"category_scores_gemma":[0.000027800843,0.00012925494,0.000071912735,0.00011824749,0.000084787906,0.0000435079,0.0026288603,0.00040155405,0.0005163744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002449164,0.0001613724,0.0032543044,0.00009677516,0.00008113608,0.000083528874,0.00022498555,0.000012127415,0.00012686187,0.48104385,0.1195629,0.3953497],"study_design_scores_gemma":[0.00038259925,0.00020113119,0.058385853,0.00035845084,0.000024017934,0.00004506598,0.000018907236,0.03518293,0.0057718116,0.8157359,0.082354166,0.0015391664],"about_ca_topic_score_codex":0.00002647679,"about_ca_topic_score_gemma":0.0000051790735,"teacher_disagreement_score":0.7651631,"about_ca_system_score_codex":0.000027465278,"about_ca_system_score_gemma":0.00007523975,"threshold_uncertainty_score":0.6637121},"labels":[],"label_agreement":null},{"id":"W2896098622","doi":"10.1145/3267782.3267798","title":"Look to Go","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer vision; Optical head-mounted display; Computer science; Artificial intelligence; Eye tracking; Virtual reality; Head (geology); Terrain; Tracking (education); Eye movement; Simulation; Computer graphics (images); Geography; Psychology","score_opus":0.015817946082270235,"score_gpt":0.26174141389369565,"score_spread":0.2459234678114254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896098622","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023763897,0.0000027088288,0.8759976,0.0045673507,0.00020235246,0.000026668191,9.059387e-8,0.0005293391,0.09491001],"genre_scores_gemma":[0.87375504,1.2345393e-7,0.11913354,0.0013945965,0.000049721497,0.000002205286,3.706905e-8,0.0000016364515,0.005663097],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960697,0.000005536408,0.00004485487,0.0001627128,0.000052360097,0.00012753981],"domain_scores_gemma":[0.9995985,0.000010603578,0.000007670744,0.00030473375,0.000041046533,0.000037430065],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000057101042,0.00003644582,0.00004023143,0.000051826857,0.000040625815,0.000029151686,0.0004805339,0.000022164148,0.000053970878],"category_scores_gemma":[0.000018315226,0.000028985052,0.00001110745,0.00020387216,0.00003675757,0.000053902444,0.00015836404,0.000030352214,0.0055047516],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.96481e-7,0.000025204834,0.0018609208,7.401431e-7,0.0000037648722,0.0000053530503,0.00011105465,4.5663225e-7,0.0032893158,0.71065587,0.08915067,0.19489577],"study_design_scores_gemma":[0.00026750824,0.0007802996,0.06798761,0.000014150511,0.000002432419,0.000032205236,0.000023008746,0.0045776987,0.15318973,0.03659527,0.7361469,0.00038312218],"about_ca_topic_score_codex":0.000006426805,"about_ca_topic_score_gemma":0.000014902097,"teacher_disagreement_score":0.84999114,"about_ca_system_score_codex":0.000007629145,"about_ca_system_score_gemma":0.0000098371975,"threshold_uncertainty_score":0.9952696},"labels":[],"label_agreement":null},{"id":"W2896218777","doi":"10.3390/s18124280","title":"Highly Accurate and Fully Automatic 3D Head Pose Estimation and Eye Gaze Estimation Using RGB-D Sensors and 3D Morphable Models","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Pose; Artificial intelligence; Computer science; Computer vision; RGB color model; Gaze; 3D pose estimation; Head (geology); Estimator; Articulated body pose estimation; Pattern recognition (psychology); Mathematics","score_opus":0.029044016276368316,"score_gpt":0.2890268859712641,"score_spread":0.25998286969489576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896218777","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78267056,0.000080719314,0.21591999,0.0005836223,0.00011554494,0.00016102637,0.0000036718495,0.0003609829,0.000103885555],"genre_scores_gemma":[0.7223324,0.000013848799,0.27750117,0.00005186568,0.000024326046,0.000002811714,0.0000018875925,0.000012694815,0.00005898559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985773,0.00009059676,0.00028417635,0.00051972334,0.00018628057,0.00034191133],"domain_scores_gemma":[0.9991448,0.00010832271,0.00016966052,0.00035103402,0.00012005395,0.000106151776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028785452,0.00023096394,0.00026367695,0.00023410204,0.00032398128,0.00025186248,0.00015424956,0.00013784206,0.0000029399175],"category_scores_gemma":[0.000106008694,0.00021945847,0.000018642977,0.00029654146,0.00031867766,0.00069467176,0.00015632813,0.00014122792,0.000014363133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007169152,0.00022406771,0.0031857663,0.00055175147,0.00017509563,0.00016131942,0.0080880355,0.26864108,0.024523458,0.0249185,0.00028750094,0.66917175],"study_design_scores_gemma":[0.00038004213,0.00013481024,0.005494033,0.00010754241,0.00002739695,0.00014338245,0.000039925777,0.9857313,0.0014391339,0.0062459083,0.00001925698,0.00023728075],"about_ca_topic_score_codex":0.00010955202,"about_ca_topic_score_gemma":0.00001438414,"teacher_disagreement_score":0.7170902,"about_ca_system_score_codex":0.00004214642,"about_ca_system_score_gemma":0.000034552686,"threshold_uncertainty_score":0.89492565},"labels":[],"label_agreement":null},{"id":"W2898541412","doi":"10.1145/3242671.3242699","title":"Empirical Evaluation of Hybrid Gaze-Controller Selection Techniques in a Gaming Context","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer science; Controller (irrigation); Context (archaeology); Teleportation; Human–computer interaction; Panning (audio); Selection (genetic algorithm); Eye tracking; Artificial intelligence; Engineering","score_opus":0.0501917607562271,"score_gpt":0.34489134300477065,"score_spread":0.29469958224854353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898541412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47242948,0.000025096346,0.5216532,0.0005257953,0.000055433364,0.00018177107,1.898368e-7,0.00027441504,0.004854639],"genre_scores_gemma":[0.9825018,0.0000010900353,0.017237248,0.0001426178,0.000028272423,0.00002877327,2.5987416e-7,0.0000034600957,0.000056485387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990288,0.00012370618,0.00021534346,0.0002409427,0.00023954581,0.00015167863],"domain_scores_gemma":[0.9993142,0.000058353744,0.00007741134,0.00016022174,0.00037272714,0.000017095812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010731866,0.00007327112,0.00014599571,0.00024512297,0.000035243036,0.000017817394,0.0002425347,0.000045619545,0.00003310816],"category_scores_gemma":[0.00018043432,0.00006285835,0.000029851086,0.00037952795,0.000080323385,0.00014935355,0.000055431305,0.00009546829,0.000015569285],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022899554,0.00018936953,0.037206072,0.000005805915,0.000017505074,0.0000018507329,0.0003003305,0.000008845359,0.026214745,0.0141130155,0.0019539525,0.9199656],"study_design_scores_gemma":[0.0010297445,0.00045848993,0.06500631,0.000052662854,0.00001288534,0.000030779272,0.000047153582,0.36253434,0.5549101,0.014743999,0.0010005535,0.00017293342],"about_ca_topic_score_codex":0.000069543836,"about_ca_topic_score_gemma":0.00011856816,"teacher_disagreement_score":0.91979265,"about_ca_system_score_codex":0.00007707731,"about_ca_system_score_gemma":0.00006442355,"threshold_uncertainty_score":0.2563289},"labels":[],"label_agreement":null},{"id":"W2902433052","doi":"10.1162/jocn_a_01362","title":"Eye Movements in the “Morris Maze” Spatial Working Memory Task Reveal Deficits in Strategic Planning","year":2018,"lang":"en","type":"article","venue":"Journal of Cognitive Neuroscience","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Multidisciplinary University Research Initiative","keywords":"Psychology; Eye movement; Working memory; Task (project management); Spatial memory; Cognition; Cognitive psychology; Neuropsychology; Security token; Audiology; Neuroscience; Computer science; Medicine","score_opus":0.05621714099661521,"score_gpt":0.3099664005480795,"score_spread":0.25374925955146427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902433052","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9573667,0.00005323724,0.039530586,0.0005553601,0.0006929463,0.00011034999,9.265968e-7,0.000018153414,0.0016717148],"genre_scores_gemma":[0.9980775,0.0000053652234,0.0005054709,0.0012766747,0.000109531065,0.0000032872658,9.85823e-8,0.000005407627,0.000016633929],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99793804,0.0002780165,0.0004662603,0.00033192066,0.0005748843,0.00041086864],"domain_scores_gemma":[0.99890333,0.00026785908,0.0004241559,0.00017603562,0.0001751541,0.00005347645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013336452,0.00014786627,0.00021439054,0.00046344067,0.00013018325,0.00018268605,0.0014074569,0.00005701968,0.0000018694204],"category_scores_gemma":[0.00051392853,0.000108056054,0.000051808423,0.0012043417,0.00034491217,0.00041494402,0.00016056844,0.0006422761,0.0000055870346],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035409865,0.001597847,0.6610852,0.000031329655,0.000018701801,0.0078073326,0.021742962,0.00067053013,0.12423137,0.0062321965,0.00018249669,0.17604592],"study_design_scores_gemma":[0.0010980165,0.0011476511,0.97998196,0.0006760483,0.0000057473794,0.00026174262,0.0015578886,0.0063621947,0.004134259,0.0044952054,0.000055253713,0.00022404904],"about_ca_topic_score_codex":0.000021609021,"about_ca_topic_score_gemma":0.00002509613,"teacher_disagreement_score":0.31889674,"about_ca_system_score_codex":0.000043639582,"about_ca_system_score_gemma":0.00013669496,"threshold_uncertainty_score":0.4406398},"labels":[],"label_agreement":null},{"id":"W2904276074","doi":"10.1016/j.cognition.2018.12.005","title":"Gaze allocation in face-to-face communication is affected primarily by task structure and social context, not stimulus-driven factors","year":2018,"lang":"en","type":"article","venue":"Cognition","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Gaze; Psychology; Stimulus (psychology); Cognitive psychology; Face perception; Social cognition; Face (sociological concept); Task (project management); Cognitive science; Communication; Cognition; Neuroscience; Perception; Linguistics","score_opus":0.017827581327770688,"score_gpt":0.2620722259024923,"score_spread":0.24424464457472161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904276074","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88105756,0.000034976296,0.115412086,0.0028212543,0.00007151075,0.00027636957,0.00005982724,0.00019065729,0.0000757752],"genre_scores_gemma":[0.9972872,0.000006470764,0.0017929007,0.00070848805,0.000019172692,0.0000137676825,0.00013914451,0.000008865113,0.00002396795],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990736,0.000112615904,0.0001614537,0.00032640563,0.00013970675,0.00018618607],"domain_scores_gemma":[0.9993769,0.00007038609,0.00010364945,0.00024475023,0.00016625856,0.000038039867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009569036,0.00013539186,0.00014919459,0.00013542591,0.00020322578,0.00008440449,0.0003460185,0.00015927806,0.000010041838],"category_scores_gemma":[0.00006463174,0.00013915634,0.00001889064,0.00030485805,0.00016298017,0.00025573146,0.00015608012,0.00019252642,0.000023595276],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009690214,0.0003251313,0.009312787,0.00007598868,0.000075742246,0.0000025283935,0.028453354,0.000015864933,0.5759776,0.0044953115,0.008451141,0.3727176],"study_design_scores_gemma":[0.0021280136,0.00051598926,0.7143127,0.00016802501,0.000042341053,0.0000065861973,0.0008243185,0.022530898,0.25236362,0.004351643,0.0020712477,0.0006846147],"about_ca_topic_score_codex":0.00003773369,"about_ca_topic_score_gemma":0.00013413056,"teacher_disagreement_score":0.7049999,"about_ca_system_score_codex":0.00007619136,"about_ca_system_score_gemma":0.000026615586,"threshold_uncertainty_score":0.5674631},"labels":[],"label_agreement":null},{"id":"W2906945768","doi":"10.1016/j.cognition.2018.12.014","title":"Distinct roles of eye movements during memory encoding and retrieval","year":2018,"lang":"en","type":"article","venue":"Cognition","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Psychology; Eye movement; Encoding (memory); Cognitive psychology; Cognitive science; Cognition; Encoding specificity principle; Communication; Neuroscience","score_opus":0.015731729292731467,"score_gpt":0.2535090727238313,"score_spread":0.2377773434310998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906945768","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9790494,0.000031204134,0.018909639,0.00006692101,0.00008546504,0.00003941031,0.0000031505638,0.000099568395,0.0017152544],"genre_scores_gemma":[0.9986435,0.000004440104,0.0012402303,0.000017688219,0.00004067522,0.0000013154234,0.000001788277,0.000002676997,0.000047649206],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995464,0.000015661233,0.000095580064,0.00015990768,0.000084792715,0.000097645265],"domain_scores_gemma":[0.99970776,0.000019559771,0.00006240348,0.00011245509,0.00007832675,0.00001950936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008901107,0.00005069601,0.00006793973,0.000066264976,0.00008640631,0.00001884492,0.00012321383,0.00003373035,0.0000061835926],"category_scores_gemma":[0.00006211589,0.000049683513,0.000013029032,0.00012114284,0.00011287922,0.00011889556,0.00007934171,0.000046961428,0.0000092805685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000062963976,0.00014366402,0.032703232,0.000119273886,0.00004749668,0.000021827584,0.0010516344,4.2666218e-7,0.85299915,0.0038306091,0.00006903953,0.1089507],"study_design_scores_gemma":[0.00034814945,0.00009816871,0.3284462,0.00007845325,0.000006774553,0.000003387858,0.000051202693,0.00058525114,0.66559803,0.0046783434,0.00002574733,0.000080267986],"about_ca_topic_score_codex":0.0000012107876,"about_ca_topic_score_gemma":0.0000037640716,"teacher_disagreement_score":0.295743,"about_ca_system_score_codex":0.000008769086,"about_ca_system_score_gemma":0.0000061654223,"threshold_uncertainty_score":0.20260347},"labels":[],"label_agreement":null},{"id":"W2907014035","doi":"10.1109/newcas.2018.8585522","title":"Evaluation of a Wearable and Wireless Human-Computer Interface Combining Head Motion and sEMG for People with Upper-Body Disabilities","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"","keywords":"Wearable computer; Computer science; Usability; Inertial measurement unit; Input device; Interface (matter); Wireless; Gesture recognition; Electromyography; Accelerometer; Gesture; Human–computer interaction; Simulation; Computer vision; Computer hardware; Embedded system; Physical medicine and rehabilitation","score_opus":0.03424024341845324,"score_gpt":0.31348697375615914,"score_spread":0.2792467303377059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907014035","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5817143,0.00002155406,0.4176929,0.00020379823,0.000033604385,0.00013920682,5.534203e-7,0.000058630758,0.00013548638],"genre_scores_gemma":[0.9822953,0.000001122815,0.01759258,0.000011718702,0.000018862569,0.000023727836,7.0053676e-7,0.0000052348923,0.000050782804],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921715,0.00005599373,0.00013075148,0.00028331362,0.00017454734,0.00013821691],"domain_scores_gemma":[0.99930984,0.000098387434,0.000060417613,0.00019997061,0.00030779888,0.000023559242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062978757,0.00008874421,0.00015999052,0.00006573124,0.00013078775,0.000059699538,0.00013491695,0.000043444536,0.000005642617],"category_scores_gemma":[0.000024971196,0.00006960163,0.000012904761,0.000104351304,0.00021817925,0.0002149929,0.00010068715,0.00004693891,9.4314566e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000081274324,0.00055395614,0.17893724,0.0003612597,0.00017750221,3.3537592e-7,0.015949989,0.00017786748,0.02927419,0.23388898,0.0003109273,0.5402865],"study_design_scores_gemma":[0.0015813306,0.0020005356,0.14021055,0.00015887525,0.000043403823,0.00001814014,0.0007522175,0.8200534,0.023641024,0.011327295,0.000018843359,0.00019437064],"about_ca_topic_score_codex":0.00009213734,"about_ca_topic_score_gemma":0.00026800897,"teacher_disagreement_score":0.81987554,"about_ca_system_score_codex":0.000021489253,"about_ca_system_score_gemma":0.000018741224,"threshold_uncertainty_score":0.2838272},"labels":[],"label_agreement":null},{"id":"W2907765090","doi":"10.3390/s19010109","title":"Ultrasonic Tethering to Enable Side-by-Side Following for Powered Wheelchairs","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wheelchair; Conversation; Ultrasonic sensor; Distraction; Computer science; Human–computer interaction; Simulation; Trajectory; Engineering; Psychology; Acoustics; Communication","score_opus":0.013498964110694964,"score_gpt":0.2590613611135193,"score_spread":0.24556239700282434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907765090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8213017,0.00006514544,0.17090032,0.002517623,0.0008164801,0.00037121263,0.000005370806,0.0007793947,0.0032427334],"genre_scores_gemma":[0.9512647,0.0000016973895,0.04688426,0.00029150557,0.00008970147,0.00003168179,0.00000112723,0.000022170967,0.0014131718],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985657,0.000029017343,0.00018821568,0.0005065306,0.00015054832,0.0005600205],"domain_scores_gemma":[0.99907845,0.00015667548,0.000056431472,0.000528826,0.00007472936,0.00010488727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002672934,0.00016722392,0.00020482707,0.00012117901,0.00022285659,0.00009147368,0.00064106396,0.000097399505,0.00000537466],"category_scores_gemma":[0.0002557394,0.00016163824,0.00011448672,0.0003565565,0.00005520475,0.00012182686,0.000112934074,0.00011159468,0.00016091242],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008237456,0.00021290316,0.0027139997,0.000056248387,0.00026967505,0.00008677169,0.004921373,0.000733025,0.69681454,0.031594727,0.020156562,0.24235784],"study_design_scores_gemma":[0.00160073,0.0012921043,0.0037924086,0.00020259041,0.000037079604,0.000049909915,0.0007791056,0.016168453,0.7071468,0.010892777,0.25675523,0.0012827709],"about_ca_topic_score_codex":0.000050239745,"about_ca_topic_score_gemma":0.00004130088,"teacher_disagreement_score":0.24107505,"about_ca_system_score_codex":0.0000635889,"about_ca_system_score_gemma":0.000033017892,"threshold_uncertainty_score":0.6591416},"labels":[],"label_agreement":null},{"id":"W2912346386","doi":"10.1155/2019/4125865","title":"Driver Distraction Identification with an Ensemble of Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":300,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Technische Universität München; Deutsche Forschungsgemeinschaft","keywords":"Distraction; Convolutional neural network; Computer science; Identification (biology); Artificial intelligence; Machine learning; Segmentation; Ensemble learning; Phone; Deep learning; Distracted driving; Artificial neural network; Face (sociological concept); Pattern recognition (psychology)","score_opus":0.007240715379631352,"score_gpt":0.2370285528321021,"score_spread":0.22978783745247075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912346386","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57254195,0.000020689882,0.4271132,0.00006894031,0.00019358666,0.00003982931,0.0000010308848,0.0000144733085,0.0000062815225],"genre_scores_gemma":[0.98214376,0.0000104002,0.017777402,0.00000994088,0.000025622498,0.0000010954457,0.000014043613,0.000004641158,0.000013122409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991807,0.000021705413,0.00035419996,0.00012721609,0.00022540009,0.000090818314],"domain_scores_gemma":[0.9988161,0.000031912507,0.00063280587,0.00014404564,0.00034159663,0.00003358478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013588138,0.00006960361,0.00013854071,0.00010207559,0.000029896742,0.000014314412,0.00019232865,0.000045191246,0.00000432126],"category_scores_gemma":[0.0000044824405,0.00005862028,0.000046269903,0.00019515908,0.000035613422,0.0011857351,0.0000013680065,0.00015207533,0.0000010536602],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032222824,0.00022836814,0.05132218,0.00002879926,0.000043516888,0.000017493287,0.00043953856,0.78371084,0.10500163,0.019227948,0.0000076928745,0.03964979],"study_design_scores_gemma":[0.00067356863,0.0005556302,0.9715878,0.000032784697,0.000019404266,0.000030341653,0.00010879695,0.020614536,0.0052472646,0.0010210339,0.000033327815,0.00007551347],"about_ca_topic_score_codex":0.0000029138616,"about_ca_topic_score_gemma":0.000016309445,"teacher_disagreement_score":0.9202656,"about_ca_system_score_codex":0.000025541409,"about_ca_system_score_gemma":0.000030951975,"threshold_uncertainty_score":0.23904656},"labels":[],"label_agreement":null},{"id":"W2917632758","doi":"10.1186/s12984-019-0482-3","title":"Augmented feedback for powered wheelchair training in a virtual environment","year":2019,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université de Montréal; Centre for Interdisciplinary Research in Rehabilitation","funders":"AGE-WELL","keywords":"Wheelchair; Augmented reality; Task (project management); Motor learning; Computer science; Test (biology); Transfer of training; Virtual reality; Transfer (computing); Training (meteorology); Physical medicine and rehabilitation; Significant difference; Simulation; Baseline (sea); Human–computer interaction; Psychology; Medicine; Engineering","score_opus":0.00634513652933181,"score_gpt":0.2030367961822785,"score_spread":0.1966916596529467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917632758","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8402898,0.000049357608,0.1577402,0.0015257288,0.00026463563,0.000100263605,4.5021508e-7,0.000021301727,0.000008281571],"genre_scores_gemma":[0.97309875,0.000013029061,0.02682751,0.000016314545,0.000015924265,0.0000033932242,1.7863837e-7,0.0000056844992,0.000019204344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939024,0.000022238673,0.0002482451,0.0001241058,0.00009606338,0.00011908998],"domain_scores_gemma":[0.99945897,0.00029021935,0.000098456774,0.000094041374,0.00002619994,0.000032139837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024480288,0.000069956484,0.00014863396,0.00022044401,0.0000151880095,0.00001955502,0.00012255782,0.000030340858,8.538184e-7],"category_scores_gemma":[0.000115919516,0.00006205393,0.00004829758,0.00008960943,0.000015390311,0.00017368216,0.000021129541,0.00012659076,0.0000010582869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031187048,0.00043884266,0.015297744,0.00039836197,0.0000989866,0.000051888674,0.018538274,0.33810592,0.23032086,0.039813515,0.00021171685,0.35641202],"study_design_scores_gemma":[0.006502996,0.012659844,0.40683937,0.00061554945,0.000021125428,0.00025743744,0.0024803896,0.5564232,0.0010637215,0.0049259868,0.007692513,0.0005178576],"about_ca_topic_score_codex":4.7456297e-7,"about_ca_topic_score_gemma":2.1735717e-7,"teacher_disagreement_score":0.39154163,"about_ca_system_score_codex":0.000034934932,"about_ca_system_score_gemma":0.000015878097,"threshold_uncertainty_score":0.2530486},"labels":[],"label_agreement":null},{"id":"W2918463687","doi":"10.1016/j.aap.2019.02.022","title":"Texting while walking: An expensive switch cost","year":2019,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; HEC Montréal","funders":"","keywords":"Poison control; Human factors and ergonomics; Injury prevention; Transport engineering; Occupational safety and health; Engineering; Computer science; Suicide prevention; Physical medicine and rehabilitation; Computer security; Forensic engineering; Simulation; Medical emergency; Medicine","score_opus":0.021229972360660414,"score_gpt":0.2935343457409218,"score_spread":0.27230437338026137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918463687","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75512236,0.000054961358,0.24315117,0.00021880936,0.00014332312,0.0001870472,5.1864642e-8,0.0002712206,0.00085104146],"genre_scores_gemma":[0.9925408,0.0000089547175,0.006348117,0.000075974094,0.000038516657,0.000024255345,0.00003218611,0.000009205765,0.0009219949],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.998245,0.0001418559,0.00034668267,0.00064128084,0.00031561856,0.0003095681],"domain_scores_gemma":[0.9984925,0.000054282857,0.00029707895,0.0009135895,0.00017463729,0.00006792966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005515208,0.0001628943,0.00031630602,0.00052209664,0.00013442789,0.00022523591,0.0008653399,0.00010125968,0.00027481944],"category_scores_gemma":[0.000041841766,0.00015984674,0.0002780181,0.0014555195,0.000017010798,0.0009170358,0.00025164685,0.00017072319,0.00035904543],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000053279723,0.00017639145,0.80330384,0.0000018023189,0.0005288506,0.000011380737,0.0005236042,0.00065509125,0.004920952,0.009463145,0.00016016365,0.18024948],"study_design_scores_gemma":[0.0005652821,0.00020573733,0.925428,0.000047085814,0.0005722009,0.0000108770655,0.00063560554,0.056986805,0.0071543637,0.0069363406,0.0009510706,0.0005066302],"about_ca_topic_score_codex":0.00007541507,"about_ca_topic_score_gemma":0.0005101053,"teacher_disagreement_score":0.23741843,"about_ca_system_score_codex":0.00007358545,"about_ca_system_score_gemma":0.000027148066,"threshold_uncertainty_score":0.6518361},"labels":[],"label_agreement":null},{"id":"W2926332035","doi":"10.1145/3302509.3313320","title":"Towards an emotionally-aware smart wheelchair","year":2019,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wheelchair; Feeling; Human–computer interaction; Perception; Computer science; Intervention (counseling); Affect (linguistics); Psychological intervention; Applied psychology; Psychology; Multimedia; World Wide Web; Social psychology; Communication","score_opus":0.01303725038977193,"score_gpt":0.24341700376967504,"score_spread":0.2303797533799031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2926332035","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32483283,0.000011801507,0.6343185,0.004340492,0.0005338401,0.000101129124,0.0000015811062,0.0012790145,0.034580823],"genre_scores_gemma":[0.96985394,0.0000010578934,0.026596561,0.00050862134,0.000023578745,0.0000033927288,0.000003076237,0.0000052899873,0.0030044867],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9991608,0.000025308296,0.00010357028,0.00033957727,0.00016745781,0.00020327658],"domain_scores_gemma":[0.99926424,0.000015711015,0.000029421895,0.0005598897,0.00007317566,0.0000575868],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001351786,0.000091269125,0.00010601153,0.00008617887,0.000046802415,0.000061882354,0.00075983634,0.00007151431,0.00023019662],"category_scores_gemma":[0.0000079249785,0.00007600051,0.00003901778,0.00019051915,0.00002736676,0.00031427835,0.00016675012,0.00011126711,0.0009199126],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018339589,0.00012770269,0.028318655,0.000008520766,0.000013065584,0.000010218049,0.00012426155,0.000029071993,0.0010072609,0.8646926,0.0017953358,0.10387146],"study_design_scores_gemma":[0.0007860558,0.00062736106,0.83848774,0.000030083793,0.000004799288,0.00007809952,0.0000959163,0.07541922,0.006564419,0.047896292,0.029373536,0.00063645263],"about_ca_topic_score_codex":0.000050626277,"about_ca_topic_score_gemma":0.000023914758,"teacher_disagreement_score":0.8167963,"about_ca_system_score_codex":0.00002222988,"about_ca_system_score_gemma":0.00005193988,"threshold_uncertainty_score":0.99985796},"labels":[],"label_agreement":null},{"id":"W2930507225","doi":"10.22215/etd/2018-12994","title":"Empirical Studies on Selection and Travel Performance of Eye-tracking in Virtual Reality","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Eye tracking; Selection (genetic algorithm); Eye movement; Optical head-mounted display; Task (project management); Computer science; Head (geology); Eye tracking on the ISS; Virtual reality; Artificial intelligence; Computer vision; Simulation; Human–computer interaction; Engineering","score_opus":0.05270653742871621,"score_gpt":0.3790242368132027,"score_spread":0.32631769938448646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2930507225","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966129,0.00005019988,0.0017229713,0.00011518382,0.000321277,0.00008975413,8.079032e-7,0.00009201399,0.0009949093],"genre_scores_gemma":[0.99804205,0.000079004334,0.0011857781,0.000026439315,0.000029766456,0.000010053012,0.0000097372995,0.000007273162,0.0006099104],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988177,0.00005089455,0.00034013443,0.00043599444,0.00018563503,0.00016967133],"domain_scores_gemma":[0.99933374,0.0000824763,0.00019312078,0.00019348206,0.00017628074,0.000020889447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036948168,0.00017443679,0.0003395102,0.00030039393,0.00007295592,0.000020633099,0.00025611493,0.0002322288,0.0000017034355],"category_scores_gemma":[0.00011091592,0.0001482245,0.000031530537,0.0003638448,0.00007317917,0.00012474354,0.000038373466,0.00030738176,0.000004886256],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088977156,0.0016404884,0.28199166,0.0020028139,0.00053639675,0.000028478522,0.04465148,0.00047745078,0.017619053,0.08429834,0.0029514288,0.56291264],"study_design_scores_gemma":[0.00027887744,0.0012071579,0.9311728,0.00038963108,0.000013094148,0.000003744782,0.0012912984,0.014321012,0.050232183,0.0007790945,0.000039083992,0.000272012],"about_ca_topic_score_codex":0.00002470508,"about_ca_topic_score_gemma":0.00063643197,"teacher_disagreement_score":0.6491811,"about_ca_system_score_codex":0.00005618732,"about_ca_system_score_gemma":0.00005596224,"threshold_uncertainty_score":0.60444194},"labels":[],"label_agreement":null},{"id":"W2936701675","doi":"10.1007/s11548-019-01964-8","title":"A novel gaze-supported multimodal human–computer interaction for ultrasound machines","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Gaze; Human–computer interaction; Artificial intelligence; Task (project management); Computer vision","score_opus":0.02199561684195725,"score_gpt":0.2864085644504389,"score_spread":0.26441294760848166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2936701675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42848194,0.000053758547,0.56618935,0.00095383875,0.0041752015,0.00007007182,0.0000049262326,0.00004932047,0.00002157285],"genre_scores_gemma":[0.9131454,0.000020524312,0.085371144,0.00060770207,0.00078061706,0.0000045116844,0.000018972103,0.000011643961,0.00003948682],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99835676,0.000105121835,0.0007224492,0.00034005044,0.0002281307,0.0002474686],"domain_scores_gemma":[0.99684,0.0016714055,0.00064447394,0.00020310779,0.00055850315,0.00008248613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068546727,0.0002075158,0.00050824694,0.00064177567,0.00008588448,0.00014108859,0.0007166636,0.0001790427,0.000018420304],"category_scores_gemma":[0.0000650482,0.00017591545,0.00029534355,0.00012059736,0.000100527854,0.00048648787,0.00012214937,0.00035050727,0.000005928156],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068986206,0.0013895645,0.31789663,0.00009072963,0.0032643601,0.00045169276,0.00081597717,0.0025921974,0.06791969,0.024355989,0.01637091,0.5641624],"study_design_scores_gemma":[0.003399724,0.0007304728,0.8146163,0.0002934863,0.000056554476,0.021963602,0.000023533055,0.14667924,0.0009711312,0.002414926,0.008261462,0.00058954424],"about_ca_topic_score_codex":0.000008581623,"about_ca_topic_score_gemma":0.0000026203102,"teacher_disagreement_score":0.5635728,"about_ca_system_score_codex":0.00006690066,"about_ca_system_score_gemma":0.0000856055,"threshold_uncertainty_score":0.7173624},"labels":[],"label_agreement":null},{"id":"W2942309445","doi":"10.1145/3290605.3300332","title":"Finding Information on Non-Rectangular Interfaces","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Engineering and Physical Sciences Research Council; Agence Nationale de la Recherche","keywords":"Computer science; Human–computer interaction; Interface (matter); User interface; Visual search; Artificial intelligence; Programming language","score_opus":0.01765705441306397,"score_gpt":0.26013771930919,"score_spread":0.24248066489612602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942309445","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.109323144,0.000015290347,0.8487718,0.0011631418,0.0014121083,0.00022566039,0.000003633424,0.00072072167,0.038364515],"genre_scores_gemma":[0.97523737,0.0000053193144,0.023924662,0.00028539027,0.00001832157,0.000015474241,0.000010996035,0.0000052476503,0.0004972375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903834,0.00002028358,0.00022724408,0.0003155393,0.00019243882,0.00020617919],"domain_scores_gemma":[0.9988607,0.00005378463,0.00017753366,0.0008181372,0.00006316426,0.000026685646],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018935962,0.00019281432,0.00021433741,0.00033930215,0.00004732241,0.00026895248,0.0013252894,0.0002822876,0.000019214895],"category_scores_gemma":[0.000044077016,0.00016337608,0.00006648211,0.00013071315,0.000024287387,0.00030519968,0.0012578018,0.00059043296,0.0012705014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025279964,0.00019736176,0.004504728,0.00058943353,0.0002495835,0.000021431884,0.0034265097,0.06337801,0.0005615576,0.2880434,0.03514492,0.60385776],"study_design_scores_gemma":[0.0011808124,0.000883925,0.037087876,0.0018438379,0.00003859607,0.000028883034,0.00031090277,0.7961455,0.0688003,0.06498357,0.026220512,0.0024752936],"about_ca_topic_score_codex":0.000033811048,"about_ca_topic_score_gemma":0.0000019286067,"teacher_disagreement_score":0.8659142,"about_ca_system_score_codex":0.000077884666,"about_ca_system_score_gemma":0.00006045208,"threshold_uncertainty_score":0.9995071},"labels":[],"label_agreement":null},{"id":"W2944151869","doi":"10.23919/fusion43075.2019.9011436","title":"Tracking the Progression of Reading Through Eye-gaze Measurements","year":2019,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Gaze; Eye tracking; Computer science; Reading (process); Artificial intelligence; Tracking (education); Computer vision; Psychology; Linguistics","score_opus":0.05196506971191676,"score_gpt":0.3178189606706287,"score_spread":0.2658538909587119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944151869","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61486197,0.0002253526,0.31341213,0.00364845,0.0007005044,0.00046894015,4.88687e-7,0.0006451229,0.06603707],"genre_scores_gemma":[0.9747111,0.000003269367,0.024851486,0.0000951391,0.000011447082,0.000005248651,2.4070036e-7,0.0000049295863,0.0003171042],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905336,0.000047443104,0.00017348868,0.00024056112,0.00028984423,0.000195332],"domain_scores_gemma":[0.99919665,0.00005140427,0.00011378753,0.0005349288,0.000089045796,0.000014161098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039216044,0.000086646745,0.0001272615,0.000038194637,0.00007257806,0.0000409148,0.0008160319,0.000053534128,0.000018760573],"category_scores_gemma":[0.000038544076,0.000050044127,0.000046892063,0.000270364,0.000053365457,0.00026064736,0.00016689983,0.00012374758,0.00006305925],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010118942,0.0002017007,0.31700456,0.000055555236,0.00006401996,0.0000042466595,0.0015820477,0.000050667495,0.11848916,0.2953652,0.0007730633,0.26639965],"study_design_scores_gemma":[0.00089843303,0.00041252782,0.19303928,0.0004661859,0.000018930681,0.000017130185,0.0003334854,0.0059062834,0.7806627,0.01139363,0.0064347195,0.00041667948],"about_ca_topic_score_codex":0.000014436114,"about_ca_topic_score_gemma":0.0000016191048,"teacher_disagreement_score":0.66217357,"about_ca_system_score_codex":0.000013221161,"about_ca_system_score_gemma":0.000021025213,"threshold_uncertainty_score":0.20407404},"labels":[],"label_agreement":null},{"id":"W2948103097","doi":"","title":"Toward Discreet Interactions and Publicly Explicit Activities","year":2019,"lang":"en","type":"preprint","venue":"R-libre (Université Téluq)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Université du Québec à Montréal","funders":"","keywords":"Computer science; Human–computer interaction; Optical head-mounted display; Internet privacy; Psychology; Artificial intelligence","score_opus":0.02677493373082135,"score_gpt":0.2234557603867706,"score_spread":0.19668082665594924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948103097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78148544,0.00027299544,0.19078167,0.015120157,0.0013548327,0.00034309275,0.000072366296,0.0013297149,0.009239722],"genre_scores_gemma":[0.9910295,0.0001030569,0.004065058,0.00011856915,0.00007126347,0.000009410456,0.000028548822,0.000023533283,0.0045510842],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99822354,0.000071154485,0.00015858692,0.0009333807,0.00022910716,0.0003842267],"domain_scores_gemma":[0.99835604,0.00013391249,0.00022836644,0.0010862884,0.000073538096,0.000121878926],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010312934,0.00033647846,0.00042507,0.00029491348,0.00020247248,0.00042020105,0.0016446892,0.00030948414,0.00003496938],"category_scores_gemma":[0.000017778497,0.00037288255,0.00015899437,0.00030975978,0.00011491542,0.0011667529,0.004291413,0.0009337167,0.000057155816],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029135612,0.00016226343,0.0038342145,0.00022011666,0.00032038824,0.00015083664,0.0026172365,0.00011561255,0.00068045495,0.1151043,0.0072201206,0.86954534],"study_design_scores_gemma":[0.0027439133,0.0006103581,0.58872414,0.0012129106,0.00033157773,0.00058126834,0.0059849154,0.052869275,0.0031773292,0.040000364,0.29969928,0.004064664],"about_ca_topic_score_codex":0.0002938922,"about_ca_topic_score_gemma":0.000091128575,"teacher_disagreement_score":0.86548066,"about_ca_system_score_codex":0.00014738561,"about_ca_system_score_gemma":0.00013831929,"threshold_uncertainty_score":0.9998723},"labels":[],"label_agreement":null},{"id":"W2954133274","doi":"","title":"Non-Contact Calibration-Free Eye-Tracker","year":2011,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Calibration; Computer vision; Artificial intelligence; Computer science; Mathematics; Statistics","score_opus":0.020704267236694513,"score_gpt":0.22943944568677693,"score_spread":0.2087351784500824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954133274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8585583,0.000078069024,0.05903755,0.0019721377,0.0005292143,0.0002680334,0.000002694215,0.0016765028,0.07787749],"genre_scores_gemma":[0.9713995,0.0000049928753,0.02779784,0.000257009,0.00006301524,0.000027948716,7.008032e-7,0.00001342949,0.00043557986],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99888897,0.0000027087976,0.00019416622,0.00042091077,0.00016730634,0.00032594943],"domain_scores_gemma":[0.9993309,0.000011399283,0.000096842494,0.00033126125,0.00014884288,0.000080759426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015023931,0.00016067222,0.00016309932,0.00013237077,0.00013530467,0.000120708646,0.0014090426,0.00012378993,0.000033356813],"category_scores_gemma":[0.000072510535,0.00014373208,0.000053243435,0.00035359088,0.00008035728,0.00083405094,0.00030996496,0.00019908894,0.00011661626],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015087227,0.00021804654,0.15049522,0.00005283608,0.000044652465,0.000028024226,0.003003854,1.6109762e-7,0.021755155,0.78270704,0.033169236,0.008510702],"study_design_scores_gemma":[0.0010866007,0.00053988467,0.72704744,0.00010917185,0.000028905817,0.00006562762,0.00030766457,0.007799724,0.1920568,0.06424606,0.0058228173,0.00088930107],"about_ca_topic_score_codex":0.000030940435,"about_ca_topic_score_gemma":0.000003222899,"teacher_disagreement_score":0.718461,"about_ca_system_score_codex":0.000024770949,"about_ca_system_score_gemma":0.00003154865,"threshold_uncertainty_score":0.5861224},"labels":[],"label_agreement":null},{"id":"W2954814858","doi":"10.15557/an.2019.0001","title":"Entering the new era of cognitive scoring? Eye-tracking assessment in neurodegenerative disorders","year":2019,"lang":"pl","type":"article","venue":"Aktualności Neurologiczne","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Medicine; Cognition; Cognitive impairment; Physical medicine and rehabilitation; Cognitive Assessment System; Neuroscience; Psychiatry; Psychology","score_opus":0.01750936180595509,"score_gpt":0.2913752932575397,"score_spread":0.2738659314515846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954814858","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96858495,0.00053785427,0.016056923,0.010899804,0.0017162177,0.00093606726,0.00001322651,0.00017999017,0.0010749329],"genre_scores_gemma":[0.99728084,0.00032000107,0.00066988054,0.0012962285,0.00010021563,0.000028497461,0.000005883708,0.000041357387,0.00025711773],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99543226,0.0007332027,0.0008789912,0.0014221657,0.00055481447,0.000978559],"domain_scores_gemma":[0.99677455,0.0013173331,0.00064619916,0.000992319,0.00015282561,0.00011675076],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00073430565,0.00059112173,0.0007762242,0.00030151097,0.00019845746,0.00019699274,0.0018586082,0.00027306398,0.00007557898],"category_scores_gemma":[0.00033030554,0.0004698559,0.00020849584,0.0012854586,0.00044543855,0.00046691103,0.001198691,0.002351248,0.000092991126],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029103266,0.001484786,0.77529436,0.00015559005,0.0002762553,0.0002441633,0.009663897,0.009816684,0.031174917,0.025226122,0.00027626852,0.14609592],"study_design_scores_gemma":[0.0022702033,0.0031939952,0.9661462,0.0005236531,0.00007706103,0.000026956272,0.00076842203,0.014625012,0.008662067,0.0026847846,0.00034091435,0.0006807785],"about_ca_topic_score_codex":0.00036852338,"about_ca_topic_score_gemma":0.00030639395,"teacher_disagreement_score":0.1908518,"about_ca_system_score_codex":0.000059689497,"about_ca_system_score_gemma":0.00029955865,"threshold_uncertainty_score":0.99995035},"labels":[],"label_agreement":null},{"id":"W2954931328","doi":"10.1007/978-981-13-5859-3_78","title":"Detecting and Counting Eyes Blinking Using Haar Cascade—A Handy Way to Diagnose Dry Eyes Disease","year":2019,"lang":"en","type":"book-chapter","venue":"World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Feature (linguistics); Haar-like features; Artificial intelligence; Optometry; Medicine; Pattern recognition (psychology); Face detection","score_opus":0.013720118513976491,"score_gpt":0.24564332360605665,"score_spread":0.23192320509208014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954931328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25244144,0.039208535,0.5670674,0.01746373,0.034771167,0.008527409,0.0011721859,0.010202104,0.069146045],"genre_scores_gemma":[0.93542886,0.001133179,0.00783017,0.003713815,0.003230902,0.00008504465,0.00012637333,0.0004766075,0.04797503],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952521,0.00005195577,0.0008136246,0.0013849436,0.0015205705,0.0009768605],"domain_scores_gemma":[0.99650854,0.0007528427,0.00036130557,0.0009795029,0.00012886859,0.0012689219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006998343,0.00092649827,0.0010959213,0.0006610376,0.00031612496,0.0003295259,0.0012286378,0.0006332281,0.00009193295],"category_scores_gemma":[0.0002676938,0.0008416153,0.00022067755,0.00033610497,0.0005566205,0.0002395756,0.0013504443,0.0020058677,0.00006043485],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013334476,0.00078049145,0.0016807581,0.0029055772,0.0015090205,0.0045611,0.00076836994,0.0010955461,0.0013978516,0.21286376,0.011946461,0.76035774],"study_design_scores_gemma":[0.0025506702,0.00041355687,0.001402433,0.01225713,0.00039077213,0.00008859593,0.000017668579,0.45846018,0.0004356578,0.0052271523,0.51528794,0.0034682844],"about_ca_topic_score_codex":0.000041091043,"about_ca_topic_score_gemma":0.0000555734,"teacher_disagreement_score":0.75688946,"about_ca_system_score_codex":0.00011884813,"about_ca_system_score_gemma":0.000193373,"threshold_uncertainty_score":0.9994035},"labels":[],"label_agreement":null},{"id":"W2956508261","doi":"10.1177/0018720819853682","title":"How to Observe Users’ Movements in Virtual Environments: Viewpoint Control in a Power Wheelchair Simulator","year":2019,"lang":"en","type":"article","venue":"Human Factors The Journal of the Human Factors and Ergonomics Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Umm Al-Qura University","keywords":"Virtual reality; Human–computer interaction; Computer science; Observer (physics); Simulator sickness; Simulation; Wheelchair; Driving simulator; Viewpoints; Virtual machine; Cursor (databases); Artificial intelligence","score_opus":0.017904758327177837,"score_gpt":0.225320795411699,"score_spread":0.20741603708452117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2956508261","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.996691,0.0000661522,0.0019403072,0.0005660747,0.00037211867,0.00033184967,0.0000085843485,0.000015132474,0.000008778972],"genre_scores_gemma":[0.9989357,0.000021583586,0.000052647494,0.00063489814,0.000025836516,0.0000020293946,7.287435e-7,0.000020371517,0.00030622087],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982282,0.00018121646,0.0005750489,0.0003026799,0.0002868002,0.00042604437],"domain_scores_gemma":[0.9985358,0.00018253864,0.00053563353,0.0006016893,0.000029785542,0.00011456511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076436094,0.00032280493,0.00051587494,0.00011959836,0.00026599792,0.00018111149,0.0015675809,0.00016322554,0.000013519458],"category_scores_gemma":[0.000031269064,0.0001882485,0.00032810273,0.00017387718,0.0001437895,0.00042907736,0.00048061775,0.0007766123,0.0000031705472],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034437355,0.00024986258,0.94964695,0.00001042601,0.00017005498,0.0000019859417,0.016916152,0.0032127865,0.026500693,0.0028355422,0.000341092,0.00008001042],"study_design_scores_gemma":[0.0016598616,0.0003560287,0.9905113,0.00007769846,0.000014049305,0.0000021762105,0.0034757145,0.0006537355,0.0010418849,0.0008048742,0.0011322375,0.00027043672],"about_ca_topic_score_codex":0.00006605079,"about_ca_topic_score_gemma":0.00009317255,"teacher_disagreement_score":0.040864345,"about_ca_system_score_codex":0.0004227742,"about_ca_system_score_gemma":0.00003511654,"threshold_uncertainty_score":0.7676551},"labels":[],"label_agreement":null},{"id":"W2957426165","doi":"10.1109/globalsip45357.2019.8969561","title":"A Novel Slip-Kalman Filter to Track the Progression of Reading Through Eye-Gaze Measurements","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Gaze; Computer science; Eye tracking; Desk; Computer vision; Artificial intelligence; Reading (process); Track (disk drive); Eye movement; Tracking (education); Psychology","score_opus":0.09313811808775489,"score_gpt":0.33839684059747804,"score_spread":0.24525872250972314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2957426165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04601869,0.0001102661,0.9305136,0.005747422,0.0014593471,0.001172327,0.000011178243,0.0005679988,0.014399131],"genre_scores_gemma":[0.8394581,0.000006181524,0.15910432,0.00038032312,0.00005598172,0.00007960811,0.0000041010176,0.000020687534,0.00089068466],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9974007,0.00008166908,0.00048680356,0.0009391401,0.0006649703,0.00042672612],"domain_scores_gemma":[0.99722874,0.00008254534,0.00037576497,0.00198862,0.00026824264,0.00005608252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070077134,0.00034903496,0.000470762,0.0001508893,0.00010165069,0.00012925938,0.0029787156,0.00031827932,0.00002008479],"category_scores_gemma":[0.000111357,0.00021998753,0.00018032899,0.00033145945,0.00009373469,0.00015044649,0.002573311,0.0006674991,0.00010613398],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017905995,0.0034127696,0.073456645,0.001825506,0.0013260865,0.000038751037,0.01670143,0.0062047173,0.25966838,0.15119033,0.048036635,0.4379597],"study_design_scores_gemma":[0.0033810092,0.0018106928,0.24388385,0.009141457,0.00029462812,0.00008112796,0.0005488273,0.032802757,0.65156585,0.02152471,0.03088499,0.0040800828],"about_ca_topic_score_codex":0.000104859195,"about_ca_topic_score_gemma":0.000012396877,"teacher_disagreement_score":0.79343945,"about_ca_system_score_codex":0.00006291618,"about_ca_system_score_gemma":0.00012027859,"threshold_uncertainty_score":0.89708316},"labels":[],"label_agreement":null},{"id":"W2963256079","doi":"","title":"Highly accurate gaze estimation using a consumer RGB-D sensor","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Artificial intelligence; Gaze; Rendering (computer graphics); Computer vision; Normalization (sociology); Pose; RGB color model; Face (sociological concept)","score_opus":0.1571272691481265,"score_gpt":0.3472728779864402,"score_spread":0.1901456088383137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963256079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06424735,0.0000067056044,0.9212631,0.0110081965,0.0012450328,0.00013854726,0.00001774058,0.00035149156,0.0017218457],"genre_scores_gemma":[0.96548074,0.000028268747,0.033851966,0.0002833018,0.00008288469,0.000017047381,0.0000027034891,0.000012306943,0.00024079693],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788535,0.00008393767,0.0005688799,0.00063698064,0.000469791,0.00035506423],"domain_scores_gemma":[0.9983662,0.00022076414,0.0002795039,0.00049409835,0.0005385294,0.00010094579],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00029663288,0.00024401616,0.000218778,0.00034967813,0.000128751,0.00027807936,0.0010123211,0.00012020383,0.00024366807],"category_scores_gemma":[0.00051560916,0.00018393286,0.00009346416,0.00025391532,0.00025665032,0.0005286939,0.00020263708,0.00019702999,0.0014321216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019724504,0.00008143157,0.00006156752,0.0000021224548,0.000023417646,0.000028052322,0.000077124925,0.00040999518,0.05937929,0.63217324,0.0000501232,0.30769387],"study_design_scores_gemma":[0.00008794496,0.00012909966,0.00054944324,0.00031634184,0.000008705088,0.00005959529,0.00006523893,0.52542496,0.2939445,0.17838606,0.0006080351,0.00042007858],"about_ca_topic_score_codex":0.000048046368,"about_ca_topic_score_gemma":0.00001917576,"teacher_disagreement_score":0.9012334,"about_ca_system_score_codex":0.00016680034,"about_ca_system_score_gemma":0.00013903482,"threshold_uncertainty_score":0.99934536},"labels":[],"label_agreement":null},{"id":"W2967330611","doi":"10.1109/uemcon.2018.8796799","title":"SmartEye: An Accurate Infrared Eye Tracking System for Smartphones","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Eye tracking; Computer vision; Tracking (education); Gaze; Tracking system; Calibration; Phone; Mathematics","score_opus":0.03170176207312449,"score_gpt":0.3012886270537058,"score_spread":0.2695868649805813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967330611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14178249,0.000015560941,0.8494925,0.0005217119,0.0009559539,0.00020694925,0.000003184791,0.0018576002,0.005164035],"genre_scores_gemma":[0.93304276,5.0443487e-7,0.06572001,0.00014773253,0.00019465323,0.000040563144,0.0000029017585,0.000012904842,0.00083797105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987177,0.000044798962,0.00024491627,0.000478117,0.00012633755,0.00038808063],"domain_scores_gemma":[0.99878883,0.00007745637,0.00010250816,0.00068319653,0.000264574,0.00008340535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033332926,0.00015794214,0.00019705674,0.00013888109,0.0002851717,0.00022626859,0.0009670658,0.00011554075,0.0000105500585],"category_scores_gemma":[0.000057162055,0.00013240382,0.00006105065,0.00032963065,0.00010946417,0.00059117127,0.00012199829,0.000085839514,0.00010890439],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000058528018,0.00027082174,0.008493282,0.00016202642,0.00009686932,0.00003094285,0.0020293444,0.000017076487,0.024508165,0.6613935,0.008198133,0.2947413],"study_design_scores_gemma":[0.004029174,0.0034955305,0.13886818,0.00031082646,0.000068784255,0.00014736899,0.0021845703,0.3766832,0.3725766,0.024066087,0.075326435,0.0022432236],"about_ca_topic_score_codex":0.000026662521,"about_ca_topic_score_gemma":0.00005048175,"teacher_disagreement_score":0.79126024,"about_ca_system_score_codex":0.000039597195,"about_ca_system_score_gemma":0.000045878616,"threshold_uncertainty_score":0.5399271},"labels":[],"label_agreement":null},{"id":"W2967647151","doi":"10.1080/09588221.2019.1647251","title":"Exploring the frontiers of eye tracking research in language studies: a novel co-citation scientometric review","year":2019,"lang":"en","type":"article","venue":"Computer Assisted Language Learning","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Education, Nanyang Technological University; National Institute of Education; Nanyang Technological University; Paragon Testing Enterprises; Ministry of Education - Singapore; Max Planck Instituut voor Psycholinguïstiek; International Business Machines Corporation","keywords":"Citation; Adjective; Scientometrics; Tracking (education); Multitude; Eye tracking; Eye movement; Scopus; Computer science; Citation analysis; Citation index; Data science; Psychology; Noun; Artificial intelligence; World Wide Web","score_opus":0.223935623871368,"score_gpt":0.4243013599204514,"score_spread":0.20036573604908342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967647151","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8554422,0.023121933,0.11899327,0.00076282735,0.0006211425,0.0004715276,9.2780215e-7,0.00024735878,0.00033879455],"genre_scores_gemma":[0.9759919,0.00045879418,0.023176713,0.00014208548,0.00005818489,0.000044051292,0.000004998107,0.00001802755,0.00010521636],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99704075,0.0005903849,0.0004713713,0.000593573,0.0007266252,0.00057731126],"domain_scores_gemma":[0.9978341,0.00087618857,0.00024921427,0.0007048313,0.00028684962,0.000048854094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00444838,0.00018410991,0.00046365982,0.0018338858,0.0002023793,0.000120509954,0.0013071689,0.000059101905,0.000004134518],"category_scores_gemma":[0.00064042944,0.0001421064,0.00010661212,0.0068517933,0.00016411774,0.00050265196,0.00047949696,0.0009918939,0.000038192826],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001168399,0.00020087798,0.036895677,0.0012739042,0.00013194926,0.00010659748,0.046483416,0.0016644871,0.010025541,0.0011278989,0.00052851817,0.90154946],"study_design_scores_gemma":[0.0032545573,0.0009373585,0.80269337,0.013445201,0.000064687985,0.000087065986,0.0528757,0.11632978,0.005228433,0.00007408959,0.0037702622,0.0012395061],"about_ca_topic_score_codex":0.00005743301,"about_ca_topic_score_gemma":0.00000846376,"teacher_disagreement_score":0.9003099,"about_ca_system_score_codex":0.00017328281,"about_ca_system_score_gemma":0.000057929978,"threshold_uncertainty_score":0.57949305},"labels":[],"label_agreement":null},{"id":"W2969392895","doi":"10.1007/978-3-030-29387-1_36","title":"A Comparative Study of Pointing Techniques for Eyewear Using a Simulated Pedestrian Environment","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Eyewear; Computer science; Touchscreen; Phone; Human–computer interaction; Simulation; Computer vision","score_opus":0.04304703850177877,"score_gpt":0.30053128210406255,"score_spread":0.2574842436022838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969392895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022278676,0.000086617845,0.97541857,0.000076228695,0.00029353963,0.0014563274,0.0000052328,0.00018150939,0.0002033081],"genre_scores_gemma":[0.71822804,0.0000028539882,0.28160527,0.00004576652,0.00005364484,0.000003589949,0.0000012785958,0.000018214812,0.000041331077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969521,0.000045118642,0.00061870296,0.0013231949,0.0005695579,0.00049132184],"domain_scores_gemma":[0.9974639,0.00055135076,0.00059035455,0.0011476193,0.00018515627,0.000061623134],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007384256,0.00045239116,0.0008605927,0.00086176855,0.00018464738,0.00014118227,0.0021494634,0.00027683208,0.0000036822598],"category_scores_gemma":[0.000049636845,0.00041808066,0.000120416815,0.00037463327,0.00047473772,0.0002330705,0.0010305418,0.00055125455,0.000005040289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007678079,0.0010836122,0.0019587127,0.00027978636,0.00020015023,0.000090017376,0.008827679,0.46605,0.008831386,0.010647783,0.000008001821,0.5019461],"study_design_scores_gemma":[0.00078243075,0.0018803689,0.00022675723,0.00048450253,0.00003329165,0.00002191922,0.0000052273203,0.9620547,0.012479715,0.021101484,0.00023215935,0.0006973958],"about_ca_topic_score_codex":0.000045815792,"about_ca_topic_score_gemma":0.000015381416,"teacher_disagreement_score":0.6959494,"about_ca_system_score_codex":0.00024381482,"about_ca_system_score_gemma":0.00029509986,"threshold_uncertainty_score":0.9998271},"labels":[],"label_agreement":null},{"id":"W2971199229","doi":"10.1016/j.jneumeth.2019.108420","title":"A novel dual and triple shifted RSVP paradigm for P300 speller","year":2019,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Rapid serial visual presentation; Computer science; Gaze; Dual (grammatical number); Character (mathematics); Speech recognition; Artificial intelligence; Neuroscience; Psychology; Cognition","score_opus":0.06524426554079871,"score_gpt":0.37024554267188065,"score_spread":0.30500127713108194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2971199229","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11218317,0.00007140818,0.88298744,0.0029011304,0.0015961996,0.00012790546,0.0000014343253,0.000031101576,0.0001002004],"genre_scores_gemma":[0.2428663,0.000014622096,0.7562208,0.0005303663,0.000052869615,0.0000016595078,3.448155e-8,0.0000070287124,0.00030631368],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986249,0.00013615657,0.00036286397,0.00032786376,0.00025242256,0.00029579015],"domain_scores_gemma":[0.99864894,0.00047634798,0.00037055355,0.00029482463,0.000095735326,0.00011361908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020838683,0.00012446662,0.00030494272,0.00028423546,0.00009193629,0.00014985754,0.00079944474,0.000063317275,0.0000025422944],"category_scores_gemma":[0.00070267543,0.00009479155,0.00010181684,0.000574045,0.00015004739,0.00048237448,0.00015287098,0.00026397276,0.0000023517118],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039682236,0.00016412506,0.0011922077,0.000020289093,0.000007635509,0.000026546437,0.00020907416,0.00008453203,0.87087345,0.037049733,0.00031045813,0.09002229],"study_design_scores_gemma":[0.0065837833,0.0061362963,0.39537236,0.00015989649,0.00007112422,0.006403018,0.00009513408,0.27536255,0.11527387,0.061428897,0.13207489,0.0010381737],"about_ca_topic_score_codex":0.0000013733629,"about_ca_topic_score_gemma":3.105833e-7,"teacher_disagreement_score":0.75559956,"about_ca_system_score_codex":0.000018952818,"about_ca_system_score_gemma":0.00010076943,"threshold_uncertainty_score":0.3865487},"labels":[],"label_agreement":null},{"id":"W2974209378","doi":"10.1167/19.10.157a","title":"Learning and visual attention across neurodevel-opmental conditions: Using Multiple Object-Tracking as a descriptor of visual attention","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Autism; Psychology; Cognition; Cognitive psychology; Task (project management); Eye tracking; Autism spectrum disorder; Proxy (statistics); Developmental psychology; Object (grammar); Artificial intelligence; Machine learning; Computer science; Neuroscience","score_opus":0.01620804106820385,"score_gpt":0.3365191436782357,"score_spread":0.3203111026100319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974209378","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9596446,0.0001020388,0.039521106,0.00007029212,0.00051732146,0.0000942942,0.0000010977222,0.000038578557,0.000010673026],"genre_scores_gemma":[0.99504024,0.00002215532,0.0048274836,0.00002009992,0.00004214019,5.488413e-7,0.000002239165,0.00001148494,0.000033629083],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998514,0.00012642212,0.0005176076,0.00023906381,0.0003766042,0.00022627611],"domain_scores_gemma":[0.99874675,0.00010852044,0.0007316632,0.000083003186,0.00026773306,0.00006232532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000607281,0.00013441964,0.00026998197,0.0002557428,0.00020071231,0.00013663377,0.00019516025,0.00009913922,0.000008688664],"category_scores_gemma":[0.00015535118,0.00012324302,0.00011676531,0.00026172714,0.000075387055,0.0009427767,0.00018159511,0.00036321938,0.000011449057],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041842504,0.00017785821,0.17554516,0.00002846668,0.000024605599,0.000020388565,0.00020776762,0.00009338458,0.7966601,0.00007211962,0.000008138445,0.027120158],"study_design_scores_gemma":[0.0017333187,0.0022574628,0.8979521,0.0006459368,0.000025635989,0.0006599333,0.0010571077,0.08229853,0.012906432,0.00018991131,0.00007264234,0.00020103186],"about_ca_topic_score_codex":0.000013581663,"about_ca_topic_score_gemma":0.0000018844157,"teacher_disagreement_score":0.7837537,"about_ca_system_score_codex":0.00007361827,"about_ca_system_score_gemma":0.000049276052,"threshold_uncertainty_score":0.50257045},"labels":[],"label_agreement":null},{"id":"W2979521244","doi":"10.1109/embc.2019.8857881","title":"A Geometrical Approach to Human Saccade Simulation","year":2019,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Saccade; Eye movement; Computer science; Human eye; MATLAB; Simulation; Computer vision; Pulley; Artificial intelligence; Movement (music); Engineering; Acoustics; Physics","score_opus":0.02942462695390054,"score_gpt":0.2877291909506345,"score_spread":0.25830456399673396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979521244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1960344,0.0000032983169,0.7674876,0.00028959048,0.000050766197,0.0000993979,1.1840277e-7,0.00037094738,0.035663895],"genre_scores_gemma":[0.94184446,5.1828042e-8,0.05647905,0.0002549788,0.000013768546,0.0000042929273,6.7203297e-7,0.000003308545,0.0013994044],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929565,0.000013990924,0.000095632306,0.00028987863,0.00013606207,0.0001687575],"domain_scores_gemma":[0.9994916,0.000048698676,0.000020387803,0.00035980664,0.00003223593,0.00004728648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000113853916,0.0000626383,0.000091875394,0.00027751984,0.000042043033,0.0000517122,0.00050540693,0.00005849636,0.000019379428],"category_scores_gemma":[0.00002997612,0.000052254352,0.000026688522,0.0008362169,0.000009305468,0.00010704462,0.00016261992,0.00009193738,0.0005933311],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000022185661,0.00024780986,0.020742234,0.000009782299,0.000012277235,0.0000015847367,0.00015327893,0.021630535,0.0040754667,0.89579374,0.0010559686,0.056275133],"study_design_scores_gemma":[0.0005731983,0.00035003308,0.32133013,0.000007995913,0.0000039429524,0.0000072918797,0.00003191766,0.6532882,0.0028649617,0.007028078,0.014081049,0.00043318298],"about_ca_topic_score_codex":0.000010808842,"about_ca_topic_score_gemma":3.6172833e-7,"teacher_disagreement_score":0.88876563,"about_ca_system_score_codex":0.000023904875,"about_ca_system_score_gemma":0.0000067586993,"threshold_uncertainty_score":0.76262695},"labels":[],"label_agreement":null},{"id":"W2981034728","doi":"10.1145/3332165.3347933","title":"Learning Cooperative Personalized Policies from Gaze Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Heuristics; Reinforcement learning; Variety (cybernetics); Task (project management); Gaze; Context (archaeology); Object (grammar); Human–computer interaction; Artificial intelligence; Annotation; Machine learning","score_opus":0.035679351924152045,"score_gpt":0.2825086278478804,"score_spread":0.24682927592372833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981034728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6506619,0.00015131308,0.31972623,0.004341912,0.0002963485,0.00013225278,0.000015724794,0.0011526927,0.023521628],"genre_scores_gemma":[0.97144216,0.000009522431,0.020520963,0.00036970343,0.000027189373,0.0000019398642,0.000026683792,0.000005530539,0.0075963074],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990858,0.000059551436,0.000098216,0.00043278106,0.00012991167,0.0001937474],"domain_scores_gemma":[0.9989366,0.0001372266,0.000040053223,0.00080421864,0.00004919214,0.000032736545],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00012612672,0.00009692473,0.00014764305,0.000054071013,0.00008001323,0.000114122995,0.0014446931,0.000053248586,0.00028909958],"category_scores_gemma":[0.00007308462,0.00007711484,0.000020058333,0.00019573045,0.00006604983,0.0003370057,0.000742907,0.00019755069,0.00091548084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017168897,0.000151925,0.07967433,0.000009777185,0.00016825352,0.0000259078,0.004273833,0.00023177636,0.04195615,0.80330825,0.012449909,0.05773269],"study_design_scores_gemma":[0.003364789,0.00079774356,0.13414927,0.0001245241,0.000033181797,0.00003563725,0.004035428,0.5220051,0.019047879,0.0069041722,0.3080121,0.0014901882],"about_ca_topic_score_codex":0.00036543922,"about_ca_topic_score_gemma":0.000021350048,"teacher_disagreement_score":0.7964041,"about_ca_system_score_codex":0.0000142086465,"about_ca_system_score_gemma":0.00003888497,"threshold_uncertainty_score":0.99986243},"labels":[],"label_agreement":null},{"id":"W2985011011","doi":"10.7939/r3-jbsv-rj58","title":"Reproducibility and Application of the Gaze and Movement Assessment (GaMA)","year":2019,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"U.S. Navy; University of Alberta","keywords":"Reproducibility; Gaze; Movement (music); Computer science; Artificial intelligence; Mathematics; Statistics; Art","score_opus":0.004616161944061187,"score_gpt":0.18316209347677687,"score_spread":0.17854593153271567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985011011","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98774654,0.000025418802,0.0041915462,0.0037551147,0.000023413679,0.00012785022,6.9272687e-7,0.000020342408,0.0041090744],"genre_scores_gemma":[0.9926883,0.000011132117,0.0064847344,0.0000431157,0.0000016470542,7.5124575e-8,6.779655e-7,0.0000013409384,0.0007689641],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994686,0.000028259048,0.000057185687,0.0003342787,0.0000611394,0.000050487906],"domain_scores_gemma":[0.99905854,0.0000614126,0.000078896934,0.0007740526,0.000011460638,0.00001563617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000100031015,0.000039187937,0.0000820077,0.000027265556,0.000034694818,0.0000058932437,0.0003158844,0.000028583408,0.000008701784],"category_scores_gemma":[0.000007821211,0.00003365644,0.000018350549,0.000105968385,0.00011147759,0.0002336208,0.0004791793,0.000050789884,9.499635e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052047158,0.000057118694,0.8561327,0.000036517104,0.000013545675,2.084885e-7,0.00052399427,0.0000068977683,0.0023708153,0.1327614,0.00016913688,0.007922458],"study_design_scores_gemma":[0.00018341414,0.00004709036,0.9868006,0.000014578264,0.000004816719,6.440226e-7,0.00012495524,0.0027485958,0.004285009,0.004278138,0.0014687078,0.000043421234],"about_ca_topic_score_codex":0.00023004826,"about_ca_topic_score_gemma":0.000013688525,"teacher_disagreement_score":0.13066794,"about_ca_system_score_codex":0.000005067705,"about_ca_system_score_gemma":0.000024586774,"threshold_uncertainty_score":0.13724698},"labels":[],"label_agreement":null},{"id":"W2986928166","doi":"10.1109/tnsre.2019.2950619","title":"Proof of Concept of an Assistive Robotic Arm Control Using Artificial Stereovision and Eye-Tracking","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Joystick; Artificial intelligence; Computer science; Robotic arm; Computer vision; Set (abstract data type); Object (grammar); Eye–hand coordination; Tracking (education); Robot; Obstacle; Simulation; Psychology","score_opus":0.01224041938508491,"score_gpt":0.24746782006205467,"score_spread":0.23522740067696976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986928166","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5015466,0.000036841127,0.49790162,0.000029849727,0.00025892875,0.00018664678,0.0000031960833,0.00003514479,0.000001132018],"genre_scores_gemma":[0.9955028,4.4268793e-7,0.0044629956,0.000002315641,0.000010303479,0.000008173153,2.689301e-7,0.000009022052,0.000003640542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990699,0.00006448004,0.00034131962,0.00025966793,0.00013884538,0.00012577137],"domain_scores_gemma":[0.9992445,0.00029193898,0.0001291257,0.0001869498,0.00010384795,0.000043626904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018056444,0.000120282726,0.00028074696,0.00020458747,0.000050944673,0.00003169293,0.00009058228,0.00007703454,8.822346e-7],"category_scores_gemma":[0.000014758108,0.00010825489,0.00004546211,0.00018078733,0.00007340724,0.00030536458,0.0000019363442,0.0001288869,1.5086917e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016241833,0.00009203235,0.0002446919,0.0002178994,0.000021078124,6.7416823e-7,0.00046017018,0.92204714,0.06142687,0.0014354811,8.1760795e-8,0.014037627],"study_design_scores_gemma":[0.00036458691,0.00082626165,0.0066871936,0.00022337056,0.000016598036,0.0000096149,0.00021953594,0.97646403,0.01504473,0.000031127358,0.0000010345414,0.00011193683],"about_ca_topic_score_codex":0.000042810858,"about_ca_topic_score_gemma":0.0000019032051,"teacher_disagreement_score":0.4939562,"about_ca_system_score_codex":0.000024317125,"about_ca_system_score_gemma":0.000011818477,"threshold_uncertainty_score":0.44145063},"labels":[],"label_agreement":null},{"id":"W2990854389","doi":"10.1167/19.13.17","title":"The Neyman Pearson detection of microsaccades with maximum likelihood estimation of parameters","year":2019,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Microsaccade; Saccadic masking; Saccade; Computer science; Artificial intelligence; Eye tracking; Eye movement; Computer vision; Gaze; Pattern recognition (psychology); Algorithm","score_opus":0.006026315587519112,"score_gpt":0.24123603621235296,"score_spread":0.23520972062483383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990854389","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8023736,0.00010395641,0.19694212,0.00036325067,0.0001423712,0.00004513069,1.9460141e-7,0.0000093779345,0.000019995412],"genre_scores_gemma":[0.9725049,0.000022818624,0.02744866,0.000007472581,0.00000664086,2.5677232e-7,6.7932035e-8,0.0000035317062,0.000005627003],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992501,0.0000456179,0.0002897355,0.000081956714,0.00023509708,0.00009748914],"domain_scores_gemma":[0.9988662,0.00011372176,0.000629234,0.00019928903,0.00016989751,0.000021640828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004465011,0.000060543556,0.00014839972,0.0001290736,0.00004521294,0.00003146547,0.00035353412,0.00004683362,6.614784e-7],"category_scores_gemma":[0.000044016728,0.000034735855,0.000059952057,0.00020889899,0.000055458808,0.00023328356,0.00003831492,0.00016041519,0.0000026116948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001131861,0.00009213246,0.0023469322,0.00002800776,0.00003186791,0.0000030241831,0.00021036982,0.0011265748,0.35880795,0.00035389772,0.00003061433,0.6368555],"study_design_scores_gemma":[0.0013356272,0.006747481,0.2753742,0.00060538325,0.00003990739,0.00024064726,0.00022488207,0.045397494,0.66052383,0.009128037,0.00022720957,0.00015527052],"about_ca_topic_score_codex":0.000008230594,"about_ca_topic_score_gemma":0.0000026441026,"teacher_disagreement_score":0.6367002,"about_ca_system_score_codex":0.000021560956,"about_ca_system_score_gemma":0.000031371652,"threshold_uncertainty_score":0.14164871},"labels":[],"label_agreement":null},{"id":"W2992421674","doi":"10.48550/arxiv.1912.02083","title":"Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI's Eye-Tracking HTC Vive","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Linearity; Computer science; Saccade; Monocular; Computer vision; Artificial intelligence; Eye movement; Engineering","score_opus":0.42636393586181276,"score_gpt":0.3696953513777433,"score_spread":0.05666858448406947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992421674","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6380733,0.00005822319,0.36000565,0.00003466087,0.0005294276,0.000774056,0.00016846143,0.00030189697,0.00005431792],"genre_scores_gemma":[0.99794436,0.000006867926,0.0017923696,0.000020740566,0.00011444733,0.0000020715122,0.00004123129,0.000037592057,0.000040324172],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9920453,0.0029498178,0.0010639122,0.0028257065,0.00049268646,0.0006225731],"domain_scores_gemma":[0.9890391,0.0015223731,0.0021509975,0.006449564,0.0007091097,0.00012883045],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0057305982,0.00063937186,0.0012306314,0.00035134293,0.00061140186,0.00031729354,0.006422624,0.00050228985,0.000009419338],"category_scores_gemma":[0.0005522898,0.0006031897,0.00030113794,0.00095158763,0.00035343983,0.00080278335,0.008687456,0.0015586881,0.00001498338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020760953,0.0012479036,0.11698789,0.0006200232,0.0017316336,0.005468954,0.010358973,0.8350029,0.0051190844,0.015291142,0.000022357533,0.007941517],"study_design_scores_gemma":[0.0010198605,0.00025847112,0.02364365,0.0006420081,0.0005979989,0.000041203777,0.018513475,0.95267344,0.00029057023,0.0015777182,0.000002834951,0.0007387583],"about_ca_topic_score_codex":0.023253875,"about_ca_topic_score_gemma":0.00082833745,"teacher_disagreement_score":0.35987103,"about_ca_system_score_codex":0.0005127979,"about_ca_system_score_gemma":0.0005553699,"threshold_uncertainty_score":0.99964195},"labels":[],"label_agreement":null},{"id":"W2995679171","doi":"10.1109/tvcg.2019.2958540","title":"Gaze-Driven Adaptive Interventions for Magazine-Style Narrative Visualizations","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer science; Narrative; Style (visual arts); Visualization; Human–computer interaction; Multimedia; Data visualization; Computer graphics (images); Computer vision; Artificial intelligence; Visual arts","score_opus":0.03227684527770374,"score_gpt":0.30834596895433736,"score_spread":0.2760691236766336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995679171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007207631,0.000034635475,0.99050105,0.00015187751,0.000823375,0.00062163704,0.000035766738,0.000534656,0.000089390094],"genre_scores_gemma":[0.9893687,0.00006302277,0.00957105,0.00049630745,0.00003260109,0.00012276294,0.000021898597,0.000027324852,0.00029634722],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846154,0.00011388068,0.0003741316,0.0005867532,0.00019562725,0.00026807966],"domain_scores_gemma":[0.9989365,0.00018102684,0.00015637667,0.0003760575,0.00025311304,0.00009696264],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013068927,0.00024765055,0.0002814116,0.0006049746,0.0004262527,0.00016682666,0.00036274,0.0001583636,0.000021668633],"category_scores_gemma":[0.0000038466237,0.00025362527,0.00023334846,0.0009093601,0.00011060802,0.00040600935,0.000010167567,0.00019111596,0.00002906876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022418382,0.00044025757,0.000110180816,0.000077259974,0.00011592078,0.0000012141797,0.0018219677,0.0015615259,0.00008747815,0.98957443,0.00047833394,0.0057089864],"study_design_scores_gemma":[0.00095448387,0.0009852027,0.00059927383,0.0001773536,0.000036425736,0.000011736767,0.00016541762,0.9929587,0.0009550166,0.0018517422,0.0009801156,0.00032451592],"about_ca_topic_score_codex":0.000003932638,"about_ca_topic_score_gemma":0.00003239794,"teacher_disagreement_score":0.9913972,"about_ca_system_score_codex":0.000027249258,"about_ca_system_score_gemma":0.00003701121,"threshold_uncertainty_score":0.9999916},"labels":[],"label_agreement":null},{"id":"W2998885320","doi":"10.1109/sensors43011.2019.8956542","title":"An Oculomotor Sensing Technique for Saccade Isolation of Eye Movements using OpenBCI","year":2019,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Saccade; Computer vision; Computer science; Artificial intelligence; Electrooculography; Eye movement; Noise (video); Saccadic suppression of image displacement; SIGNAL (programming language)","score_opus":0.02263210919314387,"score_gpt":0.30878181654371656,"score_spread":0.2861497073505727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998885320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26280555,0.0000034263094,0.73632157,0.000049546437,0.00007628184,0.0003476668,0.0000014171407,0.00011380279,0.00028073916],"genre_scores_gemma":[0.60007286,2.3106324e-7,0.39981076,0.000044845016,0.000006893314,0.000002165316,9.737217e-7,0.000004486206,0.00005680113],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993008,0.000022971723,0.00016951544,0.0002548836,0.00009408801,0.00015778576],"domain_scores_gemma":[0.9993672,0.00002693951,0.000105962186,0.00038270856,0.00009384527,0.000023381535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022702056,0.0000800909,0.00013123879,0.00011679495,0.0000468765,0.000033459317,0.00034731778,0.0000815321,0.000006626162],"category_scores_gemma":[0.000013396094,0.00007417044,0.000036010533,0.00016856482,0.000019075482,0.00034598744,0.00008182195,0.000057828525,0.0000043933624],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000049163723,0.000038357804,0.0055814018,0.000016314176,0.000007952778,3.1519528e-7,0.000043154596,0.0002543478,0.96829605,0.016439298,0.000009726447,0.009308169],"study_design_scores_gemma":[0.0002709597,0.00019410609,0.0059146048,0.00003816729,0.0000034156399,0.0000024694507,0.000026626485,0.39326286,0.5978174,0.002240972,0.000101462065,0.00012694664],"about_ca_topic_score_codex":0.000055573724,"about_ca_topic_score_gemma":0.0000026828761,"teacher_disagreement_score":0.39300853,"about_ca_system_score_codex":0.000032265096,"about_ca_system_score_gemma":0.000027403086,"threshold_uncertainty_score":0.3024583},"labels":[],"label_agreement":null},{"id":"W2999734205","doi":"10.3390/s20020543","title":"Hybrid Eye-Tracking on a Smartphone with CNN Feature Extraction and an Infrared 3D Model","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer vision; Artificial intelligence; Computer science; Eye tracking; BitTorrent tracker; Robustness (evolution); Gaze; Convolutional neural network; Feature extraction; Mobile device; Feature (linguistics); Tracking system; Kalman filter","score_opus":0.019978474437803356,"score_gpt":0.26585590937766346,"score_spread":0.2458774349398601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999734205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9158578,0.000022211028,0.07893856,0.003686888,0.00005446946,0.000087852684,0.0000031086201,0.00054221967,0.0008069045],"genre_scores_gemma":[0.9534897,0.000006138183,0.04561238,0.0006186432,0.000053992222,0.0000034025925,0.0000031808518,0.000013588019,0.0001989818],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900645,0.000037315465,0.00009278292,0.00047321932,0.00017053424,0.00021969515],"domain_scores_gemma":[0.9994311,0.000029709618,0.000070890834,0.00030124257,0.00005273557,0.00011435703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000067490975,0.00015460046,0.00015297419,0.00007637685,0.00012437518,0.000105801344,0.00022950866,0.00007349905,0.0000018363467],"category_scores_gemma":[0.000035604375,0.00012954733,0.000020715092,0.00019189979,0.00006286589,0.00026917085,0.000050736788,0.000344007,0.000015950187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010717667,0.0009521743,0.020309798,0.00022921302,0.00028606673,0.0022238987,0.016435249,0.22322549,0.12545998,0.038329817,0.008727772,0.5627488],"study_design_scores_gemma":[0.0005808493,0.0005738736,0.019605367,0.00004188228,0.00001307479,0.00006467726,0.00010318355,0.96188337,0.014775516,0.0010765002,0.00095959444,0.00032213586],"about_ca_topic_score_codex":0.0000060477205,"about_ca_topic_score_gemma":0.0000039510724,"teacher_disagreement_score":0.73865783,"about_ca_system_score_codex":0.000019306872,"about_ca_system_score_gemma":0.000025301859,"threshold_uncertainty_score":0.52827865},"labels":[],"label_agreement":null},{"id":"W3007742448","doi":"10.1142/s2424905x19500053","title":"Comparison of Attentive and Explicit Eye Gaze Interfaces for Controlling Haptic Guidance of a Robotic Controller","year":2019,"lang":"en","type":"article","venue":"Journal of Medical Robotics Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Interface (matter); Gaze; Haptic technology; Computer science; Human–computer interaction; User interface; Eye tracking; Eye–hand coordination; Control (management); Robot; Simulation; Artificial intelligence","score_opus":0.1045833355095055,"score_gpt":0.4490616434765896,"score_spread":0.3444783079670841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3007742448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22432269,0.0020778484,0.7672051,0.005834291,0.00023297171,0.0002662192,0.0000010331249,0.000008616345,0.000051204894],"genre_scores_gemma":[0.9756242,0.00011297502,0.024107853,0.000024391911,0.00004663787,0.0000035550768,1.3374189e-7,0.000008254083,0.00007202165],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965452,0.00024209169,0.00094206503,0.00021456987,0.0016804083,0.00037564329],"domain_scores_gemma":[0.995043,0.0026021167,0.00056360295,0.00023952955,0.001370047,0.00018171902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047102612,0.00011320802,0.0008672541,0.00039816598,0.000054403587,0.00004406451,0.0011102179,0.00017552671,0.000011998397],"category_scores_gemma":[0.0029162029,0.000083836676,0.000121336074,0.00032135603,0.00036002116,0.00013248589,0.00029996267,0.000800548,0.0000034277837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0025632873,0.0038914634,0.21791565,0.0025892905,0.0021894886,0.00016553208,0.0055092885,0.081629016,0.20725062,0.36937186,0.003131193,0.10379331],"study_design_scores_gemma":[0.00498786,0.0028257922,0.008178299,0.0014835675,0.000037325623,0.00003356582,0.0007306272,0.97025204,0.007061014,0.0041716513,0.00010661851,0.00013163693],"about_ca_topic_score_codex":0.000008808169,"about_ca_topic_score_gemma":0.000003144534,"teacher_disagreement_score":0.88862306,"about_ca_system_score_codex":0.000043751006,"about_ca_system_score_gemma":0.00022146158,"threshold_uncertainty_score":0.3491178},"labels":[],"label_agreement":null},{"id":"W3008347462","doi":"10.21037/aes.2019.ab005","title":"AB005. The effect of audio quality on eye movements in a video chat","year":2019,"lang":"en","type":"article","venue":"Annals of Eye Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Conversation; Session (web analytics); Computer science; Perception; Context (archaeology); Variety (cybernetics); Quality (philosophy); Sound quality; Reading (process); Compensation (psychology); Multimedia; Face (sociological concept); Videoconferencing; Speech recognition; Psychology; Communication; Artificial intelligence; World Wide Web; Social psychology; Linguistics","score_opus":0.03519355314089821,"score_gpt":0.3693193215161483,"score_spread":0.3341257683752501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008347462","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9945068,0.00004463094,0.00066157436,0.0020397238,0.00018622143,0.0001913646,0.0000017977329,0.00003185032,0.0023360585],"genre_scores_gemma":[0.9992803,0.000008075684,0.00020039218,0.00034460667,0.0000053289127,0.0000068442823,1.1322486e-7,0.000002392944,0.00015199251],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982624,0.00013649545,0.00028339576,0.00041277826,0.000570344,0.000334608],"domain_scores_gemma":[0.9984558,0.00028464262,0.00022794034,0.00088423863,0.000109570974,0.000037767226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036472776,0.000107520966,0.0002473499,0.00023060257,0.00006504287,0.000030296042,0.002043916,0.000041302566,0.0000065924396],"category_scores_gemma":[0.00041359052,0.000068126756,0.00006629036,0.0011871455,0.0005394072,0.00028991434,0.00033401157,0.00014422146,0.000042306066],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000691952,0.00024928414,0.6771036,0.00010300791,0.000019253353,0.0000048910915,0.0012611198,0.0004636504,0.16338062,0.079372585,0.00015235784,0.07782047],"study_design_scores_gemma":[0.00021062218,0.00061525294,0.66299343,0.00008063941,5.2292035e-7,1.7984414e-7,0.000017580056,0.0010415795,0.33347955,0.0013605095,0.00012442103,0.0000757099],"about_ca_topic_score_codex":0.00013041488,"about_ca_topic_score_gemma":0.000008911941,"teacher_disagreement_score":0.17009892,"about_ca_system_score_codex":0.000014884435,"about_ca_system_score_gemma":0.000059132515,"threshold_uncertainty_score":0.37981382},"labels":[],"label_agreement":null},{"id":"W3008957978","doi":"10.1080/17483107.2020.1729874","title":"Effect of feedback and target size on eye gaze accuracy in an off-screen task","year":2020,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Gaze; Fixation (population genetics); Eye tracking; Computer science; Modalities; Eye movement; Computer vision; Modality (human–computer interaction); Frame of reference; Task (project management); Human–computer interaction; Artificial intelligence; Psychology; Engineering; Medicine","score_opus":0.008211020349319397,"score_gpt":0.2796742188781743,"score_spread":0.27146319852885487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008957978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9719931,0.00012796285,0.00285734,0.023952369,0.000043112654,0.0005185855,0.000024590228,0.00036927353,0.00011366839],"genre_scores_gemma":[0.987924,0.00001203075,0.011828442,0.00014204672,0.000011319633,0.000063602245,0.000005010541,0.000010674512,0.0000028696004],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976264,0.00049251324,0.00046913797,0.00094671146,0.00017409895,0.00029112966],"domain_scores_gemma":[0.99547863,0.0035348078,0.00019286743,0.0005777455,0.00010537281,0.0001105861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073788816,0.00026426982,0.00056685833,0.00018034551,0.000095767406,0.000030357638,0.0004822465,0.00033871827,0.000008480185],"category_scores_gemma":[0.0066000586,0.00022491413,0.00006662353,0.0009059206,0.0020607319,0.00030336014,0.00026626128,0.00044710955,0.000005482054],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020102695,0.00027313706,0.8485417,0.0001561013,0.000013695383,0.0000028456516,0.00032576872,0.000012589933,0.0025549235,0.01777341,0.000021288213,0.13012351],"study_design_scores_gemma":[0.0011819922,0.006150568,0.982449,0.000062193416,0.000010902164,0.0000020223558,0.0003685859,0.0029108522,0.0022625327,0.0041785487,0.00019492826,0.00022787582],"about_ca_topic_score_codex":0.000020282296,"about_ca_topic_score_gemma":0.000024064535,"teacher_disagreement_score":0.13390729,"about_ca_system_score_codex":0.00006481466,"about_ca_system_score_gemma":0.000027716598,"threshold_uncertainty_score":0.9171732},"labels":[],"label_agreement":null},{"id":"W3012343221","doi":"10.48550/arxiv.1905.02823","title":"Tracking the Progression of Reading Through Eye-gaze Measurements","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Eye tracking; Computer science; Fixation (population genetics); Eye movement; Reading (process); Artificial intelligence; Computer vision; Saccade; Population","score_opus":0.1094803255898752,"score_gpt":0.22432310898978303,"score_spread":0.11484278339990783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012343221","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8310322,0.000036564044,0.16147341,0.00020998585,0.00021326385,0.00016693847,6.026884e-7,0.00018755678,0.0066794637],"genre_scores_gemma":[0.9980652,0.0000101466085,0.0014816483,0.0000396467,0.000010109238,2.7753546e-7,4.4950204e-7,0.0000060860575,0.0003864866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99908435,0.00007859261,0.00011835818,0.00038668409,0.00010378989,0.00022820511],"domain_scores_gemma":[0.999012,0.000062314815,0.00015704254,0.00063273916,0.00010913429,0.000026755124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002782109,0.00011050392,0.00014572524,0.00007250855,0.00011961544,0.000027011945,0.0010496203,0.00007297073,0.000010295042],"category_scores_gemma":[0.00002915139,0.00008650976,0.00007354573,0.0006103363,0.00010103963,0.0004189118,0.00023111804,0.0001647009,0.00006556759],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002922122,0.00017451015,0.40586567,0.000042745483,0.000082139355,0.000044073044,0.00065364427,0.0031986013,0.014193269,0.56357116,0.00012977284,0.012015209],"study_design_scores_gemma":[0.0055161985,0.0014408068,0.46397313,0.0014772584,0.00023007969,0.000041219137,0.0017271015,0.19617629,0.22604112,0.09324644,0.008176828,0.0019535332],"about_ca_topic_score_codex":0.000019489162,"about_ca_topic_score_gemma":0.0000029956364,"teacher_disagreement_score":0.47032472,"about_ca_system_score_codex":0.000037247442,"about_ca_system_score_gemma":0.000031478434,"threshold_uncertainty_score":0.3527766},"labels":[],"label_agreement":null},{"id":"W3013706699","doi":"10.2196/15581","title":"Value of Eye-Tracking Data for Classification of Information Processing–Intensive Handling Tasks: Quasi-Experimental Study on Cognition and User Interface Design","year":2020,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eye tracking; Computer science; Cognition; Interface (matter); Value (mathematics); Human–computer interaction; Tracking (education); User interface; Artificial intelligence; Machine learning; Psychology; Operating system; Neuroscience","score_opus":0.13769646499320776,"score_gpt":0.37924156302768863,"score_spread":0.24154509803448088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013706699","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60730636,0.0000103376315,0.3920117,0.000078258054,0.000024000778,0.00048391137,0.000014481521,0.00006436926,0.0000066029224],"genre_scores_gemma":[0.9969133,3.5176424e-7,0.00295211,0.000039116745,0.000010211617,0.000029397557,0.00004731309,0.000007006636,0.0000011826976],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902594,0.0000538704,0.00033907255,0.00028146073,0.00018449541,0.0001151577],"domain_scores_gemma":[0.9989153,0.000089831796,0.00037971823,0.00028922502,0.00029109913,0.00003478263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019631704,0.00012519186,0.00020232343,0.00013573394,0.00011197343,0.000081181046,0.00048721582,0.00006051123,9.805576e-7],"category_scores_gemma":[0.00018859033,0.00011295074,0.000022091968,0.00016607081,0.000078882025,0.0008760614,0.00016232677,0.00010840735,0.0000011650019],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043921682,0.0039904495,0.0521943,0.00068913726,0.0002450246,0.0000019658912,0.12414041,0.0003244326,0.7895813,0.009191135,0.0007703518,0.018432295],"study_design_scores_gemma":[0.0023063116,0.008842518,0.24446195,0.0003490968,0.00006532104,0.0000012108875,0.038556173,0.095520966,0.6090668,0.00030042903,0.000106363674,0.00042285465],"about_ca_topic_score_codex":0.000006261222,"about_ca_topic_score_gemma":8.7214113e-7,"teacher_disagreement_score":0.38960695,"about_ca_system_score_codex":0.000022068492,"about_ca_system_score_gemma":0.000026189136,"threshold_uncertainty_score":0.46059972},"labels":[],"label_agreement":null},{"id":"W3014193006","doi":"10.1109/tim.2020.2983525","title":"Tracking the Progression of Reading Using Eye-Gaze Point Measurements and Hidden Markov Models","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Eye tracking; Computer science; Scrolling; Computer vision; Artificial intelligence; BitTorrent tracker; Hidden Markov model; Point (geometry); Kalman filter; Reading (process); Line (geometry); Tracking (education)","score_opus":0.12360041253797482,"score_gpt":0.3023073997490084,"score_spread":0.17870698721103356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014193006","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14090809,0.00007805064,0.8559104,0.0024017768,0.00016089031,0.00031585194,0.0000027101241,0.00009139444,0.00013079726],"genre_scores_gemma":[0.9807002,0.00004224409,0.01897188,0.00024147482,0.000010175699,0.000021650549,3.1356427e-7,0.0000089207415,0.0000031385935],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856585,0.000101419806,0.0002918086,0.0003191143,0.00055548595,0.00016629718],"domain_scores_gemma":[0.9994404,0.000018964403,0.00014624772,0.00017107964,0.00014030469,0.00008300656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046865758,0.00014674525,0.00015616212,0.00009903339,0.00028437137,0.0000799588,0.00018047534,0.00005233622,0.000004001115],"category_scores_gemma":[0.0000071970835,0.000114226044,0.00004280547,0.00022684639,0.0000869828,0.0003734687,0.0000060871016,0.00016086666,8.0874105e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006464689,0.00013002806,0.0004450829,0.00007576278,0.00009770625,0.0000015435893,0.0032027604,0.0012886772,0.14537835,0.0007180531,0.0000150734095,0.8485823],"study_design_scores_gemma":[0.0028439276,0.00077917345,0.003465629,0.00065281836,0.00017580931,0.000028673356,0.001905948,0.24479981,0.74369866,0.0010830434,0.00008110541,0.00048538798],"about_ca_topic_score_codex":0.00002219412,"about_ca_topic_score_gemma":0.000007763947,"teacher_disagreement_score":0.8480969,"about_ca_system_score_codex":0.00006273095,"about_ca_system_score_gemma":0.000043630287,"threshold_uncertainty_score":0.4658003},"labels":[],"label_agreement":null},{"id":"W3019459555","doi":"10.1007/s11548-020-02143-w","title":"Hand-eye coordination-based implicit re-calibration method for gaze tracking on ultrasound machines: a statistical approach","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Gaze; Computer science; Eye tracking; Computer vision; Calibration; Artificial intelligence; Benchmark (surveying); Tracking (education); Tracking system; Human–computer interaction; Kalman filter; Mathematics; Statistics; Psychology","score_opus":0.03678703279457799,"score_gpt":0.3078458196009692,"score_spread":0.27105878680639117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019459555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0117009245,0.00008034983,0.9747995,0.012395979,0.00081007427,0.00009674773,0.0000220935,0.000061750485,0.000032574666],"genre_scores_gemma":[0.72041553,0.0000054335824,0.27644596,0.002672577,0.00040684416,0.000007053701,0.000034239332,0.0000095036685,0.0000028747104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981723,0.00033646444,0.00065544463,0.0003548682,0.0002735732,0.00020737365],"domain_scores_gemma":[0.99430764,0.0045947726,0.00047711396,0.0001245519,0.0003705767,0.00012534582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085290463,0.0001888676,0.00048137733,0.00035013282,0.0001293351,0.00021402724,0.000556689,0.00015935114,0.0000068852523],"category_scores_gemma":[0.0005469444,0.00015880744,0.00018499486,0.00017624043,0.00012484686,0.00024791938,0.000042619704,0.0003376295,8.271489e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001719248,0.0010102054,0.05430705,0.00014599181,0.0016767828,0.00046660873,0.001077052,0.017283687,0.009426664,0.120824866,0.03615488,0.75590694],"study_design_scores_gemma":[0.0013049269,0.00047899256,0.06539427,0.0000646581,0.000037735623,0.0007159961,0.0000158845,0.926618,0.0007356961,0.0027832577,0.0015977726,0.00025280382],"about_ca_topic_score_codex":0.0000031312475,"about_ca_topic_score_gemma":5.864755e-7,"teacher_disagreement_score":0.9093343,"about_ca_system_score_codex":0.000047445465,"about_ca_system_score_gemma":0.00012506172,"threshold_uncertainty_score":0.64759797},"labels":[],"label_agreement":null},{"id":"W3019640167","doi":"10.3758/s13414-020-02019-w","title":"Digit eyes: Learning-related changes in information access in a computer game parallel those of oculomotor attention in laboratory studies","year":2020,"lang":"en","type":"article","venue":"Attention Perception & Psychophysics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Fixation (population genetics); Generalizability theory; Eye movement; Accommodation; Computer science; Set (abstract data type); Cognitive psychology; Human–computer interaction; Psychology; Artificial intelligence; Neuroscience; Population; Developmental psychology","score_opus":0.034842137332891196,"score_gpt":0.31519618029760743,"score_spread":0.2803540429647162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019640167","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94404477,0.000034578596,0.05243546,0.002746357,0.00022464253,0.00030005188,0.0000037392008,0.00016853305,0.00004189282],"genre_scores_gemma":[0.99771583,0.00024900096,0.001536463,0.00034781604,0.00003573275,0.000056405846,0.000037806,0.000009064308,0.00001189834],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983864,0.00017526152,0.0005816007,0.00036420315,0.00025522182,0.0002373475],"domain_scores_gemma":[0.99918616,0.00003802352,0.00032849362,0.00022081991,0.00018608976,0.00004043382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022132727,0.00018557624,0.0003181042,0.00043471967,0.000035608125,0.000084981555,0.0004216198,0.0001384683,0.000008769945],"category_scores_gemma":[0.00004026843,0.00019539829,0.00006993939,0.0016218682,0.00008797017,0.0017487442,0.00015167825,0.00038863227,0.00008018791],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001559385,0.0010251027,0.5798716,0.000531845,0.00009258594,0.000018401188,0.024771878,0.014719466,0.023848748,0.003941443,0.00049563946,0.35052738],"study_design_scores_gemma":[0.0016755705,0.0002375982,0.9496164,0.00028937598,0.00000605669,7.8322694e-7,0.0009802161,0.04549789,0.00004044043,0.0012651769,0.00015740792,0.00023312008],"about_ca_topic_score_codex":0.000029067538,"about_ca_topic_score_gemma":0.00005715388,"teacher_disagreement_score":0.3697448,"about_ca_system_score_codex":0.00010700877,"about_ca_system_score_gemma":0.000024708323,"threshold_uncertainty_score":0.7968111},"labels":[],"label_agreement":null},{"id":"W30224377","doi":"10.1158/0008-5472.can-18-0730","title":"Tracking eye movements to uncover the nature of visual-linguistic interaction in static and dynamic scenes","year":2008,"lang":"en","type":"dissertation","venue":"Cancer Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Concordia University; Georgia Clinical and Translational Science Alliance","keywords":"Verb; Noun; Linguistics; Psychology; Sentence; Transitive relation; Referent; Artificial intelligence; Natural language processing; Cognitive psychology; Computer science; Mathematics","score_opus":0.0382799008484128,"score_gpt":0.44783255596385724,"score_spread":0.4095526551154444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30224377","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9966406,0.0011894273,0.00048737112,0.0003577172,0.0006217852,0.0003718515,0.0000075806342,0.000030296642,0.00029335555],"genre_scores_gemma":[0.9980552,0.0005528309,0.00018327514,0.000051256528,0.000030842013,0.000113500435,0.000018478886,0.000017283075,0.000977296],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9982576,0.0001469633,0.0002586112,0.0004548443,0.0005486254,0.0003333879],"domain_scores_gemma":[0.99883425,0.00026167484,0.00011243931,0.00031465566,0.0004340447,0.000042919728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048397115,0.00014873406,0.00022959997,0.0006353086,0.00014028492,0.00007947951,0.00066433114,0.00020820979,0.0000070573597],"category_scores_gemma":[0.00045905076,0.00011809598,0.000031205072,0.00090383645,0.00008536456,0.000093607974,0.00012386196,0.0012802166,0.000004894273],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000812024,0.0008783072,0.03559694,0.0034177543,0.00036759596,0.00034311146,0.04235862,0.0013780636,0.08767114,0.010480435,0.0016213025,0.8150747],"study_design_scores_gemma":[0.0016090896,0.0010233608,0.8920415,0.0076049,0.000030691426,0.000012154504,0.0062166695,0.024484908,0.048894413,0.011531304,0.005425328,0.0011256912],"about_ca_topic_score_codex":0.0007149488,"about_ca_topic_score_gemma":0.0026331244,"teacher_disagreement_score":0.85644454,"about_ca_system_score_codex":0.00028731773,"about_ca_system_score_gemma":0.0002663996,"threshold_uncertainty_score":0.5561975},"labels":[],"label_agreement":null},{"id":"W3023702875","doi":"","title":"The relationship between delay period eye movements and visuospatial memory.","year":2014,"lang":"en","type":"article","venue":"Oxford University Research Archive (ORA) (University of Oxford)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Baycrest Hospital","funders":"","keywords":"Fixation (population genetics); Eye movement; Similarity (geometry); Psychology; Task (project management); Cognitive psychology; Set (abstract data type); Working memory; Computer science; Artificial intelligence; Cognition; Neuroscience","score_opus":0.03271162623093718,"score_gpt":0.2693330849828336,"score_spread":0.2366214587518964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023702875","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.639889,0.000021382302,0.34156713,0.0037671411,0.000057377514,0.00029772616,0.00003841858,0.00014489438,0.014216939],"genre_scores_gemma":[0.98381186,0.00009521137,0.013181613,0.000011671522,0.00002665059,1.8270278e-7,0.000015095405,0.000009323018,0.002848369],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973404,0.00067877263,0.00013566785,0.0005670336,0.00062722136,0.00065094634],"domain_scores_gemma":[0.99707013,0.0015076549,0.00015490389,0.0007522648,0.00026272083,0.00025231892],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012895452,0.00017865706,0.00026762212,0.000574759,0.002649311,0.00009008828,0.0024048875,0.00013020106,0.000012221076],"category_scores_gemma":[0.00031414666,0.00018822443,0.00011608329,0.0008317676,0.001537055,0.00057012384,0.0020460556,0.00076141715,0.000010922891],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018394864,0.00010462887,0.5817107,0.000048894646,0.00013091923,0.00008471892,0.002459477,0.00001988232,0.00024898694,0.34056583,0.0010448263,0.07339721],"study_design_scores_gemma":[0.0014135895,0.00059476885,0.8524868,0.000050460207,0.00002394485,0.0000054062557,0.0030958073,0.006349678,0.00004043251,0.025200527,0.11046112,0.00027748593],"about_ca_topic_score_codex":0.00042583386,"about_ca_topic_score_gemma":0.00059883523,"teacher_disagreement_score":0.34392288,"about_ca_system_score_codex":0.00012516968,"about_ca_system_score_gemma":0.0001568633,"threshold_uncertainty_score":0.9986491},"labels":[],"label_agreement":null},{"id":"W3031080888","doi":"10.1145/3396339.3396390","title":"Tracing shapes with eyes","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Sketch; Gaze; Computer science; Tracing; Pointer (user interface); Computer vision; TRACE (psycholinguistics); Artificial intelligence; Eye tracking; Set (abstract data type); Human–computer interaction; Interface (matter); Computer graphics (images); Algorithm","score_opus":0.020960974943466958,"score_gpt":0.21893987667724613,"score_spread":0.19797890173377916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031080888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03860551,0.00003449684,0.937816,0.013478646,0.000012752294,0.000025367202,1.3344226e-7,0.00084357033,0.009183486],"genre_scores_gemma":[0.9255141,8.864049e-7,0.07335199,0.0010506558,0.000014513622,0.0000016638809,7.485353e-8,0.0000024413043,0.00006366192],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995819,0.000006733304,0.00004951707,0.00018386947,0.00006890697,0.00010912076],"domain_scores_gemma":[0.99981856,0.000019399686,0.00001608264,0.00008857768,0.000017111417,0.00004024733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000025273925,0.000051368293,0.000064157786,0.000019766023,0.000039267303,0.000043132444,0.00034057436,0.000019687235,0.000017704639],"category_scores_gemma":[0.000011171763,0.000034700995,0.000012628125,0.00019663636,0.000025796784,0.00011957402,0.00005785058,0.00006894814,0.00006888192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010169859,0.00005593948,0.008135974,0.000022491578,0.00003478085,0.000120049976,0.0012495097,0.00022352742,0.004336014,0.5183756,0.0025854113,0.46485057],"study_design_scores_gemma":[0.0027677997,0.0031568795,0.14332567,0.0001298323,0.00003806235,0.0002068404,0.0010036958,0.5425159,0.23465644,0.015432565,0.05479949,0.0019668413],"about_ca_topic_score_codex":0.0000044672843,"about_ca_topic_score_gemma":0.000003675767,"teacher_disagreement_score":0.8869086,"about_ca_system_score_codex":0.0000031493478,"about_ca_system_score_gemma":0.000011591613,"threshold_uncertainty_score":0.14150655},"labels":[],"label_agreement":null},{"id":"W3031472910","doi":"10.1145/3334480.3381062","title":"EMICS'20: Eye Movements as an Interface to Cognitive State","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Eye movement; Human–computer interaction; Computer science; Usability; Interface (matter); Cognition; User interface; Eye tracking; State (computer science); Modality (human–computer interaction); Cognitive science; Psychology; Artificial intelligence; Neuroscience","score_opus":0.02457432227010464,"score_gpt":0.30384228937451035,"score_spread":0.2792679671044057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3031472910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42363125,0.0000045625966,0.5685133,0.004217704,0.00006177893,0.00009162524,0.0000044882295,0.00044060647,0.0030347197],"genre_scores_gemma":[0.9792208,0.0000011381037,0.012318056,0.0073723984,0.000014598848,0.000008211704,0.0000016809911,0.0000073455426,0.0010557773],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903077,0.000029354956,0.00013084745,0.00043756643,0.00013428727,0.00023716052],"domain_scores_gemma":[0.99946487,0.000024706575,0.000035108766,0.00020958038,0.00008435792,0.00018136256],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000070213944,0.0001123363,0.00011735617,0.00005454751,0.000050232837,0.00008860886,0.00070294196,0.000033864882,0.000051994608],"category_scores_gemma":[0.00006741218,0.00010255032,0.00002241457,0.00030720409,0.000030847663,0.00021833928,0.00039927958,0.00013111928,0.0009896016],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017164227,0.00067053345,0.024075368,0.0000372607,0.00021872387,0.00025422333,0.02062014,0.00028789122,0.0870047,0.055637572,0.008083079,0.8029389],"study_design_scores_gemma":[0.0029923415,0.0069893305,0.03752442,0.00015670387,0.000023933326,0.0000158725,0.0037700841,0.090388104,0.79841167,0.031154787,0.02665721,0.0019155659],"about_ca_topic_score_codex":0.000040567593,"about_ca_topic_score_gemma":0.00001737419,"teacher_disagreement_score":0.8010233,"about_ca_system_score_codex":0.00001782621,"about_ca_system_score_gemma":0.000026248399,"threshold_uncertainty_score":0.9997882},"labels":[],"label_agreement":null},{"id":"W3032358219","doi":"10.1145/3313831.3376132","title":"BlyncSync: Enabling Multimodal Smartwatch Gestures with Synchronous Touch and Blink","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Smartwatch; Gesture; Computer science; Synchronicity; Set (abstract data type); Human–computer interaction; Modalities; Wearable computer; Speech recognition; Artificial intelligence; Embedded system; Psychology","score_opus":0.011757555853588815,"score_gpt":0.20973991796101493,"score_spread":0.19798236210742612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3032358219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33585897,0.00016144235,0.6418659,0.018790033,0.00005857746,0.00013619573,8.686628e-7,0.0010821573,0.0020458556],"genre_scores_gemma":[0.9065048,0.000012192157,0.09233197,0.0009914605,0.000052315245,0.000006855666,6.700812e-7,0.000008720439,0.000090976144],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894845,0.000016155625,0.00012611372,0.0004876425,0.00014453416,0.00027709096],"domain_scores_gemma":[0.9994355,0.000074306365,0.000043445234,0.00026743254,0.000054128763,0.00012521376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000063249376,0.00015178601,0.00017719885,0.00005837466,0.00011789185,0.00013568158,0.0004574163,0.00008072715,0.000012079823],"category_scores_gemma":[0.000041315267,0.00010974499,0.000022423654,0.00029052267,0.00009874756,0.00018616437,0.00022667658,0.00021823468,0.00002955433],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011711811,0.0004003646,0.122800335,0.00019527068,0.00027720176,0.00084674085,0.0065549645,0.0008795357,0.029062973,0.15112808,0.005547088,0.6821903],"study_design_scores_gemma":[0.008835345,0.005996432,0.19458732,0.00023766902,0.0001400827,0.0009316805,0.0015791338,0.5755308,0.15080513,0.005709329,0.05188299,0.003764059],"about_ca_topic_score_codex":0.00006544911,"about_ca_topic_score_gemma":0.000023483597,"teacher_disagreement_score":0.67842627,"about_ca_system_score_codex":0.00001162872,"about_ca_system_score_gemma":0.0000392113,"threshold_uncertainty_score":0.4475271},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.08764614001397125,"score_gpt":0.3826528556737595,"score_spread":0.29500671565978825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033938431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27439034,0.00040864758,0.60046464,0.12193233,0.00004733775,0.0015892226,0.00005361828,0.0005176421,0.00059619785],"genre_scores_gemma":[0.99205565,0.00007245311,0.0067297257,0.00038610966,0.00007936622,0.0006135621,0.000011153605,0.000017629725,0.000034334593],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99783933,0.00010913219,0.00028520898,0.0008793643,0.00033157674,0.0005553636],"domain_scores_gemma":[0.99775213,0.0010910318,0.000073438474,0.00071224774,0.00017985752,0.0001913178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008861754,0.00017583382,0.0002473348,0.00021111073,0.0005216272,0.00027300036,0.00089141453,0.00012734895,9.675725e-7],"category_scores_gemma":[0.0004195194,0.00015940619,0.000041797786,0.0007798158,0.00034751376,0.00022029519,0.00025930005,0.00054617843,0.000004873007],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023782179,0.00086324953,0.06287866,0.0004960063,0.000066091445,0.000033488755,0.0025420692,0.001034771,0.12743579,0.4739437,0.0011528045,0.3293155],"study_design_scores_gemma":[0.008142316,0.00509453,0.5162286,0.0011523404,0.00006172172,0.000012528968,0.002453933,0.18214616,0.113827005,0.09306955,0.07520949,0.0026017963],"about_ca_topic_score_codex":0.000042541426,"about_ca_topic_score_gemma":0.000034044333,"teacher_disagreement_score":0.7176653,"about_ca_system_score_codex":0.00006909185,"about_ca_system_score_gemma":0.00009972163,"threshold_uncertainty_score":0.65003955},"labels":[],"label_agreement":null},{"id":"W3034330414","doi":"10.1142/s1793351x20500014","title":"Discriminative Robust Head-Pose and Gaze Estimation Using Kernel-DMCCA Features Fusion","year":2020,"lang":"en","type":"article","venue":"International Journal of Semantic Computing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Alcohol Countermeasure Systems (Canada); Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Discriminative model; Computer science; Pose; Pattern recognition (psychology); Robustness (evolution); Computer vision; Gaze; Search engine indexing; Kernel (algebra); Feature extraction; Mathematics","score_opus":0.03979552839053952,"score_gpt":0.307149082528898,"score_spread":0.2673535541383585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034330414","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42900172,0.0001099056,0.5653563,0.005005052,0.0004215106,0.000028554505,6.8050815e-7,0.00003515947,0.00004109099],"genre_scores_gemma":[0.8778791,0.000012334314,0.121596955,0.00025315213,0.00024708733,9.913243e-8,8.000111e-7,0.0000063556517,0.0000041624826],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988026,0.00005545897,0.0003845441,0.00021137892,0.00040199573,0.00014404091],"domain_scores_gemma":[0.9988175,0.00015727353,0.00046290667,0.00008279754,0.00039798976,0.00008153823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002757634,0.0001247257,0.00020135446,0.00018464273,0.00010002198,0.00019875978,0.00064585166,0.000048824728,0.0000032107632],"category_scores_gemma":[0.00028588605,0.00010844549,0.0000655764,0.00016760343,0.00006361373,0.00036952947,0.000367515,0.00029725838,0.0000021883682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000123589,0.00027230816,0.013259816,0.00009777126,0.00039123988,0.001043878,0.013676653,0.13071643,0.02976561,0.027970962,0.000645024,0.7820367],"study_design_scores_gemma":[0.0005610121,0.00013828013,0.028172329,0.00038976106,0.000020406622,0.0008748445,0.00023496932,0.9656805,0.0018858133,0.0018623995,0.000046727317,0.00013299317],"about_ca_topic_score_codex":0.00001832459,"about_ca_topic_score_gemma":0.0000020624998,"teacher_disagreement_score":0.83496404,"about_ca_system_score_codex":0.00005917542,"about_ca_system_score_gemma":0.00004896903,"threshold_uncertainty_score":0.44222787},"labels":[],"label_agreement":null},{"id":"W3040062227","doi":"10.3390/app10134508","title":"Collaborative Use of a Shared System Interface: The Role of User Gaze—Gaze Convergence Index Based on Synchronous Dual-Eyetracking","year":2020,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Dyad; Eye tracking; Computer science; Convergence (economics); Human–computer interaction; Index (typography); Construct (python library); Dual (grammatical number); Interface (matter); Task (project management); Artificial intelligence; Psychology; World Wide Web; Social psychology; Engineering; Computer network","score_opus":0.0183423364340466,"score_gpt":0.23970495923794435,"score_spread":0.22136262280389776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3040062227","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.698005,0.00022485194,0.2930167,0.002565463,0.00034189504,0.0010139464,0.00008767184,0.0005302795,0.0042141993],"genre_scores_gemma":[0.99341655,0.000002506744,0.0063271313,0.00019556715,0.000015273681,0.000030729894,5.843206e-7,0.0000065743297,0.000005111978],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809676,0.00008148249,0.00038508244,0.00057155715,0.00056763046,0.000297485],"domain_scores_gemma":[0.9981824,0.0007183238,0.00039670794,0.00045171505,0.00018131705,0.000069509544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047527227,0.0001842235,0.00031692506,0.00013347004,0.0002223937,0.00013673786,0.0014762306,0.00008424542,0.000012293074],"category_scores_gemma":[0.00019636442,0.0001279192,0.000052434338,0.0020203174,0.0008200596,0.00024928938,0.0002590094,0.00020250086,0.000019349085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020459414,0.00058428285,0.086084984,0.00033115566,0.00012717929,0.00003123374,0.012335722,0.07054934,0.28748524,0.51743233,0.0012088283,0.023625085],"study_design_scores_gemma":[0.000567357,0.00085903856,0.023765653,0.00030253598,0.000020865451,0.0000047572594,0.0052310354,0.661256,0.3060631,0.00046923055,0.0010526362,0.00040775604],"about_ca_topic_score_codex":0.000035075213,"about_ca_topic_score_gemma":0.000008483749,"teacher_disagreement_score":0.5907067,"about_ca_system_score_codex":0.000038016737,"about_ca_system_score_gemma":0.0002448982,"threshold_uncertainty_score":0.52163935},"labels":[],"label_agreement":null},{"id":"W3041989270","doi":"10.1080/17483107.2020.1786734","title":"A novel design and implementation of wheelchair navigation system using Leap Motion sensor","year":2020,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wheelchair; Computer science; Bluetooth; Gesture; Movement (music); Motion (physics); Simulation; Real-time computing; Artificial intelligence; Wireless; Telecommunications","score_opus":0.026514738353227835,"score_gpt":0.2904788847013963,"score_spread":0.2639641463481685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041989270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48561102,0.000028576706,0.5093403,0.004388784,0.000030457308,0.00032592713,0.000008468728,0.0002620189,0.0000044401195],"genre_scores_gemma":[0.782745,0.0000014230466,0.21718134,0.000020183252,0.000008468977,0.000032520235,0.0000039731917,0.0000066622943,4.1170588e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99839854,0.00017273366,0.00045888915,0.00062383764,0.00015561325,0.00019035669],"domain_scores_gemma":[0.998757,0.0003928774,0.00028075458,0.00027915585,0.00022624701,0.000063974425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046934758,0.00017230732,0.00031517117,0.0001515324,0.00016208991,0.000028536384,0.00018582211,0.00021042857,0.0000015891985],"category_scores_gemma":[0.00031362314,0.00016700615,0.000047429578,0.00069281977,0.001022171,0.00028017143,0.00015333804,0.00017843662,0.000001257964],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007147992,0.00028732658,0.5295212,0.001048098,0.00007475593,0.0000028400223,0.0019196507,0.00042510536,0.11912527,0.19843714,0.000011458385,0.14907566],"study_design_scores_gemma":[0.0013784056,0.0012477898,0.8380706,0.00014078442,0.00005588555,0.00006521649,0.012085015,0.1324476,0.011742033,0.0023676732,0.000036625715,0.00036237703],"about_ca_topic_score_codex":0.00006192206,"about_ca_topic_score_gemma":0.0000043806904,"teacher_disagreement_score":0.30854937,"about_ca_system_score_codex":0.00012489801,"about_ca_system_score_gemma":0.000036821435,"threshold_uncertainty_score":0.6810313},"labels":[],"label_agreement":null},{"id":"W3042439002","doi":"10.1109/hsi49210.2020.9142677","title":"FELiX: Fixation-based Eye Fatigue Load Index A Multi-factor Measure for Gaze-based Interactions","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Eye tracking; Gaze; Computer science; Fixation (population genetics); Dwell time; Artificial intelligence; Eye tracking on the ISS; Measure (data warehouse); Eye movement; Computer vision; Tracking (education); Psychology; Data mining; Medicine","score_opus":0.09074373180188988,"score_gpt":0.31751406294523865,"score_spread":0.22677033114334877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042439002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00482535,0.00001546137,0.9723686,0.02099051,0.00025299456,0.00031964865,0.00001889694,0.00096227194,0.00024624675],"genre_scores_gemma":[0.8986461,1.5161812e-7,0.09908277,0.0019888065,0.000048853082,0.00009352712,0.000010408036,0.000012916404,0.000116451345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987098,0.00004508639,0.00024378941,0.000488513,0.00024116463,0.00027162928],"domain_scores_gemma":[0.9988328,0.000256048,0.00012386126,0.00038129836,0.0002783242,0.00012762393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010352491,0.000184178,0.00019995139,0.00011470159,0.0001623423,0.00011778873,0.0006514424,0.00010284726,0.00006491925],"category_scores_gemma":[0.0005075678,0.00016355292,0.0001331102,0.00043909156,0.000051768548,0.00019658616,0.0000488528,0.00022798307,0.000060015478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007157774,0.0042166053,0.19204059,0.00066358806,0.00059822167,0.00010947296,0.0038260212,0.04838647,0.17730469,0.031515576,0.054812614,0.48581037],"study_design_scores_gemma":[0.0016125103,0.00015950193,0.0137268035,0.00003450858,0.000008951889,5.6552443e-7,0.000027903752,0.95240533,0.024519714,0.000074352094,0.007167607,0.0002622782],"about_ca_topic_score_codex":0.000051438197,"about_ca_topic_score_gemma":0.00014008886,"teacher_disagreement_score":0.9040188,"about_ca_system_score_codex":0.00009722164,"about_ca_system_score_gemma":0.00029254536,"threshold_uncertainty_score":0.66694945},"labels":[],"label_agreement":null},{"id":"W3046719602","doi":"10.1111/infa.12358","title":"Using pupillometry to investigate predictive processes in infancy","year":2020,"lang":"en","type":"article","venue":"Infancy","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Child Health and Human Development; Natural Sciences and Engineering Research Council of Canada; James S. McDonnell Foundation","keywords":"Pupillometry; Psychology; Cognitive psychology; Predictive coding; Developmental psychology; Pupil; Neuroscience; Coding (social sciences); Sociology","score_opus":0.05075378723595137,"score_gpt":0.2938693377234688,"score_spread":0.24311555048751743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3046719602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67436993,0.00028197508,0.31585875,0.005996451,0.00018359892,0.0002760177,0.000009625794,0.00071899325,0.002304654],"genre_scores_gemma":[0.9563635,0.000007430245,0.04147161,0.002060368,0.000053328196,0.000021825099,6.394193e-7,0.000010986865,0.000010320039],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9987238,0.000033281238,0.00025195722,0.00047594213,0.00018464864,0.000330392],"domain_scores_gemma":[0.999317,0.00005617678,0.000077414894,0.00029784214,0.00007937137,0.00017219744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010010439,0.00015351955,0.00020982561,0.00026717238,0.000057078825,0.00006771477,0.00078624557,0.000087070126,0.000004831963],"category_scores_gemma":[0.0008422258,0.00015237227,0.000025282523,0.0027035587,0.00006094498,0.00040727764,0.00034192036,0.00025002993,0.000063950036],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043287004,0.00018119082,0.9014286,0.00029964207,0.00005253036,0.00027951418,0.012448295,0.002811968,0.010034074,0.012521608,0.0013224971,0.05857682],"study_design_scores_gemma":[0.002108035,0.0011060872,0.79896045,0.0009511586,0.000027084536,0.00006876857,0.00031921326,0.056401614,0.09511541,0.023699323,0.019442575,0.0018002732],"about_ca_topic_score_codex":0.000031971438,"about_ca_topic_score_gemma":0.000012112284,"teacher_disagreement_score":0.28199354,"about_ca_system_score_codex":0.00002227045,"about_ca_system_score_gemma":0.00017961692,"threshold_uncertainty_score":0.62135607},"labels":[],"label_agreement":null},{"id":"W3060207886","doi":"10.1016/j.visres.2020.07.013","title":"Do eye movements enhance visual memory retrieval?","year":2020,"lang":"en","type":"article","venue":"Vision Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Japan Society for the Promotion of Science; Natural Sciences and Engineering Research Council of Canada","keywords":"Saccade; Fixation (population genetics); Eye movement; Gaze; Microsaccade; Psychology; Cognitive psychology; Object (grammar); Visual search; Communication; Computer science; Computer vision; Saccadic masking; Artificial intelligence; Neuroscience; Medicine","score_opus":0.07440803606162828,"score_gpt":0.4406930778566176,"score_spread":0.36628504179498933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3060207886","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.82312804,0.0004443486,0.12966092,0.035454884,0.0003325127,0.00047723838,0.0000036525341,0.0008959798,0.009602451],"genre_scores_gemma":[0.99496263,0.000035185363,0.0031806848,0.0004654763,0.00007707802,0.000007051818,0.0000010819225,0.000010635372,0.0012601953],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972655,0.0002441251,0.00020033638,0.00067201763,0.0010973476,0.0005206656],"domain_scores_gemma":[0.99877155,0.0001842681,0.000041465348,0.00051807344,0.00028914752,0.00019551047],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012042412,0.000107993794,0.00015153633,0.00022074529,0.00025604496,0.00020839146,0.001499018,0.00010547681,0.00011481841],"category_scores_gemma":[0.00061204156,0.00009414055,0.00004689513,0.0016255674,0.00015467731,0.00021697053,0.0011676676,0.000639304,0.0012535675],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014705729,0.00047446517,0.002599581,0.00008542315,0.000040129642,0.00029169171,0.0011790873,0.000023262943,0.38058093,0.017294398,0.045439336,0.55184466],"study_design_scores_gemma":[0.0015115847,0.0039940584,0.038755458,0.00018312364,0.0000034307534,0.000004363932,0.0004837544,0.06870815,0.7986528,0.009030346,0.077937625,0.0007352867],"about_ca_topic_score_codex":0.000011075557,"about_ca_topic_score_gemma":5.286141e-7,"teacher_disagreement_score":0.5511093,"about_ca_system_score_codex":0.000048085636,"about_ca_system_score_gemma":0.00008680358,"threshold_uncertainty_score":0.99952406},"labels":[],"label_agreement":null},{"id":"W3080131322","doi":"10.16910/jemr.12.8.3","title":"MAD saccade: statistically robust saccade threshold estimation via the median absolute deviation","year":2020,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canadian Institutes of Health Research","keywords":"Saccade; Standard deviation; Estimator; Computer science; Outlier; Artificial intelligence; False positive paradox; Gaze; Pattern recognition (psychology); Computer vision; Mathematics; Algorithm; Eye movement; Statistics","score_opus":0.09899335121869737,"score_gpt":0.3604185019728075,"score_spread":0.2614251507541101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080131322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016588371,0.00013446478,0.87697977,0.105595656,0.00014519316,0.00018847897,0.000002477279,0.000040054776,0.00032556182],"genre_scores_gemma":[0.9348239,0.0000713064,0.06370285,0.0011048276,0.00020368987,0.0000083259365,0.0000018185176,0.000010775131,0.000072531955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970722,0.00022413756,0.0005553637,0.00024312033,0.0014709987,0.00043417246],"domain_scores_gemma":[0.99821514,0.00038983033,0.00028297055,0.00030782973,0.00060896785,0.00019526272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002621402,0.000122027595,0.00020217457,0.00024284655,0.00026804427,0.00025617908,0.0015125279,0.00008815505,0.000056698187],"category_scores_gemma":[0.0007308632,0.00008207164,0.000072667324,0.00077077345,0.00015085323,0.00040467942,0.00031972677,0.0010909637,0.000063356536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019690825,0.0005839497,0.014265436,0.00016828845,0.000371765,0.00052594807,0.0030352639,0.015724773,0.037299573,0.29778066,0.05695898,0.57308847],"study_design_scores_gemma":[0.0010867766,0.001463817,0.18503918,0.000120369026,0.00002780028,0.00002135067,0.00019616245,0.6856934,0.0061540273,0.117352866,0.0025794455,0.00026481014],"about_ca_topic_score_codex":0.000020406314,"about_ca_topic_score_gemma":0.0000137900415,"teacher_disagreement_score":0.9182355,"about_ca_system_score_codex":0.00015682488,"about_ca_system_score_gemma":0.0001967658,"threshold_uncertainty_score":0.4739755},"labels":[],"label_agreement":null},{"id":"W3080473149","doi":"10.14814/phy2.14533","title":"Spatiotemporal transformations for gaze control","year":2020,"lang":"en","type":"review","venue":"Physiological Reports","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Canadian Institutes of Health Research","keywords":"Gaze; Computer science; Control (management); Human–computer interaction; Medicine; Data science; Computer graphics (images); Artificial intelligence","score_opus":0.08874324496472087,"score_gpt":0.34598787728375013,"score_spread":0.2572446323190293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3080473149","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[7.9856386e-7,0.63930535,0.3585505,0.00032729853,0.00024197341,0.0008209597,0.000024977486,0.0005216445,0.0002065206],"genre_scores_gemma":[0.0050925734,0.98467547,0.008994707,0.00016825962,0.00017754869,0.00072060485,0.00011613261,0.00002053183,0.000034194658],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978765,0.00009496471,0.0008153976,0.0007237719,0.00015616503,0.00033314826],"domain_scores_gemma":[0.99837846,0.00026880755,0.0005915639,0.0005704771,0.00007885259,0.000111829184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021370423,0.00034673046,0.0015261599,0.0000775687,0.0001260341,0.00006385846,0.0006144598,0.00034717203,0.000008304152],"category_scores_gemma":[0.00035314655,0.00023175913,0.0007285096,0.00030747993,0.00007889261,0.00012251754,0.0000837397,0.0003805107,0.000035750712],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[9.880406e-7,0.000045168334,5.478849e-7,0.00090791687,0.00005369232,0.000103407656,0.0000066585203,0.000001694502,0.0000030255626,0.008463402,0.001382418,0.9890311],"study_design_scores_gemma":[0.00008734302,0.00017298479,0.000027379581,0.0005208038,0.00010513411,0.0001277342,5.692977e-7,0.0003725979,0.0000019617166,0.011847603,0.9864587,0.0002771902],"about_ca_topic_score_codex":0.0000023294244,"about_ca_topic_score_gemma":3.3865751e-7,"teacher_disagreement_score":0.9887539,"about_ca_system_score_codex":0.000048239344,"about_ca_system_score_gemma":0.00018502888,"threshold_uncertainty_score":0.9450863},"labels":[],"label_agreement":null},{"id":"W3081292922","doi":"10.1016/j.robot.2020.103626","title":"Visual–spatial attention as a comfort measure in human–robot collaborative tasks","year":2020,"lang":"en","type":"article","venue":"Robotics and Autonomous Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robot; Robustness (evolution); Inverse kinematics; Gaze; Artificial intelligence; Computer vision; Visibility; Task (project management); Constraint (computer-aided design); Kinematics; Measure (data warehouse); Human–computer interaction; Data mining; Mathematics","score_opus":0.018147983149232952,"score_gpt":0.2588664289692571,"score_spread":0.24071844582002416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081292922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0839988,0.00070464757,0.9080769,0.004053894,0.0005543029,0.00067905145,0.000008150828,0.0005160997,0.0014081895],"genre_scores_gemma":[0.9976879,0.00000734668,0.00197208,0.00012502617,0.00007659012,0.000023655148,0.000006580722,0.000012660934,0.000088171786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985914,0.00010563424,0.0003633507,0.0004638752,0.00019698958,0.00027874907],"domain_scores_gemma":[0.9993849,0.000033185235,0.00016773574,0.0002027832,0.00010122952,0.0001101622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022426652,0.00018522407,0.00035708523,0.00010757738,0.00016569564,0.0002284827,0.00033361441,0.00014163235,0.0000014720825],"category_scores_gemma":[0.00003080685,0.00017836195,0.00003828339,0.00042740867,0.000067721514,0.00014188165,0.00015822504,0.00022562867,0.000020931207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050157214,0.0008249274,0.06686966,0.0005669637,0.00028708464,0.0006309306,0.005867269,0.105300605,0.049906563,0.72521,0.0010437516,0.043442078],"study_design_scores_gemma":[0.0023698332,0.0015809918,0.036182944,0.000360997,0.000042245665,0.00008899666,0.0009109106,0.95251274,0.0009962604,0.0011431453,0.0028250583,0.000985856],"about_ca_topic_score_codex":0.00031542918,"about_ca_topic_score_gemma":0.000062667976,"teacher_disagreement_score":0.9136891,"about_ca_system_score_codex":0.00006636725,"about_ca_system_score_gemma":0.000101647456,"threshold_uncertainty_score":0.7273389},"labels":[],"label_agreement":null},{"id":"W3082858805","doi":"10.1007/s00138-020-01112-2","title":"Head and camera rotation invariant eye tracking algorithm based on segmented group method of data handling","year":2020,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer vision; Artificial intelligence; Gaze; Computer science; Eye tracking; Robustness (evolution); Rotation (mathematics); Invariant (physics); Biometrics; Geometric transformation; Mathematics; Image (mathematics)","score_opus":0.03809927564449648,"score_gpt":0.3575275842377369,"score_spread":0.3194283085932404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082858805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001686767,0.00008774859,0.9919733,0.0058060554,0.000009291567,0.0002190802,0.000043428216,0.00012096362,0.00005338253],"genre_scores_gemma":[0.52357495,0.000016382053,0.47578764,0.0005091014,0.000015876525,0.000020943158,0.00006736135,0.0000053736076,0.0000023804973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990886,0.000061210325,0.00019072015,0.00044640456,0.00011848788,0.000094576724],"domain_scores_gemma":[0.9992448,0.00014597554,0.00010164999,0.0004043186,0.000034533714,0.00006869479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028030458,0.00009911822,0.00014929961,0.000086342545,0.0001530119,0.00006289184,0.00035679515,0.000041360225,0.000003181108],"category_scores_gemma":[0.000032511354,0.00008433587,0.000015132567,0.00035096507,0.000041875635,0.00015083158,0.00020517342,0.00013375054,0.0000020562047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047726885,0.00008226283,0.00048649756,0.000018366396,0.000006115605,8.1568584e-7,0.00008166141,0.000103344915,0.008615014,0.008081891,0.000028759421,0.9824905],"study_design_scores_gemma":[0.00045228825,0.00013311362,0.007874008,0.000027682583,0.0000103409775,0.0000021020603,0.000020670499,0.9877012,0.0011306735,0.0004588275,0.0021027133,0.00008639892],"about_ca_topic_score_codex":0.000049329814,"about_ca_topic_score_gemma":0.0000058562077,"teacher_disagreement_score":0.9875978,"about_ca_system_score_codex":0.000005596545,"about_ca_system_score_gemma":0.000012512686,"threshold_uncertainty_score":0.3439117},"labels":[],"label_agreement":null},{"id":"W3093919292","doi":"10.1145/3382507.3418828","title":"A Neural Architecture for Detecting User Confusion in Eye-tracking Data","year":2020,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Confusion; Classifier (UML); Artificial intelligence; Visualization; Architecture; Random forest; Machine learning; Eye tracking; Variety (cybernetics); Deep learning; Recurrent neural network; Data visualization; Data mining; Artificial neural network","score_opus":0.0671110536644824,"score_gpt":0.315206460753134,"score_spread":0.24809540708865163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093919292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17662501,0.000024410276,0.809905,0.012668864,0.00008742965,0.00015820075,0.0000031840518,0.00040719146,0.00012069893],"genre_scores_gemma":[0.9112421,6.445856e-7,0.08748041,0.0011966511,0.000046100773,0.0000064924347,0.00000432608,0.0000073386154,0.000015903637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989397,0.000029597632,0.00016558051,0.0005155707,0.00009175156,0.00025778433],"domain_scores_gemma":[0.9992276,0.0001389096,0.000050770046,0.0005047378,0.00002895794,0.000049017934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001843809,0.00010272175,0.0001384541,0.00007301744,0.000074304626,0.000087903194,0.0013638011,0.00006421473,0.0000045476886],"category_scores_gemma":[0.00033380085,0.000087177104,0.000026681324,0.00031756933,0.000020551925,0.00022340653,0.00070826703,0.0002439147,0.0000058460305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041669035,0.00006976929,0.025169766,0.000079152785,0.000017639928,0.000063876054,0.0017380394,0.001087172,0.060926802,0.022344738,0.0010916732,0.8873697],"study_design_scores_gemma":[0.00090068765,0.00019702561,0.020665778,0.00003595584,0.0000055469927,0.000015093295,0.0001357777,0.95265436,0.0121720545,0.0015987676,0.011303368,0.0003156017],"about_ca_topic_score_codex":0.00002925659,"about_ca_topic_score_gemma":0.00011711655,"teacher_disagreement_score":0.9515672,"about_ca_system_score_codex":0.000009123187,"about_ca_system_score_gemma":0.000019974837,"threshold_uncertainty_score":0.3554979},"labels":[],"label_agreement":null},{"id":"W3096805296","doi":"10.1007/s00221-020-05967-9","title":"Effects of blocked vs. interleaved administration mode on saccade preparatory set revealed using pupillometry","year":2020,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pupillometry; Saccade; Set (abstract data type); Neuroscience; Psychology; Communication; Eye movement; Computer science; Pupil","score_opus":0.09784271095562873,"score_gpt":0.42939860246625605,"score_spread":0.3315558915106273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096805296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9908523,0.00019437692,0.005837691,0.0020670982,0.000085395586,0.00043107272,0.000004054451,0.00016775983,0.00036027434],"genre_scores_gemma":[0.9955275,0.0000020498646,0.003913506,0.000382892,0.000053798296,0.00003912212,0.0000033536799,0.000015423093,0.000062363346],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976682,0.0004656434,0.00027120946,0.00057003286,0.00063001335,0.00039486814],"domain_scores_gemma":[0.9987423,0.00053032505,0.000085321895,0.0004003718,0.00008357048,0.00015813285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047340262,0.00015502733,0.0002456885,0.00027755272,0.00014387046,0.000070227215,0.0008846246,0.0001247228,0.000014167254],"category_scores_gemma":[0.00048555463,0.00015289232,0.00007138926,0.0008036744,0.0002357015,0.00016839497,0.00041862976,0.0004541332,0.000037732512],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018993925,0.0002492195,0.00027827302,0.00006559513,0.00002438281,0.00005878107,0.0016764192,0.00001640991,0.9905176,0.0035998004,0.002427952,0.0008955974],"study_design_scores_gemma":[0.0006242884,0.0018187666,0.0012464249,0.00007512068,0.0000015219337,0.0000062918252,0.00022818724,0.018249601,0.97725916,0.00012952805,0.0002276211,0.00013347465],"about_ca_topic_score_codex":0.000020994139,"about_ca_topic_score_gemma":7.500351e-7,"teacher_disagreement_score":0.018233191,"about_ca_system_score_codex":0.0001460019,"about_ca_system_score_gemma":0.00012914935,"threshold_uncertainty_score":0.6234768},"labels":[],"label_agreement":null},{"id":"W3096855119","doi":"10.1167/jov.20.11.253","title":"Active Observers in a 3D World: The 3D Same-Different Task","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Task (project management); BitTorrent tracker; Computer science; Computer vision; Artificial intelligence; Active vision; Focus (optics); Set (abstract data type); Eye tracking; Human–computer interaction; Tracking (education); Observer (physics); Visual search; Psychology; Engineering","score_opus":0.026157923419407,"score_gpt":0.28106205087094865,"score_spread":0.2549041274515417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3096855119","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9244704,0.00015869402,0.0362632,0.038364794,0.0003029803,0.00007655061,0.0000010693593,0.00004373939,0.00031853642],"genre_scores_gemma":[0.99480903,0.000022438919,0.00435949,0.00071874156,0.000064209795,5.9870285e-7,1.2673226e-7,0.0000038096794,0.000021576354],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991246,0.00009388202,0.0002582502,0.00013013014,0.00025198775,0.00014116224],"domain_scores_gemma":[0.9993515,0.00013508246,0.0002403953,0.00015479117,0.00005708097,0.00006114173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021778625,0.000084222556,0.00017876271,0.00014737436,0.000051484905,0.000055260585,0.00074122933,0.000037298156,0.000010456687],"category_scores_gemma":[0.00009849626,0.000048390946,0.000073904725,0.00042759546,0.00004006446,0.00021502598,0.0001224765,0.00046947377,0.000013248778],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026234324,0.00044718158,0.020636031,0.000023680604,0.00008357673,0.00045975726,0.0048344596,0.0008026731,0.03187972,0.0044254335,0.009906653,0.9262385],"study_design_scores_gemma":[0.0012873342,0.0011313943,0.9528649,0.00021169794,0.000015484602,0.0000598899,0.00025282678,0.021355823,0.004562836,0.0019095879,0.016161658,0.00018657402],"about_ca_topic_score_codex":0.0000053087365,"about_ca_topic_score_gemma":0.000014015659,"teacher_disagreement_score":0.93222886,"about_ca_system_score_codex":0.000060663042,"about_ca_system_score_gemma":0.000035562454,"threshold_uncertainty_score":0.2039656},"labels":[],"label_agreement":null},{"id":"W3097112470","doi":"10.1167/jov.20.11.560","title":"Beyond the screen’s edge: eye and head movements while looking at rotated scenes in VR","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Computer vision; Oblique case; Eye movement; Head (geology); Artificial intelligence; Rotation (mathematics); Virtual reality; Oblique projection; Computer science; Fractal; Psychology; Line (geometry); Cognitive psychology; Mathematics; Geometry; Geology; Orthographic projection","score_opus":0.020101804792512147,"score_gpt":0.2849413561917306,"score_spread":0.26483955139921844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097112470","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9776368,0.0006252959,0.0089986045,0.012314471,0.00014091552,0.0000568242,6.839669e-7,0.000024092078,0.00020227184],"genre_scores_gemma":[0.99643224,0.00005438261,0.002654565,0.0007842167,0.00004869862,4.1558428e-7,2.1120572e-7,0.000004195633,0.000021081496],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991936,0.000057891073,0.00025899295,0.00013701098,0.00021633736,0.00013617336],"domain_scores_gemma":[0.99950117,0.000050230632,0.00019722864,0.00012216346,0.00007016199,0.000059039823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003366676,0.00007660772,0.0001473483,0.00008974471,0.00009733651,0.000069533744,0.00042745564,0.000042551288,0.000006228838],"category_scores_gemma":[0.0000550151,0.00004905458,0.000034963956,0.0002526205,0.000044400724,0.00022946842,0.00027001536,0.00023688181,0.000005889966],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016733436,0.00024768573,0.20232248,0.000035869292,0.000055226308,0.00035559354,0.0026379898,0.0004925157,0.10858971,0.0018459184,0.004750418,0.6784993],"study_design_scores_gemma":[0.0013573767,0.0008228773,0.9615756,0.00023623322,0.000008089686,0.000048531976,0.000119797354,0.022282345,0.0063399025,0.0021476303,0.0049125394,0.00014906932],"about_ca_topic_score_codex":0.000008675126,"about_ca_topic_score_gemma":0.000013949283,"teacher_disagreement_score":0.75925314,"about_ca_system_score_codex":0.00003773506,"about_ca_system_score_gemma":0.000020552261,"threshold_uncertainty_score":0.20003878},"labels":[],"label_agreement":null},{"id":"W3099241816","doi":"10.1016/j.ijmedinf.2020.104344","title":"Automatic eye localization for hospitalized infants and children using convolutional neural networks","year":2020,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Centre hospitalier universitaire Sainte-Justine","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Transfer of learning; Deep learning; Eye tracking; Task (project management); Machine learning; Artificial neural network; Pattern recognition (psychology)","score_opus":0.017364628279802237,"score_gpt":0.29685601366370556,"score_spread":0.27949138538390333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099241816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2330224,0.00006669335,0.7618456,0.0045580487,0.00040538833,0.000058711226,0.0000034260977,0.000033269203,0.000006447138],"genre_scores_gemma":[0.9522799,0.000035288518,0.04512148,0.0023101177,0.00024064876,0.0000010990905,0.000006519363,0.0000043089863,6.0531397e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830955,0.000022789667,0.0006643439,0.00006085343,0.0008137263,0.00012873572],"domain_scores_gemma":[0.9988246,0.00010191497,0.0004812597,0.000059267066,0.0003652882,0.00016765016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003991421,0.000089088986,0.00018092863,0.00010741047,0.00005158646,0.00011828098,0.00086294545,0.00010928672,0.00001549567],"category_scores_gemma":[0.00059853087,0.00007325795,0.00006358841,0.0001162455,0.00010265874,0.00052649464,0.00018535531,0.00022334603,9.439738e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020412295,0.00035599773,0.13513878,0.00015738055,0.0014772218,0.00011573346,0.005241537,0.082514,0.000038232687,0.08834683,0.008365409,0.67804474],"study_design_scores_gemma":[0.0011679998,0.00011037625,0.0063994955,0.00007380886,0.000012917154,0.00020928367,0.000028261817,0.99114275,0.000015113405,0.0005736309,0.00019210894,0.000074241696],"about_ca_topic_score_codex":0.0000024286173,"about_ca_topic_score_gemma":1.9796161e-7,"teacher_disagreement_score":0.90862876,"about_ca_system_score_codex":0.000036062134,"about_ca_system_score_gemma":0.00010521702,"threshold_uncertainty_score":0.29873726},"labels":[],"label_agreement":null},{"id":"W3108491126","doi":"","title":"Toward a deep convolutional LSTM for eye gaze spatiotemporal data sequence classification.","year":2020,"lang":"en","type":"article","venue":"Educational Data Mining","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Gaze; Convolutional neural network; Sequence (biology); Pattern recognition (psychology); Eye tracking; Deep learning; Computer vision","score_opus":0.43641733097289864,"score_gpt":0.39667249974994745,"score_spread":0.039744831222951194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108491126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031313058,0.0003081651,0.88152975,0.1126975,0.00054049975,0.00020525607,0.0011504337,0.0001972119,0.00023986583],"genre_scores_gemma":[0.601217,0.0000053774725,0.39010042,0.0008340552,0.00033798887,0.00003768769,0.0073971655,0.000009132331,0.000061203275],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801046,0.000055543926,0.00035473806,0.0010322864,0.00028426453,0.00026272013],"domain_scores_gemma":[0.99741954,0.00040410896,0.00021094282,0.001665807,0.00019233736,0.0001072517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050684327,0.00014681967,0.00016323973,0.00007427065,0.00022533914,0.0001532876,0.004651511,0.00007433672,0.000053785618],"category_scores_gemma":[0.0015830079,0.00015748377,0.00002378681,0.00036523482,0.00013663918,0.0012911729,0.0011673714,0.0001471727,0.00009544224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026186326,0.00035428262,0.04049923,0.000131577,0.00014001217,0.0000052784385,0.0018501307,0.00006383966,0.0020280045,0.5330694,0.25936034,0.16247179],"study_design_scores_gemma":[0.0003995887,0.000060871662,0.096304424,0.000041143532,0.000023815232,0.000016529955,0.00040474386,0.8025419,0.000066342815,0.0045112227,0.09524698,0.00038241243],"about_ca_topic_score_codex":0.00001756919,"about_ca_topic_score_gemma":0.0000053437284,"teacher_disagreement_score":0.8024781,"about_ca_system_score_codex":0.00005419761,"about_ca_system_score_gemma":0.000868141,"threshold_uncertainty_score":0.8643743},"labels":[],"label_agreement":null},{"id":"W3119827564","doi":"10.48550/arxiv.2101.02750","title":"Assistive arm and hand manipulation: How does current research intersect with actual healthcare needs?","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robotics; Activities of daily living; Artificial intelligence; Robot; Computer science; Human–computer interaction; Task (project management); Robotic arm; Psychology; Engineering","score_opus":0.1710399546350904,"score_gpt":0.25933802545467655,"score_spread":0.08829807081958616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119827564","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6643071,0.0002675645,0.33255354,0.0018314315,0.00044038886,0.00024979017,0.000011373253,0.00019557153,0.00014320257],"genre_scores_gemma":[0.9980899,0.00020428757,0.0012037684,0.000022775132,0.0000699641,0.0000029774083,0.000021660318,0.000016360455,0.00036829375],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99756515,0.00035684742,0.0001328173,0.0012716848,0.00019405609,0.0004794145],"domain_scores_gemma":[0.99784946,0.0002099155,0.00017608117,0.00096136256,0.0006195361,0.00018361113],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036503733,0.00031457221,0.00038026294,0.0006949429,0.0004253127,0.00055741164,0.0010422223,0.0002830054,0.000008613864],"category_scores_gemma":[0.000060448765,0.00026935773,0.000086056105,0.0010244353,0.00057357515,0.0003286016,0.002287604,0.0015992994,0.0000056788704],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045661305,0.0011770112,0.22714467,0.0018502625,0.00095453556,0.0047096624,0.01035994,0.006619191,0.00019990477,0.66032225,0.0008610277,0.08534495],"study_design_scores_gemma":[0.007195724,0.004099161,0.5053967,0.008603671,0.0005646971,0.00035402525,0.037568834,0.32574,0.0051182252,0.091814,0.0072671673,0.006277788],"about_ca_topic_score_codex":0.00018056328,"about_ca_topic_score_gemma":0.00057779625,"teacher_disagreement_score":0.5685082,"about_ca_system_score_codex":0.0003009503,"about_ca_system_score_gemma":0.0003497076,"threshold_uncertainty_score":0.99997586},"labels":[],"label_agreement":null},{"id":"W3124075988","doi":"10.1109/access.2021.3052851","title":"FRCNN-GNB: Cascade Faster R-CNN With Gabor Filters and Naïve Bayes for Enhanced Eye Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber Polytechnic; Sheridan College","funders":"Universiti Kebangsaan Malaysia","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Biometrics; Pattern recognition (psychology); Bayes' theorem; Computer vision; Naive Bayes classifier; Iris recognition; Gabor filter; Feature extraction; Bayesian probability; Support vector machine","score_opus":0.017312593781874514,"score_gpt":0.28457810400376854,"score_spread":0.26726551022189404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124075988","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46862987,0.000044223103,0.53026277,0.0004987626,0.0002475137,0.000083080144,0.0000032809774,0.00012609028,0.00010443198],"genre_scores_gemma":[0.99032044,0.000010681677,0.008972888,0.00028436942,0.00006465886,0.00006503912,0.000001290676,0.000011327264,0.00026928654],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990233,0.000027025062,0.00013614142,0.0004553921,0.000110119014,0.00024801347],"domain_scores_gemma":[0.9993311,0.00008892836,0.000079934944,0.00031787294,0.00013305481,0.00004910889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007805087,0.00013003527,0.00015845936,0.00008125291,0.0001370437,0.0002575748,0.0004142928,0.00008500848,0.0000045865972],"category_scores_gemma":[0.000033500102,0.00010766763,0.000032544554,0.00029191605,0.0000640614,0.0004721761,0.000100403464,0.000121071935,0.0000048053603],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011091974,0.0001756724,0.0063180653,0.00019273313,0.00017418544,0.0001245249,0.0010535632,0.00024767313,0.41845417,0.0011541436,0.0009197002,0.57107466],"study_design_scores_gemma":[0.0005295067,0.00014391748,0.01637187,0.000043727036,0.000014355945,0.000025751464,0.00004572867,0.0026894195,0.97796416,0.0007237308,0.0012498519,0.00019797307],"about_ca_topic_score_codex":0.000020636446,"about_ca_topic_score_gemma":0.0001627493,"teacher_disagreement_score":0.57087666,"about_ca_system_score_codex":0.000020788459,"about_ca_system_score_gemma":0.000036978872,"threshold_uncertainty_score":0.43905583},"labels":[],"label_agreement":null},{"id":"W3124332418","doi":"10.20944/preprints201810.0309.v1","title":"Highly Accurate and Fully Automatic 3&amp;ndash;D Head Pose Estimation and Eye Gaze Estimation Using RGB-&amp;ndash;D Sensors and 3D Morphable Models","year":2018,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Pose; Artificial intelligence; Computer science; Gaze; RGB color model; Computer vision; Head (geology); Estimator; 3D pose estimation; Face (sociological concept); Pattern recognition (psychology); Mathematics","score_opus":0.15744349464355684,"score_gpt":0.3730588155714376,"score_spread":0.21561532092788077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124332418","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64640594,0.00018301627,0.35105464,0.00059372804,0.00030972608,0.00054762984,0.00001629057,0.00074138906,0.00014766573],"genre_scores_gemma":[0.7503211,0.00010466179,0.24915944,0.000057696016,0.000054118194,0.00005315258,0.000029177945,0.000044094453,0.00017659114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956994,0.00029663442,0.0008751035,0.0019744323,0.0004841104,0.0006703097],"domain_scores_gemma":[0.99661326,0.0002116544,0.0007830399,0.0017893175,0.00034082166,0.0002618991],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012573878,0.0007282944,0.00083898794,0.0005621342,0.00048408852,0.00042981614,0.0008234792,0.00064835174,0.00002842762],"category_scores_gemma":[0.00048321483,0.0007623551,0.000088661014,0.00035309806,0.0005377673,0.0010118914,0.0034627223,0.0008598339,0.00015192498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021874868,0.0010559056,0.19924663,0.005945285,0.0011567193,0.00014147125,0.017444033,0.5399318,0.050562173,0.01372798,0.00031664042,0.17025265],"study_design_scores_gemma":[0.0004982991,0.000038988077,0.06275891,0.0006739151,0.000104929924,0.00013387785,0.000015395419,0.90075916,0.0016373257,0.03259475,0.00010289205,0.0006815789],"about_ca_topic_score_codex":0.0003449383,"about_ca_topic_score_gemma":0.00004090258,"teacher_disagreement_score":0.3608274,"about_ca_system_score_codex":0.00019742151,"about_ca_system_score_gemma":0.00020756302,"threshold_uncertainty_score":0.99948275},"labels":[],"label_agreement":null},{"id":"W3124376851","doi":"10.1167/jov.20.11.866","title":"Influence of Gaze Direction and Saccades on Hand Location and Orientation Errors in a Memory-Guided Alignment Task","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Orientation (vector space); Task (project management); Computer science; Computer vision; Artificial intelligence; Psychology; Mathematics; Geometry; Engineering","score_opus":0.01624877747467858,"score_gpt":0.2886609632444758,"score_spread":0.27241218576979725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124376851","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9811277,0.00012962897,0.016675824,0.0019548198,0.00004194113,0.000049309045,1.9695715e-7,0.000008773882,0.000011838965],"genre_scores_gemma":[0.9976666,0.0000823635,0.0021406943,0.00009279313,0.000012615089,5.9387236e-7,1.3510301e-7,0.0000021356775,0.0000020745151],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99934036,0.000040562372,0.00026720812,0.00012136394,0.00017162155,0.00005891763],"domain_scores_gemma":[0.99948573,0.000042862885,0.00026589932,0.00006094697,0.00010420093,0.00004034659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025663208,0.000055918,0.00012359273,0.00014995638,0.000033179283,0.000028268963,0.00009552501,0.00003989007,3.5498115e-7],"category_scores_gemma":[0.0001329794,0.00004573387,0.000012618817,0.0002560614,0.000047299185,0.00028618693,0.000035402343,0.00010454752,4.718868e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023173071,0.00025302573,0.040245775,0.00014393583,0.000028014048,0.000037765258,0.0075417194,0.020099286,0.74192894,0.0027450037,0.00032378084,0.18642099],"study_design_scores_gemma":[0.00084236806,0.0012105332,0.924577,0.000331054,0.0000083238365,0.000039482373,0.00013855884,0.011225264,0.06069831,0.0007892941,0.00006978107,0.00007003803],"about_ca_topic_score_codex":0.000013338378,"about_ca_topic_score_gemma":0.0000028561076,"teacher_disagreement_score":0.8843312,"about_ca_system_score_codex":0.000028834671,"about_ca_system_score_gemma":0.00001986363,"threshold_uncertainty_score":0.1864973},"labels":[],"label_agreement":null},{"id":"W3129212220","doi":"10.1109/tbme.2021.3062256","title":"A Fusion Algorithm for Saccade Eye Movement Enhancement With EOG and Lumped-Element Models","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saccade; Electrooculography; Eye movement; Computer science; Artificial intelligence; Computer vision; Kalman filter; Morlet wavelet; Noise (video); Band-pass filter; Filter (signal processing); Biosignal; Wavelet; Wavelet transform; Algorithm; Pattern recognition (psychology); Engineering; Electronic engineering; Discrete wavelet transform","score_opus":0.009319350486541214,"score_gpt":0.22175875283340163,"score_spread":0.21243940234686043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129212220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040054466,0.00007488466,0.99366593,0.0014338556,0.0003667785,0.0001881017,0.000015008494,0.00023684549,0.000013154293],"genre_scores_gemma":[0.5613813,0.00013259282,0.4378448,0.00022770671,0.000034798402,0.0002155545,0.000004787933,0.00001788396,0.0001406193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878114,0.00000949785,0.00019920075,0.00041583428,0.00028230122,0.00031203328],"domain_scores_gemma":[0.99948573,0.000052236614,0.000022199929,0.0002503546,0.000054544253,0.00013491878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011992991,0.00016646727,0.00017188487,0.00015525508,0.00011313565,0.000045139943,0.00018020999,0.0000873282,0.000012684071],"category_scores_gemma":[0.0000023297644,0.0001458591,0.000047987058,0.00031238652,0.00004199542,0.000115301154,0.0000071490163,0.00018880253,0.0000022092868],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008103188,0.00041736258,5.7178744e-7,0.000054775053,0.0001091409,0.000033079352,0.00015449502,0.012602434,0.043984905,0.001426307,0.00004817244,0.9411607],"study_design_scores_gemma":[0.0007580078,0.00033712754,0.000016361108,0.00008620203,0.000017673492,0.000009577868,0.000024611714,0.8636365,0.13278839,0.00017286277,0.0019743317,0.00017836895],"about_ca_topic_score_codex":0.0000060464517,"about_ca_topic_score_gemma":0.0000025416773,"teacher_disagreement_score":0.9409823,"about_ca_system_score_codex":0.00007697577,"about_ca_system_score_gemma":0.000049016027,"threshold_uncertainty_score":0.5947962},"labels":[],"label_agreement":null},{"id":"W3131339662","doi":"10.5220/0010220800670075","title":"Driver’s Eye Fixation Prediction by Deep Neural Network","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Fixation (population genetics); Medicine","score_opus":0.008809817392751138,"score_gpt":0.22343750991572167,"score_spread":0.21462769252297054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131339662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0535193,0.00014269381,0.93915194,0.0030369526,0.00054736907,0.00003577109,0.0000011644224,0.0006807398,0.0028840736],"genre_scores_gemma":[0.97700894,0.000008745017,0.02136546,0.00038583795,0.00007059136,0.0000052579694,0.000014849785,0.0000035698613,0.0011367227],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931055,0.00003890511,0.00010594906,0.00025795685,0.00010778613,0.00017884094],"domain_scores_gemma":[0.9995722,0.000026979596,0.000037554128,0.00026376787,0.00006785239,0.00003164844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006947661,0.000065483306,0.00007014174,0.000021930524,0.00010084454,0.00006482564,0.00022836741,0.000063642234,0.00003951819],"category_scores_gemma":[0.0000236485,0.00006158972,0.000029273271,0.00031295794,0.000023333105,0.00019469578,0.00010724355,0.00010225802,0.00004068831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005348739,0.00022428416,0.14584558,0.00001255854,0.000056869878,0.00007501785,0.00027946648,0.0069183176,0.01359757,0.20388609,0.14617711,0.48292178],"study_design_scores_gemma":[0.00039460123,0.000107203145,0.22439152,0.000010460473,0.000010051769,0.000029490595,0.00002478307,0.73384374,0.010031638,0.008317104,0.022612894,0.00022650848],"about_ca_topic_score_codex":0.000007956659,"about_ca_topic_score_gemma":0.000016494478,"teacher_disagreement_score":0.9234897,"about_ca_system_score_codex":0.000021759142,"about_ca_system_score_gemma":0.0000138969845,"threshold_uncertainty_score":0.25115559},"labels":[],"label_agreement":null},{"id":"W3131668558","doi":"10.5220/0010195701350144","title":"IDEA: Index of Difficulty for Eye Tracking Applications - An Analysis Model for Target Selection Tasks","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Fitts's law; Target acquisition; Measure (data warehouse); Eye tracking; Selection (genetic algorithm); Index (typography); Artificial intelligence; Human–computer interaction; Task (project management); Machine learning; Data mining; Engineering","score_opus":0.026106567391922453,"score_gpt":0.31250870791418167,"score_spread":0.2864021405222592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131668558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022332197,0.000017819222,0.97672373,0.0003840766,0.000017609555,0.00022877629,0.000022747145,0.0002098272,0.00006321893],"genre_scores_gemma":[0.68055475,9.248861e-7,0.31898633,0.000036749843,0.000013232274,0.00018237652,0.000037945334,0.0000043942337,0.00018327779],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990881,0.000014678345,0.00022074005,0.00040089042,0.000093317874,0.00018226169],"domain_scores_gemma":[0.99901825,0.00006193634,0.00010694292,0.00034061595,0.0004345431,0.00003770134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015164552,0.000089738314,0.0002089987,0.00016958818,0.00014206694,0.000046714344,0.0003217234,0.00009129483,0.0000030534065],"category_scores_gemma":[0.000036602483,0.0000842568,0.00015642043,0.00092590187,0.000026482465,0.00018219365,0.000041215546,0.00006163787,4.407547e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016735372,0.00086929015,0.026695691,0.00006959861,0.00045369283,4.4739855e-7,0.00043910093,0.3054529,0.04577374,0.5281791,0.00027851266,0.09177116],"study_design_scores_gemma":[0.00019373906,0.000035805308,0.013043197,0.0000016752414,0.000074247124,7.014383e-7,0.000042438227,0.9516971,0.020723883,0.013829747,0.00026044613,0.000096989184],"about_ca_topic_score_codex":0.000013416552,"about_ca_topic_score_gemma":0.00013867194,"teacher_disagreement_score":0.65822256,"about_ca_system_score_codex":0.000029479677,"about_ca_system_score_gemma":0.00006285169,"threshold_uncertainty_score":0.34358925},"labels":[],"label_agreement":null},{"id":"W3132789467","doi":"10.1177/1071181320641274","title":"Real-Time Gaze-Aware Cognitive Support System for Security Surveillance","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Thales (Canada)","funders":"","keywords":"Gaze; Vigilance (psychology); Cognition; Visual search; Computer science; Eye tracking; Visual attention; Task (project management); Human–computer interaction; Computer security; Artificial intelligence; Cognitive psychology; Psychology; Engineering","score_opus":0.016221205586683322,"score_gpt":0.23139639261331782,"score_spread":0.2151751870266345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132789467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99811643,0.0000164392,0.00038069408,0.0003791339,0.00007644856,0.00025378406,0.00010325344,0.00025362713,0.00042017095],"genre_scores_gemma":[0.9985613,0.0000145372605,0.0012325505,0.00006031378,0.000077241006,0.000011540567,0.0000045138554,0.000016599732,0.00002135716],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988154,0.000008986217,0.00031168366,0.00044396272,0.000115824034,0.00030413843],"domain_scores_gemma":[0.99896,0.00014512299,0.00039846057,0.00008173698,0.00032791175,0.00008674998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040993895,0.000201854,0.0003512544,0.000017602471,0.00046554007,0.00009869561,0.00064197864,0.00012984745,8.5927377e-7],"category_scores_gemma":[0.00011418896,0.00015980488,0.00021932698,0.00015441116,0.0001728972,0.00025047144,0.00046139583,0.00019187723,0.0000010568821],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023925479,0.00027497642,0.6299915,0.0051038763,0.0008586778,0.0000012879867,0.1388541,0.000030172521,0.113316424,0.089582264,0.01986663,0.0018808551],"study_design_scores_gemma":[0.00727333,0.0030408478,0.34091747,0.002492401,0.00041346386,0.000044655804,0.16590405,0.08225957,0.38407797,0.0067741973,0.0024404412,0.0043616197],"about_ca_topic_score_codex":0.000030498884,"about_ca_topic_score_gemma":0.0000011945053,"teacher_disagreement_score":0.289074,"about_ca_system_score_codex":0.00005169043,"about_ca_system_score_gemma":0.000030204723,"threshold_uncertainty_score":0.6516654},"labels":[],"label_agreement":null},{"id":"W3136070868","doi":"10.2196/24151","title":"Application of Eye Tracking in Puzzle Games for Adjunct Cognitive Markers: Pilot Observational Study in Older Adults","year":2021,"lang":"en","type":"article","venue":"JMIR Serious Games","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"University of Bern","keywords":"Eye tracking; Laptop; Gaze; Tracking (education); Cognition; Computer science; Psychology; Set (abstract data type); Cognitive psychology; Human–computer interaction; Artificial intelligence","score_opus":0.035230491418544445,"score_gpt":0.3286203805497299,"score_spread":0.29338988913118547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136070868","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96627134,0.00017227547,0.031439457,0.0005936374,0.00008484862,0.0012618105,0.000010312339,0.00008013849,0.000086175794],"genre_scores_gemma":[0.9946989,0.000006754481,0.0041131573,0.00007846719,0.000020319916,0.0009835704,0.000021373135,0.000012595526,0.0000648986],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984895,0.00009343462,0.0004017278,0.00055232114,0.00019682776,0.000266172],"domain_scores_gemma":[0.9989336,0.00027425468,0.00017310827,0.00031503328,0.00027558376,0.000028379805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029904657,0.00015495258,0.00029881173,0.00021335231,0.00004213217,0.00004075374,0.00036216323,0.000073710034,0.0000054580537],"category_scores_gemma":[0.00018838639,0.00016231168,0.00005216864,0.0007590191,0.000054896056,0.00025258778,0.00011877234,0.00015889476,0.0000036308807],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021062126,0.0026685607,0.69458467,0.00011842095,0.000035462675,0.000034608776,0.0034565509,0.00005155477,0.0017729138,0.0018502244,0.00008287332,0.29513356],"study_design_scores_gemma":[0.0023842147,0.00034322453,0.9860438,0.0002007994,0.000005706645,0.000003389916,0.0020221265,0.0056387265,0.00243512,0.00065047224,0.00010829266,0.00016413405],"about_ca_topic_score_codex":0.00009587246,"about_ca_topic_score_gemma":0.0013584318,"teacher_disagreement_score":0.29496944,"about_ca_system_score_codex":0.000055002994,"about_ca_system_score_gemma":0.00009248241,"threshold_uncertainty_score":0.6618878},"labels":[],"label_agreement":null},{"id":"W3140806113","doi":"","title":"3 - Le projet VAHM (Véhicule Autonome pour Handicapés Moteur): la localisation","year":2000,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wheelchair; Odometry; Computer science; Orientation (vector space); Disabled people; Matching (statistics); Ultrasonic sensor; Position (finance); Grid; Simulation; Computer vision; Artificial intelligence; Engineering; Control engineering; Mobile robot; Robot; Physical medicine and rehabilitation; Mathematics; Cerebral palsy; Acoustics","score_opus":0.021439731275005477,"score_gpt":0.23476350606206253,"score_spread":0.21332377478705705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140806113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21952097,0.0017429002,0.6166163,0.11302643,0.0009595403,0.001052977,0.0000904968,0.001390217,0.045600154],"genre_scores_gemma":[0.98053473,0.00006236922,0.007934598,0.0006294122,0.0003269757,0.00005148825,0.000024725694,0.000029667479,0.010406028],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971822,0.00037271678,0.000543505,0.0007579349,0.00043412103,0.00070952566],"domain_scores_gemma":[0.99900436,0.0001260119,0.00016652675,0.00042669554,0.000102587815,0.00017381161],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00082136615,0.0003564746,0.0003445034,0.00017950167,0.00034543456,0.00034501392,0.0007961481,0.00030011448,0.0031017975],"category_scores_gemma":[0.000019266936,0.00038103297,0.0001626029,0.00048643447,0.00046741116,0.0005779504,0.00010431408,0.00044733912,0.0011891997],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041640298,0.0014835212,0.00084882905,0.00009025154,0.00013223029,0.00021187599,0.0011778126,0.0026170383,0.0025162792,0.37942392,0.011340065,0.60011655],"study_design_scores_gemma":[0.003093225,0.0007477414,0.029909436,0.0003027859,0.00009675773,0.00025000633,0.00012257618,0.20854697,0.005907651,0.014979543,0.73512995,0.0009133795],"about_ca_topic_score_codex":0.00037924084,"about_ca_topic_score_gemma":0.000033481123,"teacher_disagreement_score":0.76101375,"about_ca_system_score_codex":0.00013734118,"about_ca_system_score_gemma":0.00055782386,"threshold_uncertainty_score":0.99986416},"labels":[],"label_agreement":null},{"id":"W3146194492","doi":"","title":"Experimental verification of the dynamic model for a quarter size self-balancing wheelchair","year":2004,"lang":"en","type":"article","venue":"American Control Conference","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wheelchair; Quarter (Canadian coin); Computer science; History","score_opus":0.008156173290394853,"score_gpt":0.24207621404525323,"score_spread":0.23392004075485837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3146194492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20958613,0.000022861921,0.78727144,0.0025977565,0.00006484996,0.0002290352,0.000008378962,0.00014918915,0.00007035754],"genre_scores_gemma":[0.96894705,0.0000014645674,0.030583497,0.00033935494,0.0000068346876,0.00009732756,5.2444216e-7,0.0000066408206,0.000017302207],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991016,0.00003358567,0.00019874074,0.00030373735,0.00013786944,0.00022446137],"domain_scores_gemma":[0.99898225,0.00010501612,0.00023792469,0.0005176127,0.00012437422,0.000032843498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010390219,0.00012284091,0.00022061513,0.000033111115,0.0000940285,0.00003244319,0.0007567602,0.000033536202,9.3503144e-7],"category_scores_gemma":[0.000053421594,0.00009517286,0.00008478008,0.00017947787,0.00021522686,0.00010980321,0.000050512434,0.00009455311,0.0000027411425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009953134,0.00069184974,0.0023905495,0.000035675435,0.00014961895,0.000002302345,0.006860523,0.007810783,0.5495723,0.3890748,0.00009237545,0.04321969],"study_design_scores_gemma":[0.0013300321,0.000265921,0.008878778,0.000025033858,0.000013801656,0.000004461663,0.00028077944,0.976123,0.009312957,0.0035726256,0.000029380784,0.0001632825],"about_ca_topic_score_codex":0.000066476525,"about_ca_topic_score_gemma":0.000021274947,"teacher_disagreement_score":0.96831214,"about_ca_system_score_codex":0.000085969325,"about_ca_system_score_gemma":0.00019995707,"threshold_uncertainty_score":0.38810363},"labels":[],"label_agreement":null},{"id":"W3150349000","doi":"10.5555/1739807.1739809","title":"Development of multi-directional compliant joint module for human-care robot","year":2007,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Joint (building); Robot; Computer science; Development (topology); Engineering; Simulation; Artificial intelligence; Structural engineering; Mathematics","score_opus":0.052309296395449914,"score_gpt":0.3241966720793265,"score_spread":0.2718873756838766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150349000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06889487,0.0000739557,0.9300711,0.0003575939,0.00048980035,0.00004894701,0.0000018383206,0.000024004701,0.00003786004],"genre_scores_gemma":[0.54758954,0.0000020916762,0.45234817,0.000012670751,0.000032913515,5.4913363e-7,0.0000026636608,0.0000021693827,0.000009244873],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990779,0.000007981261,0.00047675456,0.00009156454,0.0002597307,0.00008605611],"domain_scores_gemma":[0.9986164,0.000042842155,0.000439152,0.00005633351,0.0008132879,0.000031979354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037464715,0.00006667541,0.00012598594,0.00025220713,0.00007497257,0.000039655475,0.0002621894,0.000042162657,0.0000013783579],"category_scores_gemma":[0.00004034825,0.00005979443,0.000054143464,0.00005866001,0.00003002725,0.00014820306,0.000057243444,0.00007283574,6.4084315e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004426099,0.0006235709,0.0050344793,0.00009950807,0.00039697337,0.000027122896,0.0026836526,0.039962705,0.14610136,0.18568175,0.00015185983,0.6191928],"study_design_scores_gemma":[0.0031465092,0.00034673366,0.60523504,0.0004565279,0.00003101308,0.00029956468,0.00045391047,0.23429753,0.14587101,0.007834202,0.0016825349,0.00034539803],"about_ca_topic_score_codex":0.0000020389502,"about_ca_topic_score_gemma":0.000009740359,"teacher_disagreement_score":0.61884737,"about_ca_system_score_codex":0.00007862438,"about_ca_system_score_gemma":0.000052864812,"threshold_uncertainty_score":0.24383461},"labels":[],"label_agreement":null},{"id":"W3151616036","doi":"","title":"Modelling and Simulation of an Ultrasonic Tethering Smart Wheelchair System for Social Following","year":2019,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Wheelchair; Ultrasonic sensor; Simulation; Computer science; Tethering; Engineering; Acoustics; Telecommunications","score_opus":0.022045335261567933,"score_gpt":0.25731605647765876,"score_spread":0.23527072121609083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151616036","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72669524,0.000027316491,0.27262872,0.000032838834,0.000094494055,0.00015208025,5.948738e-7,0.00020885987,0.00015982217],"genre_scores_gemma":[0.9814711,6.372405e-7,0.018445706,0.000006510335,0.000033930788,0.000012214608,5.945823e-7,0.000011783601,0.000017499393],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992421,0.0000037083664,0.00016488966,0.00029335122,0.00010846975,0.00018743798],"domain_scores_gemma":[0.9996373,0.000052365816,0.000105867046,0.00007542755,0.00010296129,0.000026076088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026365038,0.00009774463,0.0001866726,0.000082511,0.00011937974,0.000066459754,0.00024564783,0.00008978097,2.0385502e-7],"category_scores_gemma":[0.000017466453,0.00009690226,0.000055010867,0.00013948376,0.000022827378,0.0004470512,0.00005095055,0.00007384914,0.0000013414182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009316746,0.00024025787,0.07204893,0.0037409535,0.00025542872,0.0000039035094,0.017114805,0.2454267,0.2120756,0.34967223,0.000026871328,0.09930116],"study_design_scores_gemma":[0.00032097974,0.000105502506,0.00074416574,0.00009433878,0.00000997678,0.0000027885906,0.0006442312,0.990108,0.007097985,0.0007000444,0.00005736121,0.00011463677],"about_ca_topic_score_codex":0.000009173786,"about_ca_topic_score_gemma":3.7197177e-7,"teacher_disagreement_score":0.7446813,"about_ca_system_score_codex":0.000031948366,"about_ca_system_score_gemma":0.000012793974,"threshold_uncertainty_score":0.39515597},"labels":[],"label_agreement":null},{"id":"W3152968659","doi":"10.24908/iqurcp.10467","title":"Learning Nouns and Verbs using Cross-situational Statistics","year":2018,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Verb; Noun; Linguistics; Word order; Psychology; Natural language processing; Artificial intelligence; Object (grammar); Computer science","score_opus":0.12302403095570796,"score_gpt":0.4100912208508778,"score_spread":0.2870671898951698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152968659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44150308,0.00003507,0.5517192,0.004186544,0.0001991926,0.00022703825,0.0000044803614,0.00038367644,0.0017417041],"genre_scores_gemma":[0.9490178,0.00011062435,0.050140135,0.000045260345,0.00021295586,0.000018642624,0.0000040479067,0.000020004425,0.00043049603],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968765,0.00009238474,0.00033405342,0.0008303952,0.0009923643,0.00087433105],"domain_scores_gemma":[0.9958228,0.00030246296,0.00014391307,0.00022506797,0.0032872576,0.00021852778],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0016884769,0.0002271665,0.0002441012,0.0005114644,0.0010932675,0.0013733326,0.000963123,0.00017453647,0.000016960896],"category_scores_gemma":[0.0010295099,0.00022561979,0.00003593454,0.0009095823,0.0024675496,0.000976768,0.00084914017,0.0009259646,0.000100090176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026941603,0.00006165089,0.03889258,0.00006469147,0.000048343805,0.00001778171,0.0020778603,0.0000053914114,0.008006153,0.92998886,0.00120615,0.019603599],"study_design_scores_gemma":[0.0011898825,0.0017645467,0.033836707,0.00031771322,0.000020315101,0.00016247894,0.0016897657,0.24955076,0.008575797,0.69712996,0.004838976,0.00092306075],"about_ca_topic_score_codex":0.00013635679,"about_ca_topic_score_gemma":0.000006753613,"teacher_disagreement_score":0.5075148,"about_ca_system_score_codex":0.00016107954,"about_ca_system_score_gemma":0.00047259577,"threshold_uncertainty_score":0.99966335},"labels":[],"label_agreement":null},{"id":"W3160033907","doi":"10.20380/gi2021.32","title":"A Comparative Evaluation of Techniques for Locating Out-of-View Targets in Virtual Reality","year":2021,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Virtual reality; Computer science; Augmented reality; Human–computer interaction; Computer vision; Computer graphics (images)","score_opus":0.1482756079395782,"score_gpt":0.38462328616757463,"score_spread":0.23634767822799643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160033907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008923728,0.0011855671,0.9858284,0.002714006,0.00013992122,0.00053748133,0.000027242882,0.000093846786,0.00054977805],"genre_scores_gemma":[0.76795363,0.000025455674,0.23171553,0.00013060233,0.000012247676,0.00009634439,0.000051121326,0.000005078227,0.000009982333],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827653,0.00037239149,0.0005570592,0.00029505056,0.0003135782,0.00018539687],"domain_scores_gemma":[0.9963539,0.00040220266,0.00034098834,0.0016549103,0.0012155318,0.000032433967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011997442,0.00012957226,0.00038374518,0.00004763542,0.00021118099,0.00002242882,0.0014722373,0.000082959334,0.0000024294957],"category_scores_gemma":[0.000057156263,0.00014529847,0.00011699002,0.00043892322,0.00021607924,0.00012411967,0.00057709217,0.00022474697,1.5485105e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000061229366,0.0016153457,0.0020033051,0.00048633124,0.0005646904,0.0000026962496,0.026369192,0.0036273957,0.01334645,0.5687677,0.0362642,0.34694654],"study_design_scores_gemma":[0.0013302884,0.00017318805,0.013143733,0.0005434864,0.00009282294,0.0000053671656,0.0017115744,0.90194464,0.051566027,0.015131807,0.013756018,0.00060105475],"about_ca_topic_score_codex":0.011948776,"about_ca_topic_score_gemma":0.16018932,"teacher_disagreement_score":0.8983172,"about_ca_system_score_codex":0.00035390325,"about_ca_system_score_gemma":0.001343066,"threshold_uncertainty_score":0.99463075},"labels":[],"label_agreement":null},{"id":"W3160049074","doi":"10.1109/icpr48806.2021.9412066","title":"Detection and Correspondence Matching of Corneal Reflections for Eye Tracking Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Eye tracking; Gaze; BitTorrent tracker; Eye tracking on the ISS; Rotation (mathematics); Optics; Physics","score_opus":0.04105118042782398,"score_gpt":0.3293669713504438,"score_spread":0.28831579092261983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160049074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3898552,0.000050408074,0.60966355,0.0000499116,0.00011490151,0.000031700507,1.5958078e-7,0.000112654634,0.00012150748],"genre_scores_gemma":[0.8486895,0.000005315003,0.15114585,0.000018921683,0.000014712818,0.0000035727253,2.8962194e-7,0.000005118248,0.000116736686],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929357,0.00005043769,0.00015571149,0.0002651125,0.00007870126,0.00015647004],"domain_scores_gemma":[0.99941224,0.00017831582,0.000089765934,0.00014112944,0.00015292948,0.000025637799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021007594,0.00007144636,0.00012093428,0.00010851758,0.0002625905,0.00006791272,0.00012400506,0.000063533174,0.0000037863879],"category_scores_gemma":[0.00016269492,0.00007432049,0.000037320333,0.00034470315,0.000042388896,0.00021311681,0.00008818882,0.00015301093,7.1313866e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001075935,0.000031683765,0.002522819,0.00003224004,0.000016347905,0.000010391839,0.0007079493,0.0025187915,0.81508076,0.015082222,9.400161e-7,0.16398512],"study_design_scores_gemma":[0.00042384773,0.00019330211,0.020783748,0.00007673175,0.000024809466,0.00029231486,0.0011532897,0.55417573,0.41274142,0.009577751,0.00031800315,0.00023904284],"about_ca_topic_score_codex":0.000036575508,"about_ca_topic_score_gemma":0.000114679046,"teacher_disagreement_score":0.55165696,"about_ca_system_score_codex":0.00002766703,"about_ca_system_score_gemma":0.000032467895,"threshold_uncertainty_score":0.30307016},"labels":[],"label_agreement":null},{"id":"W3160256003","doi":"10.1109/icpr48806.2021.9412857","title":"A General End-to-End Method for Characterizing Neuropsychiatric Disorders using Free-Viewing Visual Scanning Tasks","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Visual search; Artificial intelligence; Computer science; Eye tracking; Visual field; Attentional bias; Gaze; Eye movement; Pattern recognition (psychology); Psychology; Computer vision; Cognitive psychology; Cognition; Psychiatry; Neuroscience","score_opus":0.02943359691744937,"score_gpt":0.32430299118447226,"score_spread":0.2948693942670229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160256003","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17162153,0.00010832721,0.8230462,0.003712446,0.00071742025,0.00015290086,0.000004967331,0.0003705191,0.00026569987],"genre_scores_gemma":[0.31265852,0.0000050414105,0.68566066,0.0013372074,0.0001440192,0.00002094059,0.000004209753,0.00002367891,0.00014574434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978908,0.0001432041,0.00033469504,0.00081544404,0.00021397589,0.0006019217],"domain_scores_gemma":[0.99888337,0.00017179725,0.0001184636,0.00059196947,0.000112537506,0.00012185819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004420482,0.00023491937,0.0003164962,0.00031691056,0.00035324268,0.00025177593,0.0007921012,0.00009161687,0.000020389287],"category_scores_gemma":[0.0002048836,0.00023572068,0.00016335209,0.0011314484,0.000028158716,0.00030501088,0.0006174148,0.00022263231,0.0000073259275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012604224,0.00019549602,0.005117709,0.00006791092,0.00008586523,0.00004910255,0.0005758689,0.00076049985,0.3549141,0.04029497,0.00051277975,0.59741306],"study_design_scores_gemma":[0.0014701638,0.00036179967,0.03696444,0.00011052388,0.00008829787,0.00021623526,0.00018984952,0.89153045,0.040769257,0.0048664142,0.022225378,0.0012072001],"about_ca_topic_score_codex":0.000097656986,"about_ca_topic_score_gemma":0.000029878172,"teacher_disagreement_score":0.89076996,"about_ca_system_score_codex":0.000050517974,"about_ca_system_score_gemma":0.00013696928,"threshold_uncertainty_score":0.96124107},"labels":[],"label_agreement":null},{"id":"W3162354938","doi":"10.18280/ts.380214","title":"A New Approach for Eye-Blink to Speech Conversion by Dynamic Time Warping","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Dynamic time warping; Eye movement; Handwriting; Eye tracking on the ISS; Feeling; Process (computing); Computer vision; Speech recognition; Artificial intelligence; Human–computer interaction; Simulation; Psychology","score_opus":0.01346492893155706,"score_gpt":0.24863724935599987,"score_spread":0.2351723204244428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162354938","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021664687,0.00005285325,0.9731714,0.0039311647,0.00009515037,0.0002936679,0.000012716987,0.0002798751,0.00049844716],"genre_scores_gemma":[0.42889714,0.000003178379,0.5656901,0.0008928236,0.00006646463,0.000038907343,0.00008263492,0.000018072793,0.004310663],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985916,0.000037677793,0.00021284702,0.0005707857,0.00022816344,0.00035892666],"domain_scores_gemma":[0.99935406,0.000058578014,0.000059151087,0.00030370388,0.00009115993,0.00013336321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024857686,0.00015892858,0.00020107118,0.00010447465,0.00012544364,0.00011008159,0.00058337743,0.000083121944,0.00016625895],"category_scores_gemma":[0.000021361826,0.00016122416,0.00008925692,0.00037022264,0.000020977004,0.00013690161,0.00017884582,0.00011478954,0.00014906419],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000075662356,0.0005883977,0.00031980788,0.000114677365,0.00016073191,0.00006010581,0.00062609505,0.00048790345,0.55749035,0.008452658,0.15833063,0.27329296],"study_design_scores_gemma":[0.005173244,0.0008366051,0.0017726164,0.00012722095,0.000082101375,0.00006888367,0.0001697779,0.6714283,0.22600982,0.0024368945,0.090686336,0.0012081721],"about_ca_topic_score_codex":0.0000088138895,"about_ca_topic_score_gemma":0.0000010472761,"teacher_disagreement_score":0.6709404,"about_ca_system_score_codex":0.00008509837,"about_ca_system_score_gemma":0.000101364545,"threshold_uncertainty_score":0.65745306},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.048523542436609744,"score_gpt":0.34859698186763804,"score_spread":0.3000734394310283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164168002","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96451896,0.000099941004,0.022651885,0.0115173,0.00005121784,0.0005397403,0.000026013962,0.00023305143,0.00036188628],"genre_scores_gemma":[0.9950263,0.00020615401,0.0041200374,0.00010840294,0.00009113876,0.0001599985,0.00009530061,0.000017458524,0.00017519707],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99795884,0.00024848798,0.0003047719,0.0007237995,0.00042358073,0.0003405408],"domain_scores_gemma":[0.9976622,0.0005908298,0.0001385494,0.00101571,0.00043093128,0.00016181827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005754326,0.00017168075,0.00019771371,0.0003738605,0.00040050727,0.00027920993,0.00051061885,0.00013118549,0.00000433671],"category_scores_gemma":[0.00022425709,0.00016279423,0.00003243675,0.001063865,0.0002849169,0.00050996797,0.00020486246,0.0004295355,0.000007616137],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004125118,0.00064319914,0.06130982,0.00015301518,0.000042818625,0.000011482397,0.0012218398,0.00013345035,0.6769715,0.20006157,0.0003218974,0.05908814],"study_design_scores_gemma":[0.00085106824,0.0005820088,0.46682993,0.00027238738,0.000027938904,0.00000978573,0.00021499793,0.0116209565,0.48645097,0.031760864,0.00090402365,0.00047506343],"about_ca_topic_score_codex":0.00012031826,"about_ca_topic_score_gemma":0.000102935795,"teacher_disagreement_score":0.4055201,"about_ca_system_score_codex":0.00005289382,"about_ca_system_score_gemma":0.000090320915,"threshold_uncertainty_score":0.66385555},"labels":[],"label_agreement":null},{"id":"W3164603418","doi":"10.31234/osf.io/rzd6v","title":"MAD saccade: statistically robust saccade threshold estimation","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Saccade; Standard deviation; Estimator; Computer science; Outlier; Artificial intelligence; Gaze; False positive paradox; Pattern recognition (psychology); Mathematics; Computer vision; Algorithm; Eye movement; Statistics","score_opus":0.032110869299014785,"score_gpt":0.27528133215363176,"score_spread":0.24317046285461696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164603418","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00212788,0.00010648809,0.9775452,0.0033465251,0.0011359219,0.0003867764,0.000026819902,0.0014730645,0.013851343],"genre_scores_gemma":[0.48948693,0.00002145437,0.50853723,0.00030652116,0.000046963556,0.000032402306,0.000039032235,0.000021775506,0.0015077038],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973377,0.000056439218,0.00049031724,0.001152674,0.0004579445,0.0005049044],"domain_scores_gemma":[0.99761635,0.00018752489,0.00026530842,0.0016625631,0.0001612899,0.00010697083],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00035415712,0.00040870803,0.00049117,0.00028431878,0.00009395655,0.00039904675,0.0023983133,0.00066978007,0.00011591151],"category_scores_gemma":[0.00014439678,0.00037152664,0.00012192642,0.0002389001,0.00011523316,0.0002101872,0.002188598,0.0013361576,0.00084302935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058776586,0.00012290802,0.002119215,0.00018656766,0.00007550865,0.00004290166,0.00007500655,0.055394292,0.00009138427,0.8349458,0.014145429,0.09279508],"study_design_scores_gemma":[0.0002651246,0.00007495198,0.026623337,0.0001507764,0.00003048251,0.000021556118,0.000006938663,0.8320387,0.00051814585,0.13886598,0.0007807673,0.0006232621],"about_ca_topic_score_codex":0.000072388844,"about_ca_topic_score_gemma":0.000015194658,"teacher_disagreement_score":0.7766444,"about_ca_system_score_codex":0.00013223021,"about_ca_system_score_gemma":0.00025459338,"threshold_uncertainty_score":0.9999349},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.09156642605013683,"score_gpt":0.40087382504944363,"score_spread":0.3093073989993068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164626843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2871607,0.000047892863,0.6954554,0.014611295,0.000043423704,0.0012418249,0.0000121788435,0.00072771375,0.0006996165],"genre_scores_gemma":[0.9885938,0.000059343114,0.009752469,0.0001099027,0.000099309786,0.0011512012,0.000028322334,0.000021230942,0.0001844274],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99754304,0.00020181069,0.0003882518,0.00087361276,0.00036331973,0.00062996853],"domain_scores_gemma":[0.9973444,0.0011575063,0.000101471036,0.00087804644,0.00039728414,0.00012130694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011150553,0.0001959198,0.00029202344,0.0004182574,0.00066024985,0.00032687056,0.00086439855,0.00015650048,0.0000038165863],"category_scores_gemma":[0.00040668072,0.00017741724,0.00006181672,0.0011393058,0.00018797077,0.00017459526,0.00034559102,0.000671814,0.000006602262],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026685075,0.0032051806,0.11253967,0.00038843343,0.000065584856,0.000029831364,0.00050267775,0.0005397304,0.29285938,0.31706384,0.0007417534,0.27179706],"study_design_scores_gemma":[0.0021879482,0.0017523386,0.16832802,0.00067269243,0.000021746631,0.000020877262,0.00032756806,0.07283702,0.706025,0.034120426,0.01280566,0.00090073503],"about_ca_topic_score_codex":0.0000691545,"about_ca_topic_score_gemma":0.00029595313,"teacher_disagreement_score":0.70143306,"about_ca_system_score_codex":0.00017570575,"about_ca_system_score_gemma":0.00015761967,"threshold_uncertainty_score":0.7234865},"labels":[],"label_agreement":null},{"id":"W3165445705","doi":"10.1037/pag0000615","title":"Looking the same, but remembering differently: Preserved eye-movement synchrony with age during movie watching.","year":2021,"lang":"en","type":"article","venue":"Psychology and Aging","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Brock University","funders":"Natural Sciences and Engineering Research Council of Canada; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canada Research Chairs","keywords":"Psychology; Eye movement; Movement (music); Cognitive psychology; Developmental psychology; Communication; Neuroscience; Aesthetics","score_opus":0.01470571955172069,"score_gpt":0.2691583879738017,"score_spread":0.25445266842208103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165445705","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9395763,0.0010274333,0.047721654,0.009368621,0.00024739827,0.00009051592,8.097429e-7,0.0002570315,0.0017102108],"genre_scores_gemma":[0.9938031,0.00011192884,0.0042236396,0.0010984447,0.000063762796,0.000021593563,0.0000016397285,0.000013446826,0.0006624559],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984943,0.00011159262,0.00019772396,0.000617074,0.00014444374,0.00043481935],"domain_scores_gemma":[0.9990678,0.000080744576,0.00009296733,0.0006658737,0.00003772877,0.00005487596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002308697,0.00018897209,0.00021337857,0.000068673,0.00046274948,0.000144712,0.0005497814,0.0000759589,0.00001722024],"category_scores_gemma":[0.000026234104,0.00013171646,0.000041580388,0.00022408967,0.00016538487,0.00015764173,0.00045526438,0.0004326145,0.0000050029885],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006970411,0.0005012184,0.4575773,0.00031552825,0.00058933336,0.0037536595,0.004296794,0.0003079958,0.31348068,0.03605493,0.0002211156,0.18283175],"study_design_scores_gemma":[0.0011437784,0.000067137305,0.97378933,0.00026546922,0.000029721648,0.00020346335,0.00024006942,0.0012993941,0.015690504,0.0059257112,0.0009732931,0.00037212868],"about_ca_topic_score_codex":0.00002690283,"about_ca_topic_score_gemma":0.000057419344,"teacher_disagreement_score":0.51621205,"about_ca_system_score_codex":0.000019987165,"about_ca_system_score_gemma":0.000016298847,"threshold_uncertainty_score":0.53712416},"labels":[],"label_agreement":null},{"id":"W3170894329","doi":"10.1049/pbhe026e_ch16","title":"An extraocular muscle stimulation system based on EOG and FES","year":2020,"lang":"en","type":"book-chapter","venue":"IET eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Extraocular muscles; Stimulation; Eye movement; Medicine; Physical medicine and rehabilitation; Anatomy; Ophthalmology; Internal medicine","score_opus":0.019918054083558973,"score_gpt":0.22367603751597004,"score_spread":0.20375798343241106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170894329","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019131626,0.00012330746,0.30589753,0.0005811693,0.0005839536,0.00067899376,0.00003698316,0.0028504417,0.6873345],"genre_scores_gemma":[0.9869655,6.3540864e-7,0.0056553334,0.000208312,0.00009163869,0.0000076167453,0.000010261007,0.000034416047,0.007026318],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872214,0.00003047762,0.00020133611,0.00063695153,0.00024797564,0.00016112026],"domain_scores_gemma":[0.9989903,0.00005894278,0.00013883201,0.0006593361,0.000052897845,0.00009967598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00011952323,0.00026642284,0.00028505336,0.00015550702,0.00011313654,0.0001173865,0.0004625179,0.0002978818,0.000004207064],"category_scores_gemma":[0.000006668055,0.0002574456,0.000070518916,0.000015248107,0.000082327475,0.000048674523,0.00007208767,0.00031658268,0.000043441745],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009385852,0.000012116472,0.000019726545,0.0001079157,0.000021559435,0.00018828987,0.00009500314,0.00013607963,0.0005635103,0.8472457,0.00011562294,0.15148509],"study_design_scores_gemma":[0.002420991,0.0031417992,0.0070160087,0.0024236445,0.00021889695,0.00007096031,0.00005488452,0.8159266,0.0028143476,0.06026362,0.1028083,0.0028399567],"about_ca_topic_score_codex":0.0000058766736,"about_ca_topic_score_gemma":0.0000028925608,"teacher_disagreement_score":0.9850523,"about_ca_system_score_codex":0.00004965876,"about_ca_system_score_gemma":0.000048764156,"threshold_uncertainty_score":0.9999878},"labels":[],"label_agreement":null},{"id":"W3174311028","doi":"10.1109/i2mtc50364.2021.9459971","title":"A Wireless Flexible Electrooculogram Monitoring System With Printed Electrodes","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electrooculography; Computer science; Wearable computer; SIGNAL (programming language); Wireless; Channel (broadcasting); Electrode; Substrate (aquarium); Computer hardware; Eye movement; Embedded system; Artificial intelligence; Telecommunications; Chemistry","score_opus":0.010698201812553949,"score_gpt":0.23192498912260034,"score_spread":0.22122678731004639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174311028","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26572862,0.000116738265,0.7279761,0.0003852513,0.00009197493,0.000055628636,1.3467316e-7,0.0019956082,0.003649938],"genre_scores_gemma":[0.94613814,0.0000074617315,0.0530241,0.000021433636,0.00003754571,0.000027006634,7.646879e-7,0.000010559579,0.00073300576],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987248,0.00004710606,0.00014192636,0.00046554615,0.00018756888,0.00043304733],"domain_scores_gemma":[0.99911016,0.000038710383,0.00005189503,0.00054521154,0.00019613632,0.000057864494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010767717,0.00014398903,0.00019001192,0.00009271853,0.0001213132,0.00015823844,0.0005055528,0.000069811314,0.000002889637],"category_scores_gemma":[0.000011681601,0.000112068585,0.000041611987,0.0008710595,0.000036235873,0.0001546768,0.00012557535,0.00021350522,0.000025218247],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014378079,0.00016470435,0.024840591,0.00008315231,0.00012877752,0.0003557264,0.00012084278,0.000043557215,0.12895708,0.7704286,0.000086207016,0.07477638],"study_design_scores_gemma":[0.00031023842,0.0002084731,0.008961841,0.00012196551,0.000009003124,0.00042021502,0.00026452838,0.005240919,0.98351735,0.00029325575,0.0004126096,0.00023957335],"about_ca_topic_score_codex":0.000029969706,"about_ca_topic_score_gemma":0.000017948569,"teacher_disagreement_score":0.8545603,"about_ca_system_score_codex":0.00009120131,"about_ca_system_score_gemma":0.00012879986,"threshold_uncertainty_score":0.45700243},"labels":[],"label_agreement":null},{"id":"W3174575813","doi":"10.18280/ejee.230301","title":"Comparative Study Between Integrator Backstepping and Fuzzy Logic Control Applied to an Electric Powered Wheelchair","year":2021,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Control theory (sociology); Backstepping; Fuzzy logic; Integrator; Robustness (evolution); Computer science; Torque; Nonlinear system; Control engineering; Engineering; Adaptive control; Artificial intelligence; Control (management); Physics; Bandwidth (computing)","score_opus":0.018920792227856514,"score_gpt":0.24565510963300483,"score_spread":0.22673431740514832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174575813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40380967,0.0002978332,0.5950542,0.00018825477,0.000071682065,0.00010530125,3.5666315e-7,0.000116128314,0.00035655062],"genre_scores_gemma":[0.9870503,0.000005706298,0.0127065675,0.000082612205,0.00012801879,0.000001515289,3.3315874e-7,0.000017634668,0.000007340513],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99832714,0.00028738717,0.0004744598,0.00031795216,0.00023397888,0.00035907346],"domain_scores_gemma":[0.998952,0.00019526614,0.00015543088,0.0002135263,0.00022121899,0.00026261093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007561877,0.00020485139,0.0005131788,0.00038623615,0.000077325225,0.00014200636,0.00056746235,0.000030801726,0.0000014353374],"category_scores_gemma":[0.00021700592,0.0001785898,0.00005831124,0.0011396927,0.0000112826365,0.00015676403,0.00009980609,0.0007358407,0.000009627583],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023589151,0.0019992352,0.014595494,0.000048000402,0.0015304103,0.0057000597,0.006144344,0.025961645,0.5926081,0.050623,0.00035708732,0.30019674],"study_design_scores_gemma":[0.012815438,0.025715072,0.8271679,0.00027346943,0.00037900353,0.0017612843,0.0013570554,0.085297965,0.0375406,0.0012747456,0.0037039875,0.0027134486],"about_ca_topic_score_codex":4.3941395e-7,"about_ca_topic_score_gemma":2.8331763e-7,"teacher_disagreement_score":0.8125724,"about_ca_system_score_codex":0.00006725429,"about_ca_system_score_gemma":0.000053414933,"threshold_uncertainty_score":0.7282681},"labels":[],"label_agreement":null},{"id":"W3175009974","doi":"10.1145/3461778.3462068","title":"Co-Designing Interactions between Pedestrians in Wheelchairs and Autonomous Vehicles","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; University of Calgary","funders":"","keywords":"Testbed; Wheelchair; Human–computer interaction; Computer science; Work (physics); Interface (matter); Universal design; Transport engineering; Simulation; Engineering; World Wide Web","score_opus":0.029972223919970505,"score_gpt":0.2872759627687795,"score_spread":0.25730373884880897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175009974","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52394545,0.00007315746,0.4627539,0.005410837,0.00009106368,0.000053055624,0.0000016051937,0.00042587516,0.0072450684],"genre_scores_gemma":[0.9667547,0.0000060513335,0.0326367,0.0000787451,0.00001862155,0.0000048928027,0.0000015845286,0.0000040633827,0.0004946453],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992903,0.000050067356,0.00014560368,0.00028135444,0.00005912871,0.00017353414],"domain_scores_gemma":[0.9995064,0.00018522708,0.000031747564,0.00020811839,0.000028409979,0.00004009646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012245156,0.000076098084,0.00012873032,0.0001265656,0.000076348595,0.000099410456,0.0001977283,0.000047791058,0.00001169454],"category_scores_gemma":[0.00004768426,0.00007576233,0.000020857296,0.00025974875,0.000044424676,0.00019667686,0.000082856066,0.00018738293,0.000019972094],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000021545334,0.00013660752,0.5626112,0.000016671995,0.000040711453,0.00029442695,0.0011899272,0.000047547237,0.023375276,0.08326169,0.0010314252,0.32799235],"study_design_scores_gemma":[0.0008487358,0.00010895177,0.8730577,0.000086336964,0.000012423405,0.00015656331,0.0009920831,0.007231456,0.09842191,0.012533086,0.0060990113,0.0004517268],"about_ca_topic_score_codex":0.00006489162,"about_ca_topic_score_gemma":0.0001172099,"teacher_disagreement_score":0.44280925,"about_ca_system_score_codex":0.000033422453,"about_ca_system_score_gemma":0.00006542942,"threshold_uncertainty_score":0.3089498},"labels":[],"label_agreement":null},{"id":"W3177022022","doi":"10.1109/i2mtc50364.2021.9460029","title":"An Optimized Tongue Drive System for Disabled Persons","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Disabled people; Computer science; Human–computer interaction; Assistive technology; Tongue; Multimedia; Psychology; Applied psychology; Medicine","score_opus":0.016401010298635423,"score_gpt":0.2651583955306306,"score_spread":0.24875738523199517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177022022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009924748,0.000018884279,0.980066,0.0019166995,0.0002659647,0.0001197052,0.0000057232573,0.0009267032,0.006755553],"genre_scores_gemma":[0.7017226,5.5191975e-7,0.29717568,0.000089878085,0.000023543214,0.000034850378,0.0000049414175,0.0000053349645,0.00094258145],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991323,0.00004350474,0.0001178628,0.00039055326,0.00008248351,0.00023331356],"domain_scores_gemma":[0.99911344,0.0000900691,0.000035951827,0.00055282726,0.00012255456,0.00008513306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120127384,0.000093346636,0.00017229708,0.000043027976,0.00013277307,0.000116159485,0.0005028094,0.00006934154,0.000019274881],"category_scores_gemma":[0.000051050472,0.00007979118,0.0000735473,0.00020668535,0.000032591724,0.00016549967,0.000083346924,0.0000655741,0.00001967714],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007131421,0.00013352164,0.00032912748,0.000033774875,0.00003915396,0.000049122624,0.00024049277,0.00022892239,0.0091815535,0.9805704,0.0019122887,0.0072745443],"study_design_scores_gemma":[0.004041027,0.00044560683,0.0023183762,0.00010614951,0.000059299113,0.00024387402,0.0037400327,0.7923245,0.18494952,0.0047796313,0.006135999,0.0008560083],"about_ca_topic_score_codex":0.000011641809,"about_ca_topic_score_gemma":0.000014149538,"teacher_disagreement_score":0.97579074,"about_ca_system_score_codex":0.000040338295,"about_ca_system_score_gemma":0.00008011846,"threshold_uncertainty_score":0.32537898},"labels":[],"label_agreement":null},{"id":"W3178686803","doi":"","title":"Automatic segmentation of pupil using local histogram and standard deviation","year":2010,"lang":"en","type":"article","venue":"Figshare","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Histogram; Thresholding; Pupil; Artificial intelligence; Standard deviation; Robustness (evolution); Segmentation; Computer science; Computer vision; Pattern recognition (psychology); Balanced histogram thresholding; Image segmentation; Mathematics; Histogram matching; Statistics; Image (mathematics)","score_opus":0.024150393947113036,"score_gpt":0.2729825791201931,"score_spread":0.24883218517308003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178686803","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.51920646,0.00022701037,0.46825436,0.0003599027,0.00034310072,0.00047017605,0.009755759,0.00096311845,0.00042011766],"genre_scores_gemma":[0.9626816,1.6562369e-7,0.03678943,0.000016521066,0.000008101251,0.0000086357995,0.0004888938,0.0000032346209,0.0000034126201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958265,0.000012118356,0.0001016959,0.00012212373,0.00010087712,0.00008054234],"domain_scores_gemma":[0.99963385,0.00003534183,0.00009144186,0.00014310238,0.000074196265,0.000022057258],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004255103,0.000051113384,0.000075047996,0.00006344508,0.000046087043,0.000029548915,0.00014485724,0.000053289663,0.0012033733],"category_scores_gemma":[0.00012668286,0.000049288483,0.000014485094,0.00012789767,0.000014623906,0.00014762135,0.00006460401,0.00008598602,0.000017139086],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017802431,0.000030689185,0.00069111644,0.00016853631,0.000011265451,0.0000048914526,0.0002598556,0.00003267212,0.01542832,0.0022711079,0.0037926151,0.97730714],"study_design_scores_gemma":[0.0010574638,0.0003413416,0.09145772,0.0011080565,0.000029386032,0.000093388255,0.00011047351,0.75430053,0.13732906,0.004693555,0.008953854,0.0005251812],"about_ca_topic_score_codex":0.000005631318,"about_ca_topic_score_gemma":0.000012096687,"teacher_disagreement_score":0.97678196,"about_ca_system_score_codex":0.000019886622,"about_ca_system_score_gemma":0.000036863803,"threshold_uncertainty_score":0.99970967},"labels":[],"label_agreement":null},{"id":"W3179621883","doi":"10.1109/jsen.2021.3095423","title":"A Lightweight Flexible Wireless Electrooculogram Monitoring System With Printed Gold Electrodes","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Printed circuit board; Electrooculography; SIGNAL (programming language); Computer science; Bluetooth; Bandwidth (computing); Wireless; Electrode; Computer hardware; Electronic engineering; Electrical engineering; Materials science; Engineering; Telecommunications; Eye movement; Artificial intelligence; Physics","score_opus":0.012430198663446112,"score_gpt":0.23694779770689847,"score_spread":0.22451759904345236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179621883","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7440539,0.00032955967,0.2526603,0.0006503065,0.0006879308,0.0000611553,4.970688e-7,0.0006980104,0.0008583187],"genre_scores_gemma":[0.97718656,0.00006875311,0.021511396,0.000023534838,0.00039094998,0.000006333775,3.727976e-7,0.000025658263,0.0007864387],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9978368,0.00017623199,0.00034386103,0.00047495897,0.0004312908,0.0007368258],"domain_scores_gemma":[0.9985291,0.00007103607,0.00023138338,0.0005076557,0.00048224887,0.0001785857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029539556,0.00025623228,0.0003492657,0.0002458358,0.00029678654,0.0004555211,0.00071086304,0.00013424963,0.0000033141673],"category_scores_gemma":[0.000023641542,0.00020066081,0.000120638164,0.00095897756,0.000063999105,0.00029223942,0.00006024204,0.00090117933,0.000030352614],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013472547,0.0005496373,0.02153713,0.0001792729,0.00088929833,0.010331713,0.0009979209,0.0014815846,0.8458333,0.050399233,0.0017492974,0.06591686],"study_design_scores_gemma":[0.0006428419,0.0003455487,0.0034312727,0.0004171707,0.000031792755,0.014106766,0.00025834367,0.0040185954,0.97486055,0.00026957758,0.0012429028,0.00037466423],"about_ca_topic_score_codex":0.000007679094,"about_ca_topic_score_gemma":0.000005784606,"teacher_disagreement_score":0.23313263,"about_ca_system_score_codex":0.0002312175,"about_ca_system_score_gemma":0.00024290511,"threshold_uncertainty_score":0.81827104},"labels":[],"label_agreement":null},{"id":"W3181760676","doi":"10.16910/jemr.14.2.5","title":"Detecting performance difficulty of learners in colonoscopy: Evidence from eye-tracking","year":2021,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; University of Alberta","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; University of Alberta","keywords":"Computer science; Artificial intelligence; Eye tracking; Gaze; Deep learning; Eye movement; Reinforcement learning; Machine learning; Health care; Convolutional neural network; Adversarial system; Computer vision","score_opus":0.09634103197622662,"score_gpt":0.40054298066965827,"score_spread":0.30420194869343165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181760676","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9906956,0.0007932026,0.006515951,0.001662594,0.00016150961,0.00006860763,5.496593e-7,0.00001399638,0.00008798445],"genre_scores_gemma":[0.9895729,0.00034689033,0.009894823,0.000034864912,0.00005395544,0.0000031164054,2.3675997e-7,0.0000064047695,0.00008684817],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9973586,0.00034107963,0.0006043217,0.00027689096,0.0009943089,0.00042476476],"domain_scores_gemma":[0.9977422,0.000647933,0.0003265474,0.00037082174,0.0008369049,0.000075617696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029347888,0.00010333969,0.00029789677,0.00043282608,0.00011675391,0.000109180735,0.0010321634,0.00008362934,0.000022580885],"category_scores_gemma":[0.0013323388,0.00008950543,0.000081569415,0.0011070998,0.000119816454,0.0005164752,0.00046413104,0.0010607847,0.000005646895],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006849023,0.00023956168,0.5111081,0.000055836605,0.00004231552,0.00026446074,0.0012670053,0.0010226346,0.4457384,0.00024361779,0.00007043426,0.039879106],"study_design_scores_gemma":[0.00042925755,0.00058901426,0.5810374,0.0010655978,0.000004024124,0.0000044492986,0.0008326545,0.00629805,0.4090017,0.0005727453,0.00007131978,0.00009376673],"about_ca_topic_score_codex":0.00013139231,"about_ca_topic_score_gemma":0.00008060714,"teacher_disagreement_score":0.06992928,"about_ca_system_score_codex":0.00024068047,"about_ca_system_score_gemma":0.0003258648,"threshold_uncertainty_score":0.46086404},"labels":[],"label_agreement":null},{"id":"W3186035600","doi":"10.1109/tim.2021.3094817","title":"Reading Line Classification Using Eye-Trackers","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Eye tracking; Artificial intelligence; Preprocessor; BitTorrent tracker; Gaze; Kalman filter; Computer vision; Context (archaeology); Pattern recognition (psychology)","score_opus":0.08851997115834406,"score_gpt":0.3057332831984363,"score_spread":0.21721331204009223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186035600","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100808285,0.000020342892,0.89687747,0.0013054321,0.0005041094,0.000091710695,0.0000024393978,0.00015128973,0.00023894446],"genre_scores_gemma":[0.97366583,0.000050093782,0.025953894,0.00022310116,0.000013084795,0.000017521586,0.0000015648502,0.0000069079206,0.00006799274],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989045,0.00006509835,0.000212063,0.00032959357,0.0003341962,0.00015452036],"domain_scores_gemma":[0.99943095,0.000013810743,0.00007372419,0.00023182845,0.00018575006,0.00006394109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023280764,0.000112656526,0.00010358661,0.00014180329,0.0002585689,0.00009644305,0.00010307527,0.000059947863,0.0000145561],"category_scores_gemma":[0.000006931074,0.00011824044,0.000041726922,0.00030708435,0.000040134702,0.0002462648,0.0000016875314,0.00014286235,0.000011240609],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016530705,0.00032037206,0.00031632342,0.000029540293,0.00008128249,0.000008850815,0.0004679936,0.0014772447,0.4853856,0.0071661486,0.000033805703,0.5046963],"study_design_scores_gemma":[0.0012669403,0.00015331138,0.0067044077,0.0001326087,0.00006191403,0.000047472786,0.00081365224,0.06430524,0.9247817,0.0006366568,0.0007741699,0.00032194972],"about_ca_topic_score_codex":0.000012999568,"about_ca_topic_score_gemma":0.000015695898,"teacher_disagreement_score":0.8728576,"about_ca_system_score_codex":0.00019242066,"about_ca_system_score_gemma":0.000090995,"threshold_uncertainty_score":0.48217052},"labels":[],"label_agreement":null},{"id":"W3188616123","doi":"10.18260/1-2--37169","title":"Eye-Track Modeling of Problem-Solving in Virtual Manufacturing Environments","year":2024,"lang":"en","type":"article","venue":"2021 ASEE Virtual Annual Conference Content Access Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Institute of Standards and Technology; University of Alberta; National Science Foundation","keywords":"Computer science; Factory (object-oriented programming); Virtual reality; Human–computer interaction; Workstation; Headset; Virtual machine; Simulation","score_opus":0.045222023468674835,"score_gpt":0.27619835094733336,"score_spread":0.23097632747865854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188616123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8671734,0.00025044702,0.12937556,0.00089317275,0.0003338411,0.00035831452,0.000023501136,0.00028655934,0.0013052226],"genre_scores_gemma":[0.9976756,0.0001304843,0.0011925062,0.00006498251,0.00006703209,0.00006308948,0.0000073270826,0.000029958066,0.00076902186],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967785,0.000028275092,0.0008043405,0.0011275699,0.0005830986,0.0006782295],"domain_scores_gemma":[0.99909544,0.000082928775,0.00019569648,0.00027882334,0.00021104155,0.00013609453],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005401706,0.00042460204,0.0005256142,0.00065618474,0.00011581771,0.0008079865,0.0020737716,0.00023073539,0.000055228847],"category_scores_gemma":[0.00011776969,0.0003884123,0.00013495177,0.00058556994,0.00018346282,0.0035474824,0.0012062573,0.0007310067,0.000053224747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021324135,0.0010732902,0.016996842,0.0008287522,0.00040631386,0.00031221876,0.019482944,0.0071984082,0.111812316,0.4332525,0.0006944581,0.4077287],"study_design_scores_gemma":[0.001678949,0.0012648698,0.014183927,0.0034524042,0.00007613416,0.000041997428,0.011316136,0.8281265,0.12659171,0.010002138,0.0013633191,0.0019018989],"about_ca_topic_score_codex":0.00012889261,"about_ca_topic_score_gemma":0.000014344235,"teacher_disagreement_score":0.8209281,"about_ca_system_score_codex":0.00013100536,"about_ca_system_score_gemma":0.00012933915,"threshold_uncertainty_score":0.99985677},"labels":[],"label_agreement":null},{"id":"W3194767911","doi":"","title":"A Study of Applying Gaze-Tracking Control to Motorized Assistive Devices","year":2010,"lang":"en","type":"article","venue":"CMBES Proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Gaze; Eye tracking; Tracking (education); Computer science; Human–computer interaction; Interface (matter); Control (management); Assistive technology; Computer vision; Physical medicine and rehabilitation; Artificial intelligence; Psychology; Medicine","score_opus":0.017023862830345814,"score_gpt":0.2738156931997709,"score_spread":0.2567918303694251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194767911","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9794203,0.00001441417,0.017593445,0.0002599166,0.00033785054,0.0007640208,0.0000015833995,0.0004620396,0.0011464019],"genre_scores_gemma":[0.98431814,4.5039863e-7,0.0152020715,0.00010933275,0.00008539085,0.00023419796,1.8254623e-7,0.000015931066,0.000034327117],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99835086,0.000010801292,0.00034865958,0.00058052014,0.00033550782,0.00037364668],"domain_scores_gemma":[0.9987344,0.0001074781,0.00023582464,0.00027078987,0.0005350194,0.00011644861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004761456,0.00021259727,0.00038407324,0.00028887662,0.00018660589,0.00015697368,0.0011680686,0.00011555556,0.000004885741],"category_scores_gemma":[0.0003801756,0.00019228502,0.000059718855,0.0007029445,0.0000735514,0.0003957636,0.0002546078,0.00040872674,0.000028566154],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055858636,0.0008524582,0.34403288,0.000067991794,0.00012438613,0.000019547178,0.0040764404,0.0000051707316,0.5787973,0.027567495,0.00026518226,0.044135295],"study_design_scores_gemma":[0.003741877,0.0017000533,0.9305349,0.00017566694,0.00009485865,0.00005551491,0.004215905,0.0023488007,0.05150756,0.00082832284,0.003972804,0.0008237116],"about_ca_topic_score_codex":0.00003992359,"about_ca_topic_score_gemma":0.000022874938,"teacher_disagreement_score":0.586502,"about_ca_system_score_codex":0.000024001069,"about_ca_system_score_gemma":0.000033539356,"threshold_uncertainty_score":0.78411555},"labels":[],"label_agreement":null},{"id":"W3195029869","doi":"10.11159/mvml21.01","title":"The Use of Gaze in Human and Machine Vision","year":2021,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gaze; Computer science; Artificial intelligence; Computer vision; Human–computer interaction; Machine vision","score_opus":0.014023636716730405,"score_gpt":0.2232482857298358,"score_spread":0.2092246490131054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195029869","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927807,0.0013128679,0.0044687176,0.00076706125,0.00043960253,0.00012766162,6.09168e-7,0.000052288804,0.000050481973],"genre_scores_gemma":[0.99868125,0.00004688426,0.0011178514,0.000016916301,0.000013484775,0.0000035982446,2.1285233e-8,0.0000029702483,0.00011705238],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990903,0.000008158366,0.00020366284,0.00028824116,0.0002107735,0.00019883491],"domain_scores_gemma":[0.99944335,0.00016125494,0.00009105492,0.00013152912,0.0001263375,0.000046489542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036451328,0.000095348674,0.0001785639,0.00016933614,0.00017161584,0.00027324268,0.0004291058,0.000025894335,3.372896e-8],"category_scores_gemma":[0.00006924879,0.000057551213,0.000017925764,0.0009847666,0.0002206706,0.00017747101,0.00033979138,0.00017512277,2.6046045e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008082942,0.000071912655,0.014855693,0.0001417842,0.000015422718,0.0000045012753,0.00011637637,0.0011692626,0.035946622,0.8890137,0.00017577807,0.05848084],"study_design_scores_gemma":[0.00019456424,0.00012245256,0.04161813,0.00034415442,0.0000030246933,0.000047047975,0.0000034100992,0.94795084,0.0084118685,0.00019476442,0.0010074145,0.00010230142],"about_ca_topic_score_codex":0.000019626039,"about_ca_topic_score_gemma":0.0000036035206,"teacher_disagreement_score":0.9467816,"about_ca_system_score_codex":0.000013562189,"about_ca_system_score_gemma":0.000016110267,"threshold_uncertainty_score":0.26348856},"labels":[],"label_agreement":null},{"id":"W3196850713","doi":"10.1167/jov.21.9.2580","title":"How far away is your phone in this picture? Determining object distance and size in a 2D scene","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Task (project management); Set (abstract data type); Object (grammar); Phone; Block (permutation group theory); Block size; Computer science; Display size; Computer vision; Artificial intelligence; Mathematics; Statistics; Combinatorics; Engineering","score_opus":0.01455712227089958,"score_gpt":0.27306899706187343,"score_spread":0.2585118747909739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196850713","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95390767,0.0017800312,0.039035875,0.004937204,0.00021010096,0.000026474037,7.200203e-7,0.000015661786,0.00008626373],"genre_scores_gemma":[0.97261393,0.00022546569,0.026788179,0.00021765071,0.000029561246,4.3187228e-7,6.661616e-8,0.0000047014396,0.00012001903],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990941,0.00006721389,0.00026058085,0.00019790395,0.00020573106,0.00017441735],"domain_scores_gemma":[0.99935883,0.00012383948,0.00020347022,0.00018014769,0.00008849501,0.00004520537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034863898,0.00009569958,0.00024499936,0.00013473973,0.00003997814,0.00013064843,0.00027959363,0.0000793966,0.0000072382772],"category_scores_gemma":[0.00028059987,0.00007759891,0.000046639863,0.00043229436,0.000032270025,0.000323435,0.00012568136,0.000368692,0.0000011188545],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067235735,0.00036970724,0.11560291,0.00005384568,0.00002793801,0.004061139,0.004585614,0.00004667148,0.11923829,0.0004525558,0.0008191963,0.7546749],"study_design_scores_gemma":[0.002082223,0.00041572226,0.9508193,0.00087980623,0.000009197385,0.00085562473,0.0004483105,0.010416015,0.02773829,0.0026772502,0.0033957812,0.00026247956],"about_ca_topic_score_codex":0.00000440207,"about_ca_topic_score_gemma":0.000030817813,"teacher_disagreement_score":0.8352164,"about_ca_system_score_codex":0.000058298967,"about_ca_system_score_gemma":0.00006495255,"threshold_uncertainty_score":0.31643918},"labels":[],"label_agreement":null},{"id":"W3197067728","doi":"10.1167/jov.21.9.2990","title":"No evidence for gender and cultural differences in eye movements – a meta-analysis","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Warrant; Ethnic group; Meta-analysis; Psychology; Social psychology; Cognitive psychology; Political science; Medicine; Law","score_opus":0.1829800327622182,"score_gpt":0.39494916475293945,"score_spread":0.21196913199072126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197067728","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7864121,0.0063050403,0.20500875,0.0019599285,0.00017121868,0.00006379071,0.0000016488702,0.00001395871,0.00006357423],"genre_scores_gemma":[0.9671687,0.00012645127,0.03242888,0.00010768993,0.0000147217315,0.0000016837973,1.6576377e-7,0.0000013710095,0.00015036196],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991865,0.000066263565,0.0002767491,0.0001612401,0.00020477075,0.00010446888],"domain_scores_gemma":[0.9991589,0.00016418038,0.00020636803,0.00013450204,0.00030115436,0.000034865134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041599828,0.00006927473,0.0003841035,0.00017123183,0.000039800576,0.00009800084,0.00026954405,0.00003714829,0.000013398907],"category_scores_gemma":[0.00022169988,0.000042526928,0.00027577614,0.00037782057,0.0000212682,0.00037643182,0.00009589402,0.000104574116,0.0000010883517],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038745426,0.0021941897,0.569385,0.00032883376,0.10571315,0.0011303158,0.0046468372,0.0007149628,0.20060012,0.02339741,0.0035166054,0.08798514],"study_design_scores_gemma":[0.00059232267,0.0004888489,0.97061414,0.00004699319,0.006441825,0.000029299705,0.00013567101,0.014471938,0.0019157967,0.004764462,0.00033240323,0.00016632462],"about_ca_topic_score_codex":0.0000041232247,"about_ca_topic_score_gemma":0.000005732476,"teacher_disagreement_score":0.40122914,"about_ca_system_score_codex":0.00001753144,"about_ca_system_score_gemma":0.00002607265,"threshold_uncertainty_score":0.17341977},"labels":[],"label_agreement":null},{"id":"W3197415979","doi":"","title":"‘I get your point’: Gaze and finger pointing cues of other people activate different processing channels","year":2019,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Point (geometry); Communication; Computer science; Channel (broadcasting); Psychology; Artificial intelligence; Mathematics; Telecommunications","score_opus":0.014404152627862259,"score_gpt":0.24252244190425126,"score_spread":0.228118289276389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197415979","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.915332,0.000060455688,0.081820026,0.00097281294,0.00010460473,0.000093629096,5.8348786e-7,0.00021940353,0.0013964344],"genre_scores_gemma":[0.9918462,0.0000032079556,0.00723802,0.00010651052,0.0000205194,0.000004130907,2.819116e-7,0.000010793092,0.0007703509],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990952,0.000024203016,0.000172695,0.0003320524,0.00012962088,0.00024621835],"domain_scores_gemma":[0.9994593,0.00005139378,0.00012830566,0.00027040232,0.000056086392,0.000034509267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013765364,0.00013497508,0.00023536793,0.000118649376,0.000053681168,0.00007883606,0.00032267955,0.000064933054,0.000034211113],"category_scores_gemma":[0.000025143232,0.000100267556,0.00003595568,0.00016790416,0.000037928527,0.00026560697,0.00023764047,0.00012102599,0.000014308302],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025848247,0.00039927225,0.5315404,0.0004976797,0.00008389302,0.0000055822075,0.0071825837,0.00005847532,0.19664472,0.038506042,0.00027652312,0.22477895],"study_design_scores_gemma":[0.0015137834,0.00044337276,0.45611486,0.0006302195,0.0000214501,0.00005249685,0.0008790257,0.078140855,0.4481276,0.012619464,0.0005902209,0.0008666654],"about_ca_topic_score_codex":0.000046909387,"about_ca_topic_score_gemma":0.0000070335846,"teacher_disagreement_score":0.25148287,"about_ca_system_score_codex":0.0000110900955,"about_ca_system_score_gemma":0.0000139611175,"threshold_uncertainty_score":0.40887925},"labels":[],"label_agreement":null},{"id":"W3197738895","doi":"10.1167/jov.21.9.1898","title":"Using electrooculography to track closed-eye movements.","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Saccadic masking; Electrooculography; Eye movement; Saccade; Computer vision; Computer science; Artificial intelligence; Calibration; Eye tracking; Saccadic suppression of image displacement; SIGNAL (programming language); Vergence (optics); Noise (video); Artifact (error); Mathematics; Statistics","score_opus":0.022086373641699766,"score_gpt":0.3224395378025046,"score_spread":0.3003531641608049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197738895","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64641666,0.00030013916,0.3511031,0.0016263917,0.0003020405,0.00002364422,3.0111264e-7,0.000029390636,0.00019832246],"genre_scores_gemma":[0.8731949,0.00002431182,0.12622449,0.0004507436,0.0000628699,2.0600899e-7,1.3763444e-7,0.0000048862894,0.00003746876],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99902636,0.000054354823,0.000291037,0.00015865371,0.00027939441,0.0001902264],"domain_scores_gemma":[0.9991992,0.000027152442,0.00018493038,0.00022805622,0.00027813026,0.00008253464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029949337,0.0000807158,0.0001717202,0.0002697085,0.00006964659,0.00008441659,0.0004340885,0.000053965054,0.000008713792],"category_scores_gemma":[0.00006282101,0.00006773076,0.00012875986,0.00069303607,0.000015309452,0.000251625,0.00010610113,0.00021734292,0.0000075122352],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021822518,0.00038877476,0.00616652,0.000010504866,0.000071002745,0.00048421684,0.00019750997,0.00034297432,0.80103785,0.0057619177,0.001764601,0.18375233],"study_design_scores_gemma":[0.0020736866,0.0030629684,0.54279846,0.00061371375,0.000055124332,0.00088796293,0.00018293694,0.008551987,0.3723373,0.027312882,0.04149114,0.00063183723],"about_ca_topic_score_codex":0.00000169216,"about_ca_topic_score_gemma":0.0000012123057,"teacher_disagreement_score":0.53663194,"about_ca_system_score_codex":0.000043440214,"about_ca_system_score_gemma":0.00007409311,"threshold_uncertainty_score":0.27619803},"labels":[],"label_agreement":null},{"id":"W3198335583","doi":"10.1167/jov.21.9.2551","title":"Predicting cognitive performance using eye-movements, reaction time and difficulty level.","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Eye tracking; Eye movement; Cognition; Fixation (population genetics); Task (project management); Cognitive psychology; Computer science; Artificial intelligence; Psychology; Tracking (education)","score_opus":0.027240053757103037,"score_gpt":0.29528527928478193,"score_spread":0.2680452255276789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198335583","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711058,0.00013401726,0.02827868,0.00017967758,0.00017542217,0.000020471,0.0000012718262,0.000022326009,0.0000823355],"genre_scores_gemma":[0.99087214,0.000059054662,0.008882059,0.000040968025,0.00005972629,1.285399e-7,6.946986e-7,0.0000035349349,0.000081669554],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9993035,0.00004061031,0.00021894278,0.000128297,0.00019963272,0.00010901291],"domain_scores_gemma":[0.9992842,0.000052343643,0.0002607292,0.00007887444,0.00028412818,0.000039717805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000264186,0.000066445704,0.00012592923,0.00008667992,0.00012220374,0.00006019562,0.00011191766,0.000049123362,0.0000022002034],"category_scores_gemma":[0.0001056261,0.00005449135,0.000033006698,0.00016902956,0.000026311283,0.0004845096,0.00010833895,0.00020399937,0.0000033792699],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002268229,0.00014589276,0.025663173,0.000019573228,0.00004155914,0.00009600154,0.00023470935,0.000026211146,0.76854134,0.000048181857,0.000048211903,0.20511249],"study_design_scores_gemma":[0.0007114305,0.00036542217,0.8957705,0.00069151353,0.000022289278,0.0005164224,0.00010539121,0.07241226,0.029063644,0.00009640605,0.00013768782,0.00010703049],"about_ca_topic_score_codex":0.000002457953,"about_ca_topic_score_gemma":5.3206537e-7,"teacher_disagreement_score":0.87010735,"about_ca_system_score_codex":0.00004183983,"about_ca_system_score_gemma":0.000044353717,"threshold_uncertainty_score":0.22220927},"labels":[],"label_agreement":null},{"id":"W3198606375","doi":"10.1167/jov.21.9.2125","title":"Gaze behaviour: a window into quantifying task difficulty and performance using the Tower of London Task","year":2021,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Gaze; Saccade; Workspace; Task (project management); Fixation (population genetics); Cognitive psychology; Psychology; Cognition; Eye movement; Computer science; Computer vision; Artificial intelligence; Communication; Robot; Engineering; Population","score_opus":0.02662539947731933,"score_gpt":0.30185058333874726,"score_spread":0.2752251838614279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198606375","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95530945,0.000977694,0.042739816,0.00069304474,0.00023041942,0.000027193979,4.8206505e-7,0.000011383842,0.000010518598],"genre_scores_gemma":[0.9839306,0.00015549839,0.015831685,0.000033141514,0.00003297086,1.9461912e-7,1.753e-7,0.0000042780334,0.000011454417],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9990696,0.00006115992,0.00032819613,0.0001397841,0.00027035584,0.00013094238],"domain_scores_gemma":[0.99902165,0.000073556934,0.0003595645,0.0002365728,0.00026890993,0.00003975287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041776084,0.000087317596,0.00020082458,0.000098157085,0.00014104172,0.000071443996,0.00035021795,0.000060392693,0.0000029715961],"category_scores_gemma":[0.000060067916,0.00005457951,0.000073097624,0.00026988488,0.00008315003,0.0003098429,0.00020366744,0.00028771727,5.3962367e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041327527,0.00025381957,0.097134255,0.000046010762,0.000037822792,0.0001475445,0.001432178,0.00029312875,0.72688395,0.00048183385,0.00018546528,0.17306267],"study_design_scores_gemma":[0.000769406,0.0006156245,0.93143773,0.00068992836,0.00005054211,0.0015438402,0.00027492453,0.021342153,0.04248307,0.00029623866,0.00033389503,0.00016267202],"about_ca_topic_score_codex":0.000009820226,"about_ca_topic_score_gemma":0.0000038849025,"teacher_disagreement_score":0.83430344,"about_ca_system_score_codex":0.000030719493,"about_ca_system_score_gemma":0.00006293726,"threshold_uncertainty_score":0.22256877},"labels":[],"label_agreement":null},{"id":"W3199635927","doi":"10.2196/29610","title":"Adaptability of Assistive Mobility Devices and the Role of the Internet of Medical Things: Comprehensive Review","year":2021,"lang":"en","type":"review","venue":"JMIR Rehabilitation and Assistive Technologies","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Science and Innovation, South Africa","keywords":"Adaptability; Assistive device; Computer science; The Internet; Assistive technology; Internet of Things; Human–computer interaction; Multimedia; World Wide Web; Medicine","score_opus":0.02675035875715706,"score_gpt":0.32955358380213934,"score_spread":0.30280322504498225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199635927","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007211833,0.99306226,0.0009288053,0.003221514,0.00007380225,0.0016858237,0.000037333615,0.00017845204,0.00009082422],"genre_scores_gemma":[0.06547135,0.92997944,0.0040411623,0.000036503352,0.0000042846136,0.00044289173,0.0000052832634,0.000012453837,0.0000065957083],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9956349,0.0012332518,0.0014225976,0.00080092007,0.000695289,0.00021305525],"domain_scores_gemma":[0.98978055,0.0062483647,0.001829405,0.0013789333,0.00072779,0.000034963847],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0018391573,0.00042273846,0.0026170674,0.00019029154,0.00009548531,0.000028358865,0.0019880019,0.0005782995,0.000007397103],"category_scores_gemma":[0.0070718373,0.00022033716,0.0005733626,0.001121649,0.0064962986,0.0001565079,0.002200143,0.0008824462,4.5939822e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000073682577,0.00016013699,0.0014719885,0.021292616,0.00015910144,5.999735e-7,0.00018287232,3.9414648e-8,0.0000014480046,0.07490544,0.0000733766,0.901745],"study_design_scores_gemma":[0.0017273702,0.0010935985,0.10227763,0.23273101,0.0015362786,0.000109998306,0.010348492,0.0006713661,0.00028688365,0.024521248,0.62344193,0.0012541814],"about_ca_topic_score_codex":0.00008247681,"about_ca_topic_score_gemma":0.000013307086,"teacher_disagreement_score":0.9004908,"about_ca_system_score_codex":0.0000647064,"about_ca_system_score_gemma":0.00031894186,"threshold_uncertainty_score":0.9962075},"labels":[],"label_agreement":null},{"id":"W3199677408","doi":"10.32393/csme.2021.223","title":"Manual Wheelchair Stroke Time Estimation Using Hand-Mounted Sensor","year":2021,"lang":"en","type":"article","venue":"Progress in Canadian Mechanical Engineering. Volume 4","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Wheelchair; Computer science; Estimation; Stroke (engine); Manual wheelchair; Physical medicine and rehabilitation; Artificial intelligence; Medicine; Engineering; World Wide Web; Mechanical engineering","score_opus":0.008434859307545336,"score_gpt":0.24651433072081874,"score_spread":0.2380794714132734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199677408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2440318,0.0008122799,0.74880034,0.004189415,0.0008851232,0.00031639464,0.00005838498,0.0008018348,0.00010445452],"genre_scores_gemma":[0.8477161,0.0000018064393,0.15188861,0.00009218321,0.00004063148,0.000020667041,0.000012119079,0.00002370208,0.00020421509],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982087,0.00004175309,0.00028170992,0.00050007564,0.00022555735,0.0007422253],"domain_scores_gemma":[0.9989884,0.000040914943,0.00004961307,0.0004580359,0.00011046511,0.0003525854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002642595,0.00020449296,0.00026548968,0.0003625947,0.00009385936,0.00018768443,0.00049666926,0.00022823711,0.000047498135],"category_scores_gemma":[0.00020322695,0.00024274217,0.0000623751,0.0006275567,0.000048068447,0.0001993233,0.00013048419,0.0003838032,0.00007470851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003251579,0.0006584076,0.01836487,0.0004881425,0.0003875301,0.010225068,0.0007237573,0.11990187,0.039921045,0.2594015,0.0015432576,0.548352],"study_design_scores_gemma":[0.0002461309,0.00003503253,0.0022122993,0.00007344208,0.0000068741806,0.00011698009,0.0000055769215,0.99017376,0.0041531012,0.000220435,0.0024946446,0.0002617433],"about_ca_topic_score_codex":0.0033175007,"about_ca_topic_score_gemma":0.0059720557,"teacher_disagreement_score":0.87027186,"about_ca_system_score_codex":0.00047180319,"about_ca_system_score_gemma":0.00041500694,"threshold_uncertainty_score":0.9898738},"labels":[],"label_agreement":null},{"id":"W3206757400","doi":"10.1007/s40123-021-00407-5","title":"Correction to: Clinical Performance of Samfilcon A Contact Lenses in Intensive Digital Device Users: A Multicenter, Prospective Clinical Study","year":2021,"lang":"en","type":"erratum","venue":"Ophthalmology and Therapy","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bausch Health (Canada)","funders":"","keywords":"Medicine; Ophthalmology; Optometry; Medical physics","score_opus":0.06682225612530042,"score_gpt":0.37855472204098795,"score_spread":0.31173246591568754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206757400","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9752443,0.0009017813,0.00011959315,0.0001455342,0.02164413,0.00075211655,0.000011632948,0.00006758458,0.0011133421],"genre_scores_gemma":[0.99500483,0.00080898474,0.00025402455,0.00019632404,0.00020355138,0.00006461636,0.000018450626,0.000022382064,0.0034268193],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99677473,0.0005421209,0.0010010462,0.0011502523,0.00017447615,0.0003573982],"domain_scores_gemma":[0.99719375,0.0010113906,0.00045631634,0.00063276273,0.0006169636,0.00008881356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007637887,0.00036761726,0.0012155409,0.00026671917,0.00008014593,0.00007640763,0.0006489881,0.0006578164,0.000013053478],"category_scores_gemma":[0.0010238837,0.00032490678,0.00016423372,0.000470385,0.00029073108,0.00024007466,0.00035609794,0.0017910053,0.000010151697],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006142325,0.001670205,0.94669443,0.000018832816,0.00025744518,0.0005635498,0.00055616215,6.481792e-7,0.0000070483966,0.0000357005,0.0050746556,0.044507068],"study_design_scores_gemma":[0.002519997,0.011869037,0.9811123,0.00038715804,0.000018511932,0.0005685095,0.00067261886,0.0004620994,0.00004222983,0.000061589264,0.0019571544,0.00032877663],"about_ca_topic_score_codex":0.00009597145,"about_ca_topic_score_gemma":0.00004273525,"teacher_disagreement_score":0.04417829,"about_ca_system_score_codex":0.000043885386,"about_ca_system_score_gemma":0.00026815126,"threshold_uncertainty_score":0.9999203},"labels":[],"label_agreement":null},{"id":"W3207562721","doi":"10.48550/arxiv.1710.11319","title":"Learning Motion Predictors for Smart Wheelchair using Autoregressive\\n Sparse Gaussian Process","year":2017,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Joystick; Artificial intelligence; Wheelchair; Black box; Process (computing); Computer science; Motion control; Simulation; Motion capture; Engineering; Motion (physics); Computer vision; Control engineering; Robot","score_opus":0.10337288552838231,"score_gpt":0.23317649318065664,"score_spread":0.12980360765227433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207562721","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44507992,0.000047888687,0.55124354,0.00014708967,0.001576515,0.0007339539,0.000027328055,0.0005739825,0.000569769],"genre_scores_gemma":[0.9936136,0.00009835684,0.0025803316,0.000018705196,0.00029030401,0.000007672854,0.00003898074,0.00008396971,0.0032680694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940865,0.00034282112,0.0005993663,0.003356545,0.00025662276,0.0013581465],"domain_scores_gemma":[0.99386823,0.00018984993,0.0022393933,0.0023780356,0.0008951542,0.0004293572],"candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0008682422,0.0010387064,0.0010782093,0.0008643982,0.0026280384,0.0006122973,0.004392131,0.0013082242,0.000031755168],"category_scores_gemma":[0.0004972609,0.0012367747,0.00063125096,0.00054428657,0.0010794036,0.0012474972,0.0020645906,0.0020323128,0.000051347153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026963017,0.0006001569,0.17608745,0.0010484396,0.000659743,0.00076311495,0.0020334653,0.75582814,0.00028157968,0.0472647,0.00008340459,0.015080196],"study_design_scores_gemma":[0.0014815595,0.00040315549,0.020977756,0.0012396857,0.00042921188,0.000035119952,0.00038632634,0.9545802,0.00061785325,0.017954126,0.0006546267,0.001240406],"about_ca_topic_score_codex":0.0002331809,"about_ca_topic_score_gemma":0.00006819701,"teacher_disagreement_score":0.5486632,"about_ca_system_score_codex":0.00077144796,"about_ca_system_score_gemma":0.00083845085,"threshold_uncertainty_score":0.99998826},"labels":[],"label_agreement":null},{"id":"W3208502467","doi":"10.17605/osf.io/znvwb","title":"Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Gaze; Monocular; Eye tracking","score_opus":0.02015615444794315,"score_gpt":0.2439643866135396,"score_spread":0.22380823216559648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208502467","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65757567,0.0018718891,0.33824152,0.00015372151,0.001104983,0.00025523177,0.000014670102,0.00078079145,0.0000015445502],"genre_scores_gemma":[0.8573922,0.00010116921,0.14198549,0.00011650733,0.00027904843,0.000041230778,3.539353e-7,0.00008266524,0.0000013151898],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610597,0.00029565088,0.0006364668,0.0017782372,0.00041342032,0.00077026966],"domain_scores_gemma":[0.9969115,0.00008350923,0.00063543685,0.0015381338,0.00061141344,0.00022000137],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00070715364,0.0007194667,0.0007781956,0.00039095385,0.00045054802,0.0013307772,0.0010141948,0.00085213693,0.000009808185],"category_scores_gemma":[0.0003598214,0.000794758,0.00015359513,0.00080321403,0.0001628447,0.00057341467,0.001314695,0.0013891344,0.0000058066794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004508345,0.00010242284,0.008281041,0.0003807817,0.0001696089,0.00031151847,0.00009281939,0.0038884552,0.98584825,0.00078617997,0.000014317747,0.000120106764],"study_design_scores_gemma":[0.0012018398,0.00005658035,0.112678155,0.0020784547,0.00026685788,7.6257675e-7,0.000030486926,0.59766394,0.28313613,0.00000791835,0.00024284482,0.0026360252],"about_ca_topic_score_codex":0.000108397275,"about_ca_topic_score_gemma":0.0000063555185,"teacher_disagreement_score":0.7027121,"about_ca_system_score_codex":0.00039062373,"about_ca_system_score_gemma":0.0005617573,"threshold_uncertainty_score":0.9997059},"labels":[],"label_agreement":null},{"id":"W3211052139","doi":"10.1212/wnl.94.15_supplement.2184","title":"Saccadic Behaviour in an Eye-Tracking Task is Differentially Altered by Neurodegenerative Diseases (2184)","year":2020,"lang":"en","type":"article","venue":"Neurology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Western Hospital; Baycrest Hospital; University of Ottawa; St Joseph's Health Care; Sunnybrook Health Science Centre; Health Sciences Centre; London Health Sciences Centre; Queen's University","funders":"","keywords":"Antisaccade task; Saccadic masking; Saccade; Eye movement; Fixation (population genetics); Dementia; Disease; Neuroscience; Psychology; Frontotemporal dementia; Medicine; Cognition; Audiology; Abnormality; Peripheral; Physical medicine and rehabilitation; Internal medicine; Psychiatry; Population","score_opus":0.018658797190225593,"score_gpt":0.2672420732751526,"score_spread":0.248583276084927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211052139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96057665,0.00013371231,0.022092624,0.01618836,0.00037198316,0.00014673291,0.00003620665,0.00042733154,0.00002639406],"genre_scores_gemma":[0.98824066,0.000010509797,0.00034765052,0.011227849,0.00009806353,0.00002318768,0.000018240178,0.000023293918,0.00001055123],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99768555,0.00032019633,0.00030801966,0.0010163933,0.0001763503,0.0004934871],"domain_scores_gemma":[0.9990589,0.00006809267,0.00012294785,0.00049579627,0.000052294192,0.00020197625],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000049806276,0.00025355275,0.0003511989,0.0001348654,0.00010314614,0.000107515065,0.0011839968,0.00016460064,0.000027889082],"category_scores_gemma":[0.000059150872,0.00025541012,0.00006670463,0.0003692321,0.00013309435,0.00031297377,0.00031002442,0.0005600845,0.000037051566],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016274887,0.0007003224,0.81714,0.000016921767,0.00002474982,0.00054107833,0.0015721271,0.00013569785,0.14987013,0.004533357,0.004336233,0.020966666],"study_design_scores_gemma":[0.00094264804,0.0021407974,0.96456814,0.0000043352247,0.000024039206,0.00001792057,0.0000066006287,0.022321846,0.007672546,0.0006731711,0.0012455668,0.00038239107],"about_ca_topic_score_codex":0.00004636626,"about_ca_topic_score_gemma":0.00006109792,"teacher_disagreement_score":0.14742817,"about_ca_system_score_codex":0.000010501499,"about_ca_system_score_gemma":0.00004734684,"threshold_uncertainty_score":0.9999898},"labels":[],"label_agreement":null},{"id":"W3214045974","doi":"10.1109/biomdlore49470.2021.9594328","title":"Detection of Wheelchair Orientation in Human-Robot Interactions","year":2021,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wheelchair; Computer science; Artificial intelligence; Orientation (vector space); Robot; Mobile robot; Computer vision; Cluster analysis; Laser scanning; Object detection; Classifier (UML); Pattern recognition (psychology); Laser; Mathematics","score_opus":0.022408448239696007,"score_gpt":0.29781882757737316,"score_spread":0.27541037933767715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214045974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29871336,0.000008817498,0.6983065,0.00032959995,0.00017848573,0.000020424834,1.2472842e-7,0.00009444304,0.0023482542],"genre_scores_gemma":[0.9894904,0.0000013049765,0.010117197,0.00003163511,0.000006836892,0.000005090362,9.811895e-7,0.0000016999311,0.00034485522],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995609,0.000027102453,0.00013058202,0.00015231305,0.000057656805,0.00007147127],"domain_scores_gemma":[0.9996856,0.000027390564,0.000041825402,0.00018058215,0.0000544037,0.0000102277645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005876843,0.000036787915,0.00006318017,0.00012619032,0.00003183172,0.000014222185,0.000113516864,0.00002587835,0.000014810986],"category_scores_gemma":[0.00002926559,0.000037343754,0.000021526166,0.00043978015,0.000017603814,0.000168314,0.000052019586,0.00008263616,0.00000784589],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011577037,0.00013444874,0.0049488214,0.000005887424,0.000005880598,0.000011185363,0.00023566026,0.000107981454,0.82674015,0.07249625,0.000027530154,0.09528503],"study_design_scores_gemma":[0.00018513971,0.00004041226,0.105221234,0.000015660631,0.0000016437534,0.000017202965,0.00018191538,0.004298977,0.8844418,0.0050772503,0.00045598645,0.000062789135],"about_ca_topic_score_codex":0.00007314824,"about_ca_topic_score_gemma":0.0007387091,"teacher_disagreement_score":0.690777,"about_ca_system_score_codex":0.000028843499,"about_ca_system_score_gemma":0.000016492648,"threshold_uncertainty_score":0.15228342},"labels":[],"label_agreement":null},{"id":"W3215533246","doi":"10.3390/jimaging7120255","title":"Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation","year":2021,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Wheelchair; Computer vision; Artificial intelligence; Convolutional neural network; Usability; Obstacle; Histogram; Human–computer interaction; Image (mathematics)","score_opus":0.03298443671493393,"score_gpt":0.3719012958518052,"score_spread":0.33891685913687125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215533246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060062557,0.0010135194,0.9369274,0.0009972445,0.0007372061,0.0000768511,7.6356014e-7,0.00003528516,0.00014919533],"genre_scores_gemma":[0.7287604,0.0000054602197,0.27112997,0.000030194307,0.000056265362,0.000003091823,6.9510713e-7,0.0000055427067,0.00000837031],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858654,0.00030891248,0.00045140265,0.00016054015,0.00035043436,0.00014215431],"domain_scores_gemma":[0.99679893,0.00016945307,0.0006427246,0.00021037103,0.0021439113,0.000034614062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004076634,0.000083812854,0.00020778479,0.00019748225,0.000073107,0.000056988683,0.00025276464,0.000044770706,0.0000046663736],"category_scores_gemma":[0.00076129945,0.00008014783,0.00011721668,0.00031574283,0.00003115688,0.0004370863,0.000052867643,0.00017848055,7.3858223e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040699997,0.000031978154,0.0001314892,0.000011747248,0.000028240447,0.000003881689,0.00016940958,0.00042132032,0.2761106,0.0002906192,0.000023451898,0.7227732],"study_design_scores_gemma":[0.00085942954,0.00009851196,0.0032549617,0.00012638002,0.00011239891,0.00044968454,0.00016766162,0.38880947,0.59167206,0.013549892,0.0007980384,0.000101532714],"about_ca_topic_score_codex":0.0000041228086,"about_ca_topic_score_gemma":0.000001344446,"teacher_disagreement_score":0.7226717,"about_ca_system_score_codex":0.00018044484,"about_ca_system_score_gemma":0.0002960176,"threshold_uncertainty_score":0.32683337},"labels":[],"label_agreement":null},{"id":"W346301515","doi":"10.1093/oxfordhb/9780199539789.013.0018","title":"Neural control of three-dimensional gaze shifts","year":2011,"lang":"en","type":"book","venue":"Oxford University Press eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Gaze; Superior colliculus; Orientation (vector space); Reticular formation; Computer vision; Computer science; Horizontal plane; Head (geology); Artificial intelligence; Communication; Psychology; Neuroscience; Mathematics; Geometry; Geology; Nucleus","score_opus":0.024714912068834288,"score_gpt":0.19409487459360678,"score_spread":0.1693799625247725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W346301515","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005425876,0.000095830575,0.06397986,0.000037751422,0.0003587903,0.00029282717,0.00007693086,0.00042174026,0.9341937],"genre_scores_gemma":[0.09606297,0.000008246056,0.0039724014,0.0000782162,0.000055225897,9.677926e-7,0.000011996096,0.000030783212,0.8997792],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986003,0.00005558127,0.00020139656,0.0005344605,0.00028434215,0.000323921],"domain_scores_gemma":[0.99845576,0.00010779747,0.00035074202,0.0008119145,0.00017618643,0.00009761532],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000104541265,0.0003031823,0.00048364236,0.0002446352,0.00011488686,0.000019388352,0.001844505,0.00042439732,0.000009647652],"category_scores_gemma":[0.000008262054,0.00032249675,0.00022022337,0.000024193914,0.00035376605,0.00011678248,0.00067269936,0.0005177424,0.0000015605808],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007217335,0.000042022853,0.00006818175,0.000050016104,0.00016704695,0.0002155963,0.00007215491,0.000021473534,0.000034007662,0.97935534,0.0032670063,0.016634997],"study_design_scores_gemma":[0.0016287286,0.0003195643,0.00095931365,0.00019719181,0.00017685989,0.000022785009,0.000003377582,0.005425775,0.00031909096,0.0081145195,0.9822027,0.00063009874],"about_ca_topic_score_codex":0.00005490816,"about_ca_topic_score_gemma":0.000023849028,"teacher_disagreement_score":0.97893566,"about_ca_system_score_codex":0.000085605716,"about_ca_system_score_gemma":0.00022882142,"threshold_uncertainty_score":0.9999227},"labels":[],"label_agreement":null},{"id":"W36713471","doi":"10.4018/978-1-60960-165-2.ch013","title":"POMDP Models for Assistive Technology","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Partially observable Markov decision process; Computer science; Human–computer interaction; Process (computing); Task (project management); Wheelchair; Implementation; Population; Artificial intelligence; Machine learning; Markov model; Engineering; Systems engineering; Markov chain; Software engineering","score_opus":0.035549851104926485,"score_gpt":0.2508645986625059,"score_spread":0.2153147475575794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W36713471","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006800884,0.00019907861,0.38145256,0.00022414862,0.00038277952,0.00032587355,0.00009470368,0.001024548,0.6162895],"genre_scores_gemma":[0.63721365,0.000013027224,0.1408473,0.00084496976,0.00030650356,0.00026669205,0.000009527623,0.00015637469,0.22034198],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99763286,0.000010111661,0.00041521172,0.0010950005,0.00024212495,0.00060466304],"domain_scores_gemma":[0.9978645,0.000047715504,0.00035769434,0.001286187,0.0003296409,0.00011425247],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012585602,0.0005649871,0.0006682587,0.0003045946,0.0001692484,0.0000717295,0.0020688705,0.0012547753,0.0000064244623],"category_scores_gemma":[0.000026495427,0.0005577712,0.00029439092,0.00005442444,0.00033612305,0.00009126323,0.0006433946,0.00048513967,0.00014842357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011374205,0.000013398822,0.000005732936,0.0000151515815,0.00009870332,0.000042910142,0.000011782744,0.0000019534393,0.0000080202235,0.9346381,0.0016797032,0.063473195],"study_design_scores_gemma":[0.00031343423,0.00026394005,0.000021317754,0.000116880066,0.00005398168,0.00008034525,0.0000024710987,0.0004512485,0.00018395975,0.9734689,0.02450195,0.0005415563],"about_ca_topic_score_codex":0.000018722667,"about_ca_topic_score_gemma":0.000022483197,"teacher_disagreement_score":0.63720685,"about_ca_system_score_codex":0.00023688347,"about_ca_system_score_gemma":0.00024805317,"threshold_uncertainty_score":0.9996874},"labels":[],"label_agreement":null},{"id":"W37487391","doi":"10.5755/j01.itc.34.3.12014","title":"EXTENDING THE LIMITS FOR GAZE POINTING THROUGH THE USE OF SPEECH","year":2005,"lang":"en","type":"article","venue":"Information Technology And Control","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Kansainvälisen Liikkuvuuden ja Yhteistyön Keskus","keywords":"Gaze; Computer science; Human–computer interaction; Task (project management); Eye tracking; BitTorrent tracker; Degree (music); Interface (matter); Limit (mathematics); Multimodal interaction; User interface; Artificial intelligence; Computer vision; Speech recognition; Mathematics; Acoustics; Engineering","score_opus":0.025457624654833283,"score_gpt":0.2522100676138208,"score_spread":0.22675244295898753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W37487391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022248246,0.00019220634,0.92513275,0.051569644,0.00007157649,0.0003129637,0.000004383989,0.0002799854,0.0001882673],"genre_scores_gemma":[0.9701496,0.000024295658,0.028562987,0.0011595403,0.00002136301,0.000052740652,6.9964267e-7,0.0000021487776,0.000026655898],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993708,0.000017274573,0.00027172308,0.00008784558,0.000074659794,0.00017767446],"domain_scores_gemma":[0.99900264,0.00031924664,0.00021965105,0.0003339691,0.00011662722,0.000007847887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025429542,0.00008087117,0.00011902542,0.00014812291,0.00027396376,0.00007164629,0.0005101409,0.000127087,0.0000012283128],"category_scores_gemma":[0.00032082337,0.000046715406,0.000037207035,0.0003214805,0.00022073182,0.0010263082,0.00007264545,0.00017905551,0.000007495739],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004707075,0.000004326681,0.00026588386,0.00000397877,0.000013219249,9.794649e-8,0.00017239146,0.00005652778,0.00016080169,0.45300403,0.0001873672,0.54612666],"study_design_scores_gemma":[0.0026484847,0.00029084468,0.0053733056,0.00006921002,0.000054535994,0.00019585829,0.0007252388,0.18891375,0.031835366,0.08350449,0.6860752,0.00031371415],"about_ca_topic_score_codex":0.000004261924,"about_ca_topic_score_gemma":0.0000035445028,"teacher_disagreement_score":0.9479013,"about_ca_system_score_codex":0.000009538325,"about_ca_system_score_gemma":0.000014912445,"threshold_uncertainty_score":0.21071354},"labels":[],"label_agreement":null},{"id":"W4200093252","doi":"10.1109/embc46164.2021.9630471","title":"A Multimodal Direct Gaze Interface for Wheelchairs and Teleoperated Robots","year":2021,"lang":"en","type":"article","venue":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Teleoperation; Wheelchair; Interface (matter); Robot; Input device; User interface; Telerobotics","score_opus":0.03395862711411058,"score_gpt":0.30393519276079706,"score_spread":0.2699765656466865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200093252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32530415,0.00064107013,0.6575069,0.013161709,0.0026348354,0.00024362122,0.000065381995,0.00012653945,0.00031582845],"genre_scores_gemma":[0.9680368,0.00027118708,0.030479394,0.00015834508,0.00012610214,0.000031642427,0.000021330308,0.000011249316,0.00086399843],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871606,0.00006188716,0.00036066968,0.00045218616,0.00013697703,0.00027221124],"domain_scores_gemma":[0.9986548,0.00032206342,0.00011986861,0.00032882113,0.0005250913,0.000049390947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039444878,0.00020697835,0.00037649632,0.0001141889,0.00005372698,0.000031316922,0.000832148,0.0001926293,0.000027546688],"category_scores_gemma":[0.00064099545,0.00015806279,0.00011083537,0.00041286743,0.00027390805,0.00015368583,0.0002734966,0.00037827718,0.0000018796479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009921465,0.00054001453,0.026223248,0.0003210074,0.0013822335,0.00001569574,0.013559763,0.041209172,0.80799377,0.06798916,0.022656752,0.018009946],"study_design_scores_gemma":[0.0066242847,0.0006820422,0.036131322,0.0022104464,0.000099194556,0.00018680608,0.0021974295,0.8448556,0.05951442,0.0034997193,0.04260267,0.0013960551],"about_ca_topic_score_codex":0.00009993141,"about_ca_topic_score_gemma":0.00010338683,"teacher_disagreement_score":0.80364645,"about_ca_system_score_codex":0.000054014727,"about_ca_system_score_gemma":0.00008941354,"threshold_uncertainty_score":0.64456135},"labels":[],"label_agreement":null},{"id":"W4200171428","doi":"10.1145/3498367","title":"Eyelid gestures for people with motor impairments","year":2021,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gesture; Computer science; Eyelid; Gaze; Set (abstract data type); Eye tracking; Human–computer interaction; Computer vision; Psychology; Medicine","score_opus":0.027468888792387416,"score_gpt":0.2889893629671432,"score_spread":0.2615204741747558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200171428","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4002428,0.0027072185,0.07898288,0.51331437,0.0003290123,0.00087368896,0.000044736757,0.00046855144,0.0030367314],"genre_scores_gemma":[0.6257501,0.000024881769,0.37396002,0.00009002434,0.0000028572674,0.000037533846,0.0000019647487,0.000002839099,0.0001297505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99958724,0.00005178693,0.00009377504,0.00010966595,0.00006852872,0.000089018264],"domain_scores_gemma":[0.9802343,0.00023285528,0.000079513695,0.019279217,0.00016074964,0.000013313834],"candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.00009853202,0.00004901681,0.000088869274,0.000025109552,0.0001942562,0.000022442926,0.018288245,0.000026875894,0.0000010180338],"category_scores_gemma":[0.0014578607,0.000032960535,0.000048812348,0.00025347684,0.00009721308,0.000056026525,0.009634529,0.000085971296,0.0000023791038],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053428055,0.0029089565,0.18902501,0.00019746539,0.0005820216,0.000003516234,0.005601093,0.00022898494,0.081329204,0.5656042,0.11098582,0.04348029],"study_design_scores_gemma":[0.0011802805,0.0002945316,0.61436,0.00017617528,0.00006869626,0.00007121466,0.00037806065,0.0041710623,0.056533366,0.29334772,0.029099466,0.00031948657],"about_ca_topic_score_codex":0.000013644564,"about_ca_topic_score_gemma":0.00006603168,"teacher_disagreement_score":0.51322436,"about_ca_system_score_codex":0.00001301154,"about_ca_system_score_gemma":0.00006385159,"threshold_uncertainty_score":0.99837536},"labels":[],"label_agreement":null},{"id":"W4200343312","doi":"10.1016/j.dss.2021.113713","title":"A machine learning investigation of factors that contribute to predicting cognitive performance: Difficulty level, reaction time and eye-movements","year":2021,"lang":"en","type":"article","venue":"Decision Support Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Russian Science Foundation; Russian Foundation for Basic Research","keywords":"Eye tracking; Task (project management); Computer science; Eye movement; Cognition; Artificial intelligence; Pupil size; Machine learning; Fixation (population genetics); Pupil; Cognitive psychology; Psychology; Engineering","score_opus":0.03898795988675024,"score_gpt":0.2724360636054454,"score_spread":0.23344810371869518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200343312","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90705967,0.0000583429,0.09208769,0.00004663312,0.00028600782,0.0001950902,0.00005166779,0.00013367408,0.00008122973],"genre_scores_gemma":[0.99827605,0.000018323663,0.0009755753,0.000026287844,0.000016019965,0.000012559788,0.00010913915,0.000009185801,0.0005568394],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983948,0.00010696343,0.00040237175,0.00042779418,0.0004525481,0.00021555122],"domain_scores_gemma":[0.9987138,0.00030002574,0.00029436997,0.00021726207,0.00036156384,0.00011298617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005715874,0.00015271315,0.00031559725,0.0001853061,0.00017601509,0.00009247755,0.0001807568,0.0001082409,0.0000060215366],"category_scores_gemma":[0.0004410822,0.0001307005,0.00003906525,0.00040090186,0.00003778893,0.00032358657,0.00022114096,0.00018630583,0.00002902448],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002565408,0.000040289146,0.89466053,0.00005360738,0.000058606918,0.00002579862,0.0012263616,0.000039335482,0.0785081,0.00007939611,0.00011029828,0.025171991],"study_design_scores_gemma":[0.0008566561,0.00031014925,0.9277356,0.0006237319,0.000020227788,0.000054632368,0.000577843,0.028275492,0.04037305,0.00003280528,0.0009170523,0.0002227266],"about_ca_topic_score_codex":0.000054166776,"about_ca_topic_score_gemma":0.0000054039274,"teacher_disagreement_score":0.09121641,"about_ca_system_score_codex":0.000049682025,"about_ca_system_score_gemma":0.000055454777,"threshold_uncertainty_score":0.53298116},"labels":[],"label_agreement":null},{"id":"W4206300744","doi":"10.1109/smc52423.2021.9658688","title":"Algorithms for Reading Line Classification","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Preprocessor; Gaze; Eye tracking; Computer vision; Kalman filter; Line (geometry); Noise (video); Pattern recognition (psychology); Eye movement; Support vector machine; Image (mathematics); Mathematics","score_opus":0.128026174090953,"score_gpt":0.33867211704886,"score_spread":0.21064594295790703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206300744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025580028,0.00033055153,0.92747164,0.008308031,0.0046649077,0.00039841677,0.00009422242,0.000237642,0.03291458],"genre_scores_gemma":[0.9829518,0.00025027036,0.007186963,0.00017096737,0.00033138055,0.000074907184,0.000052858057,0.000013545906,0.0089673],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842536,0.000065110224,0.00036480906,0.0005899411,0.00032781248,0.0002269703],"domain_scores_gemma":[0.99845916,0.00014998222,0.00019045311,0.00038438718,0.00074183324,0.00007415615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027367627,0.00017971477,0.00023277361,0.0001431695,0.00011176739,0.0004385165,0.0005512932,0.00013978493,0.000025753425],"category_scores_gemma":[0.000093599745,0.00017749515,0.000059226084,0.0001515904,0.00006654368,0.00013903108,0.00009031272,0.00017895602,0.00005270907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008628963,0.00008252262,0.00030936295,0.000030280962,0.000060841794,0.000023808589,0.00011032035,0.000044162505,0.0063486034,0.9605031,0.0012710739,0.031207295],"study_design_scores_gemma":[0.001340448,0.0003595006,0.004717746,0.00054571335,0.00004132756,0.00018150838,0.0008703135,0.8795365,0.013419015,0.018877888,0.07939492,0.0007151055],"about_ca_topic_score_codex":0.000034350614,"about_ca_topic_score_gemma":0.000020058478,"teacher_disagreement_score":0.9573718,"about_ca_system_score_codex":0.00006984896,"about_ca_system_score_gemma":0.00010689808,"threshold_uncertainty_score":0.72380424},"labels":[],"label_agreement":null},{"id":"W4212946381","doi":"10.31224/osf.io/p5xcd","title":"Humanoid Eyes: Perspective &amp;amp; Challenges","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Perspective (graphical); Sophistication; Humanoid robot; Saccadic masking; Computer science; Human–computer interaction; Cognitive science; Artificial intelligence; Eye movement; Psychology; Sociology; Robot","score_opus":0.07893283498835894,"score_gpt":0.3211916582402596,"score_spread":0.24225882325190062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212946381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009400151,0.0056768847,0.7057978,0.017734598,0.002412196,0.00053221924,0.000010166942,0.0031951806,0.25524083],"genre_scores_gemma":[0.8842574,0.0009635114,0.09713502,0.0002926638,0.00021233431,0.000075149364,0.0000119579845,0.000039549763,0.01701243],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99741423,0.00009497329,0.00026954093,0.0014408185,0.00031779005,0.00046265998],"domain_scores_gemma":[0.9967224,0.00011781438,0.00020975561,0.0025492003,0.00032714993,0.00007370016],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003030255,0.00041658268,0.0005150907,0.00034130568,0.00009964816,0.00018713411,0.0027578957,0.00056227605,0.00009344685],"category_scores_gemma":[0.00010257025,0.00037264652,0.00022225671,0.00014122156,0.00013831092,0.00012535775,0.0030451086,0.0011377301,0.0030611225],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000029784767,0.00016611817,0.00021153405,0.00010433116,0.00013152325,0.000008346064,0.002354254,0.00014489799,0.000159962,0.97730464,0.00748383,0.011927587],"study_design_scores_gemma":[0.0008252945,0.00018216077,0.021723876,0.000465567,0.00007321237,0.00005126219,0.0013493398,0.0017499035,0.0010373846,0.74373174,0.22600675,0.0028035205],"about_ca_topic_score_codex":0.00018040561,"about_ca_topic_score_gemma":0.00024340289,"teacher_disagreement_score":0.87485725,"about_ca_system_score_codex":0.0002246963,"about_ca_system_score_gemma":0.00021186507,"threshold_uncertainty_score":0.99987257},"labels":[],"label_agreement":null},{"id":"W4214633681","doi":"10.1177/20556683221079694","title":"Preliminary testing of eye gaze interfaces for controlling a haptic system intended to support play in children with physical impairments: Attentive versus explicit interfaces","year":2022,"lang":"en","type":"article","venue":"Journal of Rehabilitation and Assistive Technologies Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Interface (matter); Gaze; Haptic technology; Human–computer interaction; Eye tracking; Psychology; Eye–hand coordination; User interface; Cerebral palsy; Computer science; Point (geometry); Cognitive psychology; Simulation; Artificial intelligence; Mathematics","score_opus":0.011586365721150153,"score_gpt":0.24854480751600422,"score_spread":0.23695844179485406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214633681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8311734,0.000077210534,0.1676977,0.0004069612,0.00009695481,0.00034872422,0.000008612905,0.00018640794,0.0000039859956],"genre_scores_gemma":[0.91923887,0.0000010275935,0.08062305,0.0000028031895,0.0000075058665,0.00011371179,6.3135343e-7,0.0000108303275,0.0000015879152],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99881476,0.00003685101,0.00045101595,0.0002699193,0.00020522597,0.00022220674],"domain_scores_gemma":[0.99841917,0.00077106757,0.00038851995,0.00016737028,0.00022648984,0.000027406955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046931492,0.0001750531,0.0004360162,0.00068593887,0.00007485991,0.000031777723,0.00048670624,0.00004637221,2.8656163e-7],"category_scores_gemma":[0.0010783767,0.00014636609,0.00006238991,0.00057242636,0.00007792641,0.00023181082,0.00029364508,0.00034399168,1.576876e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005007212,0.001657075,0.37329626,0.001069103,0.0016996919,0.00010182325,0.013444979,0.17626731,0.124514595,0.018236801,0.00011553135,0.28458962],"study_design_scores_gemma":[0.01158361,0.07810211,0.4707704,0.0025029122,0.00021110024,0.00038325757,0.07771759,0.33133164,0.025892897,0.000427479,0.00006048485,0.0010165144],"about_ca_topic_score_codex":0.0000042213114,"about_ca_topic_score_gemma":7.565261e-7,"teacher_disagreement_score":0.2835731,"about_ca_system_score_codex":0.00025875485,"about_ca_system_score_gemma":0.000036084144,"threshold_uncertainty_score":0.59686357},"labels":[],"label_agreement":null},{"id":"W4220822879","doi":"10.31234/osf.io/tmypw","title":"Reading proficiency predicts spatial eye-movement control in the first and second language","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Saccade; Eye movement; Reading (process); Eye tracking; Computer science; Psychology; Saccadic masking; Control (management); Contrast (vision); Language proficiency; Cognitive psychology; Linguistics; Artificial intelligence; Mathematics education","score_opus":0.008795732136312696,"score_gpt":0.24780164672405797,"score_spread":0.23900591458774528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220822879","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6105921,0.0005869343,0.33978885,0.019426458,0.0012632442,0.0022281315,0.0000740295,0.0008265754,0.025213638],"genre_scores_gemma":[0.9966406,0.000006888016,0.0011378372,0.0015452253,0.000042179214,0.0002358668,0.0000062323616,0.0000073461374,0.0003778235],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984164,0.00010889445,0.00026067175,0.00062423176,0.00029234597,0.00029745552],"domain_scores_gemma":[0.99894685,0.00012240588,0.00013504295,0.00074845646,0.000019814277,0.00002740884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008023132,0.0001955785,0.00023873617,0.00018026563,0.00016207513,0.0001656912,0.00147687,0.00012035751,0.00011561062],"category_scores_gemma":[0.000043604905,0.00013999769,0.000046496683,0.0001723662,0.0000744248,0.000067049754,0.0014400523,0.0007780101,0.0000038025198],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006737768,0.0017096926,0.459584,0.0011458963,0.0002793951,0.0015944673,0.059047535,0.001593515,0.0016662206,0.33369747,0.012099612,0.12751484],"study_design_scores_gemma":[0.002408573,0.0005833391,0.8262555,0.00019128778,0.000040097533,0.000027906945,0.0025944347,0.14410298,0.00070233585,0.011988644,0.010116448,0.0009884518],"about_ca_topic_score_codex":0.0004885843,"about_ca_topic_score_gemma":0.00051210553,"teacher_disagreement_score":0.38604847,"about_ca_system_score_codex":0.00008203874,"about_ca_system_score_gemma":0.000071017224,"threshold_uncertainty_score":0.570894},"labels":[],"label_agreement":null},{"id":"W4223984613","doi":"10.1109/accai53970.2022.9752589","title":"A Complete hands-free electric powered wheelchair for Quadriplegic Individuals and Home automation using IoT","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Gadget; Wheelchair; Joystick; Computer science; Gesture; Home automation; Radio control; Controller (irrigation); Simulation; Computer hardware; Telecommunications; Artificial intelligence","score_opus":0.03261759674692764,"score_gpt":0.30315732120852307,"score_spread":0.2705397244615954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4223984613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40268752,0.00082635763,0.57806134,0.0029406257,0.00053103047,0.001283752,0.00011812931,0.0005171274,0.013034106],"genre_scores_gemma":[0.9303169,0.0002915252,0.06872596,0.0004417067,0.000013256489,0.00009276794,0.00008932542,0.000009958552,0.000018594137],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984261,0.00008901244,0.00064443797,0.00026546034,0.00031607976,0.00025893268],"domain_scores_gemma":[0.99824375,0.00033663723,0.000618511,0.0006200566,0.00013043112,0.00005064323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007727617,0.0002056153,0.0002743679,0.00056332274,0.0005738056,0.00026282726,0.0018100519,0.000059229005,0.000025542631],"category_scores_gemma":[0.00007612129,0.00022588955,0.000035677724,0.00043615978,0.00010288749,0.00036704334,0.001482291,0.000468164,0.0000021655453],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029209856,0.00009105651,0.00062258646,0.000031685868,0.000020606734,2.7253603e-7,0.0016800367,0.0047571575,0.00021055598,0.94066894,0.00010781328,0.051780067],"study_design_scores_gemma":[0.002091932,0.00015822527,0.0015217537,0.000052338943,0.0000051875695,0.000022165745,0.0009337268,0.90996283,0.000067665154,0.07898193,0.005934907,0.00026736222],"about_ca_topic_score_codex":0.000010375351,"about_ca_topic_score_gemma":0.000012454635,"teacher_disagreement_score":0.90520567,"about_ca_system_score_codex":0.00016221042,"about_ca_system_score_gemma":0.00007073146,"threshold_uncertainty_score":0.9211509},"labels":[],"label_agreement":null},{"id":"W4224007206","doi":"10.1049/ipr2.12494","title":"CFN: A coarse‐to‐fine network for eye fixation prediction","year":2022,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Fixation (population genetics); Computer science; Artificial intelligence; Computer vision; Chemistry","score_opus":0.015205511521704164,"score_gpt":0.26975546476613804,"score_spread":0.25454995324443386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224007206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011992215,0.00012873813,0.98296887,0.0033389234,0.00037625743,0.00024387497,0.000014259275,0.0006423454,0.00029453717],"genre_scores_gemma":[0.705441,5.501893e-7,0.2935107,0.00035648065,0.00014916387,0.00025188638,0.00001645405,0.000012593183,0.0002611953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893206,0.000034216868,0.00018083047,0.00036696854,0.00018916707,0.000296724],"domain_scores_gemma":[0.99946404,0.000036079848,0.00011952432,0.00022070516,0.0001181629,0.000041468367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040118996,0.000100918616,0.00011697278,0.00010438125,0.0006633608,0.00015690259,0.00046006183,0.000032678,0.000008925698],"category_scores_gemma":[0.00007302107,0.000107759166,0.000037709688,0.0006613143,0.000029177665,0.00033452836,0.00023471266,0.00016681346,0.000007587658],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005265991,0.00017261239,0.0035538904,0.00010694151,0.000017568871,0.000014170001,0.0009912087,0.008693476,0.015876137,0.0038309502,0.027428886,0.9392615],"study_design_scores_gemma":[0.0009863803,0.0006461414,0.010328253,0.00009071493,0.000027321857,0.000044427063,0.00014627672,0.91249186,0.0048236107,0.0189389,0.051033586,0.0004425363],"about_ca_topic_score_codex":0.000003751236,"about_ca_topic_score_gemma":0.0000023830924,"teacher_disagreement_score":0.938819,"about_ca_system_score_codex":0.00007417277,"about_ca_system_score_gemma":0.000074110474,"threshold_uncertainty_score":0.5102102},"labels":[],"label_agreement":null},{"id":"W4224229792","doi":"10.1109/tbcas.2022.3168236","title":"A Flexible Wearable Electrooculogram System With Motion Artifacts Sensing and Reduction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wearable computer; Microcontroller; Reduction (mathematics); Noise (video); Artifact (error); Electrode; Noise reduction; Wireless; Motion (physics)","score_opus":0.017022656795807695,"score_gpt":0.21947814694221848,"score_spread":0.2024554901464108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224229792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060715,0.00020790425,0.9370704,0.0004069359,0.00069008954,0.00024684798,0.000006389281,0.0005286797,0.00012771797],"genre_scores_gemma":[0.9993801,0.000016160491,0.00033547694,0.00001997275,0.000034040473,0.000047928337,0.0000014921782,0.000011593487,0.00015321368],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984208,0.00016767894,0.00023186041,0.00048272478,0.00038920957,0.0003077646],"domain_scores_gemma":[0.9994289,0.00004401836,0.000088793044,0.00025173725,0.00004579251,0.00014074691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043520395,0.00014990373,0.00022805404,0.0003206131,0.0007552629,0.00014797607,0.0001397481,0.00008646837,0.0000027302865],"category_scores_gemma":[0.0000020700622,0.00012590596,0.00003004589,0.0006538489,0.00014008084,0.00014126035,0.0000044808094,0.00036986644,0.0000037639747],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039703573,0.00037070713,0.000030244322,0.00029644926,0.00014672644,0.000099000936,0.0007522189,0.0031001589,0.028360955,0.007047107,0.00011305382,0.95964366],"study_design_scores_gemma":[0.005897299,0.00955401,0.0015186528,0.0013183168,0.0002576135,0.029723115,0.009289546,0.9025727,0.023832068,0.0006624591,0.01324355,0.0021306572],"about_ca_topic_score_codex":0.0002269366,"about_ca_topic_score_gemma":0.0000047571293,"teacher_disagreement_score":0.95751303,"about_ca_system_score_codex":0.00012500664,"about_ca_system_score_gemma":0.000047582824,"threshold_uncertainty_score":0.5808948},"labels":[],"label_agreement":null},{"id":"W4225749806","doi":"10.3758/s13428-021-01762-8","title":"RETRACTED ARTICLE: Eye tracking: empirical foundations for a minimal reporting guideline","year":2022,"lang":"en","type":"review","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":163,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; SR Research (Canada)","funders":"National Eye Institute; National Cancer Institute; National Institute for Health and Care Research","keywords":"Guideline; Eye tracking; Foundation (evidence); Empirical research; Computer science; Empirical evidence; Quality (philosophy); Gaze; Tracking (education); Eye movement; Artificial intelligence; Psychology; Medicine; Statistics; Political science; Mathematics","score_opus":0.7737194927129117,"score_gpt":0.7106257387479584,"score_spread":0.06309375396495331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225749806","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000060055296,0.49598783,0.49885815,0.0007186715,0.0006407332,0.0026434108,0.000041608248,0.0007505485,0.00029899616],"genre_scores_gemma":[0.0000109869,0.23412877,0.7603918,0.000023562072,0.00022744697,0.0043065306,0.00013968424,0.00008970567,0.00068157417],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9874944,0.0046117553,0.0035880415,0.0016928335,0.0012599286,0.0013530483],"domain_scores_gemma":[0.98905987,0.0055762124,0.0019909365,0.002129938,0.00095525896,0.0002878018],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.038080726,0.0004710818,0.0017750757,0.0013138835,0.0011271426,0.0005156105,0.0028799612,0.000715978,0.00023050871],"category_scores_gemma":[0.035214268,0.0004239097,0.00089134305,0.003730249,0.00032388672,0.0002770353,0.0014883527,0.0039396994,0.00002400337],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031692257,0.00030610905,0.00005932039,0.0005429137,0.000038275655,0.00016153975,0.00007337592,1.888087e-7,0.000056319197,0.00091925095,0.0026747917,0.99516475],"study_design_scores_gemma":[0.0001611763,0.00028035403,0.00029018582,0.0004113384,0.00019225178,0.00020156657,0.00005110646,0.0005668189,0.000048333015,0.00059896114,0.9967848,0.00041309162],"about_ca_topic_score_codex":0.000026560827,"about_ca_topic_score_gemma":0.000004294611,"teacher_disagreement_score":0.99475163,"about_ca_system_score_codex":0.0006152037,"about_ca_system_score_gemma":0.0017123179,"threshold_uncertainty_score":0.99982125},"labels":[],"label_agreement":null},{"id":"W4226495069","doi":"10.2139/ssrn.4060743","title":"Quarter Circle Speller: Eeg Motor Imagery Signal-Based Text Entry System for Motor-Impaired People","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electroencephalography; Motor imagery; Quarter (Canadian coin); Psychology; Audiology; SIGNAL (programming language); Speech recognition; Brain–computer interface; Physical medicine and rehabilitation; Computer science; Neuroscience; Medicine; Geography","score_opus":0.005677015127915218,"score_gpt":0.20483528245650717,"score_spread":0.19915826732859196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226495069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17275335,0.0011585978,0.82046366,0.0034848691,0.000825613,0.0005069621,0.000024498153,0.0005365281,0.00024595356],"genre_scores_gemma":[0.9971651,0.000020075982,0.001629273,0.00015403944,0.00017080428,0.0000983737,0.000005188053,0.000034052726,0.0007230826],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958712,0.00023851552,0.00044497233,0.00051678816,0.00047993026,0.002448634],"domain_scores_gemma":[0.9987118,0.0001977768,0.000331555,0.00050911034,0.00012515196,0.0001245669],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019154861,0.00024327301,0.00035527648,0.0003552954,0.0008516911,0.00016900268,0.0014590768,0.00008467264,0.000042028616],"category_scores_gemma":[0.00003384079,0.0002560242,0.00033551603,0.00046360758,0.000045452354,0.00023312139,0.00016268804,0.0016617933,0.000048344413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016355154,0.0033000843,0.015498181,0.00052236696,0.0017453391,0.00041652255,0.0020210717,0.004399086,0.13844036,0.4475421,0.019276517,0.36520287],"study_design_scores_gemma":[0.028326433,0.02965568,0.053351626,0.00042086528,0.00057277305,0.0148283625,0.027177354,0.55344695,0.016252749,0.23805091,0.031770192,0.0061461334],"about_ca_topic_score_codex":0.00004411718,"about_ca_topic_score_gemma":0.00005889869,"teacher_disagreement_score":0.82441175,"about_ca_system_score_codex":0.0018783973,"about_ca_system_score_gemma":0.0021512974,"threshold_uncertainty_score":0.9999892},"labels":[],"label_agreement":null},{"id":"W4230018562","doi":"10.1155/2006/830150","title":"Robotic Measurement and Control for Chiropractic Research","year":2006,"lang":"en","type":"article","venue":"Applied Bionics and Biomechanics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary","funders":"","keywords":"Scheme (mathematics); Computer science; Chiropractic; Control engineering; Robot; Iterative learning control; Calibration; Artificial intelligence; Control (management); Simulation; Control theory (sociology); Engineering; Mathematics","score_opus":0.061065884866215135,"score_gpt":0.2855562378557473,"score_spread":0.22449035298953215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230018562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0130143445,0.0008607488,0.9806353,0.0044356105,0.00013358258,0.0005377693,0.0000045199586,0.00015331448,0.00022482553],"genre_scores_gemma":[0.98190916,0.00006631125,0.017807381,0.000094966184,0.000040087423,0.000053349424,0.000002049955,0.000008001571,0.000018686322],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99879324,0.000021325,0.00015882203,0.00040645833,0.00028021922,0.000339936],"domain_scores_gemma":[0.99938166,0.00008940609,0.000060245107,0.0002552856,0.00016241892,0.000051004394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012413459,0.000112818816,0.00015113359,0.00016479363,0.00029765602,0.00015601733,0.00024340712,0.000114737835,4.9066887e-7],"category_scores_gemma":[0.000029951965,0.00009370536,0.000022123582,0.00030736034,0.00007356295,0.00005052946,0.00013677221,0.00014717595,0.000004497799],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010307321,0.00006904695,0.000008016577,0.000021423028,0.000013223436,0.0000014629445,0.0000088454535,0.0000017779754,0.10492591,0.84241074,0.00021275533,0.05231652],"study_design_scores_gemma":[0.0032756452,0.0007455152,0.0009111724,0.000040167542,0.000050753755,0.000051155454,0.00008686713,0.13578388,0.056895733,0.7691177,0.032453466,0.0005879324],"about_ca_topic_score_codex":0.00002153787,"about_ca_topic_score_gemma":0.000013138423,"teacher_disagreement_score":0.96889484,"about_ca_system_score_codex":0.000039851886,"about_ca_system_score_gemma":0.000041427684,"threshold_uncertainty_score":0.38211936},"labels":[],"label_agreement":null},{"id":"W4233048981","doi":"10.4018/978-1-4666-4422-9.ch007","title":"POMDP Models for Assistive Technology","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Partially observable Markov decision process; Computer science; Human–computer interaction; Process (computing); Task (project management); Wheelchair; Implementation; Population; Artificial intelligence; Machine learning; Markov model; Systems engineering; Engineering; Markov chain; Software engineering","score_opus":0.024947136409490013,"score_gpt":0.24838320406627182,"score_spread":0.2234360676567818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233048981","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014399043,0.00020701152,0.33836052,0.000625456,0.0003985736,0.00049121235,0.000088123896,0.0011303147,0.65868443],"genre_scores_gemma":[0.4530153,0.000010228738,0.19211903,0.0011050756,0.00036252895,0.00045849953,0.000011532998,0.00015309254,0.35276473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975564,0.000010181212,0.00042749956,0.0010936183,0.00028234997,0.00062993285],"domain_scores_gemma":[0.9978027,0.00006608336,0.00035046984,0.0012622146,0.00039679985,0.00012178574],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00010454905,0.00057589076,0.00068328757,0.00028914,0.00017612864,0.00013542535,0.0019791832,0.0012534708,0.000010561412],"category_scores_gemma":[0.000030427842,0.0005568565,0.00028064585,0.000056410554,0.0003095441,0.00011734414,0.0006326715,0.0004956369,0.00037139587],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004697993,0.000010256625,0.0000032690825,0.000015789288,0.000088220855,0.000023638651,0.0000058102864,0.0000063816155,0.00001383556,0.92496413,0.005305953,0.069558],"study_design_scores_gemma":[0.0003223487,0.00021388012,0.000018873712,0.00011734978,0.00004180097,0.000067945475,0.0000034438365,0.0022688785,0.00012131933,0.9649955,0.03128334,0.00054531096],"about_ca_topic_score_codex":0.00001970759,"about_ca_topic_score_gemma":0.0000134732645,"teacher_disagreement_score":0.4530009,"about_ca_system_score_codex":0.00028579,"about_ca_system_score_gemma":0.00023112835,"threshold_uncertainty_score":0.99968827},"labels":[],"label_agreement":null},{"id":"W4235074058","doi":"10.1145/766098.766113","title":"Hands on cooking","year":2003,"lang":"en","type":"article","venue":"CHI '03 extended abstracts on Human factors in computer systems - CHI '03","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Human–computer interaction; Computer science; Gaze; User interface; Multimedia; User satisfaction; Natural (archaeology); User modeling; Computer user satisfaction; User experience design; User interface design; Artificial intelligence","score_opus":0.04480842999684755,"score_gpt":0.28701289919309636,"score_spread":0.2422044691962488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235074058","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9516007,0.00011256166,0.021720616,0.00009993487,0.006060887,0.0007334942,0.00001475902,0.0013382914,0.018318811],"genre_scores_gemma":[0.99709433,0.000006601805,0.0017671421,0.0002591398,0.0004336006,0.00004187363,0.000022781578,0.00007350257,0.00030102747],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99455285,0.00039269758,0.0012725551,0.0017221633,0.0008329428,0.0012267994],"domain_scores_gemma":[0.99659425,0.00038334267,0.00065262103,0.0019354753,0.00012331242,0.0003109705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008960894,0.0009012104,0.0010059141,0.0010672931,0.0005829256,0.00072275725,0.0020524042,0.00051404233,0.000021247573],"category_scores_gemma":[0.000090355774,0.00080005155,0.00026098883,0.00066881115,0.00019724529,0.00045256282,0.00023258549,0.0014191406,0.00038278455],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006111609,0.004636245,0.024766902,0.0004970019,0.00047302005,0.0014903318,0.0034797403,0.032388613,0.0014966717,0.8918764,0.010095486,0.028738456],"study_design_scores_gemma":[0.007444435,0.0047605922,0.9110964,0.0033481536,0.000054695367,0.0003675805,0.00020778926,0.01674721,0.022540778,0.009687384,0.019214258,0.004530744],"about_ca_topic_score_codex":0.00010668307,"about_ca_topic_score_gemma":0.0000456375,"teacher_disagreement_score":0.8863295,"about_ca_system_score_codex":0.0003690948,"about_ca_system_score_gemma":0.00011067841,"threshold_uncertainty_score":0.999445},"labels":[],"label_agreement":null},{"id":"W4238404627","doi":"10.31234/osf.io/r5ays","title":"Turning the (virtual) world around: patterns in saccade direction vary with picture orientation and shape in virtual reality","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Saccade; Gaze; Artificial intelligence; Rotation (mathematics); Orientation (vector space); Virtual reality; Computer science; Head (geology); Eye movement; Communication; Psychology; Mathematics; Geometry; Geology","score_opus":0.0247387419333366,"score_gpt":0.2648375122994659,"score_spread":0.24009877036612928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238404627","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6939334,0.000041492327,0.29799035,0.0065473216,0.00030507127,0.00031376944,0.0000054490674,0.00034144978,0.00052165793],"genre_scores_gemma":[0.9980666,0.0000325185,0.0011940257,0.0004566979,0.0000650834,0.000056150133,0.000019805531,0.000012085555,0.00009705324],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99812406,0.00022385342,0.0003169585,0.00085959747,0.00023794238,0.00023757524],"domain_scores_gemma":[0.9990645,0.00020898497,0.00018902458,0.00045960493,0.00003430713,0.000043601765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041406805,0.00025683964,0.00029508697,0.00029888548,0.00008064871,0.00020442577,0.0006068812,0.00020122633,0.000006423863],"category_scores_gemma":[0.000057748206,0.00017618427,0.00003223691,0.0006126096,0.000079361576,0.00019900798,0.00075560895,0.0014891855,0.0000023468149],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001243924,0.00020702882,0.616521,0.00012139099,0.00008212397,0.00017005864,0.0097554,0.015098275,0.0005571921,0.05488444,0.00022047985,0.3022582],"study_design_scores_gemma":[0.00043466804,0.00013683534,0.89136744,0.0002983439,0.000011302826,0.000015733312,0.0007587897,0.100651525,0.00029035495,0.0055649825,0.00014531941,0.00032470492],"about_ca_topic_score_codex":0.0009418976,"about_ca_topic_score_gemma":0.0062473156,"teacher_disagreement_score":0.30413315,"about_ca_system_score_codex":0.00013763848,"about_ca_system_score_gemma":0.00007284376,"threshold_uncertainty_score":0.7184586},"labels":[],"label_agreement":null},{"id":"W4238879251","doi":"10.1109/iembs.2006.4398134","title":"A Low Cost Human Computer Interface based on Eye Tracking","year":2006,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Eye tracking; Interface (matter); Tracking (education); Computer vision; Artificial intelligence; Computer graphics (images); Human–computer interaction; Operating system","score_opus":0.0235125255640756,"score_gpt":0.27639001101288097,"score_spread":0.25287748544880534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238879251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2598322,0.0000073941383,0.72940534,0.0017053429,0.00017934701,0.00021756021,0.0000014755361,0.0009428413,0.007708481],"genre_scores_gemma":[0.9868368,5.5696603e-7,0.012501634,0.00030926152,0.00010975674,0.000035039688,0.0000026545276,0.00001570322,0.00018858495],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983365,0.000010541963,0.0002704869,0.00066226843,0.00026470324,0.000455479],"domain_scores_gemma":[0.999149,0.00003719872,0.00015688356,0.00026185013,0.00032475867,0.00007027465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002069905,0.0002562765,0.0002479555,0.00023442488,0.00022291098,0.0005272263,0.0011665691,0.00013694887,0.00002445514],"category_scores_gemma":[0.000024172798,0.00024222159,0.000067891975,0.0003701006,0.0001525258,0.00034916564,0.00017290981,0.00036957947,0.000093827075],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031024218,0.0010375843,0.05448506,0.00018304713,0.00002481379,0.000047756792,0.0009245029,0.0004734003,0.06489698,0.6815425,0.012019485,0.18433382],"study_design_scores_gemma":[0.0016750754,0.00079101644,0.1207531,0.00085273077,0.00001713855,0.000019155921,0.000085169,0.66725993,0.1893,0.012569895,0.0055163433,0.0011604563],"about_ca_topic_score_codex":0.000021650607,"about_ca_topic_score_gemma":0.000004981342,"teacher_disagreement_score":0.72700465,"about_ca_system_score_codex":0.00007254436,"about_ca_system_score_gemma":0.00004865381,"threshold_uncertainty_score":0.987751},"labels":[],"label_agreement":null},{"id":"W4242975174","doi":"10.22215/etd/2021-14571","title":"What do Scanpaths Tell Us About Cognitive Processes? An Investigation in a Problem Solving Domain","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Eye movement; Computer science; Eye tracking; Context (archaeology); Cognition; Similarity (geometry); Artificial intelligence; Visual search; Object (grammar); Domain (mathematical analysis); Rapid serial visual presentation; Presentation (obstetrics); Human–computer interaction; Natural language processing; Psychology; Mathematics","score_opus":0.013229267956912327,"score_gpt":0.26827654053845357,"score_spread":0.25504727258154125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242975174","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9825077,0.0018789058,0.009706294,0.00017031762,0.0005745545,0.00058109534,0.0000037685256,0.00051843276,0.0040589077],"genre_scores_gemma":[0.956896,0.00057547947,0.039286897,0.00025862118,0.00006925189,0.0002430252,0.0007075283,0.000048844187,0.0019143403],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99738973,0.00015207655,0.00048866606,0.0011415429,0.00037812017,0.00044987263],"domain_scores_gemma":[0.99830943,0.00014024116,0.00035847654,0.0004216043,0.00066048565,0.00010974566],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00040680438,0.00038256892,0.00042506645,0.00052562397,0.00015940922,0.0011034631,0.00079542084,0.0005007632,0.000025229148],"category_scores_gemma":[0.00017215185,0.00038104603,0.000058370126,0.0014552004,0.000078531666,0.0018306016,0.00009877046,0.00062923424,0.000024935925],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012908528,0.0017161729,0.086034454,0.0057949936,0.0002697368,0.0011803126,0.12524909,0.0001101349,0.013714899,0.07777112,0.00040974456,0.6876203],"study_design_scores_gemma":[0.004673187,0.0014528692,0.4615233,0.050100293,0.00020291757,0.00021778664,0.14660262,0.007326814,0.14269793,0.17840095,0.0005530546,0.006248285],"about_ca_topic_score_codex":0.00024413224,"about_ca_topic_score_gemma":0.009921214,"teacher_disagreement_score":0.681372,"about_ca_system_score_codex":0.00010738167,"about_ca_system_score_gemma":0.0009624734,"threshold_uncertainty_score":0.9999335},"labels":[],"label_agreement":null},{"id":"W4244165738","doi":"10.32920/ryerson.14664096","title":"Robust Discriminative Analysis Framework for Gaze and Headpose Estimation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Toronto Metropolitan University","funders":"","keywords":"Gaze; Artificial intelligence; Discriminative model; Computer science; Robustness (evolution); Computer vision; Pose; Pattern recognition (psychology); Pupil","score_opus":0.0583605772870338,"score_gpt":0.3145092431379922,"score_spread":0.2561486658509584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244165738","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011893621,0.00023176915,0.98361003,0.0032192613,0.0002230793,0.00022538101,0.000011852649,0.0003381089,0.00024687775],"genre_scores_gemma":[0.4690021,0.000015734338,0.5307231,0.00006351284,0.00001344558,0.0000586183,0.000036148936,0.0000051469074,0.00008220821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984797,0.00005626993,0.00024079804,0.0008522485,0.00014863106,0.00022232244],"domain_scores_gemma":[0.99845093,0.00035091586,0.00017172925,0.00076587,0.00020225557,0.000058314843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023213123,0.00022471008,0.0004555782,0.00033991653,0.00011267736,0.00039271353,0.00057150086,0.00035508935,0.000009673712],"category_scores_gemma":[0.00032237018,0.00020247184,0.00019589253,0.00054242514,0.00008876815,0.00013422107,0.0009806468,0.0004359033,0.0000015543307],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046596374,0.00014163968,0.0013842825,0.00019455542,0.00090582983,0.000013290363,0.0012980527,0.02310384,0.000018320563,0.8311171,0.00015559368,0.14166287],"study_design_scores_gemma":[0.00009659076,0.00004520489,0.020285066,0.00012479306,0.00040065002,0.0000030349356,0.00017639431,0.8148732,0.00061943417,0.16304989,0.000019095625,0.0003066561],"about_ca_topic_score_codex":0.000058990743,"about_ca_topic_score_gemma":0.00006380694,"teacher_disagreement_score":0.7917693,"about_ca_system_score_codex":0.00004992559,"about_ca_system_score_gemma":0.00006708427,"threshold_uncertainty_score":0.8256562},"labels":[],"label_agreement":null},{"id":"W4245016735","doi":"10.1167/11.11.678","title":"Eye Movement in Face Change Detection Task","year":2011,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Fixation (population genetics); Eye movement; Psychology; Perception; Eye tracking; Computer vision; Change detection; Ocular dominance; Face (sociological concept); Artificial intelligence; Cognitive psychology; Communication; Computer science; Neuroscience; Visual cortex; Population; Medicine","score_opus":0.03826758901358732,"score_gpt":0.28287524928845614,"score_spread":0.24460766027486883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245016735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7637668,0.00016277998,0.23468529,0.00064927543,0.00044127682,0.00004823034,1.3867937e-7,0.000028584309,0.00021759955],"genre_scores_gemma":[0.9931774,0.000031177948,0.0066338447,0.000105868276,0.00003404038,0.0000010743767,2.330965e-8,0.0000024473322,0.000014082956],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99943256,0.000031122512,0.00019933985,0.00008679256,0.000144343,0.00010585082],"domain_scores_gemma":[0.9995989,0.000010205614,0.00018056594,0.0001253907,0.00005477733,0.000030122781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033308295,0.00005145423,0.00009605034,0.00023189149,0.000027569373,0.00001499185,0.0003095683,0.000046428424,0.0000048292977],"category_scores_gemma":[0.000020328054,0.00004004006,0.000037374877,0.00022260651,0.000013120939,0.00031388347,0.00006727141,0.00016939171,0.000011286843],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031198873,0.000319115,0.01282974,0.000007646993,0.000010186579,0.00012520155,0.001905636,0.0000068580284,0.065948136,0.0015265481,0.00008043195,0.9172093],"study_design_scores_gemma":[0.0003808268,0.00085860165,0.9657151,0.00008175552,0.000002731659,0.000021353151,0.00005919653,0.0019888731,0.025687164,0.004441698,0.0006905358,0.000072165414],"about_ca_topic_score_codex":0.000023445938,"about_ca_topic_score_gemma":0.0000130445615,"teacher_disagreement_score":0.9528853,"about_ca_system_score_codex":0.000047719233,"about_ca_system_score_gemma":0.000008911187,"threshold_uncertainty_score":0.16327862},"labels":[],"label_agreement":null},{"id":"W4245044940","doi":"10.22215/etd/2012-07011","title":"A non-intrusive and calibration-free gaze tracking system","year":2012,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Gaze; Tracking (education); Computer graphics (images); Computer science; Computer vision; Calibration; Artificial intelligence; Tracking system; Art; Psychology; Mathematics; Kalman filter; Statistics","score_opus":0.010178622621761512,"score_gpt":0.23566232678636853,"score_spread":0.22548370416460703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245044940","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24622367,0.0023747345,0.62530947,0.001195777,0.0061714966,0.0012674044,0.000029114639,0.0042030984,0.11322523],"genre_scores_gemma":[0.9872409,0.000010079934,0.009220727,0.000039227773,0.00013135913,0.00004437428,0.00005344019,0.000023778546,0.003236088],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998641,0.000027803337,0.00028821037,0.0004917032,0.00022531977,0.00032597536],"domain_scores_gemma":[0.99877864,0.00007068799,0.00021972072,0.0007143843,0.00012515015,0.000091441085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001627897,0.0002711047,0.00033297515,0.0002345455,0.00016397263,0.0002164284,0.0008924142,0.00038344317,0.00001424647],"category_scores_gemma":[0.00004293784,0.00024040397,0.00006408355,0.00023624301,0.000031318923,0.00041022652,0.00012702307,0.00031777518,0.00003258542],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021071186,0.000114351045,0.0031902886,0.0011335703,0.00017802227,0.00007573651,0.003868333,0.0000054634406,0.003706455,0.86345375,0.0054805786,0.11877239],"study_design_scores_gemma":[0.005144204,0.00081608206,0.6922786,0.005436727,0.0007205031,0.00087052793,0.015696466,0.12974113,0.11707929,0.018087413,0.0068528648,0.007276147],"about_ca_topic_score_codex":0.00010280694,"about_ca_topic_score_gemma":0.00021468554,"teacher_disagreement_score":0.8453663,"about_ca_system_score_codex":0.00005380723,"about_ca_system_score_gemma":0.00007729535,"threshold_uncertainty_score":0.98033893},"labels":[],"label_agreement":null},{"id":"W4245631291","doi":"10.1155/2006/134949","title":"Design, Sensing and Control of a Robotic Prosthetic Eye for Natural Eye Movement","year":2006,"lang":"en","type":"article","venue":"Applied Bionics and Biomechanics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Alberta; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Eye movement; Artificial intelligence; Computer vision; Computer science; SIGNAL (programming language); Artificial neural network; Sensor fusion; Eye tracking; Frame (networking); Engineering","score_opus":0.008226096543553633,"score_gpt":0.2116682611696438,"score_spread":0.20344216462609016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245631291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027103279,0.0004653112,0.9709914,0.0008009318,0.00009044161,0.00045995734,0.0000036444528,0.000076057295,0.000009004112],"genre_scores_gemma":[0.92363685,0.000019069164,0.07615891,0.00013425159,0.000011870341,0.0000106387015,0.0000027908873,0.000007307401,0.0000182843],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991476,0.000011785112,0.00019959547,0.00031301248,0.000099777266,0.00022824263],"domain_scores_gemma":[0.9995502,0.00005047662,0.000119951306,0.000186096,0.0000652303,0.000028043152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002454846,0.00013349255,0.00020174241,0.000102985934,0.00010714185,0.000057163135,0.00014589587,0.00009179542,1.4715481e-7],"category_scores_gemma":[0.000005504887,0.00010897707,0.000028498462,0.00018844799,0.00006224985,0.000030074947,0.000090477915,0.00006882262,5.047792e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022301369,0.00006121828,0.00001585734,0.00004484015,0.000022286758,0.0000017721972,0.000027739577,0.000026698119,0.6448699,0.2719746,0.000018123103,0.08291464],"study_design_scores_gemma":[0.001461461,0.00031348635,0.00018880467,0.000028993576,0.00003646134,0.0000064132573,0.000027132523,0.64144325,0.22794054,0.12803489,0.00025660286,0.00026192857],"about_ca_topic_score_codex":0.000011606811,"about_ca_topic_score_gemma":0.0000032251457,"teacher_disagreement_score":0.8965336,"about_ca_system_score_codex":0.000016348644,"about_ca_system_score_gemma":0.00002175077,"threshold_uncertainty_score":0.44439557},"labels":[],"label_agreement":null},{"id":"W4245916062","doi":"10.1101/2021.10.22.465332","title":"Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Gaze; Eye tracking; Monocular; Motion capture; Video tracking; Motion (physics); Object (grammar)","score_opus":0.02015615444794315,"score_gpt":0.2439643866135396,"score_spread":0.22380823216559648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245916062","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65757567,0.0018718891,0.33824152,0.00015372151,0.001104983,0.00025523177,0.000014670102,0.00078079145,0.0000015445502],"genre_scores_gemma":[0.8573922,0.00010116921,0.14198549,0.00011650733,0.00027904843,0.000041230778,3.539353e-7,0.00008266524,0.0000013151898],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610597,0.00029565088,0.0006364668,0.0017782372,0.00041342032,0.00077026966],"domain_scores_gemma":[0.9969115,0.00008350923,0.00063543685,0.0015381338,0.00061141344,0.00022000137],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00070715364,0.0007194667,0.0007781956,0.00039095385,0.00045054802,0.0013307772,0.0010141948,0.00085213693,0.000009808185],"category_scores_gemma":[0.0003598214,0.000794758,0.00015359513,0.00080321403,0.0001628447,0.00057341467,0.001314695,0.0013891344,0.0000058066794],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004508345,0.00010242284,0.008281041,0.0003807817,0.0001696089,0.00031151847,0.00009281939,0.0038884552,0.98584825,0.00078617997,0.000014317747,0.000120106764],"study_design_scores_gemma":[0.0012018398,0.00005658035,0.112678155,0.0020784547,0.00026685788,7.6257675e-7,0.000030486926,0.59766394,0.28313613,0.00000791835,0.00024284482,0.0026360252],"about_ca_topic_score_codex":0.000108397275,"about_ca_topic_score_gemma":0.0000063555185,"teacher_disagreement_score":0.7027121,"about_ca_system_score_codex":0.00039062373,"about_ca_system_score_gemma":0.0005617573,"threshold_uncertainty_score":0.9997059},"labels":[],"label_agreement":null},{"id":"W4250256697","doi":"10.32920/ryerson.14664096.v1","title":"Robust Discriminative Analysis Framework for Gaze and Headpose Estimation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Toronto Metropolitan University","funders":"","keywords":"Gaze; Artificial intelligence; Discriminative model; Computer science; Robustness (evolution); Computer vision; Pose; Pattern recognition (psychology); Pupil","score_opus":0.0583605772870338,"score_gpt":0.3145092431379922,"score_spread":0.2561486658509584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250256697","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011893621,0.00023176915,0.98361003,0.0032192613,0.0002230793,0.00022538101,0.000011852649,0.0003381089,0.00024687775],"genre_scores_gemma":[0.4690021,0.000015734338,0.5307231,0.00006351284,0.00001344558,0.0000586183,0.000036148936,0.0000051469074,0.00008220821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984797,0.00005626993,0.00024079804,0.0008522485,0.00014863106,0.00022232244],"domain_scores_gemma":[0.99845093,0.00035091586,0.00017172925,0.00076587,0.00020225557,0.000058314843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023213123,0.00022471008,0.0004555782,0.00033991653,0.00011267736,0.00039271353,0.00057150086,0.00035508935,0.000009673712],"category_scores_gemma":[0.00032237018,0.00020247184,0.00019589253,0.00054242514,0.00008876815,0.00013422107,0.0009806468,0.0004359033,0.0000015543307],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000046596374,0.00014163968,0.0013842825,0.00019455542,0.00090582983,0.000013290363,0.0012980527,0.02310384,0.000018320563,0.8311171,0.00015559368,0.14166287],"study_design_scores_gemma":[0.00009659076,0.00004520489,0.020285066,0.00012479306,0.00040065002,0.0000030349356,0.00017639431,0.8148732,0.00061943417,0.16304989,0.000019095625,0.0003066561],"about_ca_topic_score_codex":0.000058990743,"about_ca_topic_score_gemma":0.00006380694,"teacher_disagreement_score":0.7917693,"about_ca_system_score_codex":0.00004992559,"about_ca_system_score_gemma":0.00006708427,"threshold_uncertainty_score":0.8256562},"labels":[],"label_agreement":null},{"id":"W4251370682","doi":"10.1167/13.9.511","title":"Distinct stages of word identification during reading: Evidence from eye movements","year":2013,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Stimulus (psychology); Eye movement; Word lists by frequency; Gaze; Fixation (population genetics); Psychology; Speech recognition; Audiology; Cognitive psychology; Communication; Computer science; Artificial intelligence; Neuroscience","score_opus":0.020956918013451297,"score_gpt":0.30127307538905146,"score_spread":0.2803161573756002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251370682","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94161546,0.00016784045,0.057269234,0.00062450505,0.00023715138,0.000045968998,7.32315e-7,0.00002276878,0.000016326854],"genre_scores_gemma":[0.9909276,0.000051485415,0.008847896,0.000008093777,0.00003884802,0.0000010233033,2.7502122e-7,0.000003860204,0.00012095473],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99887913,0.00005001199,0.00048909825,0.00014514537,0.00032576267,0.0001108395],"domain_scores_gemma":[0.99856806,0.00011535452,0.00071603677,0.00029031723,0.00026491206,0.000045299887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034269894,0.00007233281,0.0001620438,0.00016452369,0.00005642952,0.00008859714,0.00066112366,0.000043561267,0.000022485088],"category_scores_gemma":[0.00025055284,0.000056734065,0.000059880364,0.0001908079,0.00003628592,0.00088597275,0.00013343935,0.00014684457,0.000021949514],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011201608,0.000096325464,0.12027351,0.000018182982,0.000022495678,0.000011327363,0.00023790152,0.000020968324,0.8190461,0.00012623586,0.0002667942,0.059868988],"study_design_scores_gemma":[0.00015255035,0.00009362563,0.93110543,0.00041685524,0.000005282515,0.000003218935,0.000027426953,0.000734965,0.06522365,0.0021652945,0.000020895466,0.000050796316],"about_ca_topic_score_codex":0.000049986436,"about_ca_topic_score_gemma":0.000001214182,"teacher_disagreement_score":0.8108319,"about_ca_system_score_codex":0.000045930097,"about_ca_system_score_gemma":0.000019294263,"threshold_uncertainty_score":0.2313548},"labels":[],"label_agreement":null},{"id":"W4252441607","doi":"10.1145/765978.765981","title":"EyePliances","year":2003,"lang":"en","type":"article","venue":"CHI '03 extended abstracts on Human factors in computer systems - CHI '03","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science","score_opus":0.048403723637467054,"score_gpt":0.2955110176671237,"score_spread":0.24710729402965662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252441607","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93587816,0.00044138473,0.033463873,0.00016848024,0.0094035175,0.0009146402,0.000024072158,0.0017033474,0.018002544],"genre_scores_gemma":[0.99342054,0.000012843348,0.005475304,0.0001826649,0.00042461252,0.00005043952,0.00001893234,0.000062196195,0.0003524727],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9945693,0.00038078218,0.0013172874,0.001742586,0.00076076336,0.0012292841],"domain_scores_gemma":[0.99661773,0.0003016884,0.0006668788,0.0019673968,0.00013174712,0.00031457056],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008583439,0.00086057535,0.0010114844,0.00082453573,0.0005069835,0.00069504883,0.002413796,0.0004733069,0.000026939992],"category_scores_gemma":[0.00008381431,0.0007563748,0.00025743424,0.00072532956,0.00024450436,0.0006381077,0.0002572378,0.0011996231,0.0004991163],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035397446,0.0042559695,0.043232594,0.0006926544,0.00050615176,0.0012979624,0.003460281,0.018279804,0.0016740547,0.8857144,0.012767803,0.028082909],"study_design_scores_gemma":[0.0031649312,0.0013637132,0.93578887,0.0014712226,0.000035008088,0.00024731547,0.00022531679,0.008004352,0.009077156,0.011402308,0.026169479,0.0030503422],"about_ca_topic_score_codex":0.00015815126,"about_ca_topic_score_gemma":0.00006986394,"teacher_disagreement_score":0.89255625,"about_ca_system_score_codex":0.00027853748,"about_ca_system_score_gemma":0.00012655926,"threshold_uncertainty_score":0.9994887},"labels":[],"label_agreement":null},{"id":"W4252825013","doi":"10.7287/peerj.preprints.2718","title":"Assessment of accuracy for target detection in 3D-space using eye tracking and computer vision","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; Centre Hospitalier Universitaire Sainte-Justine","funders":"","keywords":"Workspace; Computer vision; Computer science; Artificial intelligence; Eye tracking; Tracking (education); Tracking system; Adaptation (eye); Protocol (science); Gaze; Point (geometry); Robot; Kalman filter","score_opus":0.041287349291209446,"score_gpt":0.3742900513375495,"score_spread":0.3330027020463401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252825013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26690793,0.00004412891,0.73187554,0.0002153739,0.00046397367,0.00031523008,0.0000028430943,0.00010032129,0.000074673495],"genre_scores_gemma":[0.58903265,0.000010126153,0.41088536,0.0000099359895,0.000032714197,0.000011910762,0.0000017901464,0.0000075077874,0.000008030611],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984884,0.000062493695,0.000347535,0.0006883577,0.00016539887,0.00024777924],"domain_scores_gemma":[0.99835706,0.00018888006,0.0005280616,0.0007366701,0.00015387936,0.000035439592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061385025,0.00022457405,0.00043689116,0.0003315175,0.00014479672,0.000255531,0.0007470034,0.0003132158,0.0000013253679],"category_scores_gemma":[0.00007756383,0.00021118553,0.00008144403,0.00007889196,0.000085089094,0.0003007533,0.0012436402,0.00043966796,2.4079122e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020632087,0.0003949347,0.04125878,0.0009643482,0.00010407552,0.00002599004,0.00046940622,0.017723829,0.039179225,0.020101527,0.00003525739,0.879722],"study_design_scores_gemma":[0.0003165262,0.00009871706,0.18707441,0.0002748367,0.000008707888,0.000005036851,0.0000067378505,0.80138445,0.0058256374,0.004717982,0.00009445159,0.00019248722],"about_ca_topic_score_codex":0.00019076087,"about_ca_topic_score_gemma":0.000058934776,"teacher_disagreement_score":0.87952954,"about_ca_system_score_codex":0.000102437734,"about_ca_system_score_gemma":0.00010427632,"threshold_uncertainty_score":0.8611896},"labels":[],"label_agreement":null},{"id":"W4253943720","doi":"10.1145/766084.766086","title":"AuraMirror","year":2003,"lang":"en","type":"article","venue":"CHI '03 extended abstracts on Human factors in computer systems - CHI '03","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Human–computer interaction; Process (computing); Painting; Visualization; Computer graphics (images); Multimedia; Artificial intelligence; Operating system; Visual arts; Art","score_opus":0.04741470383063509,"score_gpt":0.2919412668482792,"score_spread":0.2445265630176441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4253943720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94380915,0.00022068269,0.03147574,0.0001665471,0.00835396,0.0009668874,0.000024875419,0.0017699644,0.0132121695],"genre_scores_gemma":[0.99260104,0.0000067017586,0.006333501,0.00019670295,0.00033781168,0.000051906063,0.000025853305,0.00007449654,0.0003719828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9942624,0.00043404868,0.0014102138,0.0017756497,0.0007956157,0.001322065],"domain_scores_gemma":[0.9963675,0.00031206312,0.00067138247,0.0021444054,0.00014519326,0.0003594643],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000889923,0.000921127,0.0010552992,0.00097043597,0.0005067985,0.00069542305,0.0024350712,0.00054657273,0.00002945223],"category_scores_gemma":[0.00009593835,0.0008191238,0.00028732972,0.00074797426,0.00023505208,0.0006016264,0.00029488475,0.0013552647,0.0005678061],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036852536,0.0047908765,0.024672017,0.00062195177,0.00050918205,0.0015459518,0.0037027004,0.011779201,0.002430469,0.9181159,0.010652359,0.021142526],"study_design_scores_gemma":[0.003497611,0.0013782578,0.94275266,0.0011618711,0.000038093425,0.00032640444,0.00020331808,0.009802365,0.0073965625,0.009236772,0.02116211,0.0030439568],"about_ca_topic_score_codex":0.00019525009,"about_ca_topic_score_gemma":0.00007606078,"teacher_disagreement_score":0.9180807,"about_ca_system_score_codex":0.00034996582,"about_ca_system_score_gemma":0.00013887724,"threshold_uncertainty_score":0.99942595},"labels":[],"label_agreement":null},{"id":"W4280653139","doi":"10.1145/3530883","title":"Gaze as an Indicator of Input Recognition Errors","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Glycemic Index Laboratories","funders":"","keywords":"Gaze; Computer science; Artificial intelligence; Task (project management); Computer vision; Selection (genetic algorithm); Gesture; Speech recognition; Motion (physics); Pattern recognition (psychology); Engineering","score_opus":0.05676673994921659,"score_gpt":0.31713587221940387,"score_spread":0.26036913227018726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4280653139","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.995902,0.0000047655435,0.00073020364,0.0012936488,0.0007784237,0.00018283378,0.000004032867,0.00019176018,0.00091233343],"genre_scores_gemma":[0.9909003,0.0000013378467,0.008622006,0.00028796514,0.00008299285,0.000038538114,0.000003688222,0.000013413558,0.000049767114],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99858356,0.000034911824,0.0003674618,0.00043373197,0.0003987122,0.0001816328],"domain_scores_gemma":[0.99848264,0.000059937338,0.0006905495,0.0005534858,0.00017591495,0.000037455928],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003919865,0.0001511317,0.00020652439,0.00039325398,0.00028633937,0.000055231845,0.0029857873,0.000059113663,0.000051309384],"category_scores_gemma":[0.00010817438,0.00013061213,0.00011837143,0.00041487438,0.00008021299,0.0005569981,0.0015353746,0.0004897279,0.000016931006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043424443,0.004261876,0.015203003,0.0003434419,0.00034896613,0.000008076872,0.012414219,0.00038862898,0.3062632,0.10783313,0.021110466,0.5313907],"study_design_scores_gemma":[0.0015010013,0.006091373,0.06257701,0.00041954248,0.00007264014,0.000293256,0.0012283017,0.011684509,0.7409086,0.16998287,0.0044593243,0.0007815185],"about_ca_topic_score_codex":0.00003473631,"about_ca_topic_score_gemma":0.0000011455885,"teacher_disagreement_score":0.53060925,"about_ca_system_score_codex":0.00011014257,"about_ca_system_score_gemma":0.000021514714,"threshold_uncertainty_score":0.55483854},"labels":[],"label_agreement":null},{"id":"W4282823754","doi":"10.1016/j.dib.2022.108380","title":"A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality","year":2022,"lang":"en","type":"article","venue":"Data in Brief","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Peking University; Fundamental Research Funds for the Central Universities; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Magnetoencephalography; Functional magnetic resonance imaging; Computer science; Artificial intelligence; Cognition; Brain mapping; Neuroimaging; Pattern recognition (psychology); Psychology; Neuroscience; Electroencephalography","score_opus":0.24730206189731588,"score_gpt":0.4495561048294709,"score_spread":0.20225404293215501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282823754","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78939956,0.00004685662,0.19752118,0.00041275442,0.00016384834,0.0005789337,0.011723851,0.00008609718,0.00006690265],"genre_scores_gemma":[0.98694134,5.490237e-7,0.0085482495,0.00004332377,0.000030609845,0.000049212154,0.00436719,0.00001121841,0.000008304129],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787456,0.00027589107,0.00030712126,0.0006745272,0.0005117277,0.00035617914],"domain_scores_gemma":[0.99773455,0.00012467851,0.00013408487,0.0019032166,0.00006626355,0.000037212358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00232351,0.000103102815,0.00020971119,0.00024002473,0.00038465924,0.000058531205,0.0026905602,0.00003932253,0.000016497024],"category_scores_gemma":[0.00012163983,0.000104381834,0.000014770491,0.0004982573,0.00021956356,0.0004079479,0.0027043787,0.0003571971,6.2106443e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017000206,0.00889854,0.057774834,0.0005682913,0.0004690122,0.0011317194,0.01350767,0.004741574,0.34635633,0.106489375,0.15738729,0.30097532],"study_design_scores_gemma":[0.01763208,0.009719101,0.16140148,0.0006157217,0.000101684425,0.00021522946,0.008936842,0.52408755,0.14368728,0.0070672226,0.12355428,0.0029815447],"about_ca_topic_score_codex":0.0041824896,"about_ca_topic_score_gemma":0.00038718944,"teacher_disagreement_score":0.51934594,"about_ca_system_score_codex":0.00010042376,"about_ca_system_score_gemma":0.00012229917,"threshold_uncertainty_score":0.6322701},"labels":[],"label_agreement":null},{"id":"W4283324821","doi":"10.48550/arxiv.2206.09256","title":"Multistream Gaze Estimation with Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Gaze; Artificial intelligence; Transfer of learning; Isolation (microbiology); Computer science; Estimation; Computer vision; Optometry; Medicine; Biology; Engineering","score_opus":0.031234341043397593,"score_gpt":0.19371198491375738,"score_spread":0.1624776438703598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283324821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45020333,0.00000468178,0.54854816,0.00026164466,0.00006725434,0.00015137014,0.0000041893854,0.0003828771,0.0003764983],"genre_scores_gemma":[0.9944204,0.000035159323,0.004472954,0.000023925588,0.000010432881,0.000004172253,0.000043399672,0.000020538106,0.00096901617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981569,0.0001880274,0.00015965289,0.0010904985,0.00011392701,0.00029101697],"domain_scores_gemma":[0.9989584,0.00008101099,0.00010474012,0.00066649506,0.000077139666,0.00011223303],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014881985,0.00026281853,0.0002654051,0.00030931114,0.00024893865,0.00007485718,0.000914465,0.00022360389,0.000016204052],"category_scores_gemma":[0.000027919934,0.00029628648,0.00008744914,0.0005911647,0.000088955225,0.00018770127,0.0004889582,0.000833131,0.00002226583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001358497,0.00017648295,0.016571289,0.000048196634,0.000069707574,0.00031755716,0.0004943146,0.92729837,0.0002582394,0.046238955,0.00020997967,0.008181061],"study_design_scores_gemma":[0.00052148825,0.00027228385,0.005730946,0.00009262653,0.000059685466,0.000010467711,0.000100988924,0.98995167,0.00023387608,0.0021876215,0.00038751087,0.0004508263],"about_ca_topic_score_codex":0.00029694225,"about_ca_topic_score_gemma":0.000036880967,"teacher_disagreement_score":0.5442171,"about_ca_system_score_codex":0.00034022887,"about_ca_system_score_gemma":0.00008379249,"threshold_uncertainty_score":0.9999489},"labels":[],"label_agreement":null},{"id":"W4285400512","doi":"10.1109/ur55393.2022.9826259","title":"Touchless Shared-Control for Wheelchair Navigation","year":2022,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Wheelchair; Computer science; Control (management); Human–computer interaction; World Wide Web; Artificial intelligence","score_opus":0.014197724603194895,"score_gpt":0.24431093887614275,"score_spread":0.23011321427294784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285400512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018072542,0.000024884552,0.9746907,0.005255004,0.0002857652,0.00019194954,0.000016727596,0.00055520644,0.0009072306],"genre_scores_gemma":[0.9768313,1.5859922e-7,0.021650989,0.00052460004,0.000023802562,0.00024922538,0.000008795204,0.000004739107,0.0007063501],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993328,0.000027215186,0.00010358729,0.00024456505,0.000121397854,0.00017043263],"domain_scores_gemma":[0.99954224,0.00007415224,0.000046033274,0.0002710595,0.000043566022,0.000022974698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020045249,0.00006104005,0.00008354233,0.000060690196,0.00027722528,0.000044082557,0.00062742137,0.000021912798,0.00004785283],"category_scores_gemma":[0.00001651733,0.000058899746,0.000047155852,0.00018015847,0.000014919499,0.00013305822,0.00015423338,0.000104565894,0.0000121659705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012396852,0.00010995717,0.0011649062,0.000010664732,0.000019068344,0.000008900293,0.00020275111,0.00073109986,0.002712339,0.86179274,0.0117176995,0.12151749],"study_design_scores_gemma":[0.0041528326,0.0010681342,0.020871518,0.000016636315,0.000021469898,0.00011017772,0.00042043248,0.6561923,0.0075473026,0.13968503,0.16912115,0.0007930301],"about_ca_topic_score_codex":0.000012640702,"about_ca_topic_score_gemma":0.0000018827425,"teacher_disagreement_score":0.9587588,"about_ca_system_score_codex":0.000045154196,"about_ca_system_score_gemma":0.000026046622,"threshold_uncertainty_score":0.24018618},"labels":[],"label_agreement":null},{"id":"W4286433333","doi":"10.3390/s22145462","title":"Gaze Estimation Approach Using Deep Differential Residual Network","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Residual; Gaze; Artificial intelligence; Computer science; Differential (mechanical device); Estimation; Computer vision; Machine learning; Engineering; Algorithm; Systems engineering","score_opus":0.02133739245612412,"score_gpt":0.24022423250689479,"score_spread":0.21888684005077066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286433333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42964384,0.000022412754,0.56926924,0.00014427933,0.00027411644,0.000055528333,8.1091736e-7,0.00025281066,0.00033697006],"genre_scores_gemma":[0.85937685,5.257358e-7,0.14041051,0.000039222472,0.000079353864,0.000007482705,0.0000050797607,0.000008017081,0.000072986295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886394,0.0001689527,0.0001394513,0.00031109096,0.00023405984,0.0002825316],"domain_scores_gemma":[0.99948764,0.000046327474,0.00007799862,0.0003355137,0.000020040916,0.000032483164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020630463,0.00009883574,0.000119604076,0.00008725421,0.00042168997,0.00005295984,0.00044964656,0.000039189912,0.000019231462],"category_scores_gemma":[0.000023735784,0.00010213376,0.000039038543,0.00038731,0.00003485158,0.00006172939,0.0003525605,0.00024576142,0.000007833092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007652621,0.000076766824,0.0007956676,0.0000062359018,0.000019235602,0.00002146157,0.00039740783,0.93516105,0.00044616213,0.03521789,0.00035352883,0.027496962],"study_design_scores_gemma":[0.00013860129,0.00003573653,0.0031774081,0.0000025343018,0.0000075078397,0.000057384907,0.00006107043,0.9932617,0.00012574642,0.002804231,0.00020659843,0.00012149622],"about_ca_topic_score_codex":0.000017308805,"about_ca_topic_score_gemma":0.0000015319138,"teacher_disagreement_score":0.42973298,"about_ca_system_score_codex":0.00006199167,"about_ca_system_score_gemma":0.000022051916,"threshold_uncertainty_score":0.4164894},"labels":[],"label_agreement":null},{"id":"W4286587072","doi":"10.3389/frobt.2022.885610","title":"Control of a Wheelchair-Mounted 6DOF Assistive Robot With Chin and Finger Joysticks","year":2022,"lang":"en","type":"article","venue":"Frontiers in Robotics and AI","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"College Ahuntsic","funders":"Administration for Community Living; Australian Government; National Institute on Disability, Independent Living, and Rehabilitation Research; U.S. Department of Health and Human Services","keywords":"Joystick; Wheelchair; Computer science; Chin; Robot; Physical medicine and rehabilitation; Activities of daily living; Human–computer interaction; Simulation; Medicine; Artificial intelligence; Physical therapy","score_opus":0.004438607204404041,"score_gpt":0.20568064212280573,"score_spread":0.2012420349184017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286587072","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019118087,0.0006065419,0.9776808,0.002098209,0.00023839196,0.00013167968,0.000011530318,0.00004770682,0.00006707674],"genre_scores_gemma":[0.92110807,0.000012108353,0.07854644,0.00024491022,0.0000076182146,0.000015164199,0.0000016327667,0.000007657141,0.00005641752],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904794,0.00006898286,0.00018291375,0.00031915866,0.00016530104,0.0002157008],"domain_scores_gemma":[0.99953717,0.000050535113,0.000109935914,0.00021321185,0.000043489566,0.00004566588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002093859,0.00012893419,0.00030413037,0.00020391228,0.0001386196,0.000036409772,0.00026591844,0.00004633794,0.0000022165912],"category_scores_gemma":[0.000028155646,0.00011330024,0.000021133957,0.00030008866,0.0001813342,0.000081715036,0.0001885475,0.00030538134,1.3337352e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002880176,0.0006590357,0.68459857,0.00012412429,0.0003244767,0.0002360868,0.0022948375,0.17802592,0.0007650522,0.067677796,0.0034038622,0.061602242],"study_design_scores_gemma":[0.0027480836,0.0008799967,0.19780003,0.0000768272,0.000046089768,0.000061214356,0.0004616377,0.79139316,0.00010562025,0.0048377626,0.0012049915,0.00038457484],"about_ca_topic_score_codex":0.000034745644,"about_ca_topic_score_gemma":0.000008257533,"teacher_disagreement_score":0.90198994,"about_ca_system_score_codex":0.000037164027,"about_ca_system_score_gemma":0.000044105414,"threshold_uncertainty_score":0.46202496},"labels":[],"label_agreement":null},{"id":"W4287269041","doi":"","title":"Preliminary experimental method to quantify vibrations with various powered wheelchair set-ups","year":2021,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Wheelchair; Vibration; Set (abstract data type); Manual wheelchair; Computer science; Aerospace engineering; Engineering; Acoustics; Physics; World Wide Web","score_opus":0.020514618831463206,"score_gpt":0.27305135587287893,"score_spread":0.2525367370414157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287269041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045249715,0.00073283334,0.92985094,0.016112467,0.00026177432,0.00052645686,0.000029657318,0.00088518555,0.0063509773],"genre_scores_gemma":[0.5103124,0.000023312738,0.48811522,0.00014892788,0.000009382742,0.00012966429,0.0001296079,0.00003117316,0.0011003161],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9927482,0.004106823,0.00053226127,0.0015651679,0.00053591625,0.0005116175],"domain_scores_gemma":[0.99308205,0.0009638719,0.0004216943,0.0036561938,0.0016140207,0.0002621956],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0027359661,0.00047886875,0.0005323533,0.0003705853,0.0004945848,0.0009811879,0.0028933466,0.0003836746,0.00006180103],"category_scores_gemma":[0.00045401542,0.00048932864,0.00019326938,0.00086293084,0.00018044222,0.00028014652,0.0038360949,0.0009000424,0.00004214491],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000096246986,0.0046765883,0.0031870278,0.00030831725,0.0006453498,0.0003156273,0.09291776,0.0023932334,0.061395206,0.7081425,0.0030594529,0.122862674],"study_design_scores_gemma":[0.0016771369,0.000023248444,0.023685968,0.004269192,0.00012977861,0.00036177103,0.0020566827,0.12697569,0.8235498,0.0049727987,0.009710502,0.0025873987],"about_ca_topic_score_codex":0.00090987125,"about_ca_topic_score_gemma":0.0006822354,"teacher_disagreement_score":0.76215464,"about_ca_system_score_codex":0.00017871891,"about_ca_system_score_gemma":0.0005666855,"threshold_uncertainty_score":0.99975586},"labels":[],"label_agreement":null},{"id":"W4288081273","doi":"10.3390/s22155627","title":"Usability Evaluation of the SmartWheeler through Qualitative and Quantitative Studies","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Usability; System usability scale; Qualitative research; Computer science; Engineering; Medical education; Applied psychology; Psychology; Human–computer interaction; Usability engineering; Medicine","score_opus":0.19544682755458279,"score_gpt":0.4282649563538975,"score_spread":0.2328181287993147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288081273","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951121,0.00048259314,0.000825614,0.0027104514,0.00019549152,0.00016667883,0.0000062417507,0.00003953716,0.00046130008],"genre_scores_gemma":[0.99532425,0.000006230327,0.004538391,0.000055284432,0.0000029508428,0.000025093874,2.5624922e-7,0.0000023890389,0.000045161076],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978944,0.0012340412,0.00015180248,0.00022690656,0.00039430417,0.00009853345],"domain_scores_gemma":[0.9988085,0.0005159509,0.0001203917,0.00030045837,0.00024692994,0.000007759728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017979807,0.00006536951,0.0001313531,0.000029243694,0.00024099633,0.0000068771215,0.0002575891,0.000015067403,0.000007212513],"category_scores_gemma":[0.0008725992,0.000045844765,0.000035073168,0.0003448415,0.0003854749,0.00007143306,0.00036154894,0.00012963312,0.00000152746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001862007,0.00016345881,0.0061148736,0.000033205433,0.00018827511,9.817508e-7,0.29417732,0.0036564898,0.0011899521,0.6807775,0.00064714876,0.013032175],"study_design_scores_gemma":[0.0010219452,0.0005685579,0.122879885,0.000029028704,0.0000959441,0.000015408308,0.21834841,0.04642346,0.0075374264,0.6016057,0.0011534062,0.00032080186],"about_ca_topic_score_codex":0.000043690277,"about_ca_topic_score_gemma":0.00002213476,"teacher_disagreement_score":0.11676501,"about_ca_system_score_codex":0.00006537618,"about_ca_system_score_gemma":0.0000414396,"threshold_uncertainty_score":0.18694954},"labels":[],"label_agreement":null},{"id":"W4289024097","doi":"","title":"Undermining a common language: smartphone applications for eye emergencies","year":2019,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Optometry; Computer security; Medicine","score_opus":0.19662838082988407,"score_gpt":0.5541879238205889,"score_spread":0.3575595429907048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289024097","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7410867,0.0072089015,0.24421254,0.001146412,0.00070020725,0.0012051096,0.00003540067,0.0003003615,0.0041043307],"genre_scores_gemma":[0.9917079,0.0003897496,0.0066269864,0.00026406825,0.00007022649,0.00017931535,0.000008214993,0.000026021311,0.00072747696],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982191,0.000084979234,0.0005321507,0.00046888614,0.0003267557,0.00036812865],"domain_scores_gemma":[0.9979829,0.00030936408,0.0005510678,0.00082439213,0.0002182653,0.00011396303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073831837,0.00021629051,0.0005217027,0.0005368954,0.00025249404,0.00066373387,0.0045119915,0.000102625105,0.00066451274],"category_scores_gemma":[0.00008506621,0.00020174956,0.00014884424,0.0010429932,0.00009141815,0.0012147403,0.0010879678,0.00027897581,0.00005186551],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047401554,0.00045196616,0.5362708,0.0001968244,0.0002309101,0.000015118776,0.00068221916,0.00021140579,0.21384004,0.012874812,0.02760448,0.20757404],"study_design_scores_gemma":[0.0010111557,0.000047122216,0.855912,0.00028068366,0.00006658161,0.000021055808,0.00041395432,0.002880506,0.06487997,0.033666186,0.039995216,0.0008255527],"about_ca_topic_score_codex":0.00026462035,"about_ca_topic_score_gemma":0.000048745645,"teacher_disagreement_score":0.31964123,"about_ca_system_score_codex":0.00005709509,"about_ca_system_score_gemma":0.00009937279,"threshold_uncertainty_score":0.8384478},"labels":[],"label_agreement":null},{"id":"W4291305254","doi":"10.1186/s12984-022-01066-8","title":"Evaluating surface EMG control of motorized wheelchairs for amyotrophic lateral sclerosis patients","year":2022,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"University of Central Florida","keywords":"Wheelchair; Joystick; Physical medicine and rehabilitation; Amyotrophic lateral sclerosis; Electromyography; Medicine; Neurology; Physical therapy; Psychology; Simulation; Computer science; Disease","score_opus":0.017070094774994108,"score_gpt":0.24977287233216047,"score_spread":0.23270277755716637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291305254","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8729757,0.000061846695,0.12560643,0.00076883414,0.00042753926,0.00012972255,0.0000048022616,0.00002458025,5.341281e-7],"genre_scores_gemma":[0.95174426,0.0000041860785,0.048201997,0.000013931679,0.000016488117,0.0000073634737,3.8826266e-7,0.000007222741,0.000004141996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999078,0.00009338392,0.00036155854,0.00012075288,0.00023178384,0.000114518705],"domain_scores_gemma":[0.9988703,0.00046331144,0.00028298874,0.0001060555,0.00024508848,0.000032247866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004946186,0.00007433832,0.00019717548,0.00015228637,0.00009237422,0.000015883332,0.00018530842,0.000018078177,6.7870457e-7],"category_scores_gemma":[0.0004076332,0.0000681928,0.00008712907,0.00016141897,0.000025724676,0.00014337608,0.00004316619,0.00015463587,5.0801738e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028761136,0.00030687609,0.028403353,0.00021725713,0.00007268146,0.000003033605,0.0015558378,0.6291478,0.31806928,0.0016855552,0.000086862616,0.020163804],"study_design_scores_gemma":[0.005251296,0.011016465,0.35867575,0.000111894056,0.000043416898,0.000021711312,0.00008404152,0.6227066,0.0004764131,0.0012152342,0.0002156798,0.00018149088],"about_ca_topic_score_codex":0.0000016972745,"about_ca_topic_score_gemma":3.4256153e-8,"teacher_disagreement_score":0.33027238,"about_ca_system_score_codex":0.000038882707,"about_ca_system_score_gemma":0.00002081736,"threshold_uncertainty_score":0.27808216},"labels":[],"label_agreement":null},{"id":"W4295067019","doi":"10.3758/s13428-022-01958-6","title":"Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture","year":2022,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"Canada Foundation for Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer vision; Computer science; Artificial intelligence; Gaze; Eye tracking; Monocular; Motion capture; Motion (physics)","score_opus":0.25227002656806086,"score_gpt":0.5296659863595778,"score_spread":0.277395959791517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295067019","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5312739,0.00040141348,0.46747634,0.00017776366,0.00021406442,0.00024003415,0.0000034191382,0.00018375399,0.00002931459],"genre_scores_gemma":[0.57238185,0.000006239333,0.42735663,0.000016539514,0.00003674997,0.0001173096,0.0000025254799,0.000017204498,0.00006494323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942128,0.0034088278,0.00028855877,0.0007183012,0.0006744713,0.00069703767],"domain_scores_gemma":[0.9985466,0.00042325057,0.00010930025,0.00058275607,0.00021389761,0.00012420355],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.007013314,0.00017844942,0.00025877057,0.000515182,0.0014984182,0.00032415276,0.000856672,0.000104365245,0.000054866665],"category_scores_gemma":[0.0005311446,0.00017952622,0.00006164364,0.0012397263,0.0001948249,0.0003293748,0.0012331854,0.0013381784,0.0000020514829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000071972604,0.0001193754,0.0066943346,0.000018364413,0.000011663046,0.00015596917,0.0010406844,0.0010261671,0.38535732,0.0012223275,0.000028841217,0.6043177],"study_design_scores_gemma":[0.0014885622,0.00044941777,0.06649519,0.00007402037,0.000061146144,0.0004729153,0.0021104293,0.8752515,0.048937604,0.0011729344,0.0025108599,0.00097548176],"about_ca_topic_score_codex":0.00025006564,"about_ca_topic_score_gemma":0.0000072373427,"teacher_disagreement_score":0.87422526,"about_ca_system_score_codex":0.00032834703,"about_ca_system_score_gemma":0.00015111748,"threshold_uncertainty_score":0.9998015},"labels":[],"label_agreement":null},{"id":"W4296586053","doi":"","title":"Giving a Hand to the Eyes: Leveraging Input Accuracy for Subpixel Interaction","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Subpixel rendering; Computer science; Artificial intelligence; Computer vision; Human–computer interaction; Computer graphics (images); Pixel","score_opus":0.05118555447903509,"score_gpt":0.3120583447071961,"score_spread":0.260872790228161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296586053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07964104,0.00004469259,0.89750224,0.019260067,0.0016768493,0.00073277665,0.0000035965734,0.00048095232,0.00065778405],"genre_scores_gemma":[0.9267364,0.0000071451286,0.07041633,0.0009242834,0.00018938753,0.0004471828,0.00000452194,0.000017461292,0.0012572588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983191,0.000056165034,0.00030759253,0.0007628995,0.00017222269,0.00038206013],"domain_scores_gemma":[0.9977947,0.00057484215,0.00021227235,0.0011290738,0.00022401182,0.00006510312],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00042118973,0.0002670406,0.00026755067,0.00023758525,0.00032258796,0.0010902277,0.0020536573,0.00019066299,0.000023165934],"category_scores_gemma":[0.0004900871,0.00018528254,0.00014277946,0.00018838102,0.000046651683,0.00028446014,0.0021935466,0.000638593,0.00014863595],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000173999,0.00015332112,0.0012422082,0.00027891927,0.0002051193,0.000006331523,0.006491821,0.0040702587,0.0044547813,0.051205333,0.051141474,0.880733],"study_design_scores_gemma":[0.000784738,0.00027722283,0.014261874,0.0010755116,0.00006133624,0.0000677909,0.0008229032,0.5185809,0.031063499,0.06894038,0.36228672,0.0017771273],"about_ca_topic_score_codex":0.00038008613,"about_ca_topic_score_gemma":0.00008512471,"teacher_disagreement_score":0.8789559,"about_ca_system_score_codex":0.00011609018,"about_ca_system_score_gemma":0.00009609366,"threshold_uncertainty_score":0.9999467},"labels":[],"label_agreement":null},{"id":"W4300767868","doi":"10.48550/arxiv.1712.02822","title":"Hybrid eye center localization using cascaded regression and\\n hand-crafted model fitting","year":2017,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Pattern recognition (psychology); Regression; Face (sociological concept); Computer vision; Training set; Feature (linguistics); Statistics; Mathematics","score_opus":0.10943784487354977,"score_gpt":0.23273985989324483,"score_spread":0.12330201501969507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300767868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45173678,0.00007801692,0.5470526,0.00010483485,0.00041101166,0.00022631162,0.000019538964,0.00018482654,0.00018606673],"genre_scores_gemma":[0.99409753,0.00040528385,0.0040161605,0.00009412188,0.00008433868,7.459144e-7,0.000027002448,0.000048054215,0.0012267818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995584,0.00028371133,0.00050109,0.0025776294,0.00019298619,0.00086058374],"domain_scores_gemma":[0.9957065,0.00007999407,0.0012471743,0.0021576392,0.0004987988,0.0003099168],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0005193011,0.0007785036,0.0008004408,0.0006145669,0.0022194753,0.0006639944,0.0022691316,0.0006231713,0.000012275008],"category_scores_gemma":[0.00017242036,0.00087078416,0.00027501953,0.00036179842,0.0009846458,0.00093151256,0.0042307326,0.0012168693,0.00002520842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016707997,0.00034208308,0.041469596,0.00033157784,0.00022945223,0.0012681156,0.00069442106,0.9266701,0.0017721121,0.016543122,0.00013885336,0.010373487],"study_design_scores_gemma":[0.0012853327,0.000056273733,0.0013362628,0.001454076,0.0001706863,0.00004484071,0.00005329647,0.9771915,0.0023052488,0.015141924,0.00012478726,0.00083575834],"about_ca_topic_score_codex":0.00026886136,"about_ca_topic_score_gemma":0.0000215487,"teacher_disagreement_score":0.54303646,"about_ca_system_score_codex":0.0004056778,"about_ca_system_score_gemma":0.00032755494,"threshold_uncertainty_score":0.9993743},"labels":[],"label_agreement":null},{"id":"W4304730950","doi":"10.48550/arxiv.2210.04483","title":"Auxilio and Beyond: Comparative Evaluation, Usability, and Design Guidelines for Head Movement-based Assistive Mouse Controllers","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Usability; Head (geology); Computer science; Human–computer interaction","score_opus":0.3089414370237076,"score_gpt":0.30811033849189445,"score_spread":0.0008310985318131237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304730950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20870863,0.00018007358,0.7876758,0.0012976972,0.00019126027,0.001498552,0.000089171874,0.00021818232,0.00014063696],"genre_scores_gemma":[0.9694614,0.000032391286,0.029628288,0.00049961143,0.000019748546,0.000050908653,0.000035043544,0.00001379069,0.0002588237],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721277,0.0005049986,0.0003365201,0.0014369623,0.00017896178,0.00032981823],"domain_scores_gemma":[0.99698067,0.0007351097,0.00037465958,0.00077323936,0.0010088064,0.00012751568],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015055143,0.00037377223,0.00057921547,0.0003554493,0.0004379989,0.00012605508,0.0008687723,0.00020032053,0.000022029386],"category_scores_gemma":[0.00032990708,0.000413087,0.00013077624,0.00035253234,0.00038417042,0.00018717026,0.0011198295,0.00043404615,0.0000018318583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005308153,0.00048586796,0.01534327,0.00016449485,0.00057545654,0.00003872352,0.00050605054,0.7945762,0.00032157762,0.17871916,0.0040382696,0.0047000805],"study_design_scores_gemma":[0.002711853,0.0003240872,0.0045980723,0.000026962245,0.00016890395,8.7514604e-7,0.00036230648,0.9469721,0.0003314775,0.04381176,0.0002679378,0.00042366597],"about_ca_topic_score_codex":0.00010811524,"about_ca_topic_score_gemma":0.000095211464,"teacher_disagreement_score":0.76075274,"about_ca_system_score_codex":0.0003465739,"about_ca_system_score_gemma":0.00049296283,"threshold_uncertainty_score":0.9998321},"labels":[],"label_agreement":null},{"id":"W4311803101","doi":"10.1167/jov.22.14.4447","title":"Title: Where’s Waldo?: Analyzing visual search behaviour with a web-based eye tracking system","year":2022,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Gaze; Eye tracking; Visual search; Dwell time; Computer science; Computer vision; Artificial intelligence; Tracking (education); Bar (unit); Task (project management); Psychology; Geography","score_opus":0.014189686355202005,"score_gpt":0.30418868676231875,"score_spread":0.28999900040711674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311803101","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86464417,0.00041468916,0.13238645,0.0011975226,0.00042647126,0.00006528233,0.000001575385,0.00013391061,0.00072991836],"genre_scores_gemma":[0.9940967,0.0000027222547,0.005755827,0.000019967078,0.000039622122,0.0000010932202,2.918042e-7,0.000007835788,0.00007593644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902374,0.00009724538,0.00019308394,0.00012391536,0.0004203316,0.00014168856],"domain_scores_gemma":[0.9994838,0.000036500533,0.00017267914,0.00014097847,0.00012004583,0.000045982375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060445775,0.00006760732,0.00014580885,0.00027569555,0.00016166928,0.00006886787,0.0004146559,0.000028109354,0.000059250913],"category_scores_gemma":[0.000007878202,0.000051448373,0.00006077663,0.00037032942,0.000019502642,0.0001168751,0.000091166476,0.00038574458,0.000013519033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044369465,0.002236562,0.22297573,0.00042502064,0.00029220068,0.008973909,0.0014291639,0.012691436,0.07414775,0.011325508,0.027537407,0.6375216],"study_design_scores_gemma":[0.0055560283,0.009722662,0.28141376,0.0019965835,0.00017985933,0.0027634073,0.0017347459,0.6641475,0.008232075,0.00010531801,0.02315552,0.0009925165],"about_ca_topic_score_codex":0.000004995104,"about_ca_topic_score_gemma":0.0000016689254,"teacher_disagreement_score":0.65145606,"about_ca_system_score_codex":0.00013382989,"about_ca_system_score_gemma":0.00013710953,"threshold_uncertainty_score":0.20980038},"labels":[],"label_agreement":null},{"id":"W4312465695","doi":"10.3138/cart-2021-0005","title":"Design and Construction of a Colourblind-Friendly Surabaya City Angkot Route Map Prototype","year":2022,"lang":"en","type":"article","venue":"Cartographica The International Journal for Geographic Information and Geovisualization","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Reading (process); Perception; Road map; Computer science; Affect (linguistics); Colour Vision; Software; Computer vision; Artificial intelligence; Geography; Cartography; Psychology; Communication","score_opus":0.014022238355071121,"score_gpt":0.26619615740889296,"score_spread":0.25217391905382186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312465695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16722336,0.00015291035,0.8270062,0.0038041465,0.0009903024,0.0006759742,0.000033754328,0.00007618574,0.00003712491],"genre_scores_gemma":[0.9865792,0.00018436012,0.012666757,0.00033531451,0.00003293524,0.00013562324,0.000051365052,0.000005068553,0.000009375416],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868286,0.00012590636,0.00045740235,0.00013237691,0.00044974324,0.00015168922],"domain_scores_gemma":[0.99839497,0.0001370069,0.000542552,0.00013236214,0.00074361026,0.000049489943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012837794,0.000114623566,0.0001313801,0.0007819403,0.00072539283,0.00026668038,0.0005062384,0.00005610571,0.0000102134545],"category_scores_gemma":[0.00008859308,0.000094160656,0.00008423839,0.0005517944,0.00020525331,0.00078912126,0.00018423334,0.00022482307,2.5215982e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055557746,0.00012761685,0.054691333,0.00005758539,0.0003784935,0.0000036621302,0.0024656316,0.0016629968,0.00025457435,0.84132886,0.0011170058,0.09735667],"study_design_scores_gemma":[0.008483215,0.005071158,0.07916665,0.00015927637,0.00021805687,0.003593545,0.0051646596,0.38744658,0.0018893757,0.22895367,0.2786678,0.0011860064],"about_ca_topic_score_codex":0.00002154697,"about_ca_topic_score_gemma":0.0000033983586,"teacher_disagreement_score":0.81935585,"about_ca_system_score_codex":0.00002428686,"about_ca_system_score_gemma":0.0000773603,"threshold_uncertainty_score":0.5579209},"labels":[],"label_agreement":null},{"id":"W4312712419","doi":"10.1109/iscas48785.2022.9937649","title":"A Wearable Electrooculogram System with Parallel Motion Artifact Sensing and Reduction","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Symposium on Circuits and Systems (ISCAS)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artifact (error); SIGNAL (programming language); Electrooculography; Channel (broadcasting); Wearable computer; Bandwidth (computing); Computer vision; Wireless; Computer hardware; Artificial intelligence; Embedded system; Telecommunications; Eye movement","score_opus":0.01641711575022838,"score_gpt":0.22989859018459388,"score_spread":0.2134814744343655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312712419","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7626955,0.0004244987,0.2251067,0.0025031513,0.0033679313,0.00065297686,0.000022382581,0.00065159943,0.0045752623],"genre_scores_gemma":[0.99902236,0.000026504633,0.0001234428,0.000036784208,0.00013470295,0.00007109875,0.000007835566,0.000016394042,0.00056087994],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998104,0.00019013714,0.00029146875,0.0006130704,0.0005311743,0.00027016876],"domain_scores_gemma":[0.9992884,0.00004970345,0.00021714327,0.000272715,0.000101178215,0.00007087775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050250784,0.00018759094,0.00023201769,0.00022146401,0.0004888623,0.0003097009,0.00029778611,0.000060944578,0.000004189569],"category_scores_gemma":[0.0000069360544,0.00016814312,0.00004030185,0.0002661043,0.00005009465,0.00022256428,0.000093130155,0.00031384834,0.0000071012046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027541403,0.0008545997,0.00948301,0.00052119186,0.0011375597,0.0008034896,0.004074339,0.085252844,0.23907256,0.46433735,0.0023304971,0.19185714],"study_design_scores_gemma":[0.0024482445,0.002216646,0.007181931,0.00052508345,0.000068485526,0.015347317,0.0032324633,0.95709234,0.0028336262,0.00051384995,0.007412675,0.0011273542],"about_ca_topic_score_codex":0.00026836863,"about_ca_topic_score_gemma":0.000011029983,"teacher_disagreement_score":0.87183946,"about_ca_system_score_codex":0.00027350808,"about_ca_system_score_gemma":0.0000264601,"threshold_uncertainty_score":0.6856677},"labels":[],"label_agreement":null},{"id":"W4313530925","doi":"10.1016/j.cmpb.2022.107330","title":"Mining attention distribution paradigm: Discover gaze patterns and their association rules behind the visual image","year":2022,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"Key Research and Development Projects of Shaanxi Province; Higher Education Discipline Innovation Project; Ministry of Education of the People's Republic of China","keywords":"Computer science; Gaze; Association rule learning; Eye tracking; Context (archaeology); Task (project management); Artificial intelligence; Association (psychology); Process (computing); Eye movement; Visual search; Cognition; Machine learning; Psychology","score_opus":0.02762388025553545,"score_gpt":0.32776254960255774,"score_spread":0.30013866934702227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313530925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38212198,0.00030220297,0.613657,0.0033994322,0.0002980871,0.00014341457,0.000008637883,0.00006554358,0.0000037004747],"genre_scores_gemma":[0.89362377,0.00005322259,0.10584832,0.00016081489,0.00011737225,0.000064684275,0.00010813323,0.0000071883496,0.000016469918],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99836665,0.0005660694,0.00024836118,0.0003846569,0.00016806328,0.00026621282],"domain_scores_gemma":[0.9992276,0.00033894298,0.0001652683,0.0002003724,0.000025281824,0.000042568085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022519096,0.00014726612,0.00021794738,0.00010167691,0.0002978927,0.00015585714,0.00030039734,0.000056393776,0.0000024309402],"category_scores_gemma":[0.000027377058,0.00009699692,0.000031550913,0.00030590285,0.00012913991,0.00014755513,0.0005519334,0.00027294073,3.1770887e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000468529,0.00008391786,0.06258998,0.000014909642,0.000022732384,0.0000067305323,0.0008841734,9.053797e-7,0.00046235594,0.0014814828,0.00008397957,0.93436414],"study_design_scores_gemma":[0.0013419255,0.0013133333,0.8305135,0.00012040928,0.000031889653,0.00012323272,0.0006901745,0.13946483,0.00021184295,0.00937924,0.016458409,0.00035125593],"about_ca_topic_score_codex":0.000053417567,"about_ca_topic_score_gemma":0.0000063940884,"teacher_disagreement_score":0.9340129,"about_ca_system_score_codex":0.00008142728,"about_ca_system_score_gemma":0.0000130460085,"threshold_uncertainty_score":0.39554194},"labels":[],"label_agreement":null},{"id":"W4313887286","doi":"10.1109/jstsp.2023.3235302","title":"EyeDrive: A Deep Learning Model for Continuous Driver Authentication","year":2023,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Biometrics; Authentication (law); Modality (human–computer interaction); Context (archaeology); Artificial intelligence; Deep learning; Frame (networking); Frame rate; Focus (optics); Identification (biology); Computer vision; Machine learning; Computer security; Computer network","score_opus":0.022988178606121786,"score_gpt":0.28708106719560605,"score_spread":0.26409288858948426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313887286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26143253,0.00008531163,0.7377396,0.00047028894,0.0000823093,0.000058754158,1.7314291e-7,0.00010389399,0.000027109614],"genre_scores_gemma":[0.9578164,0.000015100987,0.041787114,0.000030203237,0.00011240864,0.0000055658666,7.2376e-7,0.000010052046,0.00022240807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888515,0.000044311604,0.0003970676,0.00017970649,0.00021494424,0.00027880364],"domain_scores_gemma":[0.998793,0.00010163043,0.00036549132,0.00007926034,0.00061857054,0.000042066735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046214688,0.00010251793,0.00021643989,0.00041872964,0.00013595802,0.00010323106,0.0004704176,0.000102648904,9.2969736e-7],"category_scores_gemma":[0.00015612609,0.00009571164,0.00005269024,0.0009942305,0.000041724124,0.00035482907,0.00003192774,0.00043651697,0.0000024010815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005196206,0.00017509027,0.00795433,0.00013940655,0.000049472557,0.000080110876,0.0061171725,0.090838924,0.092381,0.0033547627,0.00022964622,0.79862815],"study_design_scores_gemma":[0.0004721142,0.00013002352,0.0035519404,0.00011880936,0.000011850604,0.0000347527,0.000059074562,0.97792524,0.004509939,0.012966309,0.00011020151,0.00010976471],"about_ca_topic_score_codex":8.075404e-7,"about_ca_topic_score_gemma":0.000002614952,"teacher_disagreement_score":0.8870863,"about_ca_system_score_codex":0.00006930454,"about_ca_system_score_gemma":0.00016321546,"threshold_uncertainty_score":0.39030075},"labels":[],"label_agreement":null},{"id":"W4316876947","doi":"10.1109/tnsre.2023.3236886","title":"Non-Intrusive Real Time Eye Tracking Using Facial Alignment for Assistive Technologies","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Eye tracking; Gaze; Convolutional neural network; Computation; Pose; Tracking (education); Face (sociological concept); Facial motion capture; Mobile device; Facial recognition system; Face detection; Pattern recognition (psychology); Algorithm","score_opus":0.013559550975330013,"score_gpt":0.2567866469808454,"score_spread":0.2432270960055154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316876947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.386608,0.000012386696,0.6107634,0.00040578766,0.00066099025,0.00040128888,0.000015411193,0.0011275944,0.00000518542],"genre_scores_gemma":[0.9904605,0.000009990266,0.009241225,0.0000030362542,0.00002678184,0.00018238691,0.0000014166209,0.000018324276,0.000056347748],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988547,0.000021429103,0.00027904572,0.00040049042,0.00015485125,0.00028951382],"domain_scores_gemma":[0.9991841,0.0004004186,0.00007322099,0.00021759656,0.000084796884,0.000039896666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021605851,0.00018437959,0.000238062,0.0004249601,0.00025239805,0.00009553841,0.00018054407,0.00013965854,4.4125008e-7],"category_scores_gemma":[0.000032252254,0.00017213036,0.00009415225,0.0004992482,0.00006621669,0.00024422447,0.0000050421427,0.00015152471,0.0000063301095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020075227,0.00006240249,0.0001259414,0.00025752163,0.00007881106,0.000008441162,0.0010324685,0.8022595,0.15411986,0.001511395,0.000053393265,0.040470175],"study_design_scores_gemma":[0.00032604943,0.0002981868,0.0029482674,0.0001228498,0.000015672797,0.000008393589,0.00042861572,0.99064755,0.004798842,0.000058684953,0.0001258257,0.00022103629],"about_ca_topic_score_codex":0.000033938388,"about_ca_topic_score_gemma":0.0000013524367,"teacher_disagreement_score":0.6038525,"about_ca_system_score_codex":0.00010921966,"about_ca_system_score_gemma":0.00001548575,"threshold_uncertainty_score":0.7019272},"labels":[],"label_agreement":null},{"id":"W4317815064","doi":"10.31234/osf.io/7924h","title":"Validation of an Open Source, Remote Web-based Eye-tracking Method (WebGazer) for Research in Early Childhood","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Université du Québec à Trois-Rivières; Ambrose University","funders":"Deutsche Forschungsgemeinschaft; National Institutes of Health; National Science Foundation","keywords":"Eye tracking; Computer science; Preprocessor; Sample (material); Gaze; Artificial intelligence; Tracking (education); Task (project management); Computer vision; Web application; Human–computer interaction; World Wide Web; Psychology; Engineering","score_opus":0.158784431161537,"score_gpt":0.4466810155885259,"score_spread":0.2878965844269889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317815064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2644184,0.000010647995,0.73098004,0.001974262,0.00024307237,0.0013524753,0.000015996073,0.00057746243,0.00042766077],"genre_scores_gemma":[0.54894155,0.00000282254,0.45057636,0.000029984742,0.000039023467,0.00009175846,0.00003695993,0.000040222734,0.00024129688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99553615,0.0009315908,0.0007262864,0.0015086433,0.00062169705,0.00067562814],"domain_scores_gemma":[0.99601096,0.0009914297,0.0003595134,0.0019576328,0.00058361003,0.000096830816],"candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":[],"category_scores_codex":[0.009246889,0.00029802718,0.0006736719,0.0016005746,0.00016953486,0.0006167468,0.005616203,0.00058166904,0.0000052804166],"category_scores_gemma":[0.00059132994,0.00029677874,0.00013017625,0.0013545537,0.00011299888,0.0003514827,0.0039155977,0.0013135071,0.000020762494],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011855137,0.0012605985,0.0065589203,0.0006862174,0.00016542625,0.00006639641,0.0032527535,0.03532596,0.011229233,0.031604968,0.0006273179,0.90910363],"study_design_scores_gemma":[0.0024292693,0.0008650134,0.14866833,0.0012160013,0.000025785577,0.00000420911,0.00019309513,0.55861175,0.09637025,0.19021446,0.00055585813,0.0008459484],"about_ca_topic_score_codex":0.002268113,"about_ca_topic_score_gemma":0.00022314304,"teacher_disagreement_score":0.9082577,"about_ca_system_score_codex":0.00013580958,"about_ca_system_score_gemma":0.0007336479,"threshold_uncertainty_score":0.99994844},"labels":[],"label_agreement":null},{"id":"W4318433499","doi":"10.1007/7854_2022_409","title":"Eye Tracking in Virtual Reality","year":2023,"lang":"en","type":"article","venue":"Current topics in behavioral neurosciences","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Eye tracking; Virtual reality; Computer science; Eye movement; Tracking (education); Perception; Human–computer interaction; Computer vision; Optical head-mounted display; Eye tracking on the ISS; Artificial intelligence; Computer graphics (images); Psychology","score_opus":0.14813899291122248,"score_gpt":0.4166185298234148,"score_spread":0.2684795369121923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318433499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9923624,0.000032594915,0.00233461,0.0012794823,0.003472998,0.000103155995,0.0000020960724,0.00034543115,0.00006726546],"genre_scores_gemma":[0.9996456,0.000048021786,0.00013276478,0.000024462817,0.000034497352,0.000018692512,0.0000013866652,0.0000036921122,0.0000909017],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982804,0.00009159831,0.00029137652,0.00058316253,0.00030766753,0.0004457767],"domain_scores_gemma":[0.999471,0.00004182079,0.000064144115,0.00034986777,0.000022765835,0.00005038177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047457998,0.00012607164,0.00016327754,0.0004657053,0.00009514174,0.00013277981,0.0011989642,0.000060782775,0.0000018749843],"category_scores_gemma":[0.00009385126,0.00011896055,0.000039649112,0.0022639679,0.00020523948,0.00041889102,0.0003245095,0.00037985103,0.000017389139],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012534838,0.00031715067,0.5902732,0.0000068881996,1.4563793e-7,0.00010915592,0.000453869,0.00026407518,0.0013765987,0.024157085,0.00010861781,0.38293198],"study_design_scores_gemma":[0.00018351784,0.00011433934,0.9831165,0.000040043633,0.0000010321708,0.0000026003627,0.000069237634,0.010402634,0.00073689973,0.0027571952,0.0023929721,0.0001829966],"about_ca_topic_score_codex":0.000046152876,"about_ca_topic_score_gemma":0.00013021982,"teacher_disagreement_score":0.39284334,"about_ca_system_score_codex":0.000045405475,"about_ca_system_score_gemma":0.000050544957,"threshold_uncertainty_score":0.48510703},"labels":[],"label_agreement":null},{"id":"W4319294551","doi":"10.1111/cogs.13247","title":"Did You Get That? Predicting Learners’ Comprehension of a Video Lecture from Visualizations of Their Gaze Data","year":2023,"lang":"en","type":"article","venue":"Cognitive Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Gaze; Comprehension; Eye tracking; Visualization; Computer science; Nonverbal communication; Face (sociological concept); Tracking (education); BitTorrent tracker; Psychology; Mathematics education; Multimedia; Cognitive psychology; Artificial intelligence; Communication; Pedagogy; Linguistics","score_opus":0.08491235177663273,"score_gpt":0.3278207999263255,"score_spread":0.24290844814969278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319294551","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7165619,0.00009587487,0.28191942,0.00020420377,0.00017515218,0.00013071018,0.00016019898,0.00029511342,0.00045743203],"genre_scores_gemma":[0.9978682,0.000023797244,0.0019524541,0.00004918535,0.000017863653,0.00000461231,0.000055291737,0.000007112888,0.00002152066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816686,0.00008218473,0.00023646152,0.0007393906,0.00046826055,0.0003068524],"domain_scores_gemma":[0.9979203,0.0005856479,0.00024203473,0.0008031832,0.000386357,0.00006251806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005790708,0.0001314533,0.00022467379,0.00036445833,0.0002692935,0.000057249737,0.002121752,0.00006398019,0.000012993896],"category_scores_gemma":[0.0009904299,0.00011008501,0.00003235264,0.0028070062,0.0010172647,0.0005863037,0.0016996065,0.0001724925,0.00002460037],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044586937,0.00059408095,0.15827121,0.00009974361,0.00013030005,0.000038028786,0.016509492,0.0010190395,0.40401757,0.0087569,0.0007161717,0.40980288],"study_design_scores_gemma":[0.000852841,0.0003139342,0.354891,0.0006886248,0.000038227245,0.000010636381,0.0034987433,0.46236834,0.17060503,0.006049356,0.0003133239,0.0003699321],"about_ca_topic_score_codex":0.00009114481,"about_ca_topic_score_gemma":0.000018347931,"teacher_disagreement_score":0.4613493,"about_ca_system_score_codex":0.00001447705,"about_ca_system_score_gemma":0.00015586366,"threshold_uncertainty_score":0.44891366},"labels":[],"label_agreement":null},{"id":"W4319601830","doi":"10.1016/j.parkreldis.2023.105316","title":"Classification and staging of Parkinson's disease using video-based eye tracking","year":2023,"lang":"en","type":"article","venue":"Parkinsonism & Related Disorders","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; Baycrest Hospital; Nova Scotia Health Authority; University of King's College; Ottawa Hospital; University Health Network; Health Sciences Centre; Queen's University; London Health Sciences Centre; St. Michael's Hospital; Sunnybrook Health Science Centre; Lawson Health Research Institute; Dalhousie University; Western University; University of Toronto; Toronto Western Hospital","funders":"Canadian Institutes of Health Research; Ontario Brain Institute","keywords":"Parkinson's disease; Eye tracking; Medicine; Optometry; Artificial intelligence; Computer science; Physical medicine and rehabilitation; Computer vision; Disease; Internal medicine","score_opus":0.022023229198155733,"score_gpt":0.27619901247159734,"score_spread":0.2541757832734416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319601830","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91119933,0.00059021666,0.083851285,0.0027123825,0.00023026009,0.00021393521,0.000006182676,0.0009585586,0.00023785286],"genre_scores_gemma":[0.99537164,0.00021908502,0.0042550396,0.000061024588,0.000007928289,0.000019734356,0.000011419068,0.000027207012,0.000026943819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981426,0.00012927927,0.00038294448,0.00062387745,0.00029064473,0.00043062726],"domain_scores_gemma":[0.99881756,0.00017713693,0.0002658566,0.0005404567,0.00006633242,0.00013268099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045703707,0.00021916733,0.00025233364,0.0005037206,0.0002735523,0.00008928868,0.000476574,0.00014194305,0.000004748106],"category_scores_gemma":[0.0001574714,0.00023136483,0.00009401272,0.0012763408,0.00025439716,0.0002979023,0.0001273783,0.00026299307,0.000016290609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006495828,0.00033734657,0.31638172,0.00023915397,0.00014519077,0.00007462773,0.0018117449,0.015869876,0.013522742,0.029648434,0.00020378947,0.6217004],"study_design_scores_gemma":[0.0007813756,0.00004162834,0.30530736,0.00011502045,0.000040779094,0.0000014064057,0.00018751415,0.6664463,0.0004711496,0.009837464,0.01644389,0.00032610557],"about_ca_topic_score_codex":0.000050894643,"about_ca_topic_score_gemma":0.000005497536,"teacher_disagreement_score":0.6505764,"about_ca_system_score_codex":0.00004619152,"about_ca_system_score_gemma":0.000112240596,"threshold_uncertainty_score":0.9434784},"labels":[],"label_agreement":null},{"id":"W4319864718","doi":"10.2139/ssrn.4329905","title":"Offline Machine Learning Algorithm for Wheelchair Control Throughpof Pressure Sensors and Neck Movements","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wheelchair; Computer science; Artificial intelligence; Focus (optics); Decision tree; Feature (linguistics); Random forest; Algorithm; Root mean square; k-nearest neighbors algorithm; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.007693552532613145,"score_gpt":0.23982775381714963,"score_spread":0.2321342012845365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319864718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04529523,0.002432615,0.9482031,0.0030694238,0.00023519031,0.0002309127,0.000013898015,0.0004687192,0.000050926177],"genre_scores_gemma":[0.98853433,0.0012900593,0.0058440785,0.00011348426,0.00017079267,0.000016786076,0.0000069040966,0.000025736796,0.0039978107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974918,0.0000760258,0.00023834,0.00032756117,0.00020944142,0.0016568583],"domain_scores_gemma":[0.99938565,0.00010105767,0.0001683026,0.00017798957,0.000101827,0.00006519184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011753816,0.0001734703,0.00024089255,0.00017306309,0.0003798982,0.00009555139,0.000449778,0.000093642455,0.0000025888587],"category_scores_gemma":[0.00008152658,0.00015024684,0.00008342355,0.00029341987,0.00004876623,0.00021077815,0.00010227538,0.0012840449,0.000011388648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049449805,0.00014330966,0.0111409975,0.00003074984,0.00093104545,0.000034820394,0.00037975863,0.0021271629,0.0014246071,0.15279578,0.0005483625,0.83039397],"study_design_scores_gemma":[0.0036741972,0.0011266893,0.0042041885,0.000030625724,0.000048700287,0.00032599282,0.00023601677,0.8402579,0.00023588675,0.13555896,0.013993173,0.00030767938],"about_ca_topic_score_codex":0.00002492168,"about_ca_topic_score_gemma":0.000040256182,"teacher_disagreement_score":0.9432391,"about_ca_system_score_codex":0.000097498836,"about_ca_system_score_gemma":0.00023685698,"threshold_uncertainty_score":0.61268884},"labels":[],"label_agreement":null},{"id":"W4322489555","doi":"10.1016/j.chb.2023.107720","title":"Using eye tracking to examine expert-novice differences during simulated surgical training: A case study","year":2023,"lang":"en","type":"article","venue":"Computers in Human Behavior","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Eye tracking; Eye movement; Fixation (population genetics); Saccade; Gaze; Task (project management); Computer science; Visual search; Cognitive psychology; Psychology; Artificial intelligence; Medicine","score_opus":0.18866253841189298,"score_gpt":0.4021758467616576,"score_spread":0.2135133083497646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322489555","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9918907,0.000013459469,0.0058464347,0.00006563281,0.00043790808,0.00054775295,0.0000012312986,0.0011884491,0.000008411368],"genre_scores_gemma":[0.99553794,5.078586e-7,0.0042561907,0.000022151193,0.00008498509,0.0000493412,0.0000018490808,0.000028267312,0.000018745966],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99734384,0.00018261278,0.00053161226,0.0009366354,0.00031845772,0.00068685523],"domain_scores_gemma":[0.99881786,0.00018304525,0.00010595656,0.00066717353,0.000068515496,0.00015747665],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038011058,0.0003225968,0.00047789165,0.000926859,0.00042468923,0.0002494109,0.0010752927,0.00012514292,0.0000067129426],"category_scores_gemma":[0.000023549266,0.00032738445,0.00007958553,0.0016438032,0.0000856213,0.00023963225,0.00070219825,0.0003755586,0.000010396996],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023944804,0.0024262546,0.58745724,0.000039376562,0.00006159251,0.21353821,0.08104251,0.008407741,0.019638373,0.00073241844,0.000017707003,0.086614594],"study_design_scores_gemma":[0.0031365475,0.00069689634,0.9006255,0.00023999465,0.000034187808,0.0020829348,0.0072498084,0.084113345,0.00069511775,0.000068316105,0.000025975343,0.0010313455],"about_ca_topic_score_codex":0.00044636137,"about_ca_topic_score_gemma":0.000119110184,"teacher_disagreement_score":0.31316826,"about_ca_system_score_codex":0.00016854059,"about_ca_system_score_gemma":0.000027522608,"threshold_uncertainty_score":0.9999178},"labels":[],"label_agreement":null},{"id":"W4323314808","doi":"10.3390/s23052846","title":"An Adaptive Pedaling Assistive Device for Asymmetric Torque Assistant in Cycling","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cycling; Torque; Assistive device; Computer science; Simulation; Physical medicine and rehabilitation; Engineering; Physics; Medicine","score_opus":0.04654834596131373,"score_gpt":0.32063487005205515,"score_spread":0.2740865240907414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323314808","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8772208,0.000049317012,0.11906726,0.00081708404,0.00043417636,0.0003071913,0.000016887843,0.0011040332,0.0009832681],"genre_scores_gemma":[0.9636805,0.0000074897503,0.035966814,0.00007739271,0.00006591813,0.000043952656,0.0000071266145,0.000020578367,0.00013019991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817616,0.00011631455,0.00030198172,0.0006513235,0.0002103191,0.0005438949],"domain_scores_gemma":[0.99857867,0.00061876833,0.00012601711,0.00044966565,0.00014004925,0.000086836626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073959265,0.00018732288,0.00027175018,0.00082628557,0.00016728553,0.00008937952,0.0006323904,0.00014654675,0.0000011158172],"category_scores_gemma":[0.00041102403,0.00018562995,0.00008160689,0.002436896,0.000057292295,0.00020822327,0.00010424598,0.00025248594,0.000051714196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028374424,0.0008430701,0.062151693,0.00016777912,0.00023336997,0.0010034281,0.0046197865,0.042243816,0.017173061,0.2784074,0.0010477185,0.5918251],"study_design_scores_gemma":[0.0009215727,0.0005446909,0.25412557,0.00010861504,0.000018174089,0.000012997866,0.0014015492,0.72597355,0.009416383,0.0053050686,0.001544763,0.00062708516],"about_ca_topic_score_codex":0.00010074984,"about_ca_topic_score_gemma":0.00014024087,"teacher_disagreement_score":0.6837297,"about_ca_system_score_codex":0.0001346828,"about_ca_system_score_gemma":0.0000726992,"threshold_uncertainty_score":0.75697696},"labels":[],"label_agreement":null},{"id":"W4352981306","doi":"10.1109/iscmi56532.2022.10068479","title":"Fuzzy Object Ambiguity Determination and Human Attention Assessment for Domestic Service Robots","year":2022,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ambiguity; Service robot; Computer science; Robot; Fuzzy logic; Artificial intelligence; Object (grammar); Service (business); Inference; Context (archaeology); Human–computer interaction; Machine learning; Programming language","score_opus":0.024289007565318585,"score_gpt":0.3225069351984005,"score_spread":0.2982179276330819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4352981306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46815813,0.000008291115,0.52770406,0.0020649196,0.0001778281,0.00024190948,0.0000029688738,0.00032205775,0.001319836],"genre_scores_gemma":[0.9516036,4.1544126e-7,0.047751155,0.00022547595,0.000011113238,0.00013172688,0.0000094710895,0.0000046242917,0.0002624199],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.999215,0.00005439094,0.00012577092,0.00030582625,0.00014485313,0.00015412229],"domain_scores_gemma":[0.9995496,0.00005832096,0.000071451344,0.00022754946,0.00006723768,0.00002579946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034287083,0.00007720336,0.00009497987,0.00010572313,0.00047976157,0.00007106492,0.00031514754,0.000026445474,0.000007259855],"category_scores_gemma":[0.000010732169,0.00007758428,0.0000269763,0.0002304007,0.000017446857,0.00014349035,0.0003190674,0.00010340901,0.0000016513612],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008629461,0.0005145557,0.025958095,0.00014598407,0.00003777948,0.000026418104,0.00023764459,0.0006507542,0.045150712,0.6655568,0.00071214634,0.2610005],"study_design_scores_gemma":[0.0009162185,0.0005775974,0.84356844,0.000009493284,0.000021148579,0.00007127608,0.00010174885,0.09686097,0.00038585954,0.056735035,0.00048950047,0.00026267907],"about_ca_topic_score_codex":0.000071997136,"about_ca_topic_score_gemma":0.00007443622,"teacher_disagreement_score":0.8176104,"about_ca_system_score_codex":0.00008052144,"about_ca_system_score_gemma":0.000024971661,"threshold_uncertainty_score":0.36899865},"labels":[],"label_agreement":null},{"id":"W4353100332","doi":"10.18280/ts.400137","title":"On a Moving Target Selection Model in Virtual Reality Based on Decision Trees","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Department of Education of Guizhou Province","keywords":"Virtual reality; Selection (genetic algorithm); Computer science; Decision tree; Decision model; Artificial intelligence; Computer vision; Computer graphics (images); Machine learning","score_opus":0.02754694645251198,"score_gpt":0.2714290012157336,"score_spread":0.2438820547632216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4353100332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35640898,8.862806e-7,0.6420902,0.0006804532,0.000057048957,0.00009999157,0.000004252566,0.00042324062,0.00023494496],"genre_scores_gemma":[0.9927778,7.900036e-7,0.006796051,0.00030866545,0.000022978931,0.00003609799,0.000008127629,0.000009404422,0.000040066294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984757,0.000077449935,0.00025048348,0.00047474043,0.0004052109,0.00031643276],"domain_scores_gemma":[0.99936885,0.00025413622,0.00006286446,0.00023209599,0.000032844237,0.000049190076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006616197,0.00015278079,0.00015744759,0.00050313247,0.000116900126,0.00005596813,0.00039669636,0.00008362873,0.000023095065],"category_scores_gemma":[0.00006338412,0.0001403518,0.000057359015,0.00082666386,0.000027191101,0.00012802165,0.000058240297,0.00021412152,0.00005728655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008301695,0.00022458243,0.00060229114,0.0000029957794,0.0000040973873,0.000016310678,0.0000945818,0.9237077,0.0024392023,0.026682077,0.0012248775,0.044918217],"study_design_scores_gemma":[0.0007458949,0.0004919432,0.028886074,0.000055356148,0.0000017342795,6.3809773e-7,0.000009585317,0.9555791,0.0019588012,0.012084655,0.000052519474,0.00013371448],"about_ca_topic_score_codex":0.000030034049,"about_ca_topic_score_gemma":0.00005784513,"teacher_disagreement_score":0.6363688,"about_ca_system_score_codex":0.00013435978,"about_ca_system_score_gemma":0.00005691467,"threshold_uncertainty_score":0.57233804},"labels":[],"label_agreement":null},{"id":"W4361732991","doi":"10.1109/hpcc-dss-smartcity-dependsys57074.2022.00295","title":"A Vision-Based Low-Cost Power Wheelchair Assistive Driving System for Smartphones","year":2022,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Science Foundation","keywords":"Computer science; Android (operating system); Wheelchair; Embedded system; Mobile device; Robot; Process (computing); Mobile phone; Human–computer interaction; Simulation; Artificial intelligence","score_opus":0.008692455537806256,"score_gpt":0.24059480072963213,"score_spread":0.23190234519182587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361732991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02381857,0.000021881902,0.96891266,0.002374001,0.0010347491,0.0004332537,0.000018952622,0.0013298336,0.0020560913],"genre_scores_gemma":[0.9756152,9.958406e-8,0.023043977,0.00031238902,0.000020548443,0.00043848087,0.0000060728944,0.000016137483,0.0005470667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983918,0.000091934075,0.00024619765,0.0005784416,0.00029768932,0.0003939325],"domain_scores_gemma":[0.9987184,0.00037161369,0.00012822142,0.0005900476,0.000119293254,0.000072466326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047661312,0.00017396259,0.00023374101,0.0002352648,0.00063391175,0.00010832048,0.0009943857,0.000054998105,0.000055549837],"category_scores_gemma":[0.00006521479,0.00015781145,0.00013568388,0.00049414753,0.000053260024,0.00013047742,0.00040956796,0.00019980212,0.000034580506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018868558,0.0021604744,0.048348173,0.00038980198,0.00025883535,0.0003538943,0.0010680148,0.008207071,0.012238423,0.6994634,0.07173201,0.1555912],"study_design_scores_gemma":[0.004792572,0.0019126105,0.116897404,0.00030845858,0.0000463255,0.00012910635,0.002433682,0.76455116,0.023966625,0.0016278777,0.0814228,0.0019113463],"about_ca_topic_score_codex":0.000024977478,"about_ca_topic_score_gemma":0.000015666408,"teacher_disagreement_score":0.95179665,"about_ca_system_score_codex":0.00023312918,"about_ca_system_score_gemma":0.0001270288,"threshold_uncertainty_score":0.6435364},"labels":[],"label_agreement":null},{"id":"W4367677811","doi":"10.1007/s10055-023-00799-8","title":"A scoping review of the use of lab streaming layer framework in virtual and augmented reality research","year":2023,"lang":"en","type":"review","venue":"Virtual Reality","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Ottawa Hospital","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Unidel Foundation; University of Delaware Research Foundation; Amazon Research Awards","keywords":"Computer science; Augmented reality; Virtual reality; Human–computer interaction; Multimedia; Task (project management); Synchronizing; Data science; Systems engineering; Telecommunications","score_opus":0.43265205144346236,"score_gpt":0.484353772767296,"score_spread":0.05170172132383366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367677811","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00017265418,0.99104863,0.0059114634,0.0004721805,0.00020679823,0.001827437,0.00015894446,0.0001401707,0.000061725135],"genre_scores_gemma":[0.0007732782,0.9984546,0.00050776,0.000041164218,0.000025153162,0.00009706661,0.000012238374,0.000025706919,0.00006305592],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9934461,0.0028202625,0.0015050375,0.0008732837,0.0008567469,0.0004985376],"domain_scores_gemma":[0.99216825,0.004590152,0.0008360304,0.002033181,0.00029126237,0.0000811545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056690667,0.00034179492,0.0020077857,0.00040112768,0.00012023464,0.00005651327,0.0016604658,0.0004900942,0.0000041167364],"category_scores_gemma":[0.007837941,0.00023884942,0.00028639703,0.003427956,0.00061916106,0.00017272831,0.0021352335,0.0015205553,0.000005715093],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003826336,0.00010734919,0.00007306729,0.10312308,0.000063802625,0.000011825293,0.000087336186,0.0000030646722,0.00000173958,0.037565473,0.00036255844,0.85859686],"study_design_scores_gemma":[0.00011828593,0.00015502906,0.00070751674,0.9732932,0.000083362735,0.000008272421,0.000023052735,0.00012526754,0.000014325891,0.0012055907,0.02398803,0.00027808512],"about_ca_topic_score_codex":0.0011205208,"about_ca_topic_score_gemma":0.0003136721,"teacher_disagreement_score":0.8701701,"about_ca_system_score_codex":0.0001661997,"about_ca_system_score_gemma":0.0007031838,"threshold_uncertainty_score":0.9739996},"labels":[],"label_agreement":null},{"id":"W4367725259","doi":"10.2139/ssrn.4435617","title":"Machine Learning Algorithm for Wheelchair Control Through Pof-Based Pressure Sensors and Neck Movements","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Wheelchair; Computer science; Control (management); Pressure sensor; Algorithm; Artificial intelligence; Engineering; Mechanical engineering","score_opus":0.014013876890875278,"score_gpt":0.257198521235995,"score_spread":0.24318464434511974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367725259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039403653,0.0049254796,0.9859323,0.003423208,0.0006422555,0.00048237425,0.00005545544,0.0005661045,0.00003243926],"genre_scores_gemma":[0.93901336,0.0027920478,0.052866243,0.00034950458,0.0004696021,0.00012387749,0.00004829936,0.00012421551,0.0042128465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99584836,0.00018885518,0.00045199355,0.00080130645,0.0003757962,0.0023336736],"domain_scores_gemma":[0.998516,0.00019027843,0.00054823735,0.00045086714,0.00020568562,0.00008892248],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0015736148,0.00043469865,0.00056808256,0.00024592408,0.00045780244,0.0002718253,0.0011292872,0.00038856236,0.000002581125],"category_scores_gemma":[0.00013344377,0.00040181083,0.00024311402,0.00017968511,0.00008437731,0.00016059626,0.00042247283,0.0052806293,0.000007682037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019656753,0.00055106095,0.014653903,0.0004353971,0.0052760965,0.000139556,0.0008098651,0.066424355,0.00031600197,0.1965853,0.0007465116,0.7138654],"study_design_scores_gemma":[0.0028785174,0.00071672705,0.00083614234,0.0001551537,0.00011790148,0.000116964424,0.00009042357,0.66625446,0.00010625399,0.32298508,0.005249751,0.00049262895],"about_ca_topic_score_codex":0.00014710044,"about_ca_topic_score_gemma":0.00008580218,"teacher_disagreement_score":0.935073,"about_ca_system_score_codex":0.00033513535,"about_ca_system_score_gemma":0.0012617344,"threshold_uncertainty_score":0.99984336},"labels":[],"label_agreement":null},{"id":"W4368240992","doi":"10.5267/j.msl.2023.4.004","title":"Voice-activated wheelchair: An affordable solution for individuals with physical disabilities","year":2023,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wheelchair; Raspberry pi; Computer science; Assistive technology; Speech synthesis; Software; Human–computer interaction; Disabled people; Arduino; Python (programming language); Voice command device; Embedded system; Speech recognition; Operating system; Internet of Things; World Wide Web; Psychology","score_opus":0.02195253575221101,"score_gpt":0.2684692214866219,"score_spread":0.24651668573441088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368240992","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.68600255,6.784492e-7,0.2996456,0.012604583,0.00014544123,0.00034509334,0.000002880191,0.0009776587,0.00027546787],"genre_scores_gemma":[0.9667527,6.351359e-7,0.032150567,0.00068861176,0.000034554072,0.00016026157,0.0000060299813,0.000009008067,0.00019765167],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980513,0.000024040577,0.00011338601,0.0006832106,0.00048430846,0.00064378284],"domain_scores_gemma":[0.9991827,0.00006692957,0.0000633901,0.00059463654,0.000024418385,0.00006789579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006259943,0.00014588062,0.00013275137,0.00040758547,0.00053359254,0.0002955687,0.0013619065,0.00002083548,0.0000011473776],"category_scores_gemma":[0.00002773229,0.00011837734,0.00003711982,0.00196724,0.00067562057,0.0012415787,0.0003540268,0.00008272174,0.000050911327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050670595,0.000850392,0.06249491,0.00030004504,0.0001805683,0.0000481093,0.0074206674,0.008665909,0.21886666,0.5071766,0.021218946,0.17272656],"study_design_scores_gemma":[0.003191541,0.0018407087,0.35847506,0.00020054232,0.000092802286,0.000013952122,0.004741771,0.50635535,0.060737424,0.036042977,0.02622048,0.0020873924],"about_ca_topic_score_codex":0.00001777489,"about_ca_topic_score_gemma":0.0000047558833,"teacher_disagreement_score":0.49768943,"about_ca_system_score_codex":0.00009026792,"about_ca_system_score_gemma":0.000011145404,"threshold_uncertainty_score":0.48272878},"labels":[],"label_agreement":null},{"id":"W4368362734","doi":"10.1093/cz/zoad020","title":"Friend or foe? Using eye-tracking technology to investigate the visual discrimination ability of giant pandas","year":2023,"lang":"en","type":"article","venue":"Current Zoology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer vision; Eye tracking; Tracking (education); PANDAS; Computer science; Psychology","score_opus":0.07401335991839855,"score_gpt":0.3947331686670998,"score_spread":0.3207198087487012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4368362734","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86296815,0.00010254096,0.1296891,0.005553229,0.00096263585,0.00022950326,0.0000046079176,0.00047267333,0.000017576549],"genre_scores_gemma":[0.9962153,0.000015465972,0.0036173412,0.00004532298,0.000040558185,0.000035805253,0.0000042129213,0.000010333408,0.000015679347],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99824446,0.00014059007,0.00040065646,0.0005458439,0.00018561837,0.00048283927],"domain_scores_gemma":[0.9987457,0.00020530561,0.00019211328,0.00061802735,0.00017460005,0.00006430155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058924797,0.0001795214,0.00030158312,0.0005501555,0.00020980166,0.00003442736,0.0010294946,0.00016082966,0.000008862439],"category_scores_gemma":[0.0008090836,0.00012253922,0.00006216118,0.0020545437,0.00058801577,0.00014698955,0.00074152363,0.00034401414,0.000041272757],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004178389,0.00031331126,0.06840324,0.00014837527,0.00005525885,0.000042036805,0.0030465948,0.0005507198,0.14963944,0.12437967,0.00044573334,0.65293384],"study_design_scores_gemma":[0.000998013,0.0010303449,0.7164343,0.00032780788,0.00008899761,0.00010271176,0.0012566388,0.11398295,0.07251779,0.088209555,0.0042331032,0.00081776094],"about_ca_topic_score_codex":0.000020975845,"about_ca_topic_score_gemma":0.00004356425,"teacher_disagreement_score":0.65211606,"about_ca_system_score_codex":0.00006409695,"about_ca_system_score_gemma":0.00012267497,"threshold_uncertainty_score":0.4997004},"labels":[],"label_agreement":null},{"id":"W4375946857","doi":"10.1139/cjce-2023-0056","title":"An eye gaze-aided virtual tape measure for smart construction","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Measure (data warehouse); Computer science; Calibration; Gaze; Point (geometry); Computer vision; Range (aeronautics); Artificial intelligence; Human–computer interaction; Simulation; Engineering; Data mining; Mathematics; Statistics","score_opus":0.012271636260653347,"score_gpt":0.21176524450146214,"score_spread":0.1994936082408088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4375946857","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10168628,0.00010647927,0.89498043,0.00097388716,0.0018398099,0.00006991252,0.0000076899005,0.00019400318,0.00014151532],"genre_scores_gemma":[0.993312,0.0000025523655,0.006481379,0.000025173238,0.00013658294,0.000003195671,0.0000013780844,0.000013683701,0.000024027158],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991971,0.000013589994,0.00021590354,0.00012965835,0.00011625817,0.00032745217],"domain_scores_gemma":[0.999172,0.000047977064,0.00008330649,0.00018734194,0.00017596288,0.00033340976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038926696,0.000101637575,0.00016294101,0.0006086524,0.00009278417,0.00008843781,0.00048256773,0.00008024071,0.000007954874],"category_scores_gemma":[0.00020110773,0.00010638009,0.00007190904,0.00045586788,0.000033993325,0.00030258766,0.000009326072,0.00020524253,0.000006235997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028479775,0.000046434,0.027296005,0.00016492643,0.00046023252,0.0012437953,0.002321189,0.31248668,0.063358106,0.3150117,0.01816058,0.25942186],"study_design_scores_gemma":[0.003372735,0.002054944,0.14548558,0.00088587526,0.00010560649,0.0017343329,0.00069300714,0.7334874,0.0104876915,0.00967334,0.090338886,0.0016805945],"about_ca_topic_score_codex":0.000082831604,"about_ca_topic_score_gemma":0.009245679,"teacher_disagreement_score":0.89162576,"about_ca_system_score_codex":0.000092881906,"about_ca_system_score_gemma":0.0004248953,"threshold_uncertainty_score":0.5159306},"labels":[],"label_agreement":null},{"id":"W4376223918","doi":"10.1080/13803395.2023.2207779","title":"Examining the validity of eye tracking during the computerized Wisconsin card sorting test in a sample of stroke patients and healthy controls","year":2023,"lang":"en","type":"article","venue":"Journal of Clinical and Experimental Neuropsychology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Psychology; Wisconsin Card Sorting Test; Test (biology); Eye tracking; Stroke (engine); Eye movement; Psychometrics; Sample (material); Test validity; Card sorting; Clinical psychology; Physical medicine and rehabilitation; Developmental psychology; Audiology; Neuropsychology; Cognition; Psychiatry; Neuroscience; Medicine; Artificial intelligence","score_opus":0.10062099466405534,"score_gpt":0.39586616130281527,"score_spread":0.29524516663875994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376223918","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975497,0.000095746036,0.0005589965,0.001198477,0.0004603039,0.000102860206,0.0000041022954,0.000014182461,0.000015616071],"genre_scores_gemma":[0.99869025,0.00006769662,0.00092815486,0.00025235343,0.000052037576,0.0000018367094,2.1072938e-7,0.00000578081,0.0000017043665],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99772465,0.0005057081,0.0011888122,0.00021653017,0.00016536783,0.00019890397],"domain_scores_gemma":[0.9958864,0.00293116,0.0008725003,0.00018722155,0.00006867494,0.000054064723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014334144,0.000102961654,0.0005212112,0.000107606014,0.0000905861,0.000021532811,0.0003817269,0.000068957175,8.603295e-7],"category_scores_gemma":[0.0009109386,0.000062118066,0.000085336615,0.00019921438,0.0005640558,0.00009845155,0.0002318549,0.00042832916,2.559902e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017245917,0.00028227636,0.96736956,0.000011229965,0.000020492724,0.000024763738,0.0006380122,0.0000134130805,0.02258899,0.00014139613,0.000012590938,0.008724807],"study_design_scores_gemma":[0.0028572672,0.0016582904,0.9922263,0.0000386587,0.000005794709,0.000041168783,0.00028219903,0.0007097927,0.0019213943,0.00018193113,0.000025832882,0.00005139394],"about_ca_topic_score_codex":0.000010333913,"about_ca_topic_score_gemma":0.0000026040955,"teacher_disagreement_score":0.024856709,"about_ca_system_score_codex":0.0000051578327,"about_ca_system_score_gemma":0.000020878697,"threshold_uncertainty_score":0.2533101},"labels":[],"label_agreement":null},{"id":"W4377014196","doi":"10.1145/3591126","title":"Classification of Alzheimer's using Deep-learning Methods on Webcam-based Gaze Data","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of the Fraser Valley; University of British Columbia","funders":"","keywords":"Gaze; Computer science; BitTorrent tracker; Eye tracking; Artificial intelligence; Classifier (UML); Computer vision","score_opus":0.2582520218858648,"score_gpt":0.4372612141484993,"score_spread":0.17900919226263445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377014196","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86366785,0.00002246158,0.13048571,0.0024482096,0.0014176107,0.00035470424,0.000003629897,0.00078174536,0.0008180815],"genre_scores_gemma":[0.8827799,0.0000023643095,0.11698905,0.00007782269,0.00009880943,0.000008036194,0.000007347519,0.0000188571,0.000017819],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826473,0.000072218936,0.0004556326,0.0006197851,0.00035021268,0.00023743893],"domain_scores_gemma":[0.9973581,0.00031837294,0.000804668,0.0012057386,0.00028045624,0.000032658656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010503369,0.00019550056,0.000274857,0.0005270577,0.00022780389,0.000102139355,0.0038043002,0.00011181938,0.000004728451],"category_scores_gemma":[0.00052248704,0.00015892152,0.000110654495,0.00084053056,0.000095084004,0.00051648816,0.0014334354,0.00048556452,0.000016854112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007560192,0.00041716886,0.003517093,0.00016023891,0.00025295714,0.0000014045418,0.00073956465,0.005787357,0.5872281,0.030293833,0.002995953,0.3685307],"study_design_scores_gemma":[0.00027209826,0.00030015508,0.03065639,0.00037775014,0.00004843203,0.000006404014,0.00008862106,0.78497064,0.17950249,0.0029320903,0.00066941936,0.00017551794],"about_ca_topic_score_codex":0.000016088688,"about_ca_topic_score_gemma":7.988885e-7,"teacher_disagreement_score":0.77918327,"about_ca_system_score_codex":0.000067834764,"about_ca_system_score_gemma":0.000020745045,"threshold_uncertainty_score":0.70693994},"labels":[],"label_agreement":null},{"id":"W4377014401","doi":"10.1145/3591137","title":"Unconscious Frustration: Dynamically Assessing User Experience using Eye and Mouse Tracking","year":2023,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"BitTorrent tracker; Eye tracking; Computer science; Task (project management); Computer mouse; Human–computer interaction; Point (geometry); Eye movement; Computer vision; Unconscious mind; Artificial intelligence; Tracking (education); Cursor (databases); Psychology; Engineering","score_opus":0.08054506340083589,"score_gpt":0.365363218046566,"score_spread":0.2848181546457301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377014401","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9878275,0.000004662784,0.009714694,0.0012115531,0.00054372073,0.00013717407,6.244666e-7,0.00043742292,0.00012265689],"genre_scores_gemma":[0.97795457,0.0000026064313,0.02171095,0.00011542353,0.00010610275,0.000009727942,6.884304e-7,0.000015162918,0.00008478706],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860686,0.000016264592,0.0003376325,0.0004965333,0.0002853318,0.00025736305],"domain_scores_gemma":[0.99887,0.0000911521,0.00034795766,0.00042654818,0.00022266354,0.0000416673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002538785,0.00018785904,0.0001984196,0.00027800832,0.00039426459,0.00054301636,0.001516777,0.00010078557,0.0000022765987],"category_scores_gemma":[0.00017129956,0.00015345764,0.000076134056,0.0005310178,0.00013190533,0.0013631157,0.0009211392,0.0003398894,0.0000057661737],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026036734,0.00024953342,0.05610158,0.0001544532,0.00008430708,0.000009264877,0.004877492,0.0007541871,0.8244028,0.02959049,0.0010461733,0.08270366],"study_design_scores_gemma":[0.00088489417,0.0004189689,0.28899062,0.0010245849,0.000043830216,0.00012158719,0.0013758935,0.34320915,0.34566364,0.016885852,0.00053674093,0.0008442398],"about_ca_topic_score_codex":0.000018338491,"about_ca_topic_score_gemma":0.000002905244,"teacher_disagreement_score":0.4787392,"about_ca_system_score_codex":0.00009313773,"about_ca_system_score_gemma":0.000016655336,"threshold_uncertainty_score":0.6257821},"labels":[],"label_agreement":null},{"id":"W4377200559","doi":"10.1007/978-3-031-32883-1_44","title":"Distraction Detection and Monitoring Using Eye Tracking in Virtual Reality","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Distraction; Computer science; Eye tracking; Relaxation (psychology); Process (computing); Virtual reality; Human–computer interaction; Cognitive load; Driving simulator; Cognition; Artificial intelligence; Computer vision; Cognitive psychology; Psychology","score_opus":0.04479556368736659,"score_gpt":0.30883080938326973,"score_spread":0.26403524569590314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377200559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027116977,0.000059767044,0.97016025,0.00020844328,0.0018706019,0.00015577243,0.0000015074615,0.00033172904,0.00009496416],"genre_scores_gemma":[0.9749073,0.000029521476,0.024741387,0.000023642717,0.000235932,0.0000037915734,7.297089e-7,0.00002258972,0.000035106088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973505,0.00003785528,0.00041227415,0.001234445,0.0004809491,0.00048396713],"domain_scores_gemma":[0.99870276,0.0003004485,0.00023098932,0.00059326773,0.000098942066,0.00007357935],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00094205403,0.00033580468,0.00036191268,0.0010179519,0.0002476548,0.0003621561,0.0009101032,0.00037242385,6.294551e-7],"category_scores_gemma":[0.00013490519,0.00034814028,0.00005405343,0.0007624018,0.00042531124,0.0006159737,0.00059029553,0.0010350606,0.0000050842377],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042205584,0.000014621851,0.0015962123,0.000022650558,0.0000045144893,0.000091400594,0.0003220456,0.031320482,0.004112289,0.0021055988,1.252612e-7,0.9604058],"study_design_scores_gemma":[0.0002795156,0.0001657389,0.04638662,0.00081766344,0.000008913921,0.00009068482,0.000001846592,0.87188065,0.009864484,0.06976864,0.00003659904,0.0006986324],"about_ca_topic_score_codex":0.0001471556,"about_ca_topic_score_gemma":0.00027766885,"teacher_disagreement_score":0.9597072,"about_ca_system_score_codex":0.00043757271,"about_ca_system_score_gemma":0.00012710785,"threshold_uncertainty_score":0.99989706},"labels":[],"label_agreement":null},{"id":"W4377724984","doi":"10.2139/ssrn.4439300","title":"Modification of a Joint Attention Assessment to Minimize the Need for Eye Gaze Modulation","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Joint attention; Joint (building); Modulation (music); Computer science; Visual attention; Psychology; Cognitive psychology; Artificial intelligence; Neuroscience; Engineering; Physics; Developmental psychology; Cognition; Acoustics; Structural engineering","score_opus":0.050085690400181015,"score_gpt":0.32584361833891523,"score_spread":0.2757579279387342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377724984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062315825,0.00010530691,0.9201297,0.01608619,0.0006367488,0.0005554079,0.000006186695,0.0001402739,0.000024330267],"genre_scores_gemma":[0.9678903,0.00021260118,0.03104967,0.000034690293,0.00014583413,0.00015466775,0.000017815852,0.00002269292,0.00047172979],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974819,0.00013603336,0.0005615647,0.00043508038,0.00036524193,0.0010202122],"domain_scores_gemma":[0.9981896,0.00008693005,0.00067460525,0.00064499263,0.00036058592,0.00004327935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029634123,0.00020009985,0.00030112223,0.00035321002,0.00020231964,0.00012846313,0.0011137797,0.00018997137,7.518972e-7],"category_scores_gemma":[0.00013175856,0.00015612743,0.00025786032,0.00031305323,0.000036643578,0.00009512175,0.00036347107,0.0016390629,0.000007681289],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004129602,0.00018515559,0.0018368845,0.000078719524,0.0004297497,9.675462e-7,0.00030599587,0.028571798,0.014217778,0.813691,0.00028441846,0.14035621],"study_design_scores_gemma":[0.00046558634,0.00022916096,0.11144812,0.00013237073,0.00005842469,0.000017923945,0.0003484633,0.18334922,0.00032123772,0.7033465,0.000062809755,0.00022019025],"about_ca_topic_score_codex":0.00004829891,"about_ca_topic_score_gemma":0.00004467728,"teacher_disagreement_score":0.9055745,"about_ca_system_score_codex":0.00091475016,"about_ca_system_score_gemma":0.0013888319,"threshold_uncertainty_score":0.7121004},"labels":[],"label_agreement":null},{"id":"W4377996913","doi":"10.1145/3588015.3588413","title":"On The Visibility Of Fiducial Markers For Mobile Eye Tracking","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CMC Microsystems (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Computer vision; Computer science; Gaze; Artificial intelligence; Eye tracking; Visibility; Distraction; Fixation (population genetics); Tracking (education); Medicine; Psychology; Optics; Physics","score_opus":0.028062180613182726,"score_gpt":0.31674763420975877,"score_spread":0.28868545359657605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377996913","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85184544,0.0000045973056,0.14271718,0.0023864629,0.0003404322,0.0003477337,0.0000045233332,0.00052001607,0.0018336115],"genre_scores_gemma":[0.99680746,9.062141e-7,0.0026809657,0.00010832655,0.000018854984,0.00005634012,8.1869774e-7,0.0000038948983,0.00032246226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99930435,0.000032518357,0.00013906584,0.00023760856,0.0001107171,0.00017576473],"domain_scores_gemma":[0.9987657,0.0006259947,0.00004965342,0.00043982576,0.00010410387,0.000014730376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000672423,0.000063692525,0.0000974301,0.000067241795,0.00009345352,0.000023110253,0.0005838112,0.000043785778,0.000019372665],"category_scores_gemma":[0.0004376802,0.0000401931,0.0000647368,0.00047941937,0.0000709166,0.000052641146,0.00009375727,0.00007441542,0.000027241298],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042148687,0.0001683789,0.0033116243,0.00004058396,0.000038978807,0.0000029279274,0.0005488185,0.00035240105,0.0044801636,0.70614105,0.029738626,0.25513428],"study_design_scores_gemma":[0.0012905041,0.001974427,0.36761516,0.00009850085,0.00002148737,0.0000020454854,0.0010248368,0.18553077,0.13952279,0.29216868,0.010118625,0.00063218205],"about_ca_topic_score_codex":0.000010239194,"about_ca_topic_score_gemma":0.0000063055413,"teacher_disagreement_score":0.4139724,"about_ca_system_score_codex":0.000012827191,"about_ca_system_score_gemma":0.000024086343,"threshold_uncertainty_score":0.1639027},"labels":[],"label_agreement":null},{"id":"W4378420697","doi":"10.1007/978-3-031-33743-7_7","title":"Machine Learning Technique for Data Fusion and Cognitive Load Classification Using an Eye Tracker","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Cognitive load; Computer science; Automation; BitTorrent tracker; Artificial intelligence; Machine learning; Eye tracking; Cognition; Engineering","score_opus":0.08067356213536118,"score_gpt":0.3051841054207794,"score_spread":0.2245105432854182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378420697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016505404,0.0040104864,0.9940038,0.00009716236,0.0003285887,0.000803922,0.000044084372,0.00023379001,0.00031315465],"genre_scores_gemma":[0.99084437,0.0006167998,0.0066588395,0.000042513395,0.00035538155,0.000063481304,0.00043628277,0.00007549033,0.0009068542],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982193,0.00008179042,0.00035168833,0.00091485766,0.0001635093,0.00026884134],"domain_scores_gemma":[0.99852103,0.00050052797,0.0002917389,0.0005110929,0.00012155353,0.0000540288],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00088600005,0.00030904537,0.0004530186,0.00019636613,0.00021162009,0.0001956492,0.0004300378,0.0007411795,7.1339264e-7],"category_scores_gemma":[0.0001345795,0.00027260632,0.00002884532,0.000110604844,0.000106706306,0.00015722321,0.00031249234,0.00077697926,6.5111675e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009229829,0.000063407526,0.0033996669,0.00064578897,0.00017062566,0.00009135152,0.00049391726,0.012595537,0.0011546368,0.05412751,0.000054002943,0.92711127],"study_design_scores_gemma":[0.00024454753,0.00013364996,0.00024275883,0.0010932713,0.000044006356,0.00004415235,0.0000066933326,0.99127233,0.000009629177,0.0046882518,0.0018934301,0.00032725453],"about_ca_topic_score_codex":0.00010133738,"about_ca_topic_score_gemma":0.00018731042,"teacher_disagreement_score":0.9906793,"about_ca_system_score_codex":0.000047741316,"about_ca_system_score_gemma":0.000048191094,"threshold_uncertainty_score":0.99997264},"labels":[],"label_agreement":null},{"id":"W4379352395","doi":"10.1007/978-3-031-34612-5_4","title":"Investigating the Navigational Behavior of Wheelchair Users in Urban Environments Using Eye Movement Data","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Université Laval","funders":"","keywords":"Computer science; Eye tracking; Eye movement; Human–computer interaction; Wheelchair; Task (project management); Fixation (population genetics); Phone; Population; Artificial intelligence; World Wide Web; Engineering","score_opus":0.05631291131843633,"score_gpt":0.2879213472531452,"score_spread":0.2316084359347089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379352395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011037924,0.000089117086,0.9868828,0.0007194622,0.0007244998,0.0003780534,0.00002735299,0.000075255106,0.00006554431],"genre_scores_gemma":[0.73636293,0.000014260181,0.26245028,0.00079903705,0.00014854223,0.000017630598,0.0000321312,0.00004231426,0.00013285055],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967302,0.000045547346,0.000576017,0.001242599,0.0009449339,0.00046070086],"domain_scores_gemma":[0.99726087,0.0003238061,0.00040721716,0.0018975445,0.00004644818,0.00006409337],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011779912,0.0003326054,0.00035562174,0.0005284437,0.00018706535,0.00013092093,0.0052526193,0.00021300132,0.0000031681132],"category_scores_gemma":[0.00011167288,0.0002758726,0.000052701333,0.00068060844,0.0012848254,0.00038970838,0.0035615666,0.0007935095,0.0000080382815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000047193457,0.00025664535,0.09270933,0.00012509625,0.00006719104,0.00023930964,0.003740646,0.24013741,0.015650077,0.05665886,0.00005503123,0.5903557],"study_design_scores_gemma":[0.00031227444,0.00009345814,0.031084955,0.0009048635,0.000018439488,0.000011882326,0.0000018706373,0.89751494,0.003554669,0.06573146,0.00017597839,0.0005952087],"about_ca_topic_score_codex":0.00009563439,"about_ca_topic_score_gemma":0.000095261305,"teacher_disagreement_score":0.72532505,"about_ca_system_score_codex":0.00025341977,"about_ca_system_score_gemma":0.00030715662,"threshold_uncertainty_score":0.99996936},"labels":[],"label_agreement":null},{"id":"W4379616756","doi":"10.1101/2023.06.06.543799","title":"Continuous Measures of Decision-Difficulty Captured Remotely: II. Webcam eye-tracking reveals early decision processing","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Eye tracking; Computer science; Gaze; Eye movement; Artificial intelligence; Human–computer interaction","score_opus":0.029947225045766433,"score_gpt":0.2623216925665892,"score_spread":0.23237446752082278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379616756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69689256,0.0011336511,0.2979075,0.00028330012,0.0013947074,0.00060477335,0.000056368695,0.0017205679,0.0000065943627],"genre_scores_gemma":[0.8726787,0.00011229728,0.12671222,0.000058773494,0.0002145487,0.00006490407,2.5636163e-7,0.00014375451,0.000014532723],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99357516,0.00021519727,0.0014948228,0.0022487678,0.0014280357,0.001038025],"domain_scores_gemma":[0.992728,0.0003962595,0.0014940441,0.0029238285,0.002146948,0.00031093697],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017687907,0.0008936874,0.0014923428,0.00097143993,0.0005162761,0.0005989626,0.0035357836,0.0012847142,0.000005459625],"category_scores_gemma":[0.0024275468,0.00089801126,0.000367041,0.0018535649,0.00029531165,0.00044327445,0.0026757298,0.0016139776,0.000083240215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009186721,0.00054645486,0.017968373,0.00059792557,0.00029767942,0.00035074496,0.00015302366,0.0005217662,0.9742196,0.0020826876,0.0012198318,0.0019500223],"study_design_scores_gemma":[0.0010082212,0.0002031198,0.8127614,0.0062980605,0.00015906278,1.8190424e-7,0.000008853518,0.0026238898,0.17381927,0.0005206663,0.0009343007,0.0016629393],"about_ca_topic_score_codex":0.00010456154,"about_ca_topic_score_gemma":0.000008151433,"teacher_disagreement_score":0.8004004,"about_ca_system_score_codex":0.0003013625,"about_ca_system_score_gemma":0.0006908835,"threshold_uncertainty_score":0.99934703},"labels":[],"label_agreement":null},{"id":"W4379768395","doi":"10.1167/jov.23.6.4","title":"Visual search for reach targets in actionable space is influenced by movement costs imposed by obstacles","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Cursor (databases); Visual search; Obstacle; Computer vision; Computer science; Artificial intelligence; Eye movement; Object (grammar); Orientation (vector space); Movement (music); Horizontal plane; Set (abstract data type); Task (project management); Communication; Psychology; Mathematics; Geography; Engineering; Geometry; Physics","score_opus":0.014075596716855553,"score_gpt":0.3301866612846573,"score_spread":0.31611106456780175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379768395","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94883406,0.00024709266,0.042834926,0.007604856,0.00022091928,0.00014534846,0.000009332347,0.00006496071,0.000038491253],"genre_scores_gemma":[0.99458444,0.000116319665,0.004759361,0.00023387888,0.000024789491,0.0000044917415,0.0000038748767,0.0000085778365,0.00026429468],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987778,0.000047230067,0.00031429878,0.00019229142,0.00038783788,0.00028050973],"domain_scores_gemma":[0.9992605,0.00016443148,0.00018109259,0.00013887705,0.00018576166,0.00006937222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000793046,0.00009403309,0.00017913662,0.00030771954,0.00009139533,0.0000767196,0.0004159097,0.00008345076,0.000005137854],"category_scores_gemma":[0.00007294051,0.00007958433,0.00006320091,0.0005311746,0.000028059632,0.00036140144,0.00010921107,0.00023888164,0.000012258191],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065427725,0.00024011935,0.0035414551,0.000016605285,0.000020871745,0.000023343806,0.00027949552,0.00011953182,0.8294642,0.00061672105,0.11036016,0.055252075],"study_design_scores_gemma":[0.003763137,0.0029491936,0.12620805,0.0003089569,0.0000102368085,0.0000225646,0.000538201,0.044883218,0.7866008,0.0045658355,0.029756453,0.00039331155],"about_ca_topic_score_codex":0.000035900433,"about_ca_topic_score_gemma":0.0000024169926,"teacher_disagreement_score":0.1226666,"about_ca_system_score_codex":0.00018957516,"about_ca_system_score_gemma":0.00006602668,"threshold_uncertainty_score":0.3245355},"labels":[],"label_agreement":null},{"id":"W4381735246","doi":"10.1007/978-3-031-36402-0_55","title":"iSTIMULI: Prescriptive Stimulus Design for Eye Movement Analysis of Patients with Parkinson’s Disease","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Parkinson's Clinic of Eastern Toronto & Movement Disorders Centre","funders":"","keywords":"Eye movement; Eye tracking; Computer science; Stimulus (psychology); Gaze; Parkinson's disease; Visual analytics; Artificial intelligence; Gaze-contingency paradigm; Visualization; Computer vision; Cognitive psychology; Psychology; Visual perception; Disease; Medicine; Perception; Neuroscience","score_opus":0.022741933322939605,"score_gpt":0.2482664951965073,"score_spread":0.22552456187356767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381735246","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051274203,0.00006469621,0.997394,0.00025896082,0.00048043352,0.00089114736,0.000114091505,0.00022482511,0.00005908034],"genre_scores_gemma":[0.5239131,0.000009748695,0.47509244,0.0004162177,0.0000582656,0.00007697971,0.000040546598,0.00004551064,0.00034722357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99634606,0.00003754159,0.00050031464,0.0015930178,0.0009293077,0.0005937622],"domain_scores_gemma":[0.99677825,0.00068361923,0.00045562355,0.0012953102,0.000616261,0.00017094781],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055736746,0.00048604954,0.000759436,0.0017936411,0.00017905764,0.00015975775,0.0024461076,0.00018419427,0.000005186053],"category_scores_gemma":[0.00017203204,0.00039838624,0.00020739294,0.001684264,0.0007093214,0.00023360719,0.000714664,0.0003374534,0.0000052703044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017869481,0.0002451524,0.013978459,0.00010091354,0.0005999766,0.000054481075,0.00054398936,0.72306025,0.000019528501,0.018786656,0.00006243149,0.24236946],"study_design_scores_gemma":[0.00060433435,0.0006427923,0.07710432,0.00022711486,0.00031271082,1.1400932e-7,1.8218452e-7,0.868835,0.00023740715,0.051212806,0.0002256675,0.00059756095],"about_ca_topic_score_codex":0.000015819594,"about_ca_topic_score_gemma":0.00004082866,"teacher_disagreement_score":0.52340037,"about_ca_system_score_codex":0.00024229652,"about_ca_system_score_gemma":0.0003888463,"threshold_uncertainty_score":0.9998468},"labels":[],"label_agreement":null},{"id":"W4382395242","doi":"10.18280/ts.400326","title":"An Examination Monocular Vision Gaze Point Tracking under the Theory of 'Machines Displacing Workers' in the Philosophy of Technology","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gaze; Computer vision; Point (geometry); Artificial intelligence; Monocular; Tracking (education); Monocular vision; Computer science; Cognitive science; Psychology; Human–computer interaction; Mathematics; Geometry","score_opus":0.021026929095419467,"score_gpt":0.2734081866968245,"score_spread":0.252381257601405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382395242","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.77479047,0.000068630914,0.22158131,0.0030416297,0.00004686212,0.00020203444,0.0000017844183,0.00013689397,0.00013036546],"genre_scores_gemma":[0.9987624,0.000006483482,0.0011007431,0.00006564692,0.000022640215,0.000026708612,0.000003906673,0.0000081073895,0.0000033572956],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99851686,0.00033796288,0.00035282248,0.00027558987,0.0003014614,0.00021527812],"domain_scores_gemma":[0.9989464,0.00033490843,0.00018156097,0.00046143887,0.00006139253,0.000014267455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002233149,0.00013296108,0.0001789728,0.0004538923,0.00011342246,0.00003285055,0.0010754074,0.000086103566,0.000007902972],"category_scores_gemma":[0.000026101548,0.0000802012,0.00006411124,0.0013113545,0.00023273642,0.000227793,0.000106760024,0.00021754953,0.0000030130514],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028714001,0.00039249944,0.016293753,0.000048199694,0.000046017845,0.000017882185,0.0038691806,0.0049184836,0.050486412,0.5994519,0.000023888553,0.32442304],"study_design_scores_gemma":[0.00073445943,0.000543107,0.6680383,0.00023649863,0.00002635815,0.000012891077,0.0052964543,0.082606286,0.014690245,0.22759627,0.000018652998,0.00020049135],"about_ca_topic_score_codex":0.000014900655,"about_ca_topic_score_gemma":0.000011501453,"teacher_disagreement_score":0.65174454,"about_ca_system_score_codex":0.000022561248,"about_ca_system_score_gemma":0.000019796187,"threshold_uncertainty_score":0.32705098},"labels":[],"label_agreement":null},{"id":"W4382814082","doi":"10.1016/j.dib.2023.109360","title":"Raw eye tracking data of healthy adults reading aloud words, pseudowords and numerals","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Haute école Spécialisée de Suisse Occidentale; Université de Lausanne","keywords":"Saccade; Eye tracking; Computer science; Eye movement; Gaze; Fixation (population genetics); Audiology; Artificial intelligence; Psychology; Speech recognition; Natural language processing; Medicine","score_opus":0.0728957807629369,"score_gpt":0.351343334178132,"score_spread":0.2784475534151951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382814082","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94070184,0.00071766076,0.045224853,0.009955102,0.0007508356,0.0004021682,0.00100165,0.0009412862,0.0003045901],"genre_scores_gemma":[0.9861843,0.00027007825,0.012422863,0.00022723261,0.000049594142,0.0000036527772,0.00080498523,0.000013223798,0.000024044159],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.997984,0.00008447703,0.00039619213,0.00091434724,0.00022232444,0.00039865836],"domain_scores_gemma":[0.9963185,0.0002377996,0.00015316614,0.0031928786,0.00003300947,0.00006463503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012548519,0.00013968373,0.000293206,0.00026699083,0.000085957174,0.00008636936,0.0034763627,0.00009373878,0.0000042109364],"category_scores_gemma":[0.00049219467,0.00014235404,0.000012138378,0.0011184561,0.00012187326,0.0009418429,0.0031344444,0.00025758348,0.000018182369],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011718542,0.00025751904,0.1092834,0.0003939366,0.000060926235,0.00026072958,0.0025531228,0.00003373999,0.0011011846,0.028105156,0.03910116,0.81873196],"study_design_scores_gemma":[0.0032132606,0.0003814118,0.6366793,0.0013379961,0.000027312402,0.00008306345,0.0007192681,0.30756724,0.0008929136,0.0050131627,0.043112308,0.000972781],"about_ca_topic_score_codex":0.0007475696,"about_ca_topic_score_gemma":0.00020940903,"teacher_disagreement_score":0.81775916,"about_ca_system_score_codex":0.000016442302,"about_ca_system_score_gemma":0.000058916667,"threshold_uncertainty_score":0.6460005},"labels":[],"label_agreement":null},{"id":"W4384133585","doi":"10.1101/2023.07.11.548447","title":"Web-based eye-tracking for remote cognitive assessments: The anti-saccade task as a case study","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University; Vassar College","keywords":"Saccade; Task (project management); Computer science; Cognition; Eye movement; Reliability (semiconductor); Eye tracking; Quality (philosophy); Human–computer interaction; Artificial intelligence; Computer vision; Psychology; Engineering","score_opus":0.03918603801406084,"score_gpt":0.32088920407623456,"score_spread":0.2817031660621737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384133585","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7533727,0.00013630517,0.23896681,0.0009912354,0.0015706962,0.0024889794,0.00016591992,0.0023042236,0.0000031068494],"genre_scores_gemma":[0.9846042,0.000019974323,0.013908724,0.00041213032,0.00025971327,0.00061723735,4.806483e-7,0.00016964672,0.000007910212],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99489164,0.00045465195,0.0007853382,0.0021995332,0.00065280794,0.001016022],"domain_scores_gemma":[0.99455297,0.0007920755,0.00081066,0.0024747087,0.0011581731,0.00021143112],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002164037,0.00084206957,0.0008693558,0.00060560234,0.00080429064,0.0009953954,0.0024878073,0.00061449065,0.0000036740598],"category_scores_gemma":[0.0009912313,0.00074912905,0.0003069994,0.0013889283,0.0002527771,0.00027580134,0.001492163,0.0016266912,0.000090367066],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051727565,0.011624768,0.29545864,0.0044851685,0.010409208,0.09975078,0.0012007115,0.0034653652,0.55200255,0.014730778,0.004113177,0.0022415915],"study_design_scores_gemma":[0.015559864,0.0030201923,0.5450259,0.005178757,0.0026030175,0.0000075732255,0.0009217901,0.26324996,0.1513008,0.00024469776,0.0045550927,0.008332359],"about_ca_topic_score_codex":0.0003656238,"about_ca_topic_score_gemma":0.000031694242,"teacher_disagreement_score":0.40070173,"about_ca_system_score_codex":0.00028151195,"about_ca_system_score_gemma":0.0013475302,"threshold_uncertainty_score":0.999496},"labels":[],"label_agreement":null},{"id":"W4384158909","doi":"10.1109/i2mtc53148.2023.10175906","title":"Assessing Driver Gaze Location in a Dynamic Vehicle Environment","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Élisabeth Bruyère Hospital; Ottawa Hospital; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Computer science; Gaze; Artificial intelligence; Computer vision; Metric (unit); Automotive industry; Eye tracking; Segmentation; Convolutional neural network; Context (archaeology); Windshield; Situation awareness; Field (mathematics); Visualization; Human–computer interaction; Engineering","score_opus":0.018666960882949724,"score_gpt":0.2700995137201774,"score_spread":0.25143255283722765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384158909","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5999222,0.000012536589,0.39710313,0.0017219421,0.00005463147,0.000042416537,7.437819e-8,0.00044126777,0.0007018364],"genre_scores_gemma":[0.9905772,0.000008116176,0.008959128,0.000051075716,0.0000032639616,0.000009189998,0.0000013344264,0.0000036159731,0.0003870849],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9993812,0.000024025327,0.0000961937,0.0002266099,0.00009916974,0.00017280271],"domain_scores_gemma":[0.9996741,0.00003527867,0.000023586308,0.00024136815,0.0000075657554,0.000018132541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015706946,0.000054835702,0.00006084455,0.00015619138,0.00004002257,0.00006149195,0.00026926576,0.000041278232,0.000009757988],"category_scores_gemma":[0.000012572279,0.000051654955,0.000012932915,0.00051183463,0.00003026312,0.00026235712,0.00013488295,0.00008330036,0.000511541],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000144332,0.00018249864,0.052845076,0.000019848278,0.000011005022,0.00014580588,0.0004929914,0.0066097346,0.033945598,0.058437437,0.00046183928,0.8468467],"study_design_scores_gemma":[0.00013002384,0.00001728097,0.6078777,0.000012628772,6.778912e-7,0.0000023661287,0.00006066355,0.38779333,0.0006962547,0.0030613902,0.00027305717,0.00007465928],"about_ca_topic_score_codex":0.000018653041,"about_ca_topic_score_gemma":0.000013887731,"teacher_disagreement_score":0.8467721,"about_ca_system_score_codex":0.00007153188,"about_ca_system_score_gemma":0.00001557827,"threshold_uncertainty_score":0.6574995},"labels":[],"label_agreement":null},{"id":"W4384300925","doi":"10.1038/s41598-023-38346-9","title":"A dual mobile eye tracking study on natural eye contact during live interactions","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Dual (grammatical number); Eye tracking; Computer science; Natural (archaeology); Eye contact; Optometry; Tracking (education); Computer vision; Artificial intelligence; Human–computer interaction; Biology; Medicine; Communication; Psychology; Art","score_opus":0.02187807452090466,"score_gpt":0.31990634635667314,"score_spread":0.29802827183576847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384300925","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9848936,0.000020204658,0.00028427836,0.00022533964,0.012442114,0.00043647317,0.0000010272648,0.0013668088,0.00033014978],"genre_scores_gemma":[0.9953806,4.949656e-7,0.00018543108,0.000011940135,0.00006902604,0.000101748796,0.000007974152,0.000014019339,0.004228783],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99718076,0.00008039514,0.0004585579,0.001215328,0.0005672525,0.0004977059],"domain_scores_gemma":[0.9980903,0.00008800035,0.00027425642,0.0012813078,0.00017848714,0.00008761092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001012995,0.00019614173,0.00022939824,0.0006375234,0.00079968467,0.00070900266,0.00045144293,0.000050827057,0.000022409477],"category_scores_gemma":[0.00021234652,0.00017481727,0.00011889277,0.0015229936,0.000110756344,0.00050874386,0.00035558364,0.00041700766,0.0003616795],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005632888,0.0037196176,0.33990997,0.00008578472,0.00042005035,0.041618824,0.044276115,0.0019149596,0.50279164,0.002066183,0.016195823,0.046944667],"study_design_scores_gemma":[0.00052867096,0.0004365347,0.94098663,0.00016418527,0.000027777265,0.00043610585,0.0052124253,0.0038768952,0.040535502,0.002019521,0.0051215533,0.00065416645],"about_ca_topic_score_codex":0.000025776137,"about_ca_topic_score_gemma":0.00004269136,"teacher_disagreement_score":0.60107666,"about_ca_system_score_codex":0.00010594867,"about_ca_system_score_gemma":0.00007183028,"threshold_uncertainty_score":0.7128841},"labels":[],"label_agreement":null},{"id":"W4385200174","doi":"10.1007/978-981-99-2100-3_22","title":"Real-time Multi-module Student Engagement Detection System","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Distraction; Convolutional neural network; Facial expression; Artificial intelligence; IRIS (biosensor); Computer vision; Eye tracking; Emotion detection; Facial motion capture; Speech recognition; Pattern recognition (psychology); Face detection; Psychology; Facial recognition system; Emotion recognition; Cognitive psychology; Biometrics","score_opus":0.024344577413007582,"score_gpt":0.25163220852756635,"score_spread":0.22728763111455877,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385200174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033069396,0.0017133027,0.9873679,0.00006330153,0.0028778582,0.0008155108,0.0000061651313,0.001254041,0.005571195],"genre_scores_gemma":[0.9867998,0.00045462488,0.0015757623,0.000018901395,0.0005642004,0.000100927784,0.00001384319,0.00008683296,0.010385153],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976976,0.00013891885,0.00055457704,0.0008667683,0.00031770443,0.000424447],"domain_scores_gemma":[0.99846274,0.00036010984,0.00031488243,0.0007093959,0.00007798375,0.00007488785],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00085571525,0.00044309153,0.00069836323,0.00033095662,0.00021367599,0.0002399204,0.0005770742,0.0007620986,0.0000016865777],"category_scores_gemma":[0.000024171466,0.00038584945,0.00010162693,0.00015841017,0.000067143716,0.000055589273,0.0003543687,0.000856932,0.000050360613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045587705,0.00016469606,0.0013954567,0.0019433766,0.00097653136,0.0015615503,0.0014758395,0.42244843,0.0010420595,0.21395391,0.0005074584,0.35448512],"study_design_scores_gemma":[0.00068928907,0.0002517785,0.0016033755,0.0024507935,0.00006236757,0.00010669167,0.000022373293,0.98958236,0.000037670845,0.00084813824,0.0034392758,0.0009058844],"about_ca_topic_score_codex":0.00014849326,"about_ca_topic_score_gemma":0.00016620633,"teacher_disagreement_score":0.9864691,"about_ca_system_score_codex":0.00024324222,"about_ca_system_score_gemma":0.000022509856,"threshold_uncertainty_score":0.99985933},"labels":[],"label_agreement":null},{"id":"W4385650119","doi":"10.3390/drones7080520","title":"Usability Comparison between 2D and 3D Control Methods for the Operation of Hovering Objects","year":2023,"lang":"en","type":"article","venue":"Drones","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Handong Global University; National Research Foundation of Korea; National Research Foundation","keywords":"Drone; Computer science; Usability; Computer vision; Motion (physics); Trajectory; Gesture; Control (management); Smoothness; Obstacle; Artificial intelligence; Simulation; Human–computer interaction; Mathematics; Geography","score_opus":0.05257637762131481,"score_gpt":0.37978843638586784,"score_spread":0.32721205876455306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385650119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15071571,0.000107282016,0.847909,0.00089763995,0.00009004403,0.00014533123,0.0000031506074,0.000114044386,0.00001780534],"genre_scores_gemma":[0.9476194,0.000004480725,0.05230496,0.00001408085,0.000020533778,0.000022492679,0.0000010466291,0.0000025660543,0.000010473075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948794,0.000067927394,0.00013114983,0.00015561191,0.000049579543,0.000107773805],"domain_scores_gemma":[0.9983741,0.0013237008,0.000050586612,0.0002068236,0.00003193162,0.000012869499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007252316,0.000051436135,0.00015131208,0.000036760674,0.00010591279,0.000029564162,0.00022542206,0.000036169047,5.422278e-7],"category_scores_gemma":[0.00019161757,0.000036261754,0.000022844935,0.00013247253,0.00007716919,0.000072427,0.00007663044,0.000051793748,0.0000016000052],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007571636,0.00002462306,0.115334846,0.00005189854,0.00005102931,2.309641e-7,0.00096636516,0.001853839,0.0076600434,0.014134966,0.00008798474,0.8598266],"study_design_scores_gemma":[0.0003262467,0.0000921165,0.40977895,0.0000081596145,0.000017734581,4.469208e-7,0.000099144316,0.57038003,0.015273966,0.0032458135,0.00070952054,0.00006783445],"about_ca_topic_score_codex":0.000029361108,"about_ca_topic_score_gemma":0.000009574661,"teacher_disagreement_score":0.8597588,"about_ca_system_score_codex":0.0000073615042,"about_ca_system_score_gemma":0.000010662715,"threshold_uncertainty_score":0.14787114},"labels":[],"label_agreement":null},{"id":"W4385654374","doi":"10.2139/ssrn.4531837","title":"On Validating a Generic Camera-Based Blink Detection System for Cognitive Load Assessment","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Cognitive load; Cognition; Attentional blink; Artificial intelligence; Computer vision; Human–computer interaction; Psychology; Neuroscience","score_opus":0.032463405119705066,"score_gpt":0.30432629820756535,"score_spread":0.27186289308786027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385654374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052576687,0.00020967999,0.9434334,0.0007271397,0.0016343173,0.00052045746,0.000015258918,0.000791894,0.00009117863],"genre_scores_gemma":[0.99169916,0.00007673815,0.0073395837,0.00006768791,0.0003415142,0.00025280012,0.000015701626,0.00005705873,0.00014973842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956136,0.00026515924,0.0005382754,0.00084901357,0.0005811155,0.0021528814],"domain_scores_gemma":[0.997598,0.0004776875,0.0008160177,0.000470763,0.00055237004,0.00008519692],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0033563422,0.00040376332,0.00047601713,0.00051730947,0.0005154304,0.00035484062,0.0010990709,0.0003720652,8.228994e-7],"category_scores_gemma":[0.00026067215,0.00038313426,0.0003463936,0.00041058997,0.000047050715,0.000090290036,0.00029430538,0.004759621,0.00002801897],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00019487503,0.00040282842,0.0003953587,0.0005636765,0.0015714009,0.000094490046,0.00020665598,0.017685551,0.0040050806,0.21594322,0.00011230282,0.7588246],"study_design_scores_gemma":[0.004613911,0.004373814,0.0009384066,0.0025711805,0.00045202384,0.000660538,0.0016644516,0.5768873,0.021188121,0.38477394,0.00012557914,0.0017507412],"about_ca_topic_score_codex":0.000079541845,"about_ca_topic_score_gemma":0.0002514381,"teacher_disagreement_score":0.9391225,"about_ca_system_score_codex":0.0055453274,"about_ca_system_score_gemma":0.0070012403,"threshold_uncertainty_score":0.9998621},"labels":[],"label_agreement":null},{"id":"W4385981513","doi":"10.1007/978-3-031-36118-0_66","title":"Design and Development of a Hybrid Eye and Mobile Controlled Wheelchair Prototype using Haar cascade Classifier: A Proof of Concept","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Wheelchair; Obstacle avoidance; Chassis; Computer science; Bluetooth; Android (operating system); Embedded system; Engineering; Artificial intelligence; Simulation; Wireless; Mobile robot; Operating system","score_opus":0.0692674357151381,"score_gpt":0.2782294434477841,"score_spread":0.208962007732646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385981513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010331526,0.009707674,0.98616064,0.00053368765,0.000029429113,0.0013991086,0.00007977623,0.0010118458,0.00004470677],"genre_scores_gemma":[0.5585686,0.0010261434,0.44006017,0.0000062998333,0.00000339355,0.00018553603,0.000047100064,0.00003501034,0.00006777323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988228,0.000022164655,0.00040237888,0.0004483846,0.000130547,0.00017374608],"domain_scores_gemma":[0.996608,0.0006253796,0.00027805113,0.0023826642,0.00008384919,0.000022058148],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002606067,0.0003009781,0.0006104034,0.00042067902,0.00013313026,0.00003996552,0.0017295326,0.0002867292,3.0160726e-7],"category_scores_gemma":[0.00030463835,0.00025521498,0.000023984656,0.00011832757,0.0004834,0.00008300451,0.002129318,0.0005749998,2.683814e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008811691,0.0001265895,0.000026872709,0.0007368312,0.00068683247,0.000013824221,0.0008945842,0.0097697275,0.00585466,0.057958607,0.000032060696,0.9238113],"study_design_scores_gemma":[0.0026765806,0.0013278668,0.000058466117,0.0034237797,0.00024700383,0.00010951026,0.00013106379,0.84248805,0.11852853,0.019534817,0.009944862,0.0015294453],"about_ca_topic_score_codex":0.000003829014,"about_ca_topic_score_gemma":0.0000034985796,"teacher_disagreement_score":0.92228186,"about_ca_system_score_codex":0.000025064559,"about_ca_system_score_gemma":0.00008874572,"threshold_uncertainty_score":0.99999},"labels":[],"label_agreement":null},{"id":"W4386047445","doi":"10.3758/s13428-023-02190-6","title":"Deep learning models for webcam eye tracking in online experiments","year":2023,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Max-Planck-Gesellschaft","keywords":"Eye tracking; Computer science; Gaze; Fixation (population genetics); Artificial intelligence; Deep learning; Computer vision; Eye movement; Tracking (education); Task (project management); Psychology","score_opus":0.49091216203907995,"score_gpt":0.610252692821539,"score_spread":0.1193405307824591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386047445","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29047495,0.0001534402,0.7076414,0.00039930057,0.00015034713,0.0004840884,0.0000018319122,0.0005624078,0.00013223161],"genre_scores_gemma":[0.49549913,0.000032151336,0.5031587,0.000009680136,0.0000323226,0.00062139635,0.000010206704,0.000026170792,0.0006102428],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962805,0.001211844,0.00032483676,0.000692278,0.00050101714,0.0009895214],"domain_scores_gemma":[0.9980659,0.0009688785,0.000055842127,0.0005505571,0.0002441756,0.00011466586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0067506284,0.00015695904,0.00027303144,0.0011014253,0.00030704978,0.00015008805,0.0012683317,0.00016728615,0.000007371305],"category_scores_gemma":[0.0009089773,0.00015625407,0.00009283137,0.0020807425,0.00013520334,0.0003543678,0.0006180576,0.0008973564,0.000022109434],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000122449255,0.00029785567,0.0067818887,0.000018851439,0.0000066236353,0.000079804966,0.0013002094,0.0014625712,0.048722185,0.007089532,0.000055401346,0.9341728],"study_design_scores_gemma":[0.0010656021,0.00041254784,0.08311347,0.00008141559,0.0000059926033,0.0000072307894,0.0018229819,0.8605145,0.035676092,0.01457366,0.0023391466,0.00038735833],"about_ca_topic_score_codex":0.00006532322,"about_ca_topic_score_gemma":0.000020902062,"teacher_disagreement_score":0.9337855,"about_ca_system_score_codex":0.00013791317,"about_ca_system_score_gemma":0.00007427586,"threshold_uncertainty_score":0.63718563},"labels":[],"label_agreement":null},{"id":"W4386242671","doi":"10.1167/jov.23.9.5797","title":"Investigation of camera-free eye tracking glasses compared to a video-based system","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Saccade; Computer science; Computer vision; Gaze; Eye tracking; Amplitude; Artificial intelligence; Tracking system; Eye movement; Calibration; Tracking (education); Significant difference; Tracking error; Simulation; Optics; Mathematics; Statistics; Physics; Psychology; Kalman filter; Control (management)","score_opus":0.03346704340078883,"score_gpt":0.3038648225619355,"score_spread":0.27039777916114666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386242671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8662415,0.00003849507,0.12958829,0.0035276327,0.00036380405,0.00006450089,0.0000015565869,0.00014090899,0.000033281423],"genre_scores_gemma":[0.97436184,0.0000019069123,0.025500335,0.00007408414,0.00004562892,0.0000012848184,6.1046694e-7,0.000006744715,0.0000075907806],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9986525,0.00009699779,0.00050930283,0.00015071829,0.00042963272,0.00016084925],"domain_scores_gemma":[0.9984979,0.0001861493,0.0004972531,0.0003544415,0.00037165728,0.00009259673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079422735,0.00009381852,0.00028134295,0.0006015974,0.00006889506,0.00006114019,0.0008371742,0.000059789145,0.0000010615106],"category_scores_gemma":[0.00027626805,0.00007708728,0.00009148425,0.0010126858,0.000039753864,0.00025226787,0.00011979223,0.0001614318,0.000014901652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016283753,0.00015484085,0.067385554,0.000507151,0.00010070824,0.0003660044,0.0023152994,0.017795077,0.8213341,0.009331791,0.020920528,0.05962606],"study_design_scores_gemma":[0.0016068541,0.001146936,0.7822282,0.002435849,0.000030930438,0.000060175258,0.0004455826,0.10606078,0.10334923,0.00166312,0.0007298326,0.00024254783],"about_ca_topic_score_codex":0.000014858754,"about_ca_topic_score_gemma":0.000005099202,"teacher_disagreement_score":0.7179849,"about_ca_system_score_codex":0.00007699124,"about_ca_system_score_gemma":0.00010092813,"threshold_uncertainty_score":0.3143528},"labels":[],"label_agreement":null},{"id":"W4386247169","doi":"10.1167/jov.23.9.5010","title":"Where’s Waldo? Exploring Gaze Strategy in a Visual Search Task Online and In-Person","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Gaze; Fixation (population genetics); Eye tracking; Eye movement; Visual search; Task (project management); Psychology; Cognitive psychology; Artificial intelligence; Computer science; Computer vision; Medicine","score_opus":0.07917692211328388,"score_gpt":0.3526562751503678,"score_spread":0.2734793530370839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386247169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99626344,0.00026308413,0.0015476325,0.0017239461,0.000112764115,0.000030635954,3.867992e-7,0.00003456734,0.000023512504],"genre_scores_gemma":[0.9979015,0.00049893657,0.0015115294,0.000013678089,0.000041021456,7.675781e-7,2.5349527e-7,0.0000052850837,0.000027010099],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99904335,0.00007276952,0.00027269454,0.00015481524,0.00024051627,0.00021588702],"domain_scores_gemma":[0.9996117,0.00009610178,0.00008681469,0.00009661072,0.000054659315,0.000054125314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000744791,0.00007521935,0.00017292208,0.0006823966,0.00002988596,0.000059694794,0.0002789374,0.000053395474,0.000002505723],"category_scores_gemma":[0.00005309801,0.00006204891,0.00002935131,0.0007416463,0.000026973083,0.00042599294,0.0001039432,0.00041742873,0.0000070284764],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000119717835,0.00060668116,0.0764741,0.000114423485,0.000020230113,0.0026398243,0.0024022725,0.004186273,0.099191844,0.0015200136,0.0004916956,0.8122329],"study_design_scores_gemma":[0.0007916064,0.00068928127,0.9302883,0.00057412504,0.0000014716679,0.0000913027,0.00089395506,0.06528249,0.0005461445,0.0005892069,0.00015749043,0.000094595336],"about_ca_topic_score_codex":0.000036101323,"about_ca_topic_score_gemma":0.00007227756,"teacher_disagreement_score":0.85381424,"about_ca_system_score_codex":0.000051585444,"about_ca_system_score_gemma":0.000048143025,"threshold_uncertainty_score":0.2530281},"labels":[],"label_agreement":null},{"id":"W4386247296","doi":"10.1167/jov.23.9.5546","title":"Visual landmark information is multiplexed with target information in the visual responses of prefrontal gaze centres.","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Landmark; Gaze; Visual field; Artificial intelligence; Computer science; Population; Psychology; Neuroscience; Computer vision; Communication; Pattern recognition (psychology); Medicine","score_opus":0.009304293127076955,"score_gpt":0.2815153879963468,"score_spread":0.2722110948692698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386247296","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96931237,0.000018961815,0.02891902,0.0013836811,0.00012296534,0.0001043213,0.0000044976227,0.00003739708,0.00009679635],"genre_scores_gemma":[0.9959709,0.000021391894,0.0038501453,0.00012575414,0.000016806003,0.0000013469195,0.000005446999,0.0000020396926,0.0000061525443],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987619,0.00010025938,0.00047416237,0.00005171057,0.000465932,0.00014604807],"domain_scores_gemma":[0.99900603,0.00016724068,0.00048962707,0.00012269679,0.00018797471,0.000026414484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008727827,0.000081493636,0.00015070634,0.00053600816,0.000053502674,0.000080752005,0.00039300445,0.000061370745,0.0000040897103],"category_scores_gemma":[0.0001772878,0.000047695594,0.000046519595,0.00061016134,0.000039632927,0.0019917213,0.00007319487,0.0002051009,0.000019316654],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0060644927,0.0013197765,0.19102377,0.0003510944,0.00019589507,0.00019294753,0.06638376,0.0080747,0.027140915,0.0044089225,0.031464145,0.6633796],"study_design_scores_gemma":[0.0017084538,0.0015615877,0.9216905,0.00020638033,0.0000067597675,0.00008336802,0.0012977985,0.06398013,0.003711991,0.00023491414,0.0054110857,0.00010700515],"about_ca_topic_score_codex":0.000014988359,"about_ca_topic_score_gemma":0.000004438916,"teacher_disagreement_score":0.73066676,"about_ca_system_score_codex":0.00003545514,"about_ca_system_score_gemma":0.00007750567,"threshold_uncertainty_score":0.19449699},"labels":[],"label_agreement":null},{"id":"W4386249120","doi":"10.1167/jov.23.9.5505","title":"Psychophysics of variable fonts: Gaze measures of reading efficiency","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Saccade; Gaze; Font; Reading (process); Computer science; Eye movement; Saccadic masking; Variable (mathematics); Contrast (vision); Psychology; Cognitive psychology; Speech recognition; Artificial intelligence; Audiology; Mathematics; Linguistics; Medicine","score_opus":0.026284544609860936,"score_gpt":0.3002159469526525,"score_spread":0.27393140234279156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386249120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5007821,0.00015722695,0.49693945,0.00044634333,0.000635137,0.000040204483,0.0000012681352,0.000056764,0.00094152073],"genre_scores_gemma":[0.9794302,0.000053710002,0.020432787,0.000011722212,0.00003247671,2.1233487e-7,1.1254123e-7,0.0000043509262,0.00003445864],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.998927,0.000043915323,0.00040788934,0.00010457989,0.00038327093,0.00013335446],"domain_scores_gemma":[0.99877006,0.00012393684,0.00050723756,0.00024068063,0.00032523065,0.000032850858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086108816,0.00006516555,0.0002340469,0.00029979198,0.000036832,0.000013667483,0.00060312107,0.000053460262,0.0000022439465],"category_scores_gemma":[0.00019012649,0.00005041931,0.000085719,0.0009295455,0.00004687572,0.0001946227,0.000082517,0.00015466748,0.0000052776036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004246575,0.00039047794,0.0024299626,0.00007037607,0.000052347732,0.000027893902,0.00048170812,0.0021227202,0.7986924,0.034993246,0.003842202,0.1568542],"study_design_scores_gemma":[0.0047321706,0.007832439,0.42412096,0.0048204483,0.00013542807,0.00026747092,0.0004695456,0.08176246,0.30069393,0.16868447,0.0057036397,0.00077702984],"about_ca_topic_score_codex":0.000005548855,"about_ca_topic_score_gemma":1.5626298e-7,"teacher_disagreement_score":0.49799845,"about_ca_system_score_codex":0.000017515993,"about_ca_system_score_gemma":0.000055457247,"threshold_uncertainty_score":0.20560397},"labels":[],"label_agreement":null},{"id":"W4386266809","doi":"10.1109/access.2023.3309810","title":"An Explainable Attention Zone Estimation for Level 3 Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robustness (evolution); Computer science; Gaze; Artificial intelligence; Situation awareness; Cluster analysis; Artificial neural network; Pattern recognition (psychology); Computer vision; Engineering","score_opus":0.07679844048639094,"score_gpt":0.35373309624950156,"score_spread":0.2769346557631106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386266809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33058348,0.0000025681375,0.66752076,0.0005098926,0.0003798361,0.00011266651,0.0000022381817,0.000841272,0.000047266552],"genre_scores_gemma":[0.9556961,0.0000013920151,0.043705672,0.000043404372,0.00005129282,0.000102382,0.000014339504,0.000009877453,0.00037555242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999126,0.000021354426,0.0001448424,0.00032791737,0.000108175554,0.00027167745],"domain_scores_gemma":[0.9993499,0.00007104016,0.00008025511,0.0003790758,0.00008106659,0.000038690938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002944653,0.00008823548,0.00010527516,0.0002112045,0.00022659647,0.00029039098,0.00091620215,0.000066360066,0.000002159426],"category_scores_gemma":[0.00004131435,0.00009030466,0.000035715788,0.00050522055,0.000024705958,0.0012318487,0.00008880553,0.00006713717,0.00005565992],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007636093,0.00019367259,0.012588246,0.00009663249,0.000031890304,0.000034270022,0.0003686799,0.015522911,0.021171024,0.044638053,0.0064462046,0.8989008],"study_design_scores_gemma":[0.00028333554,0.00008685732,0.124781005,0.000024731211,0.00000591796,0.0000059268623,0.00002231433,0.8497456,0.013099819,0.01115864,0.000612503,0.00017338266],"about_ca_topic_score_codex":0.000038454335,"about_ca_topic_score_gemma":0.000033593973,"teacher_disagreement_score":0.8987274,"about_ca_system_score_codex":0.00004325988,"about_ca_system_score_gemma":0.000031692674,"threshold_uncertainty_score":0.36825174},"labels":[],"label_agreement":null},{"id":"W4386529978","doi":"10.1080/17483107.2023.2253339","title":"SWADAPT2: benefits of a collision avoidance assistance for powered wheelchair users in driving difficulty","year":2023,"lang":"en","type":"article","venue":"Disability and Rehabilitation Assistive Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre intégré universitaire de santé et de services sociaux de la Capitale-Nationale; Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"Interreg; European Regional Development Fund; Fonds de Recherche du Québec - Santé","keywords":"Wheelchair; Collision; Task (project management); Collision avoidance; Physical medicine and rehabilitation; Driving simulator; Population; Computer science; Aeronautics; Simulation; Engineering; Medicine; Computer security; Environmental health","score_opus":0.01314474098336658,"score_gpt":0.2697035488548009,"score_spread":0.2565588078714343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386529978","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9610936,0.00013446613,0.025229035,0.011865874,0.00016313071,0.0006971099,0.00004535734,0.00072554743,0.000045891822],"genre_scores_gemma":[0.96826655,0.000028961444,0.031318948,0.000017120394,0.000007826024,0.00030435924,0.000011125161,0.000013084697,0.000032035707],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9976588,0.00013307885,0.0006335042,0.00092583575,0.00021309956,0.00043570448],"domain_scores_gemma":[0.99663633,0.0020533123,0.00027465905,0.0006951546,0.00028465467,0.00005590328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009111196,0.00023704818,0.0005149929,0.00054486276,0.00018471942,0.00002723679,0.00056242105,0.00036418423,0.0000014181774],"category_scores_gemma":[0.0031648534,0.00022432326,0.00011803025,0.0023884664,0.0014524128,0.00025690143,0.00026326752,0.00027159744,0.0000038588805],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056560846,0.00037215024,0.8159359,0.00017556136,0.000018103921,0.0000011840882,0.00035066725,0.00029790294,0.0021940595,0.14047171,0.00011734187,0.040008865],"study_design_scores_gemma":[0.0008497419,0.00045953935,0.9781699,0.00018049114,0.0000064803935,0.0000019737352,0.001009924,0.0053341584,0.0008404124,0.012562733,0.00036043633,0.0002242175],"about_ca_topic_score_codex":0.000023907445,"about_ca_topic_score_gemma":0.00038092,"teacher_disagreement_score":0.16223401,"about_ca_system_score_codex":0.00018185966,"about_ca_system_score_gemma":0.000050929102,"threshold_uncertainty_score":0.9147637},"labels":[],"label_agreement":null},{"id":"W4386542258","doi":"10.3390/s23187753","title":"Investigation of Camera-Free Eye-Tracking Glasses Compared to a Video-Based System","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Saccade; Computer vision; Eye tracking; Eye movement; Smooth pursuit; Fixation (population genetics); Artificial intelligence; Computer science; Gaze; Tracking system; Eye tracking on the ISS; Tracking (education); Saccadic masking; Kalman filter; Psychology; Medicine","score_opus":0.033866362257498894,"score_gpt":0.2731373666278565,"score_spread":0.2392710043703576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386542258","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9775176,0.000008902416,0.017753366,0.0027648828,0.00026459154,0.0001493094,0.000007126562,0.0013367672,0.00019744561],"genre_scores_gemma":[0.98550886,3.7903018e-7,0.014281565,0.00010413642,0.00002449973,0.0000124207345,0.0000042571096,0.00001127867,0.000052580173],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987502,0.00009226685,0.00027216543,0.00034447905,0.00026730346,0.00027358741],"domain_scores_gemma":[0.9987305,0.00017963341,0.00013055993,0.00072143134,0.00015493728,0.00008293529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029502093,0.0001269432,0.00023444917,0.00039558372,0.00008885905,0.00004872251,0.0007494986,0.000069062866,0.0000010399676],"category_scores_gemma":[0.00022793471,0.00012474786,0.000055765864,0.0013992571,0.00007968358,0.00007905354,0.00015500747,0.000105261366,0.000110437766],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008312564,0.00012003759,0.30720693,0.0013655237,0.00021180855,0.00045090413,0.00818486,0.123439945,0.38284564,0.133916,0.02085713,0.021318091],"study_design_scores_gemma":[0.000920595,0.00018416197,0.38777548,0.00063367444,0.000022603152,0.000011396193,0.0010472236,0.35529414,0.25127298,0.0013944686,0.00094886875,0.000494429],"about_ca_topic_score_codex":0.00013302804,"about_ca_topic_score_gemma":0.00003885641,"teacher_disagreement_score":0.23185419,"about_ca_system_score_codex":0.000058534857,"about_ca_system_score_gemma":0.00006028516,"threshold_uncertainty_score":0.50870705},"labels":[],"label_agreement":null},{"id":"W4386631367","doi":"10.1109/peds57185.2023.10246520","title":"Kinematic Optimization and Comparison of Wheelchair-mounted Assistive Robots for Activities of Daily Living","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; College Ahuntsic","funders":"Administration for Community Living; Australian Government; National Institute on Disability, Independent Living, and Rehabilitation Research; U.S. Department of Health and Human Services","keywords":"Wheelchair; Robot; Workspace; Kinematics; Activities of daily living; Physical medicine and rehabilitation; Computer science; Cover (algebra); Simulation; Physical therapy; Medicine; Engineering; Artificial intelligence","score_opus":0.029885306802188122,"score_gpt":0.30917218342589997,"score_spread":0.2792868766237119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386631367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088115536,0.000024768931,0.9109308,0.00023086811,0.00006362721,0.00012982791,0.0000035334076,0.00022158344,0.00027941616],"genre_scores_gemma":[0.86780727,0.0000039661445,0.13201271,0.00000766244,0.000004371338,0.00001464109,0.0000024677095,0.0000049392565,0.00014196266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931043,0.000028860028,0.0002349843,0.00018413394,0.00010920151,0.00013236304],"domain_scores_gemma":[0.99894744,0.00056609564,0.00018765904,0.00018438844,0.000095993644,0.00001843042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019759244,0.000081881946,0.00026208974,0.00021332549,0.00005351103,0.000020365773,0.00021281031,0.00005474137,0.0000036189074],"category_scores_gemma":[0.00017486922,0.000072915514,0.000033073324,0.00038791416,0.00008862128,0.00015792229,0.00013348366,0.000047138274,7.0887324e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000053707852,0.00095005526,0.098479465,0.0021087108,0.0004043052,0.0000036069478,0.011350044,0.5185001,0.05337637,0.20993656,0.0051531927,0.0996839],"study_design_scores_gemma":[0.00015935134,0.00015443476,0.03292222,0.00012051642,0.000010678349,9.67997e-7,0.0007679584,0.95413184,0.011319085,0.00032539607,0.0000064663495,0.000081069426],"about_ca_topic_score_codex":0.00001738713,"about_ca_topic_score_gemma":0.000011613099,"teacher_disagreement_score":0.77969176,"about_ca_system_score_codex":0.000010746292,"about_ca_system_score_gemma":0.000020159145,"threshold_uncertainty_score":0.29734084},"labels":[],"label_agreement":null},{"id":"W4386741090","doi":"10.1117/12.2692524","title":"Assessing the performance of a lens inspired by the human eye","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut National d'Optique","funders":"","keywords":"Human eye; Lens (geology); Computer science; Pupil; Computer vision; Artificial intelligence; Human visual system model; Optics; Optometry; Image (mathematics); Physics","score_opus":0.04211369820652551,"score_gpt":0.31237111246165783,"score_spread":0.27025741425513233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386741090","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98455286,0.000021539616,0.0055612326,0.005181923,0.000071847484,0.000049293372,3.8527162e-7,0.00037997408,0.0041809226],"genre_scores_gemma":[0.99845064,0.0000063479856,0.00042213537,0.0001824108,0.000010051366,0.000007154265,6.4605337e-7,0.0000033951642,0.00091723533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99940884,0.000037120662,0.0001173067,0.00013928418,0.00013287208,0.00016455949],"domain_scores_gemma":[0.9993646,0.00008195924,0.00005943795,0.00045333154,0.000032342694,0.000008289733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637899,0.00005902489,0.000071963506,0.00004181048,0.0003003098,0.00008063954,0.0009500823,0.000030642423,0.000004637902],"category_scores_gemma":[0.000017865363,0.000028652623,0.000028838194,0.0005467939,0.00015146019,0.00020787057,0.00021783718,0.00012431145,0.000034155866],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014858401,0.0001599872,0.18219861,0.000052708954,0.000080835795,0.000008872697,0.002106759,0.0002130162,0.19136116,0.30230448,0.050214786,0.2712973],"study_design_scores_gemma":[0.00030263254,0.00016384195,0.79710615,0.000048266807,0.00001024391,0.000007745352,0.0004810064,0.0914285,0.09329115,0.0017263048,0.015200598,0.00023353993],"about_ca_topic_score_codex":0.000028752334,"about_ca_topic_score_gemma":0.0000040827313,"teacher_disagreement_score":0.61490756,"about_ca_system_score_codex":0.000007574904,"about_ca_system_score_gemma":0.000016387285,"threshold_uncertainty_score":0.23097706},"labels":[],"label_agreement":null},{"id":"W4386920275","doi":"10.1109/sas58821.2023.10253976","title":"Reducing Fixation Error Due to Natural Head Movement in a Webcam-Based Eye-Tracking Method","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation, Science and Economic Development Canada; Bruyère; Carleton University; National Research Council Canada","funders":"Natural Sciences and Engineering Research Council of Canada; Bruyère Research Institute; AGE-WELL","keywords":"Computer vision; Computer science; Artificial intelligence; Eye tracking; Eye movement; Fixation (population genetics); Head (geology); Eye tracking on the ISS; Tracking (education); Geology; Psychology; Medicine","score_opus":0.03540142141613464,"score_gpt":0.3567978809468054,"score_spread":0.32139645953067075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386920275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3814405,0.000010624963,0.6040036,0.01273368,0.00035378506,0.00018165009,7.761423e-7,0.00092019525,0.00035520943],"genre_scores_gemma":[0.79134405,2.6873084e-7,0.20729561,0.0009277,0.00001984836,0.00003639132,0.0000036520782,0.000007760222,0.00036475228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985249,0.00009336641,0.00025639514,0.0005050141,0.00022583238,0.00039447364],"domain_scores_gemma":[0.9992651,0.00015040311,0.00006198099,0.00040561263,0.000063515974,0.000053412223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079765747,0.00013414475,0.00018989798,0.0005796404,0.00008843188,0.000086769,0.0005268143,0.00006947799,0.000010315031],"category_scores_gemma":[0.00018474596,0.00012307179,0.00004827726,0.0017220768,0.000015076563,0.00018217086,0.00018297517,0.00021799724,0.000117661715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000031246298,0.00024609384,0.009364793,0.00007059336,0.000031327767,0.00045046868,0.0023351044,0.046991423,0.14804304,0.06425707,0.0039380747,0.7242408],"study_design_scores_gemma":[0.0004803642,0.000101643585,0.2319133,0.00011691832,0.0000027554154,0.0000043117125,0.00014941551,0.699097,0.06422816,0.0030622403,0.00055521214,0.00028871835],"about_ca_topic_score_codex":0.00028638408,"about_ca_topic_score_gemma":0.00029860507,"teacher_disagreement_score":0.72395205,"about_ca_system_score_codex":0.000118321965,"about_ca_system_score_gemma":0.00005568968,"threshold_uncertainty_score":0.5018722},"labels":[],"label_agreement":null},{"id":"W4386920314","doi":"10.1109/sas58821.2023.10254132","title":"Design and Validation of a System to Synchronize Speech Recognition and Eye-Tracking Measurements","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruyère; National Research Council Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Eye tracking; Tracking (education); Artificial intelligence; Speech recognition","score_opus":0.10420594417408499,"score_gpt":0.29469293524327095,"score_spread":0.19048699106918596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386920314","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5357736,0.000010870418,0.46346113,0.0001599712,0.00004862124,0.00013126772,4.7289674e-7,0.0002991978,0.00011488184],"genre_scores_gemma":[0.91558355,0.0000037212635,0.08435447,0.000014152708,0.000006516359,0.0000102831145,0.000001499443,0.000004032901,0.000021754902],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928355,0.000061991064,0.00013962526,0.0002371917,0.00014943413,0.00012820437],"domain_scores_gemma":[0.9996046,0.000060829425,0.000051298306,0.0001423256,0.0001054138,0.00003552459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064655417,0.00006720637,0.00010667292,0.00018667083,0.00005651015,0.00005433804,0.00012437205,0.000043898915,0.000001277852],"category_scores_gemma":[0.00005229592,0.00006210842,0.000009169149,0.00040723846,0.000020034851,0.00016152853,0.000091653004,0.00003863126,0.00003467599],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001610663,0.000047077356,0.012121837,0.00033775737,0.000054292323,0.000023336434,0.0009681829,0.0002080867,0.19283065,0.0017146871,0.00033567077,0.7913423],"study_design_scores_gemma":[0.00054814806,0.00028902598,0.037338194,0.00047490993,0.00002257895,0.000040650448,0.00029009723,0.032815274,0.9259248,0.0019686932,0.000024121791,0.00026355215],"about_ca_topic_score_codex":0.000020972604,"about_ca_topic_score_gemma":0.0000015056332,"teacher_disagreement_score":0.79107875,"about_ca_system_score_codex":0.000026094458,"about_ca_system_score_gemma":0.000012835924,"threshold_uncertainty_score":0.2532708},"labels":[],"label_agreement":null},{"id":"W4387031219","doi":"10.1145/3577190.3614149","title":"Classification of Alzheimer's Disease with Deep Learning on Eye-tracking Data","year":2023,"lang":"en","type":"article","venue":"INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Generality; Artificial intelligence; Leverage (statistics); Classifier (UML); Confusion; Machine learning; Deep learning; Eye tracking; Pattern recognition (psychology)","score_opus":0.13781453031235621,"score_gpt":0.389955937920352,"score_spread":0.25214140760799575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387031219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.78814566,0.000014078825,0.18673296,0.011245874,0.0019230291,0.00036269033,0.00004699408,0.0012887814,0.0102399355],"genre_scores_gemma":[0.9979145,0.000020193222,0.0015778929,0.00005370117,0.000062367726,0.000022262482,0.00022805917,0.000012566427,0.000108486274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846935,0.000077404315,0.0002629347,0.00057095237,0.00044602103,0.00017331999],"domain_scores_gemma":[0.9985565,0.00014759983,0.00029193188,0.0005993336,0.0003422695,0.00006238057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022012969,0.00015593397,0.00013668225,0.00042381292,0.00009522142,0.00013575322,0.0011013236,0.00005960979,0.00004896672],"category_scores_gemma":[0.00023488201,0.00013727874,0.0000385767,0.00028555738,0.000068858615,0.00072126935,0.00020168505,0.00038447333,0.00023535066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006985211,0.0006713585,0.035655934,0.000021714468,0.00033947718,0.00008651847,0.0006257368,0.020142168,0.027840614,0.32488996,0.00031248975,0.5887155],"study_design_scores_gemma":[0.00023518634,0.00014838764,0.25704417,0.0001476847,0.000011241947,0.0000023041716,0.0001676867,0.7394617,0.0016591435,0.00056155125,0.00043571694,0.00012523333],"about_ca_topic_score_codex":0.00004787099,"about_ca_topic_score_gemma":0.000019225721,"teacher_disagreement_score":0.7193195,"about_ca_system_score_codex":0.000059851864,"about_ca_system_score_gemma":0.000047175534,"threshold_uncertainty_score":0.5598064},"labels":[],"label_agreement":null},{"id":"W4387446214","doi":"10.1145/3611659.3615693","title":"GazeRayCursor: Facilitating Virtual Reality Target Selection by Blending Gaze and Controller Raycasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Virtual reality; Controller (irrigation); Intersection (aeronautics); Ambiguity; Convergence (economics); Gaze; Object (grammar); Artificial intelligence; Computer vision; Mixed reality; Computer graphics (images); Human–computer interaction","score_opus":0.02236023430277048,"score_gpt":0.2678905715113875,"score_spread":0.24553033720861703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387446214","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1763694,0.000044440614,0.81835365,0.0013802326,0.00015675518,0.00011661118,0.000008341007,0.0019979954,0.001572595],"genre_scores_gemma":[0.98406,0.00000792847,0.014590166,0.00008974403,0.000028303222,0.000015764916,0.000005689115,0.000008452013,0.0011939752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998582,0.00009415888,0.000240024,0.0004913293,0.00018021799,0.0004122867],"domain_scores_gemma":[0.9992459,0.00038948876,0.00008141407,0.00015129881,0.000062157626,0.00006977802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007565234,0.00014929946,0.00020470389,0.00016178514,0.00032227772,0.00012634207,0.00024695433,0.000089877634,0.000011776275],"category_scores_gemma":[0.00033781992,0.00013762445,0.000037140082,0.000665673,0.00006725116,0.0002847466,0.00016512076,0.00021891065,0.000051469775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040724917,0.00013374179,0.02951131,0.00007797844,0.00016246445,0.000034280438,0.0024316595,0.0024081548,0.2022774,0.20206174,0.06326828,0.49759227],"study_design_scores_gemma":[0.0012389005,0.00028122414,0.009787312,0.000042486045,0.000009130015,0.000039350056,0.0007543284,0.9645998,0.009481837,0.007491317,0.0058025722,0.00047173022],"about_ca_topic_score_codex":0.00006650804,"about_ca_topic_score_gemma":0.0000068606087,"teacher_disagreement_score":0.96219164,"about_ca_system_score_codex":0.00003863492,"about_ca_system_score_gemma":0.000019054953,"threshold_uncertainty_score":0.5612162},"labels":[],"label_agreement":null},{"id":"W4387486534","doi":"10.1109/indiscon58499.2023.10269872","title":"Automated 20-20-20 Timers Using Facial Detection and Facial Recognition","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Heritage College","funders":"","keywords":"Timer; Computer science; Real-time computing; Artificial intelligence; Human–computer interaction; Computer hardware; Computer vision","score_opus":0.0404285693608641,"score_gpt":0.27532715726820584,"score_spread":0.23489858790734175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387486534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9043177,0.0000075728485,0.08796307,0.00046295553,0.0005251668,0.0001216427,0.000010673852,0.005537437,0.001053799],"genre_scores_gemma":[0.991592,0.0000041181816,0.0079759015,0.000058530793,0.00005147925,0.0000066046764,0.000011921052,0.000008689956,0.00029073973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990719,0.00004219145,0.00014900549,0.00034382267,0.00013249357,0.00026056712],"domain_scores_gemma":[0.9996629,0.000033554963,0.000057013094,0.00014463511,0.000054574168,0.00004736052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016173166,0.0001151081,0.00012037089,0.0002779965,0.00023499728,0.00009679612,0.00016723001,0.00013265293,0.000021014916],"category_scores_gemma":[0.000038363087,0.00011248673,0.000032835476,0.0007010015,0.000070365095,0.00025186373,0.00012163925,0.0001274648,0.00024183127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000135237415,0.000034764576,0.00077480974,0.000014850406,0.00003311117,0.00003462465,0.00024890667,0.00015208166,0.23175755,0.00020712789,0.0011338459,0.7655948],"study_design_scores_gemma":[0.0005226267,0.00013759568,0.013426332,0.000021274856,0.000016295251,0.00005257598,0.00008170761,0.9379993,0.04158961,0.0024351513,0.0033861487,0.00033138864],"about_ca_topic_score_codex":0.00007262118,"about_ca_topic_score_gemma":0.00010432385,"teacher_disagreement_score":0.9378472,"about_ca_system_score_codex":0.0000389295,"about_ca_system_score_gemma":0.00002522519,"threshold_uncertainty_score":0.45870754},"labels":[],"label_agreement":null},{"id":"W4387611818","doi":"10.1145/3607822.3614523","title":"PalmGazer: Unimanual Eye-hand Menus in Augmented Reality","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Augmented reality; Computer science; Human–computer interaction; Computer graphics (images); Computer vision","score_opus":0.0320939876130099,"score_gpt":0.30536707845370237,"score_spread":0.27327309084069246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387611818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89039606,0.000025671481,0.07599709,0.0138621265,0.00035439903,0.00018150527,0.00000512271,0.0023461785,0.016831836],"genre_scores_gemma":[0.996149,0.000008489905,0.0018151856,0.000118483644,0.000011637595,0.000012938725,0.0000043436075,0.0000044453855,0.0018754764],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9989922,0.000052558506,0.00016713393,0.00033378846,0.0001445451,0.00030974916],"domain_scores_gemma":[0.9994264,0.00005704097,0.000033046163,0.00041322116,0.000028854885,0.00004138601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037870186,0.00009182352,0.00012907521,0.00022891021,0.000070809285,0.000056568908,0.0005485985,0.000067899884,0.000015639582],"category_scores_gemma":[0.00004315571,0.000080360616,0.000027424167,0.0010689902,0.000072627205,0.00011657962,0.00027623802,0.00013664445,0.00026727226],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022776252,0.00038089132,0.106346086,0.000046021083,0.00005384741,0.00069103634,0.0013229476,0.00019754982,0.01369849,0.6061471,0.04494024,0.22615302],"study_design_scores_gemma":[0.0008595859,0.00012678267,0.92492646,0.000027420743,0.0000031592506,0.000007121188,0.00013784571,0.028323779,0.010817452,0.020885028,0.013583462,0.00030190125],"about_ca_topic_score_codex":0.00020161677,"about_ca_topic_score_gemma":0.00019994819,"teacher_disagreement_score":0.8185804,"about_ca_system_score_codex":0.00003856222,"about_ca_system_score_gemma":0.000027602517,"threshold_uncertainty_score":0.34353334},"labels":[],"label_agreement":null},{"id":"W4387781159","doi":"10.16910/jemr.14.3.6","title":"Filtering eye-tracking data from an EyeLink 1000: Comparing heuristic, savitzky-golay, IIR and FIR digital filters","year":2023,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Finite impulse response; Infinite impulse response; Root-raised-cosine filter; Computer science; Low-pass filter; Digital filter; Filter (signal processing); Filter design; Algorithm; Butterworth filter; Prototype filter; Mathematics; Computer vision","score_opus":0.18026228656718493,"score_gpt":0.4320153818137153,"score_spread":0.25175309524653033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387781159","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9666491,0.00019570906,0.029293742,0.0030053745,0.00029815626,0.00012541417,0.000038287395,0.00014236501,0.00025186426],"genre_scores_gemma":[0.9958254,0.00013629886,0.0034978604,0.00008322952,0.00025182718,0.0000031864322,0.000035364104,0.00002084339,0.0001459921],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9966982,0.00017378609,0.00061137194,0.0005921166,0.0011527544,0.0007717485],"domain_scores_gemma":[0.9975558,0.000500685,0.00024371335,0.0011065191,0.00034449127,0.0002488125],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0027952879,0.0001960272,0.00037670712,0.00069499697,0.00034109343,0.0011480892,0.003023315,0.000090957205,0.0000228712],"category_scores_gemma":[0.0004117484,0.00017453748,0.000060372437,0.00071897055,0.0001917268,0.0017528376,0.003016587,0.0010440489,0.000045612425],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019045426,0.0008328407,0.6051637,0.00015983087,0.00054495916,0.0026208707,0.0021257794,0.0011365666,0.0804073,0.0045945123,0.017771998,0.28445116],"study_design_scores_gemma":[0.0016648468,0.0010197551,0.82521117,0.00061653444,0.000024087782,0.00002239378,0.0013283446,0.14409442,0.0050359447,0.012255643,0.008181414,0.0005454364],"about_ca_topic_score_codex":0.000105434054,"about_ca_topic_score_gemma":0.000039376355,"teacher_disagreement_score":0.28390574,"about_ca_system_score_codex":0.0001129867,"about_ca_system_score_gemma":0.00011448465,"threshold_uncertainty_score":0.99988884},"labels":[],"label_agreement":null},{"id":"W4387801426","doi":"10.1145/3586183.3606734","title":"RadarVR: Exploring Spatiotemporal Visual Guidance in Cinematic VR","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Motion (physics); Field (mathematics); Computer vision; Plan (archaeology); Artificial intelligence; Virtual reality; Human–computer interaction; Geography","score_opus":0.06801581781402279,"score_gpt":0.2967922579240582,"score_spread":0.22877644011003545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387801426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7884406,0.000032903015,0.20258982,0.0032082365,0.00036423665,0.000108374064,2.7985465e-7,0.001854601,0.003400982],"genre_scores_gemma":[0.9818832,0.000005486457,0.017453328,0.000071450224,0.000021942013,0.000032329033,9.541313e-7,0.0000062412605,0.0005250574],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990509,0.00003085881,0.00021211014,0.00027970967,0.0001534372,0.0002729699],"domain_scores_gemma":[0.9995341,0.00007473649,0.000041494255,0.0002963165,0.00002221233,0.000031188156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031108878,0.0000914238,0.00014001044,0.0002950191,0.000045170545,0.00004389914,0.00047901814,0.000038538044,0.000011568551],"category_scores_gemma":[0.00008077066,0.00008260521,0.000027434935,0.0009643308,0.000028163096,0.00030122625,0.00020311544,0.00011484792,0.00037012284],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007295957,0.00022750611,0.11998495,0.00013937186,0.000023905583,0.00048173396,0.0015861754,0.0006153232,0.0052747806,0.39292535,0.0057605887,0.47297305],"study_design_scores_gemma":[0.000839423,0.00018919092,0.6788179,0.00020314207,0.0000028395707,0.000017105453,0.0003203781,0.27620354,0.017536828,0.022293864,0.0030283926,0.0005473702],"about_ca_topic_score_codex":0.00006807146,"about_ca_topic_score_gemma":0.00004869494,"teacher_disagreement_score":0.558833,"about_ca_system_score_codex":0.000027583808,"about_ca_system_score_gemma":0.000025163094,"threshold_uncertainty_score":0.4757304},"labels":[],"label_agreement":null},{"id":"W4387814364","doi":"10.1177/21695067231192924","title":"On Validating a Generic Video-Based Blink Detection System for Cognitive Load Detection","year":2023,"lang":"en","type":"article","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Task (project management); Cognitive load; Cognition; Computer science; Eye tracking; Artificial intelligence; Psychology; Engineering","score_opus":0.024394616924976336,"score_gpt":0.24254695984594365,"score_spread":0.21815234292096733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387814364","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9901625,0.00000937621,0.008782296,0.000057132398,0.0002610535,0.0002582239,0.000010947086,0.0004006798,0.000057801855],"genre_scores_gemma":[0.9986069,0.0000024436667,0.0012184675,0.0000320034,0.00006315302,0.000041947093,0.00000136926,0.000017923468,0.00001574411],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988936,0.0000103301945,0.0002716338,0.00039662945,0.00014700102,0.0002808031],"domain_scores_gemma":[0.99889183,0.0002687517,0.00039794293,0.00008374646,0.00031911142,0.00003861638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068347366,0.00017788196,0.00021051016,0.00007694429,0.00086837244,0.00012087149,0.0003351276,0.00013753778,1.0987867e-7],"category_scores_gemma":[0.00026219184,0.00014276674,0.00020214953,0.00039179603,0.00009462317,0.00019754708,0.00017952146,0.0001977144,9.110365e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002361937,0.00018799304,0.021435637,0.0022434138,0.00040501083,4.4020223e-7,0.019520981,0.0013811394,0.8856126,0.015153286,0.0005258266,0.053297494],"study_design_scores_gemma":[0.00081950316,0.00039177027,0.013423129,0.00048026364,0.000059258524,0.000002529499,0.008826553,0.097808905,0.8766367,0.0011601896,0.000052695606,0.00033851794],"about_ca_topic_score_codex":0.00003592882,"about_ca_topic_score_gemma":0.0000059725116,"teacher_disagreement_score":0.09642777,"about_ca_system_score_codex":0.000166018,"about_ca_system_score_gemma":0.000024663344,"threshold_uncertainty_score":0.6678906},"labels":[],"label_agreement":null},{"id":"W4387940128","doi":"10.1177/21695067231192631","title":"Eye Tracking-Based Adaptive Displays: A Review of the Recent Literature","year":2023,"lang":"en","type":"review","venue":"Proceedings of the Human Factors and Ergonomics Society Annual Meeting","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Eye tracking; Adaptation (eye); Computer science; Usability; Tracking (education); Human–computer interaction; Artificial intelligence; Computer vision; Psychology","score_opus":0.04490897663703944,"score_gpt":0.3000061898922111,"score_spread":0.25509721325517165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387940128","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032121073,0.99505144,0.00002306933,0.00032776978,0.00029875914,0.00074860355,0.0001157137,0.00012327809,0.00009923699],"genre_scores_gemma":[0.0017606342,0.9971416,0.0007667897,0.00011115182,0.00006509184,0.000033539556,0.0000065085815,0.000044289092,0.00007038846],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.997819,0.00004640479,0.0008894352,0.0006080691,0.00027659355,0.00036046526],"domain_scores_gemma":[0.9969358,0.00021935339,0.001955553,0.00037490384,0.000459319,0.000055065462],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00095030246,0.00049335905,0.0013931736,0.00006627046,0.00045852695,0.000113758324,0.0022399635,0.0003771763,8.517841e-7],"category_scores_gemma":[0.000352089,0.00027050142,0.0012116167,0.0010020963,0.000355733,0.0002028984,0.0010752403,0.0009427176,5.7828163e-7],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018836396,0.00046268967,0.0026756574,0.604814,0.0019348447,0.0000012150768,0.017974516,0.000014357601,0.00012037878,0.04865048,0.020708025,0.30262503],"study_design_scores_gemma":[0.00033689864,0.0001954353,0.0015290193,0.66980755,0.0011777463,0.000008898387,0.00117898,0.00027623356,0.00042890615,0.001286523,0.32258555,0.0011882788],"about_ca_topic_score_codex":0.000011823498,"about_ca_topic_score_gemma":0.0000017307,"teacher_disagreement_score":0.30187753,"about_ca_system_score_codex":0.00012765342,"about_ca_system_score_gemma":0.00013850337,"threshold_uncertainty_score":0.9999747},"labels":[],"label_agreement":null},{"id":"W4388009780","doi":"10.1101/2023.10.24.563804","title":"AI-Powered Smart Glasses for Sensing and Recognition of Human-Robot Walking Environments","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"Toronto Rehabilitation Institute; AGE-WELL","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Robot; Microcontroller; Computer vision; Inference; Deep learning; Embedded system","score_opus":0.03711632772992761,"score_gpt":0.2532311040037521,"score_spread":0.21611477627382447,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388009780","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65337884,0.00024117614,0.34328374,0.00048011617,0.0010413068,0.0006457654,0.000108260996,0.00081821805,0.0000025621591],"genre_scores_gemma":[0.94396675,0.00007956303,0.055652473,0.000079693884,0.00009158046,0.000055245164,7.0308977e-7,0.00006982358,0.000004155839],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99761677,0.00009364743,0.00051227247,0.0010687741,0.00025372114,0.00045480157],"domain_scores_gemma":[0.998067,0.00015929728,0.0005065444,0.0009826195,0.00018282614,0.00010170237],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006205473,0.0003908108,0.0005371864,0.00039662313,0.00020596174,0.00017921839,0.0005942817,0.0004512944,0.0000018250186],"category_scores_gemma":[0.00020494332,0.0004513468,0.00011788094,0.0003084545,0.00017371429,0.00017297192,0.00084537215,0.00047019427,0.00001412254],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009287717,0.00009013113,0.0033179002,0.00043135698,0.00015216686,0.000029430077,0.000020535144,0.0000112149955,0.9945094,0.0010756883,0.000107888125,0.00024501525],"study_design_scores_gemma":[0.0008931093,0.00020742352,0.21071191,0.0012869232,0.00014481675,7.560989e-8,0.000005700733,0.00412841,0.7801643,0.0007181766,0.00066468667,0.00107446],"about_ca_topic_score_codex":0.00004860822,"about_ca_topic_score_gemma":0.0000044964218,"teacher_disagreement_score":0.2905879,"about_ca_system_score_codex":0.000116150506,"about_ca_system_score_gemma":0.000108311084,"threshold_uncertainty_score":0.9997938},"labels":[],"label_agreement":null},{"id":"W4388725852","doi":"10.3758/s13428-023-02285-0","title":"Retraction Note: Eye tracking: empirical foundations for a minimal reporting guideline","year":2023,"lang":"en","type":"retraction","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; SR Research (Canada)","funders":"","keywords":"Guideline; Computer science; Eye tracking; Tracking (education); Artificial intelligence; Optometry; Psychology; Medicine","score_opus":0.6596814002210868,"score_gpt":0.6967781457215364,"score_spread":0.03709674550044961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388725852","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006989478,0.00024321643,0.9814091,0.005400933,0.0075952355,0.0018563156,0.00006877553,0.0017020289,0.0010254476],"genre_scores_gemma":[0.0010289494,0.00023476587,0.9745098,0.00004102175,0.0019497534,0.0021544197,0.0005488762,0.00017746461,0.019354999],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.989715,0.002072422,0.0029652063,0.0019661393,0.0018252636,0.0014559688],"domain_scores_gemma":[0.98797923,0.0045651584,0.0021960249,0.0018476521,0.003138062,0.00027388235],"candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["metaresearch","research_integrity"],"category_scores_codex":[0.03915319,0.0004952473,0.0009739667,0.0022724746,0.0011533713,0.00074312487,0.0018127791,0.002300074,0.00004965373],"category_scores_gemma":[0.0434816,0.0004972808,0.0006051852,0.003078166,0.00033196423,0.00056970154,0.0006604783,0.005241739,0.000120942954],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021558635,0.0002879239,0.00041164114,0.0001346046,0.000036805766,0.00015277654,0.00014295364,0.000003650512,0.005430334,0.00043397112,0.14786421,0.8450796],"study_design_scores_gemma":[0.0004198736,0.0006015696,0.0154820345,0.0002639448,0.00013003955,0.00015453938,0.00013850525,0.010479894,0.003967417,0.0025499,0.9650946,0.000717643],"about_ca_topic_score_codex":0.00021712063,"about_ca_topic_score_gemma":0.000055960747,"teacher_disagreement_score":0.84436196,"about_ca_system_score_codex":0.0009972666,"about_ca_system_score_gemma":0.001486592,"threshold_uncertainty_score":0.9997479},"labels":[],"label_agreement":null},{"id":"W4389279087","doi":"10.1016/j.trf.2023.11.019","title":"Driver’s gaze behaviour before, during and after take-over manoeuvres: Influence of agentivity associated with different automation solutions","year":2023,"lang":"en","type":"article","venue":"Transportation Research Part F Traffic Psychology and Behaviour","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Institut Universitaire de France; Agence Nationale de la Recherche","keywords":"Disengagement theory; Gaze; Overtaking; Automation; Context (archaeology); Eye tracking; Computer science; Simulation; Control (management); Human–computer interaction; Psychology; Artificial intelligence; Engineering; Transport engineering","score_opus":0.045012498751192835,"score_gpt":0.33864553747211573,"score_spread":0.2936330387209229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389279087","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979566,0.00004131482,0.00073724997,0.00044762413,0.000061798055,0.00030240638,0.000079837875,0.000364777,0.000008399662],"genre_scores_gemma":[0.99941885,0.00009084571,0.00013427845,0.000015848282,0.0000078132425,0.00014639246,0.00006411426,0.000012571528,0.00010925689],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979705,0.0001977888,0.00032809985,0.0005665555,0.00041037463,0.0005266528],"domain_scores_gemma":[0.99915755,0.00010596845,0.000119620534,0.0003162669,0.00018631197,0.00011426911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005718459,0.00017756321,0.0002468844,0.00048631136,0.0003188413,0.00003923482,0.0002650195,0.00021422394,0.000018372395],"category_scores_gemma":[0.000025841999,0.00015581034,0.000044110948,0.00077789254,0.0005758995,0.0002939525,0.00003029377,0.00044190875,0.000005628396],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000054678814,0.0003671543,0.9934828,0.00003429284,0.000042340085,0.00012405364,0.0011356317,0.00015029348,0.0009899321,0.00079343736,0.00010858914,0.0027167907],"study_design_scores_gemma":[0.0009721961,0.00029725497,0.99757,0.00012143933,0.000033743316,0.0000057568714,0.000058416237,0.00043706846,0.00016087669,0.00019228725,0.000009087515,0.00014192305],"about_ca_topic_score_codex":0.000034709527,"about_ca_topic_score_gemma":0.0014452889,"teacher_disagreement_score":0.004087144,"about_ca_system_score_codex":0.000023985729,"about_ca_system_score_gemma":0.000031866803,"threshold_uncertainty_score":0.63537616},"labels":[],"label_agreement":null},{"id":"W4389449631","doi":"10.1212/wnl.94.15_supplement.5350","title":"Video-based Eye Tracking Distinguishes Follow Up OMAS Patients from Controls (5350)","year":2020,"lang":"en","type":"article","venue":"Neurology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; Hospital for Sick Children","funders":"","keywords":"Medicine; Artificial intelligence; Ophthalmology; Computer science","score_opus":0.02119466816620231,"score_gpt":0.23840233327493654,"score_spread":0.21720766510873424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389449631","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89189726,0.00004955845,0.087962106,0.017562557,0.0010513652,0.00016948677,0.000016266275,0.0008654647,0.0004259108],"genre_scores_gemma":[0.98482156,8.173573e-7,0.0023506703,0.012622902,0.00014125514,0.000019964316,0.0000150869655,0.000020594596,0.0000071285394],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979992,0.00019210315,0.0003167547,0.00081929547,0.00021333803,0.00045930516],"domain_scores_gemma":[0.9986109,0.00046081844,0.00017853414,0.000493047,0.00011733847,0.00013936356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008684965,0.00023117574,0.00040605827,0.0000755714,0.00014028391,0.000084937914,0.0011062962,0.00019245909,0.000022795184],"category_scores_gemma":[0.0008891811,0.00022395392,0.00011736057,0.000277612,0.00012624796,0.00012982257,0.00021344823,0.00044288812,0.000110579575],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044723155,0.000300067,0.910285,0.00001868495,0.00007506087,0.00053597963,0.000694974,0.00025711564,0.00672879,0.0063935905,0.0023495352,0.07191398],"study_design_scores_gemma":[0.005458085,0.0025791079,0.8982273,0.000013480346,0.000043034535,0.000004366842,0.0000046514624,0.0581156,0.0039067925,0.003557757,0.027547343,0.000542495],"about_ca_topic_score_codex":0.0000906405,"about_ca_topic_score_gemma":0.00003319037,"teacher_disagreement_score":0.0929243,"about_ca_system_score_codex":0.000011206104,"about_ca_system_score_gemma":0.000042789266,"threshold_uncertainty_score":0.91325754},"labels":[],"label_agreement":null},{"id":"W4390031802","doi":"10.1159/000535756","title":"Examining Eye Tracking Metrics and Cognitive Function in Post-Stroke Individuals: A Comparison of Visual Searching Tasks between Those with and without Cognitive Impairment","year":2023,"lang":"en","type":"article","venue":"Cerebrovascular Diseases","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Eye tracking; Cognition; Saccade; Stroke (engine); Eye movement; Medicine; Rehabilitation; Gaze; Smooth pursuit; Physical medicine and rehabilitation; Montreal Cognitive Assessment; Audiology; Cognitive impairment; Psychology; Physical therapy; Artificial intelligence; Psychiatry; Ophthalmology; Computer science","score_opus":0.03915750365858325,"score_gpt":0.32474277710363736,"score_spread":0.28558527344505413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390031802","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96298033,0.0011681939,0.03507389,0.000034384142,0.00002668458,0.00037641596,0.000098365344,0.0002265776,0.000015177158],"genre_scores_gemma":[0.99908644,0.000036709796,0.0006992546,0.000021969696,0.00002200241,0.00004254079,0.000064930384,0.000020798641,0.0000053580698],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9979462,0.00026150374,0.00030375857,0.00062599266,0.00047108452,0.00039148537],"domain_scores_gemma":[0.99863905,0.0006766257,0.00018115222,0.00017343031,0.00018378391,0.00014598963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005603675,0.00023522819,0.00048063946,0.000916891,0.00017208378,0.00013996808,0.00018995139,0.000090960726,0.000001729633],"category_scores_gemma":[0.0004130911,0.0002095619,0.00006129374,0.0011010688,0.00027100267,0.00037180143,0.0003464121,0.00029024848,0.0000024030132],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033737033,0.0001477769,0.8341774,0.00007502935,0.00029436668,0.0000066935,0.0013250713,0.000010095819,0.00008996782,0.000040353563,0.0000010922982,0.16379847],"study_design_scores_gemma":[0.0019008721,0.0009823841,0.98560935,0.00050417177,0.00040356693,0.000004022562,0.0060297195,0.0031575398,0.0010507929,0.00011389532,0.0000023921054,0.0002413172],"about_ca_topic_score_codex":0.00006303698,"about_ca_topic_score_gemma":0.000022701324,"teacher_disagreement_score":0.16355714,"about_ca_system_score_codex":0.000024639512,"about_ca_system_score_gemma":0.00008865246,"threshold_uncertainty_score":0.8545686},"labels":[],"label_agreement":null},{"id":"W4390562181","doi":"10.22541/au.170432961.11380493/v1","title":"Low-cost Geometry-based Eye Gaze Detection using Facial Landmarks Generated through Deep Learning","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Gaze; Computer science; Artificial intelligence; Leverage (statistics); Landmark; Computer vision; Face (sociological concept); Inference; Eye tracking; Set (abstract data type); Deep learning; Machine learning; Human–computer interaction","score_opus":0.02743058286720234,"score_gpt":0.286220855223201,"score_spread":0.25879027235599866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390562181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28604537,0.00018292532,0.70915127,0.00020684504,0.0017082794,0.00021827288,0.000005293536,0.0018124712,0.0006692584],"genre_scores_gemma":[0.9422346,0.000017361106,0.05701109,0.0001205197,0.00023257842,0.000040474904,0.000034422705,0.00004297498,0.0002659839],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99724907,0.00016305156,0.0004225632,0.0012508427,0.000352878,0.00056160666],"domain_scores_gemma":[0.99873185,0.00007136396,0.00021753929,0.00071028376,0.00019401386,0.000074960015],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0003690115,0.00046877383,0.00047360535,0.00056023564,0.00029351754,0.00061177055,0.0009570678,0.00078926753,0.000052396877],"category_scores_gemma":[0.000117463656,0.00043879435,0.00022344298,0.0009854676,0.000097681586,0.00011846021,0.0015072519,0.0023363186,0.00016074411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000038271635,0.0002303908,0.0023200125,0.0005191019,0.00030974226,0.00043397813,0.00045666593,0.35313368,0.036422215,0.0035014336,0.000115434304,0.60251904],"study_design_scores_gemma":[0.00028463494,0.000054356587,0.000808247,0.00020283052,0.000049856877,0.000016056587,0.000021305299,0.92911714,0.06375355,0.00313837,0.0019390613,0.0006145756],"about_ca_topic_score_codex":0.00039290695,"about_ca_topic_score_gemma":0.00013509259,"teacher_disagreement_score":0.6561892,"about_ca_system_score_codex":0.00029288666,"about_ca_system_score_gemma":0.0002087752,"threshold_uncertainty_score":0.9999653},"labels":[],"label_agreement":null},{"id":"W4390876248","doi":"10.3390/s24020540","title":"A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Saccade; Electrooculography; Algorithm; Fusion; Constant (computer programming); Computer science; Sensor fusion; Artificial intelligence; Computer vision; Eye movement","score_opus":0.02238594253045921,"score_gpt":0.2319190399864109,"score_spread":0.20953309745595167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390876248","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0755111,0.00007280552,0.9226977,0.0010588305,0.000060802267,0.0002961059,0.000009187702,0.00022423439,0.00006922787],"genre_scores_gemma":[0.8527864,0.0000017972403,0.14705914,0.00010357327,0.0000053139165,0.000030480389,5.4892695e-7,0.0000080702375,0.000004636966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893576,0.000034379234,0.00014012303,0.0003387179,0.00031088907,0.00024013262],"domain_scores_gemma":[0.9993366,0.0001403167,0.000059458554,0.00032666087,0.00010970852,0.00002722662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004761445,0.00012474979,0.00013458237,0.00016405158,0.0000966953,0.000050344894,0.0002998506,0.000052413034,2.5785153e-7],"category_scores_gemma":[0.00003484831,0.00007388765,0.000093360235,0.00040828926,0.00012259585,0.00003564068,0.00002701747,0.0001715736,6.7974497e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018785715,0.00023312226,0.00008788366,0.00022334338,0.00018909377,0.00003034463,0.001011093,0.08269515,0.027510863,0.03694482,0.00027010913,0.85061634],"study_design_scores_gemma":[0.00021127697,0.00036534874,0.00007555269,0.00007746599,0.0000220985,0.0000031572483,0.000030045725,0.9772166,0.019970601,0.0018824538,0.00004757545,0.00009784123],"about_ca_topic_score_codex":0.000020865047,"about_ca_topic_score_gemma":0.000011251235,"teacher_disagreement_score":0.8945214,"about_ca_system_score_codex":0.00005335227,"about_ca_system_score_gemma":0.00012833571,"threshold_uncertainty_score":0.3013051},"labels":[],"label_agreement":null},{"id":"W4391527624","doi":"10.3791/2601-v","title":"JoVE Video Dataset","year":2024,"lang":"pt","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.03053062230994534,"score_gpt":0.30163468489729556,"score_spread":0.2711040625873502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391527624","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014308408,0.00521825,0.9298377,0.038344886,0.00548863,0.00019352145,0.0013524482,0.002666698,0.01546704],"genre_scores_gemma":[0.9709739,0.000068288944,0.0054221344,0.0011613255,0.00019410183,0.000008352065,0.00017437663,0.0000216348,0.021975867],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983206,0.000047627134,0.0002314141,0.0007682461,0.00021430821,0.0004177987],"domain_scores_gemma":[0.99879074,0.00015522372,0.000026906544,0.00090340787,0.000031193813,0.000092500224],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002997941,0.00020319496,0.00018092935,0.00021715024,0.000098524564,0.00063568633,0.0010853745,0.00015469099,0.00062416296],"category_scores_gemma":[0.000045232187,0.0001703505,0.00007410776,0.0007162779,0.00012781334,0.00036994365,0.00058092765,0.00040721532,0.0089013735],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011145397,0.00003934709,0.00007786483,0.000052118652,0.000043553133,0.00031193614,0.0001115899,0.0000030331646,0.0002080911,0.22400932,0.67071,0.10443202],"study_design_scores_gemma":[0.00011557904,0.00014380633,0.0011745583,0.00016763734,0.000033347496,0.00011862482,0.000050388633,0.09051746,0.0016955336,0.0045391438,0.9010991,0.0003448436],"about_ca_topic_score_codex":0.0000811543,"about_ca_topic_score_gemma":0.000032206946,"teacher_disagreement_score":0.9695431,"about_ca_system_score_codex":0.00004465872,"about_ca_system_score_gemma":0.00011808648,"threshold_uncertainty_score":0.9918703},"labels":[],"label_agreement":null},{"id":"W4391542999","doi":"10.1016/j.cag.2024.103889","title":"Does fiducial marker visibility impact task performance and information processing in novice and low-time pilots?","year":2024,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Computer science; Gaze; Distraction; Visibility; Computer vision; Fixation (population genetics); Artificial intelligence; Eye tracking; Task (project management); Psychology; Medicine; Optics; Cognitive psychology; Physics","score_opus":0.004973778187879169,"score_gpt":0.23318719004527683,"score_spread":0.22821341185739766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391542999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93809515,0.00021330969,0.06023137,0.0007007771,0.00024081701,0.00012667319,0.0000040554523,0.00033000787,0.00005785998],"genre_scores_gemma":[0.9961489,0.000073768046,0.003554053,0.00017510135,0.000030126346,0.0000049619225,0.0000039341767,0.000005520244,0.0000036788635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896646,0.000038651757,0.00024378889,0.0003516787,0.00014715226,0.00025228882],"domain_scores_gemma":[0.9995056,0.00008343165,0.000057308633,0.00023362185,0.000060411712,0.000059625025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045474147,0.0001665788,0.00017167823,0.00041044917,0.000121743986,0.00048020782,0.0002914859,0.000098704935,0.0000010433923],"category_scores_gemma":[0.00002249031,0.00011386467,0.000032894248,0.0007840839,0.00016772353,0.0020478517,0.00023825964,0.0002958663,0.000006129433],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030218816,0.00006778927,0.080861874,0.0007073163,0.000023427398,0.000020803276,0.00211562,0.00008120591,0.00033026564,0.004587896,0.00029967926,0.9108739],"study_design_scores_gemma":[0.00017061178,0.000083949824,0.47076437,0.00021426822,0.00000422258,0.00002210307,0.000007306612,0.5268396,0.000034883764,0.0012620586,0.00044516896,0.00015142607],"about_ca_topic_score_codex":0.000022188755,"about_ca_topic_score_gemma":0.0000066058165,"teacher_disagreement_score":0.9107225,"about_ca_system_score_codex":0.000037797145,"about_ca_system_score_gemma":0.000073081996,"threshold_uncertainty_score":0.46432665},"labels":[],"label_agreement":null},{"id":"W4391736069","doi":"10.5267/j.msl.2024.1.002","title":"Design and implementation of advanced sensor systems for smart robotic wheelchairs: A review","year":2024,"lang":"en","type":"review","venue":"Management Science Letters","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Human–computer interaction; Embedded system","score_opus":0.0439878795067563,"score_gpt":0.34266164516118175,"score_spread":0.29867376565442544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391736069","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.9745717e-7,0.59475565,0.40149048,0.0010694986,0.0003891128,0.0021715842,0.0000022489028,0.000107677086,0.000013254275],"genre_scores_gemma":[0.00001714387,0.942122,0.056717407,0.00035323473,0.00001449382,0.00068888004,0.0000041632097,0.000017165012,0.000065554275],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974806,0.000096176074,0.00061090617,0.0009669139,0.00040716407,0.0004382531],"domain_scores_gemma":[0.9987211,0.00010292162,0.00040759955,0.0006781702,0.000035865745,0.000054343178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016595812,0.00031711478,0.0009098262,0.0006807679,0.0001316891,0.00021697073,0.0013391856,0.000045520574,8.8577093e-7],"category_scores_gemma":[0.000018929271,0.00024696294,0.00015056587,0.0014780511,0.00025911993,0.00030061652,0.00047204277,0.00013156822,0.000014993989],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.0724362e-7,0.000008202233,7.9434454e-7,0.08686885,0.00006834078,0.0000144289515,0.000023966251,0.00007117857,0.000009428993,0.009966188,0.0030429545,0.89992535],"study_design_scores_gemma":[0.00028812245,0.00019226868,0.00001444474,0.08902264,0.0017151758,0.000057424524,0.00010532141,0.0038405359,0.000009404333,0.00019620801,0.90379757,0.0007608828],"about_ca_topic_score_codex":0.000006158701,"about_ca_topic_score_gemma":3.205541e-7,"teacher_disagreement_score":0.90075463,"about_ca_system_score_codex":0.00014044924,"about_ca_system_score_gemma":0.000041014835,"threshold_uncertainty_score":0.9999983},"labels":[],"label_agreement":null},{"id":"W4391770549","doi":"10.1109/itsc57777.2023.10422321","title":"A Solution for Cross-Calibration of Gaze Tracker System and Stereoscopic Scene System","year":2023,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Western University","funders":"","keywords":"Computer vision; Gaze; Stereoscopy; Computer science; Artificial intelligence; Calibration; Computer graphics (images); Mathematics","score_opus":0.028679870514423073,"score_gpt":0.2827849132412273,"score_spread":0.25410504272680423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391770549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25049484,0.000029255318,0.7477631,0.00023263882,0.0002090645,0.00019599037,0.000005037462,0.0008673045,0.00020281159],"genre_scores_gemma":[0.98191214,0.0000010637189,0.017745432,0.000008197477,0.00002098887,0.0000367603,0.000003587879,0.0000057534457,0.00026606527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922466,0.000026069922,0.00022362717,0.00025033334,0.00010102838,0.00017425272],"domain_scores_gemma":[0.99949634,0.00006858114,0.000088548775,0.00023863865,0.000080141275,0.00002773192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028271298,0.00007767359,0.0001560849,0.00013278962,0.00009105115,0.000074970136,0.00020526383,0.00008148315,4.9062936e-7],"category_scores_gemma":[0.000018123494,0.00006611446,0.000029759367,0.00029536866,0.000051806204,0.00021311283,0.000076755234,0.000037745904,0.0000076266965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022196766,0.000048010563,0.016218642,0.0023442141,0.000046116642,0.000008450984,0.00029951797,0.00005273115,0.022216039,0.92208445,0.0009836579,0.03567598],"study_design_scores_gemma":[0.0006190001,0.00015738593,0.03820339,0.0002279366,0.000009318037,0.000020023776,0.00016708684,0.94303054,0.017031632,0.00035144057,0.000061845705,0.00012042903],"about_ca_topic_score_codex":0.000017298154,"about_ca_topic_score_gemma":0.0000050063477,"teacher_disagreement_score":0.9429778,"about_ca_system_score_codex":0.000032564578,"about_ca_system_score_gemma":0.000021215757,"threshold_uncertainty_score":0.26960695},"labels":[],"label_agreement":null},{"id":"W4391853794","doi":"10.1109/tai.2024.3366174","title":"Multistream Gaze Estimation With Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Innovation for Defence Excellence and Security","keywords":"Gaze; Isolation (microbiology); Transfer of learning; Computer science; Artificial intelligence; Estimation; Computer vision; Optometry; Engineering; Medicine; Biology","score_opus":0.024896728066047768,"score_gpt":0.28726717062696483,"score_spread":0.26237044256091707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391853794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070114546,0.00001529039,0.92592883,0.0022887227,0.00035022726,0.00020602369,0.000005338676,0.0009594973,0.0001314928],"genre_scores_gemma":[0.9876748,0.000028223174,0.0119021665,0.000041579497,0.000018687058,0.000053457894,0.0000024452338,0.000022344999,0.00025633024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984299,0.00008136598,0.0003066225,0.0006413326,0.00024560982,0.00029518592],"domain_scores_gemma":[0.9993174,0.00017332117,0.0000232665,0.0003113503,0.00006862957,0.00010603323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015927177,0.00020615736,0.00016151245,0.0003179632,0.00024716204,0.00023199095,0.0003213106,0.00013145806,0.000019023515],"category_scores_gemma":[0.00001124454,0.0001883566,0.000073504896,0.0008606149,0.00010846788,0.000315604,0.0000019852218,0.00047567012,0.00029596404],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007359796,0.00016897336,0.000007758096,0.000017929955,0.000023912815,0.00003157748,0.0008550454,0.14471899,0.010802465,0.010841597,0.000042555825,0.8324156],"study_design_scores_gemma":[0.000025634268,0.0004070457,0.000025015095,0.00014566991,0.000020500942,0.000023516628,0.00007973363,0.78891623,0.20860963,0.0013425085,0.00018420443,0.000220317],"about_ca_topic_score_codex":0.00012678908,"about_ca_topic_score_gemma":0.00010925389,"teacher_disagreement_score":0.9175602,"about_ca_system_score_codex":0.00011905411,"about_ca_system_score_gemma":0.0000486896,"threshold_uncertainty_score":0.7680959},"labels":[],"label_agreement":null},{"id":"W4392128272","doi":"10.1016/j.sna.2024.115111","title":"Methodology based on machine learning through neck motion and POF-based pressure sensors for wheelchair operation","year":2024,"lang":"en","type":"article","venue":"Sensors and Actuators A Physical","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Alberta Heritage Foundation for Medical Research; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Wheelchair; Support vector machine; Computer science; Artificial intelligence; Linear discriminant analysis; Decision tree; Focus (optics); Random forest; Machine learning; Feature extraction; Pattern recognition (psychology); Mean squared error; Mathematics","score_opus":0.03019009562364544,"score_gpt":0.2958660018248958,"score_spread":0.26567590620125037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392128272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39818498,0.00009305309,0.5973717,0.0035596013,0.00014230027,0.00020366347,0.000013926694,0.00038104792,0.00004972739],"genre_scores_gemma":[0.96167886,0.0000049853807,0.03785577,0.00021511535,0.000117971336,0.000019960156,0.00001430783,0.000020287758,0.00007273477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864864,0.00020541842,0.00014024293,0.0006105265,0.00013969674,0.0002554872],"domain_scores_gemma":[0.9989512,0.0006996639,0.000046637382,0.00020257231,0.00004088614,0.000059037087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024306,0.0002136063,0.0002743934,0.00011462376,0.00021448355,0.00015942658,0.00012133794,0.000122031925,0.0000029196892],"category_scores_gemma":[0.00013928418,0.0001702386,0.00008189751,0.0001770447,0.00011202915,0.00017973632,0.00004458292,0.00032439706,0.000004807074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029614408,0.00068830786,0.0015336765,0.0007280142,0.00022211633,0.00008342409,0.00432907,0.071741,0.021140633,0.4042832,0.0007484516,0.49420595],"study_design_scores_gemma":[0.00043612433,0.00050375157,0.00070106966,0.000045818528,0.000036243673,0.000006755413,0.00002934001,0.98316526,0.0072174664,0.004220945,0.003448516,0.00018873054],"about_ca_topic_score_codex":0.000029930265,"about_ca_topic_score_gemma":0.0000022190516,"teacher_disagreement_score":0.9114242,"about_ca_system_score_codex":0.000015909878,"about_ca_system_score_gemma":0.000025684503,"threshold_uncertainty_score":0.69421285},"labels":[],"label_agreement":null},{"id":"W4392721602","doi":"10.16995/intransition.11328","title":"Eye-Camera-Ninagawa","year":2023,"lang":"en","type":"article","venue":"[in]Transition","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Computer graphics (images); Optometry; Medicine","score_opus":0.015082910263607172,"score_gpt":0.2548639133119358,"score_spread":0.23978100304832864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392721602","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.867419,0.000033288004,0.11949083,0.009632409,0.0002660055,0.00011631709,0.000002586203,0.0008708128,0.0021687734],"genre_scores_gemma":[0.9981295,0.000015827512,0.0014869341,0.0002178414,0.000022432432,0.000018758765,0.000007501824,0.0000061749934,0.00009504058],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991178,0.00004368161,0.00014922804,0.00029441406,0.000121173274,0.0002737333],"domain_scores_gemma":[0.9996568,0.00003309776,0.000022992423,0.00024341901,0.000019261173,0.000024460445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023598035,0.00008492641,0.00011581097,0.00035538204,0.000039760755,0.000029420413,0.00028238294,0.00008366292,0.000008851933],"category_scores_gemma":[0.000009629259,0.00009007415,0.00003411956,0.0011099445,0.00003584005,0.00019254403,0.000029905455,0.00017320202,0.00032256517],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009232896,0.0012513963,0.04591848,0.00029959163,0.000058183196,0.0037343854,0.022480918,0.016797334,0.10683177,0.26024774,0.009483515,0.53280437],"study_design_scores_gemma":[0.0015578828,0.00019988566,0.814992,0.00020664395,0.0000060772777,0.000027190816,0.00032914116,0.1325092,0.006040473,0.041163642,0.002419649,0.00054818747],"about_ca_topic_score_codex":0.0000763789,"about_ca_topic_score_gemma":0.00008208987,"teacher_disagreement_score":0.76907355,"about_ca_system_score_codex":0.00003924791,"about_ca_system_score_gemma":0.000018001738,"threshold_uncertainty_score":0.41460305},"labels":[],"label_agreement":null},{"id":"W4393103558","doi":"10.2139/ssrn.4768799","title":"Seeing Think-Aloud: Tracking Driver Visual Attention with Eye Movements and Verbalization","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Eye tracking; Think aloud protocol; Eye movement; Psychology; Cognitive psychology; Tracking (education); Computer science; Human–computer interaction; Computer vision; Communication; Artificial intelligence","score_opus":0.007346018427487837,"score_gpt":0.25602888434216287,"score_spread":0.24868286591467503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393103558","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5797179,0.0023348168,0.41633755,0.00059182546,0.0004848293,0.00013690189,0.0000012430171,0.00027619503,0.00011879311],"genre_scores_gemma":[0.9964358,0.0011479425,0.0015734101,0.00006232151,0.00017409303,0.000003915332,0.000009245831,0.000036004196,0.00055727497],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99704057,0.00010076513,0.00033743802,0.0006751115,0.0004438074,0.0014023249],"domain_scores_gemma":[0.99914855,0.000021364634,0.0003243746,0.0002867215,0.00015252782,0.00006648418],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0010293346,0.00032908577,0.0002896233,0.0003635752,0.00028361834,0.00062779966,0.00064326695,0.00026834055,0.0000023025011],"category_scores_gemma":[0.000019941632,0.00027865285,0.000100495265,0.00028278393,0.000072292205,0.00027682213,0.00076620345,0.00421165,0.00001407875],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000055918248,0.00027396617,0.023025023,0.0002513983,0.0017117697,0.00020743988,0.0032020702,0.0013028311,0.0022888826,0.59233624,0.000106365755,0.3752381],"study_design_scores_gemma":[0.0011173767,0.0008115064,0.030583343,0.0012972405,0.00021851399,0.0006720896,0.0013170445,0.03997845,0.0004367653,0.9224554,0.0001753389,0.00093698426],"about_ca_topic_score_codex":0.000045806562,"about_ca_topic_score_gemma":0.000113612055,"teacher_disagreement_score":0.41671795,"about_ca_system_score_codex":0.00084576505,"about_ca_system_score_gemma":0.0010691442,"threshold_uncertainty_score":0.99996656},"labels":[],"label_agreement":null},{"id":"W4393132487","doi":"10.16910/jemr.17.3.2","title":"Dynamics of eye dominance behavior in virtual reality","year":2024,"lang":"en","type":"article","venue":"Journal of Eye Movement Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Virtual reality; Computer science; Ocular dominance; Eye movement; Dominance (genetics); Flexibility (engineering); Human–computer interaction; Artificial intelligence; Computer vision; Replicate; Context (archaeology); Eye tracking; Cognitive psychology; Psychology; Mathematics; Geography","score_opus":0.05718249658622737,"score_gpt":0.4053120209746938,"score_spread":0.34812952438846645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393132487","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93265915,0.00044974798,0.057658423,0.0081207715,0.00033261388,0.00014783857,0.0000066605426,0.000025756333,0.00059903663],"genre_scores_gemma":[0.99734676,0.00012089786,0.0018126265,0.000018196039,0.000043176035,0.000007852773,5.262783e-7,0.000006329469,0.0006436197],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9978122,0.00019423629,0.000550093,0.00021973516,0.0008831177,0.00034062605],"domain_scores_gemma":[0.9988971,0.00020624326,0.00012904491,0.00034155298,0.0003632547,0.00006284213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038470118,0.0000874562,0.00023617817,0.00078139105,0.000045917797,0.00009194884,0.0010512393,0.00008070542,0.000014558169],"category_scores_gemma":[0.00016817493,0.00007096432,0.00009497877,0.0009842067,0.00017523389,0.00026625354,0.00033606435,0.0008777246,0.000006995848],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011532065,0.0014635621,0.08831592,0.00017630396,0.00008303312,0.0020469546,0.00072640897,0.0003189062,0.03749894,0.52000123,0.0021168902,0.34713653],"study_design_scores_gemma":[0.0016626185,0.004710526,0.8078073,0.0013327879,0.000025376487,0.000036916666,0.0007314593,0.07461056,0.03702148,0.06888608,0.0027701156,0.0004047577],"about_ca_topic_score_codex":0.00010317734,"about_ca_topic_score_gemma":0.00009961728,"teacher_disagreement_score":0.7194914,"about_ca_system_score_codex":0.00035481987,"about_ca_system_score_gemma":0.000245398,"threshold_uncertainty_score":0.38133255},"labels":[],"label_agreement":null},{"id":"W4393156087","doi":"10.1609/aaai.v38i4.28151","title":"Test-Time Personalization with Meta Prompt for Gaze Estimation","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; University of Toronto; University of Waterloo","funders":"","keywords":"Personalization; Gaze; Test (biology); Estimation; Meta-analysis; Computer science; Psychology; Artificial intelligence; Medicine; Engineering; World Wide Web; Geology","score_opus":0.07552402674694116,"score_gpt":0.2975394259052834,"score_spread":0.22201539915834223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393156087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020846257,0.00006951097,0.9582923,0.016782595,0.00022163766,0.00089518377,0.000016835602,0.000637586,0.002238069],"genre_scores_gemma":[0.97628707,0.000005973555,0.022908295,0.00007689759,0.000023755778,0.000112386646,0.000001025388,0.000012943465,0.0005716317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987306,0.000006826285,0.0002801219,0.0004605016,0.00029858664,0.00022338092],"domain_scores_gemma":[0.9989213,0.00020149852,0.00015691001,0.00018067383,0.00050411146,0.000035528723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037624998,0.0001794645,0.00022028295,0.00013753083,0.00013937797,0.00032826632,0.0009478147,0.000072702634,0.000028270108],"category_scores_gemma":[0.0003337446,0.00011214382,0.000108829474,0.0006305414,0.00019476487,0.00038345647,0.00009873234,0.00016226748,0.00005658393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002176309,0.000070158654,0.000017019547,0.00010281407,0.00006632211,3.4365465e-7,0.00049302285,0.00007988752,0.028512046,0.8959429,0.00021029987,0.07448339],"study_design_scores_gemma":[0.000013119285,0.00028989735,0.00003333245,0.0002137475,0.00008278279,0.000009184871,0.000049425904,0.54985005,0.34244543,0.106787734,0.000098650424,0.0001266267],"about_ca_topic_score_codex":0.0000055476617,"about_ca_topic_score_gemma":0.0000019987172,"teacher_disagreement_score":0.9554408,"about_ca_system_score_codex":0.00003359562,"about_ca_system_score_gemma":0.00009515493,"threshold_uncertainty_score":0.45730922},"labels":[],"label_agreement":null},{"id":"W4394798216","doi":"10.1080/21681163.2024.2337765","title":"Optimising virtual object position for efficient eye-gaze interaction in Hololens2","year":2024,"lang":"en","type":"article","venue":"Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Holland Bloorview Kids Rehabilitation Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer vision; Eye tracking; Computer science; Artificial intelligence; Ellipse; Object (grammar); Position (finance); Eye movement; Mathematics; Geometry","score_opus":0.015519254908468982,"score_gpt":0.3575236034349473,"score_spread":0.3420043485264783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394798216","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012268806,0.0004262821,0.9838775,0.00059148425,0.0021509053,0.00019324219,0.0000030602807,0.00048666203,0.0000020618565],"genre_scores_gemma":[0.45048937,0.000033157874,0.5492434,0.000070659844,0.000094567884,0.000023681561,0.000022570699,0.000020375117,0.0000022334837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984067,0.00010158166,0.00039916218,0.0005891779,0.00017372779,0.0003296352],"domain_scores_gemma":[0.9993456,0.000311169,0.00005174891,0.00017016515,0.00005187398,0.000069436675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014907907,0.00019782252,0.00022722241,0.0014866163,0.000049900886,0.00029239498,0.00024941188,0.00013093678,9.1714674e-7],"category_scores_gemma":[0.00011445095,0.00019506832,0.000052753963,0.001357012,0.000036455214,0.00022038541,0.0002019627,0.0002235343,0.0000010259328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000070092583,0.000117087715,0.000019496616,0.00018349433,0.000015228806,0.000037921272,0.00043333333,0.005760233,0.104196504,0.05880666,0.00003083077,0.8303922],"study_design_scores_gemma":[0.00030491606,0.00012443258,0.0002096713,0.0006104949,0.0000066004163,0.00003943528,0.000016266975,0.9900914,0.0063307327,0.0009959419,0.0010567881,0.00021335653],"about_ca_topic_score_codex":0.00000840441,"about_ca_topic_score_gemma":2.4167508e-7,"teacher_disagreement_score":0.98433113,"about_ca_system_score_codex":0.00017072108,"about_ca_system_score_gemma":0.000033244458,"threshold_uncertainty_score":0.7954655},"labels":[],"label_agreement":null},{"id":"W4394846989","doi":"10.3390/s24082545","title":"Pupil Response in Visual Tracking Tasks: The Impacts of Task Load, Familiarity, and Gaze Position","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Gaze; Eye tracking; Task (project management); Pupil; Position (finance); Tracking (education); Computer science; Pupillary response; Human–computer interaction; Computer vision; Cognitive psychology; Psychology; Pupil diameter; Artificial intelligence; Engineering; Neuroscience","score_opus":0.011865502866184839,"score_gpt":0.279179671472612,"score_spread":0.2673141686064272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394846989","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9937328,0.0007485709,0.0019068561,0.0031099063,0.00014244218,0.00008467665,0.0000044262356,0.00016404154,0.00010624164],"genre_scores_gemma":[0.9993564,0.0000312239,0.00048142235,0.00006649856,0.000016798685,0.0000024395197,5.5986345e-7,0.0000070758474,0.00003761517],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9989748,0.00018549767,0.0001792993,0.00027223586,0.0001812745,0.00020691649],"domain_scores_gemma":[0.9992797,0.00037695968,0.00003881863,0.00022827018,0.00004503367,0.00003125775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006735655,0.00010178724,0.0001275961,0.0001818254,0.00005342192,0.00010120059,0.00023965925,0.000082066676,0.000001414205],"category_scores_gemma":[0.00018826526,0.00007478816,0.000037642698,0.0005438688,0.00014032988,0.00014546808,0.000109103225,0.0002454219,0.00001078943],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084076275,0.0006235664,0.033208735,0.0004252935,0.00017673765,0.0018033218,0.028383883,0.0007752414,0.58685863,0.08369359,0.0012030748,0.26200718],"study_design_scores_gemma":[0.0006898852,0.0007807407,0.76120234,0.0006773155,0.000028653954,0.000412088,0.0010946324,0.18105884,0.040461928,0.012248554,0.00091184134,0.000433212],"about_ca_topic_score_codex":0.00014209148,"about_ca_topic_score_gemma":0.000055303914,"teacher_disagreement_score":0.7279936,"about_ca_system_score_codex":0.00006283931,"about_ca_system_score_gemma":0.000078864905,"threshold_uncertainty_score":0.30497727},"labels":[],"label_agreement":null},{"id":"W4395036406","doi":"10.1055/s-0044-1784028","title":"Eye-Tracker-basierte Differenzierung von Schwindelursachen: Eine mobile Möglichkeit der schnelleren und genaueren Triagierung im Notfall","year":2024,"lang":"de","type":"article","venue":"Laryngo-Rhino-Otologie","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science","score_opus":0.02596355219954072,"score_gpt":0.31193376656433136,"score_spread":0.28597021436479064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395036406","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67872024,0.23689458,0.04330723,0.012986587,0.012847529,0.0036795042,0.00045439432,0.008453456,0.0026564838],"genre_scores_gemma":[0.98113936,0.00321383,0.00834885,0.00083413505,0.0015861166,0.0005611511,0.00022701231,0.00030511475,0.0037844027],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9877041,0.00086129404,0.0021207703,0.004307417,0.001422704,0.0035837411],"domain_scores_gemma":[0.9929066,0.0013024163,0.00061909965,0.003872074,0.0004757417,0.00082410086],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0016084797,0.0021288886,0.002367877,0.001703366,0.0010649641,0.0021315203,0.005479381,0.002308148,0.000511249],"category_scores_gemma":[0.0006309034,0.001935185,0.0010557214,0.003410507,0.0012295381,0.0013645056,0.002636372,0.0041118967,0.005735668],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009919242,0.0050585754,0.07090722,0.0034971873,0.015560744,0.013418517,0.005448498,0.0014600464,0.037914902,0.117937826,0.07073133,0.65707326],"study_design_scores_gemma":[0.010329938,0.0056734905,0.15892258,0.004311177,0.007410647,0.00050099246,0.0005925372,0.20807445,0.040516466,0.028063037,0.52471864,0.010886023],"about_ca_topic_score_codex":0.00053257594,"about_ca_topic_score_gemma":0.00014799429,"teacher_disagreement_score":0.64618725,"about_ca_system_score_codex":0.00094874675,"about_ca_system_score_gemma":0.0010335511,"threshold_uncertainty_score":0.9999015},"labels":[],"label_agreement":null},{"id":"W4396217314","doi":"10.21203/rs.3.rs-3909704/v2","title":"E2GO : Free Your Hands for Smartphone Interaction","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer science; Fixation (population genetics); Eye tracking; Human–computer interaction; Event (particle physics); Jitter; Power consumption; Cover (algebra); Artificial intelligence; Power (physics); Engineering","score_opus":0.13888762735462137,"score_gpt":0.44435814710640387,"score_spread":0.3054705197517825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396217314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30828425,0.006058329,0.56429076,0.07688426,0.0117212245,0.0052223518,0.0005790834,0.006437818,0.020521931],"genre_scores_gemma":[0.9851953,0.000081119266,0.010231561,0.000026478452,0.00047792387,0.0007065938,0.000048497302,0.00004090233,0.003191622],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9970303,0.00017529541,0.0002867744,0.0010853186,0.00071100204,0.00071128015],"domain_scores_gemma":[0.9968518,0.00045772325,0.00007527069,0.0018191654,0.000674388,0.000121655095],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0015153384,0.0002452484,0.0003104556,0.0009720317,0.0002417508,0.00072394585,0.0025648344,0.0004504831,0.000023977054],"category_scores_gemma":[0.0007855749,0.00022307306,0.00023561816,0.0006441391,0.0001411374,0.00010543208,0.0070956186,0.002691688,0.00030107092],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012734455,0.00046174505,0.0005827827,0.0038068355,0.0003387179,0.00025879088,0.0018536209,0.00020015611,0.0018449571,0.1889798,0.31023535,0.49130988],"study_design_scores_gemma":[0.001363916,0.0013230048,0.005637286,0.0034700076,0.000034562225,0.000045081913,0.00044663815,0.076803334,0.015443921,0.80605173,0.08845373,0.0009267557],"about_ca_topic_score_codex":0.00015240375,"about_ca_topic_score_gemma":0.00008036575,"teacher_disagreement_score":0.67691106,"about_ca_system_score_codex":0.00032930108,"about_ca_system_score_gemma":0.0003992451,"threshold_uncertainty_score":0.9996091},"labels":[],"label_agreement":null},{"id":"W4396732117","doi":"10.3390/s24102984","title":"Early Eye Disengagement Is Regulated by Task Complexity and Task Repetition in Visual Tracking Task","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Recall; Disengagement theory; Eye movement; Task (project management); Cognition; Eye tracking; Cognitive psychology; Fixation (population genetics); Cognitive load; Repetition (rhetorical device); Psychology; Computer science; Artificial intelligence; Neuroscience; Engineering; Medicine","score_opus":0.018153130192636514,"score_gpt":0.27459644234174413,"score_spread":0.2564433121491076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396732117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.987549,0.00030335394,0.0074589597,0.00342805,0.00022335253,0.00015242072,0.000025130148,0.0006207719,0.0002389566],"genre_scores_gemma":[0.9980898,0.000028485993,0.001309222,0.00012224712,0.000028422219,0.000007149553,0.000013426184,0.00001556082,0.00038571836],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983684,0.0001363923,0.0002701006,0.0006674255,0.00022036256,0.00033731398],"domain_scores_gemma":[0.99944806,0.00007879741,0.000055204193,0.00031546204,0.000036788697,0.00006570952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035482834,0.00018245005,0.0001996321,0.00022922661,0.00013160658,0.00024568732,0.0002461073,0.000112936665,0.000011004213],"category_scores_gemma":[0.000038653718,0.00017729856,0.00004901331,0.0005634175,0.00017711292,0.00022207257,0.00014634777,0.0003764748,0.000047011294],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112305126,0.0011565482,0.057776026,0.00068471784,0.00042662202,0.0021182606,0.021874616,0.00030357894,0.3136761,0.1404864,0.024037868,0.43734697],"study_design_scores_gemma":[0.0011647972,0.0005048185,0.7132702,0.0006735442,0.000049727638,0.00006463033,0.00037978054,0.22629827,0.016645547,0.019364452,0.020478254,0.0011059691],"about_ca_topic_score_codex":0.00013934025,"about_ca_topic_score_gemma":0.00002122036,"teacher_disagreement_score":0.65549415,"about_ca_system_score_codex":0.00007644597,"about_ca_system_score_gemma":0.000018311426,"threshold_uncertainty_score":0.72300255},"labels":[],"label_agreement":null},{"id":"W4396832788","doi":"10.1145/3613904.3642915","title":"Gaze on the Go: Effect of Spatial Reference Frame on Visual Target Acquisition During Physical Locomotion in Extended Reality","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Gaze; Computer vision; Computer science; Virtual reality; Reference frame; Head (geology); Optical head-mounted display; Frame of reference; Artificial intelligence; Frame (networking); Target acquisition; Tracking (education); Movement (music); Eye tracking; Physics; Psychology","score_opus":0.013774620438767713,"score_gpt":0.2981186087881098,"score_spread":0.2843439883493421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396832788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9328144,0.0000059829595,0.06360796,0.0017277091,0.00012729965,0.00015590836,0.000002530117,0.00030077662,0.0012573837],"genre_scores_gemma":[0.99964035,0.0000015081716,0.0002116067,0.000035420948,0.000049403923,0.00002078444,0.0000032537953,0.00000558755,0.00003208512],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988092,0.00024127735,0.00015393793,0.00037953284,0.00023396782,0.00018207078],"domain_scores_gemma":[0.9992007,0.00038430464,0.000041646013,0.0003304161,0.000021794858,0.000021149559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040822817,0.00012857607,0.00016978467,0.00012413732,0.000048497113,0.000050085655,0.00032340383,0.00008147314,0.000016976739],"category_scores_gemma":[0.000058476304,0.00007876866,0.000055678327,0.00030421652,0.000068215675,0.00009898564,0.00009586169,0.00035596447,0.00007233735],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026276708,0.0009996941,0.0022140339,0.00025008543,0.00004555802,0.00009223264,0.00047790524,0.00072877837,0.0953293,0.76283634,0.0002763319,0.13648698],"study_design_scores_gemma":[0.0003855712,0.0019086633,0.35739097,0.0003806695,0.0000065468203,0.0000052572113,0.0000094912075,0.16835874,0.44796887,0.023369098,0.000028468692,0.00018764318],"about_ca_topic_score_codex":0.00010432654,"about_ca_topic_score_gemma":0.000011965474,"teacher_disagreement_score":0.73946726,"about_ca_system_score_codex":0.000068701156,"about_ca_system_score_gemma":0.0000152122275,"threshold_uncertainty_score":0.32120925},"labels":[],"label_agreement":null},{"id":"W4399021554","doi":"10.1002/9781119719830.ch13","title":"Industrial Applications, Current Challenges, and Future Directions","year":2024,"lang":"en","type":"other","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Current (fluid); Computer science; Data science; Engineering; Electrical engineering","score_opus":0.04350769475811921,"score_gpt":0.2801606108351016,"score_spread":0.23665291607698236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399021554","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[8.355227e-8,0.28096625,0.012924321,0.005734912,0.0018655449,0.00023138901,0.000010389726,0.002511685,0.6957554],"genre_scores_gemma":[0.000076187855,0.1638205,0.009859726,0.0000518004,0.006716265,0.00045236235,0.0000136895715,0.00026982566,0.81873965],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923456,0.000016376305,0.00008548343,0.00045993022,0.00008286749,0.00012080802],"domain_scores_gemma":[0.9994906,0.000010284594,0.000045060213,0.00040341407,0.0000102780095,0.00004034096],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053581305,0.00014486881,0.000143724,0.00026793338,0.000025455825,0.000050569368,0.0003219799,0.00029125804,0.000061010065],"category_scores_gemma":[0.000002343525,0.000118382915,0.0000307128,0.00017128047,0.000043346627,0.000021172093,0.00016704507,0.00039053542,0.0003600714],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.595311e-8,0.000010877144,5.932119e-7,0.000016165051,0.00000986248,5.582872e-7,0.000009149666,6.261858e-10,9.435368e-8,0.20596562,0.25347763,0.5405094],"study_design_scores_gemma":[0.00005443635,0.000010243928,0.000018964087,0.000053705877,0.000011358058,0.000008193316,0.000010940691,0.00000788786,0.0000012276219,0.004077217,0.9956193,0.00012655079],"about_ca_topic_score_codex":0.000011314854,"about_ca_topic_score_gemma":0.00004053722,"teacher_disagreement_score":0.7421416,"about_ca_system_score_codex":0.000014919196,"about_ca_system_score_gemma":0.00002689438,"threshold_uncertainty_score":0.4827515},"labels":[],"label_agreement":null},{"id":"W4399072199","doi":"10.1145/3655601","title":"GazeSwitch: Automatic Eye-Head Mode Switching for Optimised Hands-Free Pointing","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Universitas Brawijaya; European Commission","keywords":"Computer science; Head (geology); Leverage (statistics); Gesture; Artificial intelligence; Eye movement; Computer vision; Human–computer interaction","score_opus":0.053931460160588654,"score_gpt":0.35492537761544113,"score_spread":0.3009939174548525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399072199","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8476658,0.000027479797,0.14141047,0.0066108056,0.0017312138,0.0004608763,0.0000023528344,0.0012505249,0.00084046304],"genre_scores_gemma":[0.9149269,0.0000015063777,0.08435374,0.0001752237,0.00029277068,0.000058431862,0.0000010580006,0.000031256193,0.00015915382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998203,0.000011710469,0.00048544342,0.00063809543,0.00030917826,0.0003525983],"domain_scores_gemma":[0.99835646,0.0002570258,0.0003162745,0.0008053965,0.00022007522,0.00004473841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043828003,0.00026884663,0.000296907,0.00038943216,0.0003167318,0.0005850043,0.0036058014,0.00011707478,0.000004822526],"category_scores_gemma":[0.00035928498,0.00020480115,0.0002670074,0.00038115616,0.00004211257,0.0008998502,0.0012742345,0.0004863236,0.000014334025],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109209235,0.00061563577,0.0010132773,0.002356227,0.0005298447,0.0000073781976,0.0073888754,0.0013903629,0.2150722,0.24742596,0.0461328,0.47795823],"study_design_scores_gemma":[0.0009184183,0.00043433314,0.0017495777,0.0018179774,0.00004474639,0.000040889972,0.00006826241,0.84301376,0.07322046,0.07764102,0.0007368629,0.0003137164],"about_ca_topic_score_codex":0.00001770306,"about_ca_topic_score_gemma":0.000002470328,"teacher_disagreement_score":0.84162337,"about_ca_system_score_codex":0.0001399775,"about_ca_system_score_gemma":0.000023984525,"threshold_uncertainty_score":0.83515483},"labels":[],"label_agreement":null},{"id":"W4399144629","doi":"10.1109/vrw62533.2024.00357","title":"[DC] Exploring and Designing VR Locomotion Method Based on Bio-Signal for Hands-Free Context and its Improvement","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Usability; Computer science; Interrupt; Human–computer interaction; Virtual reality; Eye tracking; Context (archaeology); SIGNAL (programming language); Electroencephalography; Visualization; Computer vision; Simulation; Artificial intelligence; Computer hardware","score_opus":0.06854116563995938,"score_gpt":0.2922344591906983,"score_spread":0.22369329355073891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399144629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022190694,0.00017901503,0.97453576,0.0021418568,0.00014026876,0.00024049343,0.000002402796,0.0004003266,0.00016919697],"genre_scores_gemma":[0.87488174,0.000007177471,0.12470505,0.0001821246,0.000026199901,0.000100875535,8.5034395e-7,0.000008495241,0.00008745817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990792,0.000032935022,0.00013516161,0.00044561867,0.00010369886,0.0002033477],"domain_scores_gemma":[0.9993414,0.00034628742,0.00002704307,0.00019225602,0.000041446932,0.000051522344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051788514,0.00012671514,0.00012966532,0.00018825705,0.00012212033,0.0002015325,0.00021136149,0.000052218136,0.0000054539896],"category_scores_gemma":[0.000044886154,0.00010328378,0.000031229007,0.00014902795,0.000023793331,0.00025039824,0.000118920296,0.00010230664,0.0000029208975],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011940783,0.00002592883,0.000054659988,0.00007940164,0.000017443419,0.0000069528623,0.00012337667,0.000038487087,0.022697626,0.097219974,0.00018973419,0.8795345],"study_design_scores_gemma":[0.0009935239,0.00087686954,0.0003308275,0.00014082395,0.000012973088,0.000005438168,0.00005776734,0.6511516,0.33971843,0.005792342,0.0007607376,0.00015863855],"about_ca_topic_score_codex":0.000009313873,"about_ca_topic_score_gemma":0.0000045811826,"teacher_disagreement_score":0.8793758,"about_ca_system_score_codex":0.000027674047,"about_ca_system_score_gemma":0.000022998607,"threshold_uncertainty_score":0.42117903},"labels":[],"label_agreement":null},{"id":"W4399200332","doi":"10.1145/3649902.3656358","title":"LookToFocus: Image Focus via Eye Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Focus (optics); Eye tracking; Image (mathematics); Optics","score_opus":0.010840875905473146,"score_gpt":0.27179343442225784,"score_spread":0.26095255851678467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399200332","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053068036,0.000560612,0.9635248,0.0057448777,0.000458659,0.000057086294,6.230963e-7,0.002845473,0.021501075],"genre_scores_gemma":[0.93959653,0.0000069927896,0.058873843,0.000107813925,0.000063099564,0.000007784732,4.0799495e-7,0.000011890745,0.0013316412],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989061,0.000022776834,0.00015838815,0.0004521421,0.00015197691,0.0003086375],"domain_scores_gemma":[0.9993863,0.00006878054,0.000018891065,0.00043241825,0.000041917017,0.000051662457],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019253949,0.00013103602,0.00012272835,0.00018128753,0.00007755184,0.00037233945,0.0006981691,0.00007793288,0.00007660623],"category_scores_gemma":[0.000021895294,0.00010537877,0.00007966631,0.00048545937,0.000070662514,0.00049089454,0.00018240049,0.00022853211,0.001019607],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.5627836e-7,0.000031619886,0.00019196805,0.000018271954,0.000021464555,0.00024512762,0.00017288208,0.0000018687193,0.017779678,0.258698,0.002944399,0.71989405],"study_design_scores_gemma":[0.0005555419,0.00035611325,0.014269658,0.00026059328,0.000042854037,0.00032083847,0.00010643301,0.2231014,0.33795142,0.32643607,0.09523676,0.0013622957],"about_ca_topic_score_codex":0.000035750447,"about_ca_topic_score_gemma":0.000011209711,"teacher_disagreement_score":0.9342897,"about_ca_system_score_codex":0.00003469928,"about_ca_system_score_gemma":0.000032695385,"threshold_uncertainty_score":0.99975824},"labels":[],"label_agreement":null},{"id":"W4399203220","doi":"10.1145/3649902.3653351","title":"CSA-CNN: A Contrastive Self-Attention Neural Network for Pupil Segmentation in Eye Gaze Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Huawei Technologies (Canada)","funders":"","keywords":"Gaze; Computer science; Eye tracking; Artificial intelligence; Pupil; Computer vision; Segmentation; Tracking (education); Convolutional neural network; Psychology; Neuroscience","score_opus":0.016178079169075082,"score_gpt":0.2810789525591908,"score_spread":0.2649008733901157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399203220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12217409,0.00034228765,0.8723777,0.002273903,0.0008534203,0.00039481802,0.000002644811,0.0011662369,0.00041487947],"genre_scores_gemma":[0.9490236,0.000009946731,0.05036781,0.0001559132,0.00011211747,0.00009601873,0.0000083088125,0.000010964195,0.0002152992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987903,0.00004891464,0.00024336725,0.00043963915,0.00012222913,0.00035550227],"domain_scores_gemma":[0.99952185,0.0001860754,0.000046308818,0.00015376315,0.00005836646,0.000033622033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034499832,0.00013589604,0.00015153222,0.00015707951,0.00008503292,0.00023794235,0.00026877073,0.00008469918,0.000007429984],"category_scores_gemma":[0.000023141962,0.00012294443,0.00007641822,0.0005387394,0.000026098593,0.0004730974,0.00005184444,0.00017116225,0.000022592787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034740584,0.00023587234,0.02544699,0.00016040506,0.00012226033,0.000108789594,0.0011018595,0.00278168,0.009271194,0.2789206,0.0038708118,0.6779448],"study_design_scores_gemma":[0.00086788175,0.00027094214,0.101287425,0.00016944794,0.000027702246,0.000017689947,0.0001534023,0.8821596,0.0020794922,0.011369885,0.0012731706,0.00032337024],"about_ca_topic_score_codex":0.00001841691,"about_ca_topic_score_gemma":0.00004000864,"teacher_disagreement_score":0.8793779,"about_ca_system_score_codex":0.00009403915,"about_ca_system_score_gemma":0.00003544757,"threshold_uncertainty_score":0.50135285},"labels":[],"label_agreement":null},{"id":"W4399526356","doi":"10.1109/tcsvt.2024.3412243","title":"TTAGaze: Self-Supervised Test-Time Adaptation for Personalized Gaze Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Gaze; Adaptation (eye); Artificial intelligence; Test (biology); Computer vision; Estimation; Machine learning; Psychology; Engineering","score_opus":0.02321060151935446,"score_gpt":0.25691397730326143,"score_spread":0.23370337578390699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399526356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004647422,0.0007137625,0.98682,0.0021355664,0.0012372807,0.0011839515,0.000103826904,0.003113891,0.000044337638],"genre_scores_gemma":[0.9835782,0.000048040893,0.013964881,0.000060498416,0.000036898557,0.0012292744,0.0000063862035,0.00003746624,0.0010383723],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830085,0.00003291294,0.0003966438,0.0007194278,0.0001676933,0.00038249954],"domain_scores_gemma":[0.99850386,0.00076405716,0.00008644235,0.00040368986,0.00017959258,0.00006234417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038076073,0.00025561318,0.00036037024,0.0008107053,0.0003575382,0.00023671724,0.0003821883,0.0003548852,0.0000034511781],"category_scores_gemma":[0.000060020055,0.00024222414,0.00014154651,0.00065158383,0.00012227209,0.00032544316,0.0000032482171,0.000245797,0.000034411532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022921271,0.00027664387,0.000007240133,0.00087331614,0.00023000808,0.000015740534,0.0008952305,0.0032991348,0.021916404,0.13384445,0.0012997409,0.83731914],"study_design_scores_gemma":[0.0007884983,0.0006741669,0.000007383983,0.00021517379,0.00008205955,0.00016499916,0.00013052374,0.9802125,0.006620936,0.003462949,0.0073582334,0.00028258993],"about_ca_topic_score_codex":0.000011319976,"about_ca_topic_score_gemma":0.0000049867176,"teacher_disagreement_score":0.9789308,"about_ca_system_score_codex":0.0001118782,"about_ca_system_score_gemma":0.00010569805,"threshold_uncertainty_score":0.9877613},"labels":[],"label_agreement":null},{"id":"W4399528242","doi":"10.3389/fcomp.2024.1394397","title":"Energy-efficient, low-latency, and non-contact eye blink detection with capacitive sensing","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computer Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"HORIZON EUROPE Framework Programme; European Commission","keywords":"Latency (audio); Capacitive sensing; Computer science; Materials science; Optoelectronics; Telecommunications; Operating system","score_opus":0.005073829007036616,"score_gpt":0.20545733929817606,"score_spread":0.20038351029113943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399528242","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2012752,0.00018316238,0.7958375,0.00024645543,0.0019629449,0.000077999015,8.028254e-7,0.00029475594,0.00012112971],"genre_scores_gemma":[0.78671277,0.000009616839,0.21311817,0.00008093815,0.00005168741,0.000003120091,2.4672775e-7,0.000007812183,0.000015632086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978848,0.000040122413,0.00019832462,0.0010207144,0.00034361158,0.00051243213],"domain_scores_gemma":[0.99927247,0.00005065644,0.000055335564,0.0004148056,0.00010183759,0.000104875966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053408655,0.00020604813,0.00021782535,0.00091771537,0.00026479014,0.0005739741,0.0006964677,0.000080695616,3.0630986e-7],"category_scores_gemma":[0.000011212079,0.00017169578,0.000032210894,0.0023385568,0.00060773554,0.00062498654,0.00030371064,0.0002968621,0.0000035991268],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000865114,0.000037252685,0.0024684942,0.000028463151,0.000016072354,0.00027372016,0.0011303267,0.0018354307,0.0044287965,0.0024139555,0.000102637474,0.9872562],"study_design_scores_gemma":[0.0001918483,0.00017157153,0.011778594,0.00022176057,0.0000042019765,0.00008286144,0.00002728826,0.97473145,0.0108115245,0.0016532913,0.000087721804,0.00023786857],"about_ca_topic_score_codex":0.000067056644,"about_ca_topic_score_gemma":0.000019505933,"teacher_disagreement_score":0.98701835,"about_ca_system_score_codex":0.00017747519,"about_ca_system_score_gemma":0.00017377787,"threshold_uncertainty_score":0.7001551},"labels":[],"label_agreement":null},{"id":"W4399662956","doi":"10.1016/j.cognition.2024.105835","title":"Spatial updating of gaze position in younger and older adults – A path integration-like process in eye movements","year":2024,"lang":"en","type":"article","venue":"Cognition","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Baycrest Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Gaze; Psychology; Eye movement; Path integration; Process (computing); Cognitive psychology; Position (finance); Path (computing); Neuroscience; Psychoanalysis; Computer science","score_opus":0.006885033960581597,"score_gpt":0.25655497902320307,"score_spread":0.24966994506262147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399662956","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9207245,0.00008783681,0.07850016,0.00014610394,0.00010742456,0.00014478361,0.0000047501794,0.00007355073,0.00021089354],"genre_scores_gemma":[0.9989198,0.000009072665,0.0009315702,0.00005243582,0.000012026895,0.000037180955,0.00002886406,0.000004092182,0.000004960652],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99937874,0.000026520565,0.0001726177,0.00022841286,0.00009699665,0.00009672704],"domain_scores_gemma":[0.9997943,0.000024107227,0.000041392275,0.00006591769,0.00006241142,0.000011892791],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012877659,0.00006652432,0.0000787795,0.00021951133,0.00001747894,0.00004160345,0.000076964796,0.00005403933,0.000004107601],"category_scores_gemma":[0.000028922368,0.000064244414,0.000011344582,0.0003195892,0.00002502262,0.00034875137,0.000031121643,0.00011520447,0.0000036002803],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006550075,0.00064045447,0.036719065,0.000697705,0.000023326302,0.00011738937,0.009472235,0.000021941454,0.02465594,0.011162133,0.00003308103,0.91639125],"study_design_scores_gemma":[0.0014814329,0.00021206005,0.7299024,0.005188115,0.000011851637,0.00001265618,0.0010122688,0.20343912,0.03587066,0.022593379,0.000004694011,0.0002714041],"about_ca_topic_score_codex":0.00004386542,"about_ca_topic_score_gemma":0.00006467539,"teacher_disagreement_score":0.9161198,"about_ca_system_score_codex":0.000024212644,"about_ca_system_score_gemma":0.000021017659,"threshold_uncertainty_score":0.2619811},"labels":[],"label_agreement":null},{"id":"W4399783202","doi":"10.1007/s11548-024-03173-4","title":"Head motion-corrected eye gaze tracking with the da Vinci surgical system","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Computer science; Eye tracking; Gaze","score_opus":0.01895561517645774,"score_gpt":0.2720180330526739,"score_spread":0.2530624178762162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399783202","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3527719,0.0015503992,0.63073796,0.010302286,0.0042795637,0.00004561425,0.0000026842122,0.00019538394,0.00011419499],"genre_scores_gemma":[0.99572456,0.000054010212,0.0032583706,0.00019142701,0.00072387024,0.000003094174,0.0000034148438,0.000010250915,0.00003098784],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9983234,0.00032256253,0.00050324394,0.00028528858,0.00034110498,0.00022440434],"domain_scores_gemma":[0.9974376,0.0016493209,0.00025452094,0.00018360825,0.0004002945,0.000074655065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010002704,0.0001777798,0.0003859684,0.00046963996,0.00012238199,0.00035393718,0.000749024,0.00013403024,0.000005273619],"category_scores_gemma":[0.000030412006,0.000105321335,0.00018452636,0.0003069979,0.00022342587,0.00034920455,0.00010487325,0.0005491274,0.0000057087923],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026045003,0.00019481161,0.038977116,0.00008212096,0.0020699329,0.016947405,0.0006566008,0.0010990147,0.00027540844,0.061894108,0.01113719,0.86640584],"study_design_scores_gemma":[0.0017433703,0.000601499,0.5168884,0.0024634076,0.00016695113,0.19273505,0.00019473663,0.21527657,0.00040866632,0.0007239769,0.067957066,0.00084036594],"about_ca_topic_score_codex":0.0000059177114,"about_ca_topic_score_gemma":0.0000028691686,"teacher_disagreement_score":0.8655655,"about_ca_system_score_codex":0.00009657434,"about_ca_system_score_gemma":0.00015075518,"threshold_uncertainty_score":0.42948794},"labels":[],"label_agreement":null},{"id":"W4400014636","doi":"10.21203/rs.3.rs-4463582/v1","title":"Non-Contact, Non-Visual, Multi-Person Hallway GaitMonitoring","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Psychology","score_opus":0.10783555905240158,"score_gpt":0.42714497554868597,"score_spread":0.3193094164962844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400014636","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67655814,0.007863164,0.25288758,0.013642265,0.01349805,0.0051564793,0.00012679893,0.006374947,0.023892581],"genre_scores_gemma":[0.9856074,0.00014467069,0.010792092,0.00001772828,0.0006537705,0.00038816145,0.00001767054,0.00007819327,0.0023003444],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946414,0.00024054752,0.00038701337,0.0018996291,0.0013967764,0.0014346108],"domain_scores_gemma":[0.9967788,0.00032818623,0.000109904686,0.0018308506,0.000647733,0.00030451713],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00198739,0.00050847454,0.0005916353,0.001338515,0.00041356514,0.0012029595,0.003294578,0.00081020367,0.000020098907],"category_scores_gemma":[0.00024997574,0.00047399133,0.00032114558,0.0011330445,0.00019876388,0.00016654264,0.006625494,0.006010047,0.0014490589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016812187,0.0047628772,0.05809826,0.026339885,0.0021334793,0.014256801,0.022131864,0.0012135598,0.06556873,0.045435105,0.08541901,0.67447233],"study_design_scores_gemma":[0.002551469,0.0024071895,0.19962084,0.02014582,0.000094058785,0.00011788699,0.0034346657,0.6787268,0.06114318,0.013542966,0.014020691,0.0041944603],"about_ca_topic_score_codex":0.00082864234,"about_ca_topic_score_gemma":0.000053478147,"teacher_disagreement_score":0.67751324,"about_ca_system_score_codex":0.0006134967,"about_ca_system_score_gemma":0.0007746996,"threshold_uncertainty_score":0.9998339},"labels":[],"label_agreement":null},{"id":"W4400042780","doi":"10.1109/tim.2024.3417595","title":"Vehicle Heading Enhancement Based on Adaptive Sliding Window Factor Graph Optimization for Gyroscope/Magnetometer","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"National Natural Science Foundation of China","keywords":"Magnetometer; Gyroscope; Heading (navigation); Control theory (sociology); Accelerometer; Computer science; Physics; Acoustics; Engineering; Artificial intelligence; Magnetic field; Aerospace engineering","score_opus":0.052150242126629255,"score_gpt":0.2743818822945031,"score_spread":0.22223164016787386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400042780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0115869,0.000039098188,0.9855203,0.00094466755,0.0009903141,0.00052555633,0.000017465658,0.00028042562,0.000095287665],"genre_scores_gemma":[0.9580098,0.00002122642,0.041306045,0.00033574592,0.000016532917,0.00025953123,0.0000027722826,0.000014611175,0.00003375266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862725,0.000049977218,0.00023145879,0.00045820264,0.00040946683,0.00022365797],"domain_scores_gemma":[0.99952346,0.000075016986,0.000051302217,0.00017581585,0.00010353545,0.00007086474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025807339,0.00018101992,0.00013206531,0.00042752188,0.00029299725,0.00019453447,0.000126815,0.000062859166,0.00003443249],"category_scores_gemma":[0.0000053665517,0.00017118554,0.000079994,0.00034665628,0.000029166376,0.00030063133,0.0000014803327,0.00014113467,0.000009558839],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019364616,0.00049833395,0.000036556576,0.00015279702,0.00016761993,0.0000036740917,0.00081598054,0.092153475,0.08773282,0.0045890533,0.00012530567,0.81353074],"study_design_scores_gemma":[0.0010668108,0.0012109365,0.0002048514,0.00025964534,0.000035351713,0.0000014426464,0.00007781995,0.6429601,0.353572,0.00016663686,0.0002196822,0.00022472662],"about_ca_topic_score_codex":0.000007695175,"about_ca_topic_score_gemma":0.0000072419634,"teacher_disagreement_score":0.9464229,"about_ca_system_score_codex":0.0002719357,"about_ca_system_score_gemma":0.00006455352,"threshold_uncertainty_score":0.69807434},"labels":[],"label_agreement":null},{"id":"W4400114502","doi":"10.1109/i2mtc60896.2024.10560724","title":"A Cost-Effective Webcam Eye-Tracking Algorithm for Robust Classification of Fixations and Saccades","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bruyère; National Research Council Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; AGE-WELL","keywords":"Computer science; Eye tracking; Computer vision; Artificial intelligence; Tracking (education); Algorithm; Psychology","score_opus":0.05263269247291695,"score_gpt":0.335395325759418,"score_spread":0.28276263328650103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400114502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052784537,0.00022968513,0.99146694,0.0014436911,0.00015109709,0.00056931406,0.0000110888295,0.0003915056,0.00045823274],"genre_scores_gemma":[0.7206276,0.000010762335,0.27874348,0.000023040879,0.000025233103,0.00024944852,0.000003476401,0.0000062659215,0.00031068388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933213,0.00002193636,0.00014220973,0.00029865504,0.000073364725,0.00013171887],"domain_scores_gemma":[0.99932444,0.0003273132,0.000045391716,0.00017287032,0.000104555314,0.000025434802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020292081,0.000081006656,0.000110765126,0.00016749869,0.00007965727,0.00010426781,0.00019174875,0.00007251814,0.000003370639],"category_scores_gemma":[0.000054947617,0.0000689144,0.000040164126,0.00031627284,0.00007739427,0.00025348892,0.00005292759,0.00009492161,0.0000044605576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.482019e-7,0.000031714713,0.00033428957,0.000034218014,0.000020623409,7.6942626e-7,0.00017058807,0.000016618835,0.0027514405,0.1236812,0.00030028683,0.8726576],"study_design_scores_gemma":[0.00017017977,0.00013882508,0.04367013,0.0000708742,0.000016754573,0.000007952245,0.00011867548,0.9328693,0.011314503,0.0068133073,0.0046867873,0.00012270032],"about_ca_topic_score_codex":0.000007900003,"about_ca_topic_score_gemma":0.000009705427,"teacher_disagreement_score":0.9328527,"about_ca_system_score_codex":0.000028067832,"about_ca_system_score_gemma":0.000029008293,"threshold_uncertainty_score":0.28102475},"labels":[],"label_agreement":null},{"id":"W4400114561","doi":"10.1109/i2mtc60896.2024.10560882","title":"Multi-Class Gaze Detection in a Dynamic Environment","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; Élisabeth Bruyère Hospital; Carleton University","funders":"","keywords":"Gaze; Computer science; Class (philosophy); Artificial intelligence; Human–computer interaction; Computer vision","score_opus":0.010339131683659722,"score_gpt":0.23614205477067746,"score_spread":0.22580292308701774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400114561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06301986,0.00017149374,0.9345161,0.0009984617,0.00020716208,0.000055092965,2.9992518e-7,0.0005894614,0.00044208913],"genre_scores_gemma":[0.97715163,0.00001766238,0.02197782,0.0000360661,0.000004267362,0.000015133391,2.4103437e-7,0.0000042681945,0.0007928984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993935,0.000017641973,0.000091950336,0.00028215462,0.00006918878,0.00014551979],"domain_scores_gemma":[0.9997412,0.0000258031,0.000008549822,0.0002034887,0.0000026727828,0.000018272434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010349889,0.00006563211,0.0000582851,0.00016148137,0.00002084483,0.000052249317,0.0002201178,0.000052801057,0.000014637094],"category_scores_gemma":[0.000006035632,0.000056300945,0.000024980563,0.00020387839,0.000025758596,0.00010731538,0.000089224624,0.00014167014,0.0003423461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000012043065,0.00010788319,0.00032610242,0.00001768,0.000010708775,0.000116794065,0.00016850134,0.00030822106,0.062921286,0.012873672,0.00004338439,0.9231046],"study_design_scores_gemma":[0.00011411841,0.000038759637,0.018024284,0.000017461552,0.0000012939809,0.00001775957,0.0000133764615,0.97054356,0.0061269393,0.0011140362,0.003897059,0.000091372734],"about_ca_topic_score_codex":0.000027175065,"about_ca_topic_score_gemma":0.00012324293,"teacher_disagreement_score":0.9702353,"about_ca_system_score_codex":0.00010531843,"about_ca_system_score_gemma":0.000009541323,"threshold_uncertainty_score":0.44002807},"labels":[],"label_agreement":null},{"id":"W4400648810","doi":"10.1109/iv55156.2024.10588743","title":"SCOUT+: Towards Practical Task-Driven Drivers’ Gaze Prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_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":"Gaze; Computer science; Task (project management); Human–computer interaction; Artificial intelligence; Computer vision; Engineering; Systems engineering","score_opus":0.024393856291002,"score_gpt":0.29261045235601923,"score_spread":0.26821659606501724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400648810","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006567249,0.00007852857,0.9575874,0.013493801,0.0010918449,0.0000866369,0.0000048787633,0.002876247,0.018213397],"genre_scores_gemma":[0.9341956,0.000016326734,0.06411101,0.00016344177,0.00008867976,0.0000103534785,0.0000025250636,0.000007922099,0.0014041823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889666,0.000034076857,0.0001438675,0.00044760047,0.00023382393,0.00024395775],"domain_scores_gemma":[0.9994346,0.00006971476,0.000021152006,0.00035361576,0.000052102914,0.000068831156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016609015,0.00011284208,0.0001063083,0.00015708274,0.00007458924,0.00022280304,0.0003703838,0.00011360813,0.000070987466],"category_scores_gemma":[0.0000623675,0.00009225451,0.00006315738,0.00041252203,0.00008294399,0.000522767,0.00018815538,0.00028790467,0.00059268274],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000026604523,0.00009272871,0.0012879651,0.000027405296,0.00006804539,0.00030481964,0.00036085967,0.000031396245,0.002907521,0.81461704,0.062359042,0.1179405],"study_design_scores_gemma":[0.0005125713,0.00061976595,0.03951573,0.00020094149,0.00006924363,0.000613343,0.00017322283,0.6558139,0.007984158,0.022893187,0.27098188,0.0006220808],"about_ca_topic_score_codex":0.00001792531,"about_ca_topic_score_gemma":0.0000046456216,"teacher_disagreement_score":0.92762834,"about_ca_system_score_codex":0.00006562583,"about_ca_system_score_gemma":0.00013731755,"threshold_uncertainty_score":0.7617935},"labels":[],"label_agreement":null},{"id":"W4400980206","doi":"10.1007/978-3-031-58396-4_20","title":"Can We Replicate Impaired Vision with Simulation Glasses in Computer-Based Task? An Eye Tracking Validation Study","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in information systems and organisation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Replicate; Eye tracking; Task (project management); Computer science; Tracking (education); Artificial intelligence; Computer vision; Psychology; Engineering; Mathematics","score_opus":0.017317607583558724,"score_gpt":0.2678919511195458,"score_spread":0.25057434353598707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400980206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06704107,0.00008367291,0.9297324,0.00064549636,0.00031585433,0.0014213398,0.000014933401,0.0005101035,0.00023515096],"genre_scores_gemma":[0.99784726,0.00000636038,0.0017306209,0.000058073554,0.00007501545,0.00003090097,0.00019248633,0.000028618488,0.000030641637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99799323,0.0000739506,0.000812602,0.0005030672,0.0004167738,0.00020038958],"domain_scores_gemma":[0.9985366,0.00018242408,0.00053323404,0.00051669346,0.00018242154,0.000048666654],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054589607,0.00036296662,0.0004065019,0.0009946857,0.00011811382,0.0007398235,0.00025433028,0.00038902313,0.0000027217277],"category_scores_gemma":[0.00003209563,0.00030897485,0.000030398656,0.00030603318,0.000039663737,0.0012427937,0.00006540118,0.00043242882,0.000013401591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006608355,0.000131985,0.0100404145,0.0007656661,0.000053862033,0.000028144512,0.01766542,0.7770894,0.00018752467,0.017390817,0.000004796346,0.17657588],"study_design_scores_gemma":[0.0011230469,0.0011036868,0.012432943,0.0013853668,0.000033165237,0.000017275315,0.00013464673,0.9756778,0.00029676798,0.0066191438,0.0005573783,0.00061877334],"about_ca_topic_score_codex":0.00019032469,"about_ca_topic_score_gemma":0.00038202835,"teacher_disagreement_score":0.9308062,"about_ca_system_score_codex":0.00031846436,"about_ca_system_score_gemma":0.00011981977,"threshold_uncertainty_score":0.9999362},"labels":[],"label_agreement":null},{"id":"W4401024987","doi":"10.24963/ijcai.2024/161","title":"Hypernetwork Aggregation for Decentralized Personalized Federated Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Tongji University; National Natural Science Foundation of China","keywords":"Computer science; Gaze; Adaptation (eye); Human–computer interaction; Estimation; Artificial intelligence; Computer vision; Engineering; Psychology; Neuroscience","score_opus":0.019383378754774177,"score_gpt":0.27133097515129245,"score_spread":0.25194759639651826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401024987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016555924,0.0008930498,0.9767255,0.0022289613,0.00038192543,0.00013678522,4.768489e-7,0.00202228,0.0010550872],"genre_scores_gemma":[0.9440125,0.00003840695,0.051667355,0.00009742123,0.000046838108,0.000034847788,0.00000793805,0.000010701291,0.0040839813],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991955,0.000036519858,0.000119734505,0.00031621644,0.00009377713,0.0002382571],"domain_scores_gemma":[0.9996167,0.00016857058,0.00002250395,0.00009828059,0.000058428657,0.00003555985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001986457,0.00009436215,0.00010872652,0.0000763508,0.0001636149,0.00034262703,0.00021208543,0.00006955058,0.000037049387],"category_scores_gemma":[0.000069132744,0.00007847418,0.00007025972,0.00034368577,0.000033543918,0.00016210297,0.000038711685,0.00012652691,0.000063621614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010816577,0.000019239324,0.00048862514,0.00002786816,0.00004476144,0.000012787403,0.00017564288,0.00013100027,0.0015132226,0.60183555,0.0041673854,0.39157313],"study_design_scores_gemma":[0.0005170966,0.00009793121,0.00035665795,0.000075968266,0.000012759278,0.000025053912,0.000038344857,0.8089204,0.0039625997,0.010138066,0.1756498,0.00020533205],"about_ca_topic_score_codex":0.000008097602,"about_ca_topic_score_gemma":0.0000041109115,"teacher_disagreement_score":0.92745656,"about_ca_system_score_codex":0.000035845667,"about_ca_system_score_gemma":0.000043581833,"threshold_uncertainty_score":0.33039603},"labels":[],"label_agreement":null},{"id":"W4401072489","doi":"10.1109/memea60663.2024.10596718","title":"Metrics in a Dynamic Gaze Environment","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; Élisabeth Bruyère Hospital; Carleton University","funders":"","keywords":"Gaze; Computer science; Human–computer interaction; Artificial intelligence; Computer vision","score_opus":0.009333416552452359,"score_gpt":0.23679062036732842,"score_spread":0.22745720381487605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401072489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020276647,0.0008051178,0.9707578,0.0027310585,0.00017357235,0.000042556683,4.0951068e-7,0.00053011946,0.004682744],"genre_scores_gemma":[0.9674043,0.00004597899,0.03142843,0.00005872584,0.0000032235434,0.000006195914,2.87751e-7,0.0000031334453,0.0010497151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994447,0.000011599234,0.000083073945,0.00023510253,0.00008758727,0.00013789178],"domain_scores_gemma":[0.9997077,0.00005185995,0.000006179769,0.00021417494,0.0000022562008,0.000017857592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012867716,0.000053851007,0.000058253565,0.0002981027,0.0000118003845,0.000054933444,0.0003138417,0.000037836977,0.000028156886],"category_scores_gemma":[0.0000104694755,0.000044141045,0.00002175593,0.0005403292,0.000020823838,0.00008636579,0.00012221605,0.00011554235,0.00037304222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.5581778e-7,0.00006038723,0.0013659893,0.000013151418,0.000008518926,0.0001936643,0.00010176204,0.00008556002,0.0009780499,0.43037006,0.00052447023,0.56629807],"study_design_scores_gemma":[0.00016748838,0.0000947283,0.038991634,0.00004139199,0.0000037952047,0.00003791329,0.000028027853,0.87949526,0.0013838774,0.036046505,0.043451708,0.00025766643],"about_ca_topic_score_codex":0.0000115946605,"about_ca_topic_score_gemma":0.000008045232,"teacher_disagreement_score":0.94712764,"about_ca_system_score_codex":0.0000717924,"about_ca_system_score_gemma":0.000012805587,"threshold_uncertainty_score":0.47948274},"labels":[],"label_agreement":null},{"id":"W4401351949","doi":"10.31234/osf.io/48en3","title":"Bridging reading and attention through connectivity with the frontal-eye-fields","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge; Simon Fraser University; University of Saskatchewan","funders":"","keywords":"Bridging (networking); Reading (process); Psychology; Cognitive psychology; Computer science; Linguistics; Philosophy; Computer security","score_opus":0.01776161422005526,"score_gpt":0.26116069116190516,"score_spread":0.2433990769418499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401351949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32159445,0.0001831881,0.65751183,0.012170234,0.0003410435,0.00014132382,0.0000014166436,0.0006312677,0.0074252584],"genre_scores_gemma":[0.9826515,0.000013061683,0.016332809,0.0002016758,0.00006520291,0.000028161085,0.0000014922325,0.000011741301,0.0006944103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987586,0.00005476851,0.00011990463,0.00069427514,0.00015617644,0.0002162502],"domain_scores_gemma":[0.9991277,0.00009376731,0.00008427775,0.00061734696,0.0000541589,0.000022763144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027981866,0.00020559314,0.0002153476,0.00006496662,0.00016111905,0.00039749488,0.00054088206,0.00019579672,0.0000027427495],"category_scores_gemma":[0.000021617927,0.0001239427,0.00005058518,0.00017581099,0.00012568918,0.00010904775,0.0019194894,0.0009298954,0.000017295244],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001481749,0.00008409786,0.013009603,0.00042179195,0.00043092953,0.00019647833,0.0024154542,0.00028799244,0.0012133643,0.73164994,0.007944372,0.24233116],"study_design_scores_gemma":[0.0007926039,0.000491946,0.23763742,0.0019993333,0.0003262664,0.00039917548,0.00077815854,0.3197567,0.0049564405,0.4284367,0.0024264294,0.001998868],"about_ca_topic_score_codex":0.00049824675,"about_ca_topic_score_gemma":0.00016372006,"teacher_disagreement_score":0.661057,"about_ca_system_score_codex":0.00004395013,"about_ca_system_score_gemma":0.00005303049,"threshold_uncertainty_score":0.50542367},"labels":[],"label_agreement":null},{"id":"W4401411007","doi":"10.3758/s13428-024-02482-5","title":"A tutorial: Analyzing eye and head movements in virtual reality","year":2024,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Virtual reality; Human–computer interaction; Eye movement; Head (geology); Optical head-mounted display; Computer graphics (images); Artificial intelligence; Computer vision; Cognitive psychology; Psychology","score_opus":0.29592612692404874,"score_gpt":0.5960021332841007,"score_spread":0.30007600636005194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401411007","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5301912,0.0007698074,0.46576953,0.0012988634,0.0006932396,0.00035275889,0.0000057613124,0.00044992715,0.00046895078],"genre_scores_gemma":[0.88592345,0.000060106162,0.113152735,0.00001318488,0.00007793961,0.00015895136,0.0000016890679,0.000015414264,0.0005965055],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99657,0.0015467517,0.00025560122,0.0006665823,0.00042676588,0.00053431914],"domain_scores_gemma":[0.99862796,0.00062229036,0.000019344157,0.00052557385,0.0000929921,0.0001118663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009565595,0.0001191622,0.00020457654,0.0007477926,0.00012727521,0.00034549515,0.000645635,0.00012256429,0.000009750576],"category_scores_gemma":[0.000508325,0.00010807482,0.000043743803,0.0015675961,0.0002120724,0.00026854145,0.00067135197,0.00081399095,0.000012626698],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005693474,0.00010231023,0.013063265,0.000020869775,0.000007649913,0.00023620183,0.00043834912,0.0000018587338,0.02700294,0.028921844,0.0001353165,0.9300637],"study_design_scores_gemma":[0.001089633,0.0009551333,0.85117006,0.00045883746,0.000016332584,0.000031335738,0.00048679224,0.030531842,0.025749672,0.03675036,0.052012123,0.00074790145],"about_ca_topic_score_codex":0.0006311278,"about_ca_topic_score_gemma":0.00006876843,"teacher_disagreement_score":0.9293158,"about_ca_system_score_codex":0.0001554238,"about_ca_system_score_gemma":0.00012287819,"threshold_uncertainty_score":0.44071633},"labels":[],"label_agreement":null},{"id":"W4401910133","doi":"10.1145/3689434","title":"HeadShift: Head Pointing with Dynamic Control-Display Gain","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Computer-Human Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"European Commission","keywords":"Head (geology); Computer science; Automatic gain control; Geology; Telecommunications","score_opus":0.014783611003136538,"score_gpt":0.2966373885628007,"score_spread":0.2818537775596642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401910133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05219322,0.000044588498,0.93948644,0.004282928,0.0016521966,0.000210606,0.000009641218,0.0019051246,0.00021525307],"genre_scores_gemma":[0.96308714,0.000005877285,0.03613208,0.00031093287,0.00010192457,0.000052321644,0.000009117756,0.00003645896,0.0002641679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979802,0.000114784045,0.00037944352,0.0008312654,0.00027692883,0.00041737483],"domain_scores_gemma":[0.9984173,0.00040045704,0.00009682998,0.00090179435,0.00009231619,0.000091301394],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022677364,0.000330015,0.00028537933,0.0006188112,0.00046610882,0.0005804028,0.0008294232,0.00013284624,0.000038163045],"category_scores_gemma":[0.00000786932,0.00028988667,0.00017029504,0.00058987667,0.00008617097,0.00092340715,0.000022720838,0.00087197573,0.00020272085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012098436,0.0007772651,0.00010939657,0.00016529828,0.00052380434,0.0003342521,0.0010500563,0.025647325,0.005725312,0.031359076,0.0005207621,0.93366647],"study_design_scores_gemma":[0.0014313367,0.0021009953,0.0033348291,0.0011853017,0.00008845432,0.0008162155,0.000065230015,0.9755394,0.0029693355,0.004494045,0.0072019002,0.0007729874],"about_ca_topic_score_codex":0.00011099381,"about_ca_topic_score_gemma":0.0003121802,"teacher_disagreement_score":0.94989204,"about_ca_system_score_codex":0.0002577504,"about_ca_system_score_gemma":0.000049324204,"threshold_uncertainty_score":0.99995536},"labels":[],"label_agreement":null},{"id":"W4402128879","doi":"10.1080/0144929x.2024.2394881","title":"Cognitive evaluation based on regression and eye-tracking for layout on human–computer multi-interface","year":2024,"lang":"en","type":"article","venue":"Behaviour and Information Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Computer science; Eye tracking; Human–computer interaction; Interface (matter); Cognition; Regression; Artificial intelligence; Psychology; Operating system; Neuroscience","score_opus":0.0440920330924712,"score_gpt":0.37418011712989396,"score_spread":0.33008808403742274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402128879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31795043,0.00008493673,0.6789312,0.001485171,0.00022733789,0.00044482798,0.000013442913,0.00074825104,0.00011444466],"genre_scores_gemma":[0.9880552,0.000006357371,0.011526299,0.00022045981,0.00001597944,0.00011941576,0.000028577824,0.000007691527,0.000019989926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990495,0.000028132612,0.00025496504,0.00030273173,0.00016793658,0.00019672538],"domain_scores_gemma":[0.99938667,0.00009110604,0.00009814556,0.0002080172,0.00018407921,0.000031961885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003781758,0.00016814328,0.00015002384,0.00082687644,0.00021676997,0.00020830172,0.00019361604,0.00025738045,0.0000037994962],"category_scores_gemma":[0.00008388111,0.00013959853,0.000033668875,0.00026721877,0.00010572942,0.000776868,0.000094644165,0.0002699442,0.000019933119],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023284476,0.000086626715,0.004124038,0.00006666094,0.00001404057,0.0000032277821,0.00047976352,0.000110286725,0.00039100632,0.062426463,0.00031335323,0.93196124],"study_design_scores_gemma":[0.0018478767,0.0014189973,0.044239137,0.0007743453,0.00004805474,0.000021571512,0.00022986437,0.92952234,0.018461993,0.0021482997,0.0009506563,0.00033686895],"about_ca_topic_score_codex":0.000003040703,"about_ca_topic_score_gemma":0.0000014383578,"teacher_disagreement_score":0.93162435,"about_ca_system_score_codex":0.000049053193,"about_ca_system_score_gemma":0.000030638148,"threshold_uncertainty_score":0.5692663},"labels":[],"label_agreement":null},{"id":"W4402400738","doi":"10.1016/j.trf.2024.09.002","title":"Easy listening or driving distraction? The relationship between audiobook complexity level and driving performance on simple routes","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part F Traffic Psychology and Behaviour","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Distraction; Active listening; Simple (philosophy); Poison control; Computer science; Human factors and ergonomics; Engineering; Psychology; Medicine; Cognitive psychology; Medical emergency; Communication","score_opus":0.26777004696046774,"score_gpt":0.43575978098513835,"score_spread":0.1679897340246706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402400738","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98348093,0.000096592514,0.010783274,0.0047377828,0.00017342181,0.0002395807,0.000024391973,0.00034949003,0.000114555],"genre_scores_gemma":[0.9983754,0.00006158255,0.0007684911,0.00006005431,0.00007409292,0.00005299766,0.000046952377,0.000014080831,0.00054630596],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9980825,0.00022288361,0.0003249444,0.0006212278,0.00032286518,0.0004255826],"domain_scores_gemma":[0.99798614,0.0014241674,0.000056620054,0.00033065726,0.0000751258,0.00012731511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012352468,0.0001705095,0.00018161755,0.00025608976,0.0009793015,0.00020665562,0.00037431877,0.00016860625,0.000021395148],"category_scores_gemma":[0.00010069447,0.00012325721,0.00004452254,0.00051854306,0.00061868445,0.00036254284,0.00002115959,0.0011007638,0.00002143608],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019257212,0.000055144585,0.9477949,0.00003874288,0.000022729997,0.00004253137,0.00077885087,0.000035654797,0.000039543218,0.018516438,0.00092018617,0.031736024],"study_design_scores_gemma":[0.0002483647,0.0002454962,0.9948703,0.00015495306,0.00002146711,0.000021662947,0.000097856806,0.0012681322,0.00002748333,0.0008952502,0.0020061852,0.00014282083],"about_ca_topic_score_codex":0.000019641662,"about_ca_topic_score_gemma":0.00036286138,"teacher_disagreement_score":0.04707543,"about_ca_system_score_codex":0.000025489993,"about_ca_system_score_gemma":0.00008566582,"threshold_uncertainty_score":0.7532095},"labels":[],"label_agreement":null},{"id":"W4402401772","doi":"10.1109/tvcg.2024.3456153","title":"Filtering on the Go: Effect of Filters on Gaze Pointing Accuracy During Physical Locomotion in Extended Reality","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"H2020 European Research Council; European Commission","keywords":"Gaze; Computer science; Saccade; Computer vision; Context (archaeology); Artificial intelligence; Noise (video); Eye tracking; Human–computer interaction; Eye movement","score_opus":0.018117018962442145,"score_gpt":0.29666083455570114,"score_spread":0.27854381559325897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402401772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39362195,0.0000057870375,0.6055595,0.00020091124,0.00024436257,0.00013682972,0.0000029674143,0.00020323854,0.000024405945],"genre_scores_gemma":[0.99967676,0.000034165267,0.000102727354,0.00010563955,0.00003173396,0.00002482992,0.0000017519778,0.000013290983,0.000009126047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986723,0.00025698738,0.00024559497,0.00041813374,0.00022266302,0.00018433708],"domain_scores_gemma":[0.99886847,0.0006782109,0.00006919819,0.0003164973,0.000032889202,0.000034739147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003410861,0.00018937062,0.0001983725,0.0004451256,0.00017128291,0.00013439906,0.00024871208,0.00007731206,0.0000023348682],"category_scores_gemma":[0.000011308127,0.00014278824,0.00010597695,0.0008607657,0.00008471758,0.00018723196,0.000008091876,0.00035324538,0.0000055280593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012715132,0.000764744,0.0001748561,0.00051442004,0.000115744246,0.00003681334,0.0023523883,0.009491766,0.0028946637,0.8644791,0.00009500804,0.11895335],"study_design_scores_gemma":[0.00038411244,0.00067030714,0.0057625435,0.0006058373,0.000013281171,0.000011080329,0.000010371219,0.9314505,0.059814807,0.0010801278,0.000032730117,0.00016429393],"about_ca_topic_score_codex":0.000010885848,"about_ca_topic_score_gemma":0.000006141024,"teacher_disagreement_score":0.92195874,"about_ca_system_score_codex":0.000034059765,"about_ca_system_score_gemma":0.000012012538,"threshold_uncertainty_score":0.58227354},"labels":[],"label_agreement":null},{"id":"W4402722190","doi":"10.1145/3641825.3687743","title":"Evaluating Gaze Interactions within AR for Nonspeaking Autistic Users","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Gaze; Computer science; Human–computer interaction; Autism; Psychology; Computer vision; Developmental psychology","score_opus":0.09483475742566341,"score_gpt":0.40009690172912543,"score_spread":0.305262144303462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402722190","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010558124,0.00006317148,0.98131794,0.0026607523,0.0014172107,0.00013093879,0.0000024549695,0.0013942033,0.0024551845],"genre_scores_gemma":[0.7218127,4.1948246e-7,0.27684706,0.00010868506,0.000040961397,0.000034562738,0.0000012345696,0.00000786357,0.0011465105],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991071,0.000024772782,0.00018294797,0.00036513593,0.00011322513,0.00020677262],"domain_scores_gemma":[0.99925417,0.0003557204,0.000037063477,0.00027026382,0.00005526705,0.000027539092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038770103,0.00009678918,0.000096264666,0.00016980003,0.0001481453,0.00024679664,0.00037334912,0.00003787645,0.000025882091],"category_scores_gemma":[0.00022302497,0.000082874336,0.000061757724,0.0003203319,0.000037166326,0.0002568463,0.00009697839,0.00018027549,0.00010751339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000023265886,0.000025297824,0.00011706319,0.000045874116,0.000044521636,0.000016643426,0.0005087008,0.0007549509,0.0055359313,0.8616325,0.002119977,0.12919618],"study_design_scores_gemma":[0.000096458956,0.00008995239,0.0004381539,0.00013463071,0.000017353286,0.000045371387,0.00006336647,0.97492236,0.0016851652,0.017887305,0.0044852444,0.00013463871],"about_ca_topic_score_codex":0.000020717865,"about_ca_topic_score_gemma":0.000038250084,"teacher_disagreement_score":0.9741674,"about_ca_system_score_codex":0.00006263425,"about_ca_system_score_gemma":0.000074242824,"threshold_uncertainty_score":0.33795172},"labels":[],"label_agreement":null},{"id":"W4402904747","doi":"10.1167/jov.24.10.728","title":"Can people determine object distance from its visual size and position in a correctly scaled 2D scene displayed on a large screen with aligned ground plane?","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Position (finance); Computer vision; Plane (geometry); Ground plane; Object (grammar); Artificial intelligence; Computer science; Geometry; Optics; Geography; Geodesy; Physics; Mathematics; Telecommunications; Economics","score_opus":0.005941807636264346,"score_gpt":0.259608152270303,"score_spread":0.25366634463403864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402904747","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96683127,0.0003472719,0.031141298,0.0012987278,0.00020695449,0.00008443775,0.00002010724,0.000050212686,0.000019697593],"genre_scores_gemma":[0.99742484,0.000033370143,0.0023581495,0.00008814074,0.000060161914,0.0000016399171,0.0000049103655,0.000010470513,0.000018293658],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99883986,0.00008218334,0.00028414393,0.00028025333,0.0003066555,0.00020690924],"domain_scores_gemma":[0.99922913,0.000341982,0.00015463581,0.0001344203,0.0000619857,0.000077852455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029323588,0.00014591486,0.00027367647,0.00020827881,0.00007550797,0.00018615741,0.00023662295,0.00007509751,0.000004754505],"category_scores_gemma":[0.000056753448,0.00010362528,0.000040772236,0.0003795256,0.000026154376,0.00030704227,0.00006324883,0.0002312062,0.0000031526406],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.008432633,0.0036411644,0.11279849,0.00037828743,0.00049172854,0.016110355,0.008902172,0.00029980118,0.54745036,0.007790714,0.0015805305,0.29212376],"study_design_scores_gemma":[0.0022992527,0.0034281146,0.92066044,0.0022720548,0.0000372238,0.0005417396,0.00007502073,0.06418932,0.005229405,0.00093804026,0.00006881632,0.00026059395],"about_ca_topic_score_codex":0.00009977875,"about_ca_topic_score_gemma":0.0012010029,"teacher_disagreement_score":0.8078619,"about_ca_system_score_codex":0.000094316725,"about_ca_system_score_gemma":0.000061411825,"threshold_uncertainty_score":0.42257163},"labels":[],"label_agreement":null},{"id":"W4402905413","doi":"10.1167/jov.24.10.938","title":"EEG-based decoding of shapes and their categories in visual working memory","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Decoding methods; Cognitive psychology; Electroencephalography; Computer science; Working memory; Visual short-term memory; Visual memory; Psychology; Cognitive science; Speech recognition; Neuroscience; Cognition; Algorithm","score_opus":0.017822790126404888,"score_gpt":0.2947611668274261,"score_spread":0.2769383767010212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402905413","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84822226,0.0029118808,0.14752825,0.00096209924,0.00024347613,0.000019915047,1.6210576e-7,0.000030064608,0.0000819272],"genre_scores_gemma":[0.9949577,0.000043433087,0.0049420795,0.00002245749,0.000025958092,2.2533922e-7,4.7028934e-8,0.0000036858987,0.0000044148296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994025,0.000035334822,0.00025393645,0.000102052545,0.00011164007,0.00009452346],"domain_scores_gemma":[0.9995379,0.00022708338,0.000113276605,0.00005358589,0.000044378306,0.000023820194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005262575,0.00006476185,0.00015837488,0.00036900386,0.000028028066,0.000071491086,0.00020755362,0.000045909004,0.0000022894276],"category_scores_gemma":[0.000047934547,0.000044064072,0.000044483982,0.00029093144,0.000045306937,0.00019204472,0.000063963846,0.0001998663,6.039372e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021646505,0.00007548824,0.0067301723,0.00006379756,0.000018122135,0.00013688361,0.0011192126,0.00029271244,0.046152227,0.0047790087,0.00007639404,0.94053435],"study_design_scores_gemma":[0.0011290496,0.0015636998,0.25200272,0.0047601126,0.00002093965,0.00030910852,0.001033174,0.64784396,0.0702845,0.019631768,0.0010823911,0.0003385794],"about_ca_topic_score_codex":0.0000050073736,"about_ca_topic_score_gemma":0.000005896915,"teacher_disagreement_score":0.94019574,"about_ca_system_score_codex":0.000024672845,"about_ca_system_score_gemma":0.000055645127,"threshold_uncertainty_score":0.17968808},"labels":[],"label_agreement":null},{"id":"W4402978052","doi":"10.22306/atec.v10i3.217","title":"Development of a robotic wheel door opener system assistive device","year":2024,"lang":"en","type":"article","venue":"Acta Tecnología","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds de Recherche du Québec - Santé; Centre for Interdisciplinary Research in Rehabilitation","keywords":"Computer science; Human–computer interaction; Automotive engineering; Embedded system; Engineering","score_opus":0.025846280791463946,"score_gpt":0.2619702882236122,"score_spread":0.23612400743214823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402978052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31208935,0.0014775569,0.66690266,0.0041656783,0.0019392867,0.00046170593,0.0000065156632,0.0038269402,0.009130328],"genre_scores_gemma":[0.8819973,0.0000033130484,0.11758674,0.000041059408,0.000019755214,0.000036164536,0.000002089033,0.000013001275,0.00030060732],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984785,0.000048554313,0.0003720386,0.00054369407,0.00021459936,0.00034262892],"domain_scores_gemma":[0.9990707,0.000120119184,0.00009999841,0.00056256045,0.00009238353,0.000054235854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030918317,0.00019295876,0.0003004894,0.00028131367,0.000110580484,0.000105866035,0.001177488,0.00016421979,0.000009209708],"category_scores_gemma":[0.000054768843,0.00015462203,0.00006677376,0.0006592987,0.000098656245,0.00024382604,0.00047603453,0.00025777676,0.00019164578],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015514863,0.000290614,0.0018412109,0.0009644508,0.0006684331,0.00046005158,0.0022546202,0.00005623302,0.083175056,0.37655306,0.00476241,0.5289583],"study_design_scores_gemma":[0.0019359172,0.0007625174,0.21384674,0.0055384496,0.0003311859,0.0010675321,0.0026807482,0.09785907,0.34695345,0.0022637285,0.32347935,0.0032813014],"about_ca_topic_score_codex":0.000016705062,"about_ca_topic_score_gemma":0.000018766908,"teacher_disagreement_score":0.5699079,"about_ca_system_score_codex":0.0001625254,"about_ca_system_score_gemma":0.00025650059,"threshold_uncertainty_score":0.6305303},"labels":[],"label_agreement":null},{"id":"W4403327790","doi":"10.2196/64353","title":"High-Resolution Eye-Tracking System for Accurate Measurement of Short-Latency Ocular Following Responses: Development and Observational Study","year":2024,"lang":"en","type":"article","venue":"JMIR Pediatrics and Parenting","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministero della Salute; European Commission","keywords":"Preprint; Latency (audio); Eye tracking; Computer science; Artificial intelligence; Telecommunications; World Wide Web","score_opus":0.10845097579262156,"score_gpt":0.3315360191273409,"score_spread":0.22308504333471932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403327790","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86395425,0.001348483,0.13366349,0.00006690023,0.00041258475,0.00038793753,0.0000032615085,0.00015809946,0.000004999357],"genre_scores_gemma":[0.98614347,0.000009609134,0.013648299,0.0000030442225,0.00007001817,0.00010134511,0.0000043465707,0.000009670633,0.000010174972],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99851733,0.000054534357,0.00042827998,0.00041060575,0.00036531882,0.00022391406],"domain_scores_gemma":[0.99938524,0.00015938416,0.00010210778,0.0001435771,0.00016960419,0.00004010579],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015305317,0.00013859422,0.000197483,0.00022971371,0.00026415053,0.0002109287,0.00017866452,0.00006244883,1.7764582e-7],"category_scores_gemma":[0.000106432795,0.00012788469,0.00004934854,0.00037563633,0.000013282826,0.00023362602,0.00015003707,0.00010316296,4.708551e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000319521,0.00024819406,0.8887067,0.0018891344,0.00024032667,0.000080889826,0.0039444515,0.00022144361,0.0034411552,0.018722584,0.000078351724,0.082394846],"study_design_scores_gemma":[0.0005550562,0.00022165349,0.9573386,0.00045255435,0.00013057473,0.0000071433738,0.00096893933,0.03806235,0.0009921887,0.00031079256,0.00055334525,0.00040683956],"about_ca_topic_score_codex":0.000011458359,"about_ca_topic_score_gemma":0.0000070325777,"teacher_disagreement_score":0.12218925,"about_ca_system_score_codex":0.000067047295,"about_ca_system_score_gemma":0.000120618875,"threshold_uncertainty_score":0.5214986},"labels":[],"label_agreement":null},{"id":"W4403338496","doi":"10.1145/3672539.3686312","title":"NeuroSight: Combining Eye-Tracking and Brain-Computer Interfaces for Context-Aware Hand-Free Camera Interaction","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Computer vision; Eye tracking; Context (archaeology); Hands free; Artificial intelligence; Tracking (education); Human–computer interaction; Computer graphics (images); Psychology","score_opus":0.02279073288510286,"score_gpt":0.2949175988273254,"score_spread":0.2721268659422225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403338496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14852226,0.00028442926,0.8364562,0.011903551,0.0015886099,0.00017170972,0.0000036416368,0.0008966895,0.00017296699],"genre_scores_gemma":[0.9887781,0.0000075354474,0.00971697,0.0009941482,0.00009370213,0.000025710457,0.0000024924664,0.000018702376,0.00036263606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987186,0.00003895402,0.00024098952,0.0006087942,0.00010670806,0.00028591725],"domain_scores_gemma":[0.9989214,0.00053317274,0.000055097098,0.00035734664,0.00007745569,0.00005555518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019921205,0.00019454485,0.00020990976,0.0002246394,0.0002092148,0.0010095848,0.0005868787,0.00009164217,0.000008998948],"category_scores_gemma":[0.000060576505,0.00016408283,0.00006663132,0.0002043462,0.00011797307,0.00067180104,0.00039219586,0.00028086308,0.000016047234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017855597,0.000062889754,0.00082326535,0.00014681638,0.00008744404,0.000050331193,0.002437302,0.00004212202,0.010933951,0.12917227,0.022068955,0.8341568],"study_design_scores_gemma":[0.0013558237,0.0011603527,0.0052920356,0.0007557327,0.000036096735,0.00023230215,0.0005328487,0.85233384,0.060627222,0.01173251,0.06516568,0.0007755335],"about_ca_topic_score_codex":0.000031320254,"about_ca_topic_score_gemma":0.000039861858,"teacher_disagreement_score":0.85229176,"about_ca_system_score_codex":0.000030683485,"about_ca_system_score_gemma":0.000024732264,"threshold_uncertainty_score":0.9735449},"labels":[],"label_agreement":null},{"id":"W4403616769","doi":"10.5821/dissertation-2117-416263","title":"Objective evaluation on the effectiveness of vergence vision training based on the analysis of eye movements","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vergence (optics); Eye movement; Training (meteorology); Artificial intelligence; Computer vision; Computer science; Optometry; Psychology; Physical medicine and rehabilitation; Medicine; Geography","score_opus":0.02819695961474217,"score_gpt":0.3381828653401465,"score_spread":0.30998590572540435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403616769","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.95403427,0.00004960608,0.028499667,0.00021798818,0.00065047323,0.000829708,0.0000161614,0.00010116152,0.0156009635],"genre_scores_gemma":[0.9994762,0.0000031729978,0.00013845411,0.000052072694,0.0000056341673,0.00011459295,0.000060936512,0.000009715868,0.00013924573],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744004,0.00078938593,0.00029596686,0.00049397314,0.00084245135,0.0001381759],"domain_scores_gemma":[0.99648577,0.0021312241,0.00030063998,0.00070671015,0.00036222613,0.000013450032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032998384,0.00020353854,0.00036091366,0.00064448244,0.0000981009,0.000034915938,0.00080313964,0.00014362433,0.000056054534],"category_scores_gemma":[0.00049746863,0.00010899517,0.0002623957,0.002158259,0.000055719738,0.000046841113,0.00003800276,0.00029662382,0.00000865173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083567813,0.0010472232,0.0018682221,0.0006385692,0.00605764,0.0000067180185,0.009005625,0.08328737,0.050955087,0.59108895,0.0001602491,0.25504866],"study_design_scores_gemma":[0.00014215727,0.00036749657,0.35906336,0.00064818247,0.0005692042,2.5537556e-8,0.0008700434,0.57448316,0.057986148,0.00572637,0.0000044912126,0.00013933776],"about_ca_topic_score_codex":0.00007551356,"about_ca_topic_score_gemma":0.000074691365,"teacher_disagreement_score":0.5853626,"about_ca_system_score_codex":0.00009777589,"about_ca_system_score_gemma":0.00015560324,"threshold_uncertainty_score":0.4444694},"labels":[],"label_agreement":null},{"id":"W4403715560","doi":"10.1145/3696762.3698055","title":"Effects of Increasing Command Capacity of Spatial Memory Menus in Tablets","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Universitas Brawijaya","keywords":"Computer science","score_opus":0.00927387974738805,"score_gpt":0.22356474161259923,"score_spread":0.21429086186521118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403715560","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8745575,0.00015225382,0.123103835,0.00014314736,0.00015512841,0.00006684789,7.3600495e-7,0.00011368044,0.0017068597],"genre_scores_gemma":[0.9914816,0.0000033822785,0.008460844,0.000014972332,0.000007984772,0.000002890607,1.902327e-7,0.0000031081338,0.000025054891],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99937266,0.00007397289,0.00016003293,0.00016653328,0.000104980536,0.00012183181],"domain_scores_gemma":[0.9993952,0.0003441985,0.00003490687,0.00018464077,0.000023677303,0.000017363947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030630257,0.00006519916,0.00016944733,0.0001756067,0.0000133683625,0.0000145889035,0.0002813308,0.000053231917,0.000004080747],"category_scores_gemma":[0.000077441786,0.000055127377,0.00002838551,0.0002750972,0.000083777915,0.00009426922,0.00012133786,0.00011904165,0.0000032660525],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024639894,0.00040069863,0.016353123,0.0014994026,0.00007633453,0.00021566892,0.0015874546,0.00008264891,0.59640646,0.1484674,0.0002998394,0.23458631],"study_design_scores_gemma":[0.00043084665,0.00015055551,0.18246578,0.00041025676,0.000010884715,0.000025456504,0.00001572514,0.02909494,0.7812643,0.005891754,0.00010093145,0.00013856024],"about_ca_topic_score_codex":0.002967187,"about_ca_topic_score_gemma":0.00015901397,"teacher_disagreement_score":0.23444775,"about_ca_system_score_codex":0.00002056391,"about_ca_system_score_gemma":0.000028856382,"threshold_uncertainty_score":0.4485519},"labels":[],"label_agreement":null},{"id":"W4403912788","doi":"10.1101/2024.10.28.620734","title":"Managing Gaze Competition when Acting On and Monitoring the Environment in Parallel","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Eye–hand coordination; Computer science; Human–computer interaction; Computer vision; Communication; Psychology","score_opus":0.016736976284574132,"score_gpt":0.21762817184461059,"score_spread":0.20089119556003646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403912788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.926416,0.0030011006,0.055446245,0.0107777985,0.0021425395,0.0007895199,0.000016764527,0.0013320678,0.000078005876],"genre_scores_gemma":[0.9843378,0.00038701945,0.014855426,0.00007664499,0.00015940126,0.00013505257,3.6898264e-8,0.00004475461,0.000003857513],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9977597,0.00012377193,0.00033413863,0.0010366002,0.0003002577,0.00044556023],"domain_scores_gemma":[0.99846774,0.00012911386,0.00018519198,0.0011058558,0.000032632513,0.00007945208],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066686905,0.00038582,0.00031523456,0.00032913568,0.0001729116,0.00044740393,0.0009168123,0.0002620379,0.0000029927887],"category_scores_gemma":[0.00004349849,0.0003398673,0.00006649916,0.00023243735,0.00011147047,0.000103851045,0.0016703053,0.001426042,0.000059606402],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007388869,0.0009998552,0.095579386,0.003110541,0.0008862645,0.002132468,0.001057463,0.013548237,0.39548647,0.48156926,0.0003828463,0.0051733037],"study_design_scores_gemma":[0.0015758858,0.00029726527,0.7698302,0.009457578,0.00020149734,2.8635512e-7,0.000081113794,0.06316762,0.1380186,0.0072443374,0.006581378,0.0035442538],"about_ca_topic_score_codex":0.00004117807,"about_ca_topic_score_gemma":0.0000012232584,"teacher_disagreement_score":0.67425084,"about_ca_system_score_codex":0.00028697186,"about_ca_system_score_gemma":0.00006882633,"threshold_uncertainty_score":0.99990535},"labels":[],"label_agreement":null},{"id":"W4403913151","doi":"10.1145/3678957.3688384","title":"HumanEYEze 2024: Workshop on Eye Tracking for Multimodal Human-Centric Computing","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Eye tracking; Human–computer interaction; Computer vision; Artificial intelligence; Computer graphics (images); Multimedia","score_opus":0.039428586607421275,"score_gpt":0.3405728741669387,"score_spread":0.30114428755951744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403913151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08023279,0.00027505704,0.9087139,0.0019690658,0.0014519808,0.00030564677,0.000002399457,0.0023676937,0.00468151],"genre_scores_gemma":[0.96721727,0.0000035296036,0.029301932,0.00023941472,0.00022263649,0.000015357293,0.000004123711,0.000022840788,0.002972917],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822325,0.00003100299,0.0002817287,0.0007535079,0.00020123349,0.0005092897],"domain_scores_gemma":[0.99892545,0.0004450671,0.000049608392,0.0004483629,0.00006504445,0.00006648284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035078323,0.00021695341,0.00021521513,0.00036416424,0.00033507633,0.00046276095,0.00080734404,0.00013646578,0.000041161027],"category_scores_gemma":[0.00007109664,0.00018787113,0.00015102285,0.0005932375,0.000058156653,0.00020041647,0.00018332101,0.00037051403,0.00013218407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000045393126,0.00015887937,0.0004362766,0.000085506836,0.00005665263,0.0000799916,0.00056555733,0.0007270237,0.001674921,0.56109715,0.006407798,0.4287057],"study_design_scores_gemma":[0.0009999422,0.00035031507,0.010842039,0.0006267219,0.00003605835,0.000025125048,0.00021930746,0.9386196,0.005919297,0.014670553,0.026789716,0.00090130005],"about_ca_topic_score_codex":0.000011367413,"about_ca_topic_score_gemma":0.000010245258,"teacher_disagreement_score":0.9378926,"about_ca_system_score_codex":0.00008810507,"about_ca_system_score_gemma":0.000034924593,"threshold_uncertainty_score":0.76611626},"labels":[],"label_agreement":null},{"id":"W4403922372","doi":"10.1145/3702319","title":"An Investigation of Multimodal Kinematic Template Matching for Ray Pointing Prediction for Target Selection in VR","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Computer-Human Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Kinematics; Selection (genetic algorithm); Computer science; Matching (statistics); Artificial intelligence; Template matching; Computer vision; Mathematics; Physics; Image (mathematics); Statistics","score_opus":0.025648376975372335,"score_gpt":0.3123002958854843,"score_spread":0.286651918910112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403922372","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2289121,0.0000072147996,0.76853174,0.00036696554,0.0010778179,0.00052760105,0.000017865177,0.00055387855,0.000004803365],"genre_scores_gemma":[0.6720116,0.0000016654651,0.32760638,0.00003146433,0.00011244511,0.00017552006,0.000029473795,0.000020277492,0.000011181391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99830306,0.000092642265,0.00060455216,0.0005886664,0.00015711674,0.00025396544],"domain_scores_gemma":[0.99884456,0.00045504444,0.00017956825,0.00032288334,0.00014906925,0.00004887881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000504717,0.00021246092,0.00024996704,0.0009192772,0.0002777613,0.00019913088,0.00040478414,0.00015049164,0.000008170071],"category_scores_gemma":[0.000018587318,0.00022751425,0.00015001708,0.00049582287,0.000036406207,0.0013325562,0.000010288619,0.00035759955,0.0000034225939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016976707,0.0006837875,0.0004921862,0.001090457,0.00023100882,0.0000043362115,0.0057870164,0.39082542,0.21727122,0.0084939785,0.00021631445,0.3747345],"study_design_scores_gemma":[0.0005849285,0.00094719674,0.0034027437,0.0005711294,0.000023735425,0.000028513809,0.00007592399,0.94922554,0.024375793,0.020449199,0.00012377102,0.00019154008],"about_ca_topic_score_codex":0.00011517968,"about_ca_topic_score_gemma":0.00007363924,"teacher_disagreement_score":0.5584001,"about_ca_system_score_codex":0.00020750433,"about_ca_system_score_gemma":0.000043024138,"threshold_uncertainty_score":0.92777616},"labels":[],"label_agreement":null},{"id":"W4404688984","doi":"10.1109/icds62089.2024.10756434","title":"Drowsiness Detection Through Yawning and Eye Blinking Models Using Convolutional Neural Networks and Transfer Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Convolutional neural network; Transfer of learning; Computer science; Artificial intelligence; Pattern recognition (psychology); Machine learning","score_opus":0.030534688583600485,"score_gpt":0.2598773689095004,"score_spread":0.22934268032589994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404688984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33580792,0.0007438927,0.6624495,0.00026830944,0.00018139275,0.000034193312,1.2961668e-7,0.00043106958,0.000083607214],"genre_scores_gemma":[0.98972666,0.000023611676,0.010086195,0.00005231565,0.000059423557,0.0000028861677,6.590958e-7,0.000010299591,0.00003793329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991074,0.000041435964,0.00013671254,0.00038602622,0.000101695994,0.00022669365],"domain_scores_gemma":[0.999757,0.00008042275,0.000013044789,0.000084216714,0.000035955047,0.000029387094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017482843,0.00012194888,0.00011900813,0.00010491936,0.00028916146,0.0003008372,0.00010813374,0.0001011749,0.000002107932],"category_scores_gemma":[0.0000068939794,0.00011083626,0.000026648184,0.00032142748,0.000102273494,0.0007824912,0.00008957675,0.00034351932,3.559945e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008484286,0.000015293279,0.0024370619,0.0000639332,0.000048676426,0.00004237599,0.00090278586,0.48640892,0.0071611316,0.3357174,0.0000034641705,0.16719048],"study_design_scores_gemma":[0.00011751158,0.00003158087,0.000872971,0.000045500637,0.000011471556,0.0001063973,0.000039914725,0.9912325,0.00035508507,0.006988213,0.00006545284,0.00013335925],"about_ca_topic_score_codex":0.000046396322,"about_ca_topic_score_gemma":0.000010053069,"teacher_disagreement_score":0.65391874,"about_ca_system_score_codex":0.0000237851,"about_ca_system_score_gemma":0.000014642681,"threshold_uncertainty_score":0.45197716},"labels":[],"label_agreement":null},{"id":"W4405230022","doi":"10.3758/s13428-024-02565-3","title":"DerLex: An eye-movement database of derived word reading in English","year":2024,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Eye movement; Word (group theory); Computer science; Reading (process); Movement (music); Natural language processing; Artificial intelligence; Linguistics; Art","score_opus":0.25296962608578827,"score_gpt":0.5626719829348331,"score_spread":0.30970235684904485,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405230022","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6628756,0.00049287797,0.33458784,0.00028396412,0.00038578906,0.00038206557,0.000008733311,0.00063396036,0.00034918947],"genre_scores_gemma":[0.53848094,0.000032009248,0.46117634,0.00001015417,0.000031765576,0.00014590261,0.000003405853,0.000018374589,0.00010110391],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99626404,0.0015638245,0.00034267298,0.0006906567,0.0005386426,0.0006001735],"domain_scores_gemma":[0.9979304,0.00064147543,0.000034814395,0.0010060244,0.00025442458,0.0001328518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008482085,0.00013638295,0.0002374525,0.0010160614,0.00008908848,0.00017910347,0.0013057676,0.00010779922,0.000029160783],"category_scores_gemma":[0.0007939253,0.00012758939,0.000055920278,0.0018078861,0.00022746583,0.0004971911,0.0007232462,0.0008212205,0.000011004875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006698679,0.00025937974,0.0061892397,0.000058587157,0.000007275006,0.00026428382,0.001212363,0.000002295152,0.33913374,0.023341918,0.000096925236,0.62942725],"study_design_scores_gemma":[0.00063157786,0.0006293617,0.20165329,0.00071918574,0.000021901573,0.000011342214,0.0015152827,0.011736512,0.7687407,0.008223526,0.0055720513,0.0005452313],"about_ca_topic_score_codex":0.00028169592,"about_ca_topic_score_gemma":0.000055687215,"teacher_disagreement_score":0.62888205,"about_ca_system_score_codex":0.00014124703,"about_ca_system_score_gemma":0.00020482756,"threshold_uncertainty_score":0.5202944},"labels":[],"label_agreement":null},{"id":"W4405273443","doi":"10.3758/s13428-024-02517-x","title":"The PSR corpus: A Persian sentence reading corpus of eye movements","year":2024,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reading (process); Sentence; Eye movement; Persian; Computer science; Natural language processing; Artificial intelligence; Linguistics; Philosophy","score_opus":0.19681685782045238,"score_gpt":0.5401224052394038,"score_spread":0.3433055474189514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405273443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22349352,0.0039721713,0.7621227,0.0036060503,0.0017257121,0.0009790708,0.000017018698,0.00086663046,0.0032171286],"genre_scores_gemma":[0.7303469,0.00029082116,0.26462802,0.000022789574,0.000050359973,0.0002227968,0.0000015145448,0.000028016413,0.0044087754],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965098,0.00120464,0.00030032083,0.0005553207,0.0007670236,0.00066292397],"domain_scores_gemma":[0.99730045,0.001275555,0.000057807454,0.0009488554,0.00030459376,0.00011276782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073891757,0.00013809747,0.00019275793,0.0003876267,0.00042207414,0.00032304245,0.0018811525,0.00009863045,0.000010936878],"category_scores_gemma":[0.00066145626,0.00009755045,0.00011183354,0.0015157327,0.0006105558,0.0001933968,0.00077530637,0.00078528054,0.000035860463],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006179456,0.00007882542,0.00524155,0.000030483596,0.00002202588,0.00013991327,0.0004695406,6.9524015e-7,0.104202874,0.056900717,0.00038424833,0.8325229],"study_design_scores_gemma":[0.001092867,0.0021009268,0.23727785,0.001345943,0.00009095601,0.0002700853,0.0044034105,0.047099065,0.4799243,0.04920219,0.1759145,0.0012779214],"about_ca_topic_score_codex":0.0001937797,"about_ca_topic_score_gemma":0.0000089386185,"teacher_disagreement_score":0.831245,"about_ca_system_score_codex":0.00012763806,"about_ca_system_score_gemma":0.00017548286,"threshold_uncertainty_score":0.3977992},"labels":[],"label_agreement":null},{"id":"W4405758937","doi":"10.1080/19312458.2024.2443396","title":"Automated object detection in mobile eye-tracking research: comparing manual coding with tag detection, shape detection, matching, and machine learning","year":2024,"lang":"en","type":"article","venue":"Communication Methods and Measures","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mount Royal University","funders":"Office of the Vice President for Research, University of Minnesota","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Coding (social sciences); Eye tracking; Matching (statistics); Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.1041294768580692,"score_gpt":0.427102956582468,"score_spread":0.3229734797243988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405758937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40616897,0.005811211,0.5865399,0.000107030646,0.00008005077,0.00019009064,3.1495074e-7,0.0009336853,0.00016872703],"genre_scores_gemma":[0.9291977,0.00082932145,0.06979062,0.000010802402,0.000015968326,0.000096208896,0.0000014880924,0.000022031274,0.000035807254],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966506,0.0019682108,0.00031741837,0.00050427724,0.00024630173,0.00031321714],"domain_scores_gemma":[0.99833184,0.0008039811,0.00009701403,0.0004962491,0.00020151337,0.00006937933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055701085,0.0001843743,0.00025704375,0.0007007897,0.0009139293,0.0005417032,0.00043059795,0.00013414596,0.0000025854995],"category_scores_gemma":[0.0003206548,0.00016591577,0.00002869931,0.0010590102,0.00022872299,0.00046143975,0.00035719437,0.001223651,0.00000409048],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020466161,0.000024244104,0.0023244082,0.00007532806,0.000029195,0.0000039316474,0.0022284219,0.00048681456,0.13687056,0.00073891337,0.0000014935827,0.8571962],"study_design_scores_gemma":[0.00046862883,0.0003216135,0.058853343,0.00052302337,0.000023346049,0.00017198923,0.0012727436,0.78371555,0.1467613,0.003284573,0.004230143,0.00037378608],"about_ca_topic_score_codex":0.00041742396,"about_ca_topic_score_gemma":0.001364509,"teacher_disagreement_score":0.85682243,"about_ca_system_score_codex":0.00011117116,"about_ca_system_score_gemma":0.000032537337,"threshold_uncertainty_score":0.70292974},"labels":[],"label_agreement":null},{"id":"W4405927601","doi":"10.1371/journal.pone.0316469","title":"Updating the remembered position of targets following passive lateral translation","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Translation (biology); Position (finance); Computer science; Biology; Genetics","score_opus":0.03544065337920705,"score_gpt":0.24204558679099342,"score_spread":0.20660493341178637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405927601","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88661444,0.00051001593,0.10130086,0.009244314,0.00018048765,0.00017378318,0.000003863917,0.000511376,0.0014608715],"genre_scores_gemma":[0.97545475,0.000004321595,0.02441883,0.000038722676,0.000029635376,0.000008897359,0.0000044504127,0.0000052902606,0.000035073994],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99936676,0.00003822637,0.00014135777,0.00016586625,0.00017362286,0.00011418887],"domain_scores_gemma":[0.9996596,0.00008387315,0.000037892838,0.0001757793,0.00003089662,0.000011966545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013532572,0.000059164064,0.000095026095,0.000062134735,0.00007179804,0.000055750104,0.00022509572,0.000040719697,0.0000051179677],"category_scores_gemma":[0.000020747068,0.000044085078,0.000052725594,0.0002376869,0.00002119573,0.00019807699,0.000032914795,0.000121158904,0.000013420879],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000062432528,0.00030398864,0.001108629,0.0001597469,0.0004108936,0.00005116349,0.0025716203,0.000029671943,0.86381996,0.06175034,0.00012681696,0.06966095],"study_design_scores_gemma":[0.00038968134,0.00022232544,0.012664516,0.0012254067,0.00019459751,0.000009335426,0.00008477221,0.20943715,0.7491459,0.026193565,0.00013823983,0.00029450972],"about_ca_topic_score_codex":0.000008893953,"about_ca_topic_score_gemma":0.0000035907613,"teacher_disagreement_score":0.20940746,"about_ca_system_score_codex":0.000016664459,"about_ca_system_score_gemma":0.00001577577,"threshold_uncertainty_score":0.17977372},"labels":[],"label_agreement":null},{"id":"W4405935164","doi":"10.1109/irc63610.2024.00010","title":"Autonomous Wheelchair – Object Measurement","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Wheelchair; Computer science; Object (grammar); Human–computer interaction; Computer vision; Artificial intelligence; World Wide Web","score_opus":0.02704344782854747,"score_gpt":0.2443132460146344,"score_spread":0.2172697981860869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405935164","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015280754,0.0007265486,0.94788325,0.006139491,0.00075600273,0.000058318892,3.3321044e-7,0.0035279198,0.039380047],"genre_scores_gemma":[0.98157823,0.000004021074,0.016889606,0.00016264076,0.000031954136,0.000010189841,1.4930175e-7,0.000005207977,0.0013180227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991839,0.0000160658,0.00009812346,0.00030781468,0.00020555506,0.0001885569],"domain_scores_gemma":[0.9995705,0.000019585246,0.000009382407,0.00032253136,0.00004405078,0.0000339364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029063015,0.00008175128,0.000075448595,0.000116361276,0.00004684864,0.00015923535,0.00045419173,0.00004235052,0.000031657128],"category_scores_gemma":[0.000018145221,0.00006322572,0.000047166537,0.00027751303,0.00002612679,0.00013594891,0.000115043964,0.00011470921,0.0005601632],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.070959e-7,0.000024701416,0.000082937186,0.000014007184,0.000023248722,0.000060943043,0.00010101029,0.000009420611,0.002215573,0.6818602,0.009962292,0.30564535],"study_design_scores_gemma":[0.00037503216,0.0004152522,0.014755669,0.00020571858,0.000024051265,0.0002314469,0.00006535736,0.19575863,0.061969217,0.08166857,0.6436173,0.0009138203],"about_ca_topic_score_codex":0.000021030011,"about_ca_topic_score_gemma":0.00001573286,"teacher_disagreement_score":0.98005015,"about_ca_system_score_codex":0.00008265094,"about_ca_system_score_gemma":0.000093078575,"threshold_uncertainty_score":0.7199951},"labels":[],"label_agreement":null},{"id":"W4406208667","doi":"10.1002/alz.090577","title":"Evaluating the use of eye tracking tasks as language‐ and culturally‐neutral assessments of cognition in a multi‐ethnic cohort of older adults","year":2024,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Baycrest Hospital; Queen's University; University of Toronto; Sunnybrook Hospital","funders":"","keywords":"Ethnic group; Cohort; Eye tracking; Cognition; Tracking (education); Psychology; Developmental psychology; Cognitive psychology; Medicine; Computer science; Sociology; Artificial intelligence; Psychiatry","score_opus":0.09957291364017891,"score_gpt":0.4026957870253441,"score_spread":0.3031228733851652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406208667","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97863936,0.01670902,0.004059699,0.00009018974,0.00010829134,0.0003135833,0.000006934367,0.000050896666,0.000022048056],"genre_scores_gemma":[0.98470986,0.00004401585,0.015171535,0.00002606994,0.0000059723575,0.000023386434,0.000008117451,0.0000079782185,0.000003088433],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987979,0.00012443574,0.00038623658,0.00029923613,0.00021952004,0.00017268163],"domain_scores_gemma":[0.9992909,0.00015835708,0.00017404194,0.00024598223,0.00011081485,0.000019939314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003897455,0.00011927256,0.00019495783,0.00012550308,0.000031770367,0.000039788738,0.00028014305,0.000066771325,0.000008930246],"category_scores_gemma":[0.000046029134,0.000088838875,0.000056723286,0.00033745263,0.000119689845,0.00032538926,0.00014220715,0.00016897381,0.0000018258646],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004502439,0.00042960688,0.05472494,0.00015157237,0.004802007,0.00003414808,0.013702648,0.00021755199,0.09830571,0.0026138234,0.000048899627,0.82492405],"study_design_scores_gemma":[0.00094943744,0.00031655488,0.79659206,0.00079418294,0.0024502284,0.000012218207,0.00087333965,0.072000615,0.12559693,0.00019638572,0.000011765202,0.0002063014],"about_ca_topic_score_codex":0.00037701108,"about_ca_topic_score_gemma":0.0000813293,"teacher_disagreement_score":0.82471776,"about_ca_system_score_codex":0.0000036353536,"about_ca_system_score_gemma":0.000037279253,"threshold_uncertainty_score":0.3622744},"labels":[],"label_agreement":null},{"id":"W4406369718","doi":"10.1121/10.0035052","title":"Towards the detection of Alzheimer’s disease through eye movement changes using a hearable","year":2024,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Eye movement; Movement (music); Disease; Neuroscience; Medicine; Optometry; Physical medicine and rehabilitation; Psychology; Art; Aesthetics; Pathology","score_opus":0.03924573278666174,"score_gpt":0.29722960264455334,"score_spread":0.25798386985789157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406369718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016102929,0.0040292935,0.9469198,0.03240969,0.00037553115,0.00010167557,0.00000404354,0.00002922723,0.000027801225],"genre_scores_gemma":[0.973722,0.00045860888,0.024677347,0.0010219715,0.00009689503,0.0000012247554,3.149764e-8,0.0000075297603,0.000014429397],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998851,0.00013933866,0.00027526767,0.00010186611,0.0004385819,0.00019396363],"domain_scores_gemma":[0.9989584,0.00020951367,0.0002850691,0.0003807828,0.00012701454,0.00003920922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006417773,0.00010401912,0.00020968863,0.000018075796,0.00018717794,0.000031286985,0.0009318585,0.00003959783,0.0000050691033],"category_scores_gemma":[0.000082466446,0.000045705634,0.00030385083,0.00048006364,0.00063287467,0.00010049311,0.00031648693,0.00038002167,0.0000010339878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017658922,0.0005899151,0.00017192769,0.00029671876,0.0020569323,0.000012191071,0.007818661,0.03697398,0.43252146,0.0013800623,0.008413082,0.5095885],"study_design_scores_gemma":[0.00020396715,0.00046317835,0.004153763,0.00035023945,0.00081827387,0.000036937363,0.001355132,0.9292135,0.041953437,0.018811425,0.002492216,0.00014789708],"about_ca_topic_score_codex":0.00011624374,"about_ca_topic_score_gemma":5.290898e-7,"teacher_disagreement_score":0.957619,"about_ca_system_score_codex":0.000051550225,"about_ca_system_score_gemma":0.00012184283,"threshold_uncertainty_score":0.2331853},"labels":[],"label_agreement":null},{"id":"W4406380149","doi":"10.1186/s12967-024-06044-3","title":"Diagnosis of Parkinson’s disease by eliciting trait-specific eye movements in multi-visual tasks","year":2025,"lang":"en","type":"article","venue":"Journal of Translational Medicine","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities; Dalian Medical University","keywords":"Trait; Disease; Eye movement; Parkinson's disease; Medicine; Physical medicine and rehabilitation; Psychology; Neuroscience; Cognitive psychology; Computer science; Pathology","score_opus":0.026784425494808816,"score_gpt":0.32158928496136724,"score_spread":0.29480485946655843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406380149","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6376669,0.0042360863,0.33658555,0.020854281,0.0003905649,0.00011919464,0.00001697766,0.00002164479,0.00010881881],"genre_scores_gemma":[0.9927056,0.00019836507,0.006737114,0.00026813798,0.00004982857,0.0000056127133,0.000004094074,0.0000043213704,0.000026916809],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984757,0.00006473185,0.0007234539,0.00016467688,0.00041606074,0.00015539999],"domain_scores_gemma":[0.9990851,0.000278055,0.00029188202,0.0001099614,0.00015752662,0.00007744531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004985311,0.00011227697,0.0003053281,0.00047551602,0.000037854694,0.000008682861,0.0004200463,0.000051918643,0.00002324502],"category_scores_gemma":[0.00012586107,0.00009159731,0.000076694996,0.0004919702,0.00011179521,0.00016325217,0.000018050243,0.00024903278,5.0432493e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014659612,0.00087149814,0.7091737,0.00010188101,0.00012124964,0.00009440873,0.00092489395,0.00063805864,0.009680685,0.006048578,0.0030898594,0.2691086],"study_design_scores_gemma":[0.0031397338,0.00014907146,0.97795266,0.00085807935,0.000017907632,0.0000027632705,0.00008150279,0.0059957686,0.0013908937,0.0031856971,0.0071296203,0.00009630786],"about_ca_topic_score_codex":0.000014507822,"about_ca_topic_score_gemma":0.000005567566,"teacher_disagreement_score":0.35503873,"about_ca_system_score_codex":0.0000376287,"about_ca_system_score_gemma":0.00008388173,"threshold_uncertainty_score":0.37352297},"labels":[],"label_agreement":null},{"id":"W4406509500","doi":"10.1016/s0197-2510(07)72337-9","title":"10.1016/s0197-2510(07)72337-9","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Eye protection; Optometry; Business; Medicine; Optics; Physics","score_opus":0.006213229148106932,"score_gpt":0.1753407399383803,"score_spread":0.16912751079027338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406509500","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051055383,0.000035576348,0.0005752934,0.001681811,0.0000031089887,0.000095667696,0.0000030885217,0.0009255801,0.9961693],"genre_scores_gemma":[0.0019110079,1.1969732e-7,0.004205725,0.00007365177,0.00005545534,0.000013621648,0.0000019624388,0.000012364719,0.9937261],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988623,0.000036825168,0.00016320868,0.00040006224,0.00017308147,0.00036448304],"domain_scores_gemma":[0.9991168,0.000049999915,0.000032211177,0.0006434062,0.000046161014,0.00011145823],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001539756,0.00014741551,0.00016838864,0.000112644615,0.00010664185,0.00008362005,0.0010157678,0.000085887936,0.96338207],"category_scores_gemma":[0.00003290953,0.00014098814,0.0000554194,0.00040032069,0.000051145547,0.00014742507,0.00014463515,0.00016033366,0.99594957],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005538568,0.00004109516,3.3342226e-7,0.0000017182057,0.000007303969,0.000013048545,0.000011168369,0.000027617289,0.00005538951,0.000073739,0.14292604,0.85683703],"study_design_scores_gemma":[0.00014358872,0.00014686541,0.00019093536,0.000011775615,0.00000406577,0.000021997374,3.0222208e-7,0.0013407839,0.00030148562,0.00014458896,0.99751186,0.00018172193],"about_ca_topic_score_codex":0.0000137461575,"about_ca_topic_score_gemma":1.3691272e-7,"teacher_disagreement_score":0.8566553,"about_ca_system_score_codex":0.000030926,"about_ca_system_score_gemma":0.000028445089,"threshold_uncertainty_score":0.57493293},"labels":[],"label_agreement":null},{"id":"W4407155957","doi":"10.1080/00085030.2025.2459195","title":"Development of a competency test model to evaluate forensic identification officers on crime scene processing","year":2025,"lang":"en","type":"article","venue":"Canadian Society of Forensic Science Journal","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Regional Municipality of Niagara; Montreal Police Service; Trent University","funders":"","keywords":"Forensic science; Crime scene; Identification (biology); Test (biology); Psychology; Criminology; Applied psychology; History; Archaeology","score_opus":0.03307738718726991,"score_gpt":0.2970630294662097,"score_spread":0.26398564227893984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407155957","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60495967,0.00008638841,0.38736975,0.0049531106,0.00044830976,0.00023369903,0.000005868619,0.00005338848,0.0018898179],"genre_scores_gemma":[0.631149,0.000002393229,0.36836523,0.00041597392,0.000008152425,0.000002611652,3.739809e-7,0.000003224163,0.000053067684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979267,0.000011914254,0.0005299861,0.00038686267,0.00064072,0.00050383375],"domain_scores_gemma":[0.9979846,0.000038888942,0.00031471837,0.00036400688,0.0009925228,0.0003052453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015656885,0.00014055676,0.00022889602,0.0005973726,0.0007655612,0.00014373622,0.0013766974,0.00006698379,0.000001927347],"category_scores_gemma":[0.00022473355,0.0001322968,0.00010408719,0.0020122663,0.0006932094,0.00033153084,0.000101427504,0.00023504552,0.0000038365524],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052511446,0.00008689402,0.0014384993,0.00005903506,0.00003099825,0.0000025989027,0.0059826327,0.0033458676,0.08879026,0.01857981,0.0055223126,0.87615585],"study_design_scores_gemma":[0.0007266288,0.00020520674,0.114046894,0.0011392356,0.000038062626,0.000043737673,0.0019654247,0.48684755,0.3827518,0.011188962,0.0005723911,0.00047413792],"about_ca_topic_score_codex":0.00020497586,"about_ca_topic_score_gemma":0.0010448511,"teacher_disagreement_score":0.8756817,"about_ca_system_score_codex":0.0007520935,"about_ca_system_score_gemma":0.010280951,"threshold_uncertainty_score":0.99532986},"labels":[],"label_agreement":null},{"id":"W4407226018","doi":"10.1145/3716175","title":"Improving Human–Robot Collaboration through Augmented Reality and Eye Gaze","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Human-Robot Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Gaze; Augmented reality; Human–computer interaction; Human–robot interaction; Computer science; Eye tracking; Robot; Psychology; Artificial intelligence","score_opus":0.061074467752535236,"score_gpt":0.3857506476495536,"score_spread":0.32467617989701836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407226018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04439303,0.00005448851,0.94729203,0.004891166,0.0009909247,0.00032443367,0.000008002815,0.00076666573,0.0012792866],"genre_scores_gemma":[0.98493916,0.000023070585,0.013261498,0.00035878088,0.00003924923,0.00009927937,0.000018725059,0.000015672002,0.0012445954],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99812037,0.00014777576,0.0004715981,0.00074599806,0.00020712567,0.00030715426],"domain_scores_gemma":[0.998349,0.00016018782,0.00021491417,0.0010073642,0.00021939338,0.000049116323],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023786379,0.00027454045,0.00027152282,0.00040078466,0.001014718,0.00033199252,0.00058686646,0.00020313358,0.00005549512],"category_scores_gemma":[0.00006380257,0.00029074698,0.00009246171,0.00075004896,0.00012379122,0.0011966401,0.00004526719,0.00058311987,0.000024353736],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018812204,0.0019340802,0.0006188703,0.00028602692,0.000503567,0.000033934903,0.0023050662,0.0073855347,0.53428054,0.102846995,0.0019002713,0.34771702],"study_design_scores_gemma":[0.009208143,0.0035965503,0.09635491,0.0020581705,0.00077652157,0.00014290088,0.00459033,0.047826957,0.6845849,0.12527348,0.02207986,0.0035072898],"about_ca_topic_score_codex":0.0005539276,"about_ca_topic_score_gemma":0.00045186133,"teacher_disagreement_score":0.9405461,"about_ca_system_score_codex":0.00031606606,"about_ca_system_score_gemma":0.00005482235,"threshold_uncertainty_score":0.99995446},"labels":[],"label_agreement":null},{"id":"W4408175764","doi":"10.31357/ait.v4i02.8022","title":"Web-Based Visual Acuity Testing under Low-Resource Settings","year":2025,"lang":"en","type":"article","venue":"Advances in Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Visual acuity; Web application; Resource (disambiguation); Web resource; World Wide Web; Optometry; Medicine; Ophthalmology","score_opus":0.006912786665291693,"score_gpt":0.28532164027630136,"score_spread":0.27840885361100964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408175764","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3362805,0.0022584854,0.62583464,0.019374771,0.0004501254,0.0003060788,0.0000023965547,0.005169914,0.010323115],"genre_scores_gemma":[0.9492474,0.000013600611,0.049497098,0.0010579875,0.000009515563,0.00005432247,0.000001108642,0.000009608814,0.00010932658],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982756,0.000047759295,0.00032557972,0.00067994907,0.00012956189,0.000541547],"domain_scores_gemma":[0.99871624,0.0004001026,0.0001364365,0.0006408127,0.00008093725,0.000025462317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027281552,0.0002087628,0.00029390218,0.0010408104,0.00014467363,0.000034873996,0.0014636947,0.00031813481,0.0000027125207],"category_scores_gemma":[0.00060370733,0.00020993891,0.000039253733,0.0032917368,0.00044708283,0.00024233095,0.0005133853,0.0006123954,0.000019804807],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000642693,0.00018125889,0.04215622,0.000044985904,0.000009315002,0.00005244562,0.000009339592,0.0013183189,0.006857941,0.24681872,0.00016750713,0.7023775],"study_design_scores_gemma":[0.004252268,0.00063878327,0.0172778,0.0011799599,0.000024974548,0.00006090898,0.00041615163,0.18298838,0.20189092,0.44917294,0.14071317,0.0013837591],"about_ca_topic_score_codex":0.0000043095565,"about_ca_topic_score_gemma":0.000033966833,"teacher_disagreement_score":0.7009938,"about_ca_system_score_codex":0.00011992825,"about_ca_system_score_gemma":0.00013672744,"threshold_uncertainty_score":0.85610604},"labels":[],"label_agreement":null},{"id":"W4408212485","doi":"10.1101/2025.03.02.641038","title":"Steering in the presence of a gaze-contingent occlusion over a quarter of the visual field","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gaze; Quarter (Canadian coin); Visual field; Field (mathematics); Psychology; Computer vision; Optometry; Computer science; Neuroscience; Medicine; History; Mathematics; Archaeology","score_opus":0.009060037313196422,"score_gpt":0.23523194532356734,"score_spread":0.2261719080103709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408212485","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9756159,0.00036356173,0.021490784,0.0010272113,0.00080683146,0.00053205603,0.000017180486,0.00011737291,0.00002909706],"genre_scores_gemma":[0.99739903,0.000023789087,0.0023156295,0.0001625882,0.000037093978,0.00004906273,1.2567691e-8,0.000009736033,0.0000030460094],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99795467,0.00023234331,0.0005198599,0.0005752275,0.00040487896,0.0003130533],"domain_scores_gemma":[0.99738073,0.0003106899,0.00045535827,0.0016203333,0.00020363808,0.000029252587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083013444,0.00026481895,0.0003944667,0.00023583707,0.00007875891,0.00006986646,0.0025441106,0.00030712193,0.0000042631536],"category_scores_gemma":[0.00034860842,0.00018560386,0.00015633745,0.00083467196,0.000113991926,0.000089745685,0.0018080273,0.00075520825,0.0000011851486],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006116666,0.0010590635,0.25395098,0.0018447795,0.00023755594,0.00008189452,0.00075044803,0.00058121723,0.69328856,0.046794552,0.0011983532,0.0001514338],"study_design_scores_gemma":[0.00039817207,0.000101334816,0.6744618,0.0019088443,0.000038925486,2.9163594e-8,0.000014612449,0.009269468,0.31297433,0.000038079284,0.00044265285,0.0003517824],"about_ca_topic_score_codex":0.00017007341,"about_ca_topic_score_gemma":0.000014543217,"teacher_disagreement_score":0.42051077,"about_ca_system_score_codex":0.000060001432,"about_ca_system_score_gemma":0.00028823453,"threshold_uncertainty_score":0.75687057},"labels":[],"label_agreement":null},{"id":"W4408566208","doi":"10.1109/imc-ssgp63352.2024.10919763","title":"Comparative Analysis of SLAM Algorithms for Voice-Controlled Autonomous Wheelchair","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Wheelchair; Computer science; Simultaneous localization and mapping; Speech recognition; Algorithm; Mobile robot; Artificial intelligence; Robot","score_opus":0.032954404220541855,"score_gpt":0.31776916293712837,"score_spread":0.2848147587165865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408566208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071903886,0.0003713636,0.9855913,0.0014302424,0.0002559561,0.00026475196,0.0000147015635,0.0006618017,0.0042194976],"genre_scores_gemma":[0.9113866,0.0000026979578,0.08662917,0.00004842793,0.000021028814,0.00007299017,0.0000051473503,0.0000037871168,0.0018301387],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891603,0.000031621148,0.00030943297,0.0004052546,0.00012496489,0.00021270814],"domain_scores_gemma":[0.99894434,0.00045502503,0.000073268406,0.00035724635,0.00013434514,0.00003575495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002912282,0.00013157507,0.0006403574,0.0006371698,0.000052746334,0.000084418025,0.00052734086,0.0000706003,0.000018873234],"category_scores_gemma":[0.000026297657,0.000098032535,0.00037387243,0.0013622282,0.000071609524,0.00018185296,0.00008199312,0.000092613845,0.000024622203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029399816,0.00016177073,0.00021247867,0.0000354321,0.004833306,0.000011412297,0.001091151,0.0011099555,0.0017878378,0.93518066,0.0020225323,0.053524073],"study_design_scores_gemma":[0.0006772553,0.00014705297,0.0017844647,0.000010195528,0.0004260344,0.0000016237528,0.00004549746,0.9852175,0.0031891104,0.0049377675,0.0034341896,0.0001293038],"about_ca_topic_score_codex":0.000035558467,"about_ca_topic_score_gemma":0.000057192185,"teacher_disagreement_score":0.98410755,"about_ca_system_score_codex":0.000034025503,"about_ca_system_score_gemma":0.00006852571,"threshold_uncertainty_score":0.39976507},"labels":[],"label_agreement":null},{"id":"W4408882981","doi":"10.23977/acss.2025.090114","title":"Research on the design and realization of interactive wearable blindness guidance system based on computer vision","year":2025,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Realization (probability); Blindness; Wearable computer; Computer science; Human–computer interaction; Computer vision; Optometry; Artificial intelligence; Embedded system; Medicine; Mathematics","score_opus":0.04342589581592771,"score_gpt":0.35668640173512206,"score_spread":0.31326050591919435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408882981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072671995,0.0014020653,0.9893982,0.00037423515,0.0005861644,0.00048895885,0.0000015013982,0.00006903184,0.00041268065],"genre_scores_gemma":[0.98910695,0.00008112661,0.010587776,0.000106313586,0.000040706283,0.000053598636,5.3130054e-7,0.0000059875574,0.000016984077],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997472,0.0010768864,0.00038541286,0.00052282267,0.0003072585,0.00023562809],"domain_scores_gemma":[0.9964514,0.002663808,0.0001591297,0.00046121926,0.00023463175,0.000029860288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022700888,0.00015452095,0.00031874672,0.00045006193,0.0001861003,0.00018330054,0.0005308328,0.000086928005,2.626673e-7],"category_scores_gemma":[0.0000257875,0.000105085186,0.00002433675,0.0007721554,0.00015108057,0.00024308564,0.00018330236,0.00025357885,0.0000017344761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015176136,0.0002195765,0.0009633356,0.0005516276,0.00002379495,0.000026000096,0.0002706413,0.5130087,0.00022992662,0.3722983,0.00062627625,0.111630045],"study_design_scores_gemma":[0.0003700876,0.00073830684,0.001218179,0.0040544407,0.0000019609665,0.0000049997075,0.00007667921,0.9909209,0.00073538424,0.0013198034,0.00046281327,0.000096472344],"about_ca_topic_score_codex":0.00003342763,"about_ca_topic_score_gemma":0.0000034023476,"teacher_disagreement_score":0.9818398,"about_ca_system_score_codex":0.00006310846,"about_ca_system_score_gemma":0.000040802126,"threshold_uncertainty_score":0.42852494},"labels":[],"label_agreement":null},{"id":"W4409215437","doi":"10.21203/rs.3.rs-6247198/v1","title":"Exercise Intensity improves performance on a Spatial Memory Task","year":2025,"lang":"en","type":"preprint","venue":"Research Square","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Ministerio de Economía y Competitividad","keywords":"Task (project management); Computer science; Intensity (physics); Cognitive psychology; Psychology; Economics; Physics; Optics","score_opus":0.04449937300787781,"score_gpt":0.35072976323203486,"score_spread":0.30623039022415705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409215437","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90166306,0.0012480879,0.053968567,0.009962661,0.0040048165,0.0034796002,0.00014455264,0.0029115633,0.022617089],"genre_scores_gemma":[0.9947668,0.00035001972,0.002109233,0.00007048012,0.00013287783,0.00022046913,0.000018806428,0.00001392901,0.002317421],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9963617,0.00022718642,0.00029903316,0.0012563588,0.0010203117,0.0008354196],"domain_scores_gemma":[0.9963827,0.00025231537,0.000102750026,0.0022029204,0.00091914635,0.00014012736],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0014202811,0.00032338558,0.0004929941,0.0009780101,0.00038227183,0.00027694268,0.0029675975,0.00050591206,0.000011371171],"category_scores_gemma":[0.00040780715,0.00029179495,0.000166866,0.00065391127,0.00032995635,0.000113223876,0.0059882887,0.0034343794,0.00020014145],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001656986,0.00039450362,0.0009777525,0.0017276679,0.00005881507,0.00013081483,0.0006096334,0.00022940466,0.0008334645,0.0042054835,0.007869334,0.98279744],"study_design_scores_gemma":[0.002376814,0.0037386662,0.5055834,0.025754299,0.00006876229,0.000032080246,0.0005774321,0.3183046,0.093880445,0.033261836,0.0133372685,0.0030843844],"about_ca_topic_score_codex":0.0006227264,"about_ca_topic_score_gemma":0.00003776363,"teacher_disagreement_score":0.979713,"about_ca_system_score_codex":0.0002936476,"about_ca_system_score_gemma":0.0006816148,"threshold_uncertainty_score":0.99995345},"labels":[],"label_agreement":null},{"id":"W4409537179","doi":"10.1007/978-3-031-85933-5_2","title":"Analysis of Driver Attention to Objects While Driving","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Thesaurus; Information retrieval; Artificial intelligence","score_opus":0.025922107292989893,"score_gpt":0.2870758553090325,"score_spread":0.26115374801604263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409537179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006798183,0.00007923414,0.8379336,0.0008760478,0.00018260998,0.00023600887,0.000010890393,0.00010906295,0.15989274],"genre_scores_gemma":[0.82036793,0.0004135715,0.176864,0.00056820386,0.000007663815,0.000018776427,0.000041791725,0.0000038520325,0.0017142],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871135,0.000021046893,0.0005538954,0.0002588394,0.0002961252,0.00015873244],"domain_scores_gemma":[0.99710363,0.00017129915,0.0002897686,0.0019167108,0.00046379428,0.00005480049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066549436,0.00014238142,0.0003101412,0.0038902047,0.00023269375,0.00020135615,0.0029076596,0.00010246238,0.0000037793745],"category_scores_gemma":[0.000053161915,0.00014847239,0.00007290267,0.0020774999,0.00043738115,0.002082394,0.0025678289,0.00024078965,0.000014251971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[6.1233254e-7,0.000015638841,0.0015465728,0.000015849993,0.000046192683,1.5559937e-7,0.00088913704,0.0005747352,0.000017227121,0.7550105,0.00015669895,0.24172673],"study_design_scores_gemma":[0.00022911193,0.00008652374,0.14698529,0.0005342586,0.00011493635,0.0000034804389,0.000035931673,0.8114349,0.000042146505,0.004209449,0.035876352,0.00044760184],"about_ca_topic_score_codex":0.000012526012,"about_ca_topic_score_gemma":0.000037352755,"teacher_disagreement_score":0.81968814,"about_ca_system_score_codex":0.00010059754,"about_ca_system_score_gemma":0.0001640119,"threshold_uncertainty_score":0.60545284},"labels":[],"label_agreement":null},{"id":"W4409720489","doi":"10.1145/3706599.3721274","title":"Demonstration of GazeNoter: Enhancing AR Note-Taking Through Gaze-Based Selection of LLM Suggestions","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Selection (genetic algorithm); Computer science; Human–computer interaction; Artificial intelligence","score_opus":0.013571583303128817,"score_gpt":0.28364746364227106,"score_spread":0.27007588033914226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409720489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054524038,0.000026574446,0.9383322,0.0006721928,0.00014193119,0.00008533108,9.581937e-7,0.00024967358,0.005967083],"genre_scores_gemma":[0.8523531,0.0000024776161,0.14748038,0.00006647504,0.0000073988836,0.000005767357,0.0000014197825,0.000003062347,0.00007989421],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990916,0.000047070982,0.00033041314,0.00024475082,0.00013099125,0.00015515723],"domain_scores_gemma":[0.9991206,0.0001748218,0.00022677514,0.0002698165,0.0001950653,0.000012950908],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019767898,0.00009611006,0.00017614951,0.00022372669,0.000075008225,0.000019903257,0.00029397718,0.000104720326,0.000012253146],"category_scores_gemma":[0.00012452982,0.000093665156,0.00006160014,0.0008653521,0.00008019531,0.00017903707,0.000045738005,0.00013065657,0.000002899933],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009627367,0.00024745864,0.01150289,0.00008760546,0.000026899632,0.0000011087897,0.0001327343,0.0023276592,0.41546497,0.51111,0.00016891363,0.058920108],"study_design_scores_gemma":[0.00024406584,0.0001235812,0.019240357,0.00016516924,0.00001644361,0.0000023621549,0.000021376329,0.043355186,0.93075895,0.005872223,0.000106942694,0.00009333707],"about_ca_topic_score_codex":0.00014402076,"about_ca_topic_score_gemma":0.0001770187,"teacher_disagreement_score":0.7978291,"about_ca_system_score_codex":0.000045700795,"about_ca_system_score_gemma":0.00015749282,"threshold_uncertainty_score":0.38195542},"labels":[],"label_agreement":null},{"id":"W4409720870","doi":"10.1145/3706599.3720273","title":"Exploring and Modeling Gaze-Based Steering Behavior in Virtual Reality","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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Gaze; Virtual reality; Computer science; Human–computer interaction; Computer vision; Computer graphics (images)","score_opus":0.1474634749289316,"score_gpt":0.3059342789579178,"score_spread":0.1584708040289862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409720870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4729009,0.000015396221,0.5260197,0.00037368346,0.000062866995,0.00003735356,1.7037591e-7,0.00019339957,0.00039652543],"genre_scores_gemma":[0.9908079,0.0000062630083,0.009025054,0.00006629683,0.000003953187,0.000045207253,2.8232873e-7,0.0000026771775,0.00004236488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992996,0.000022014367,0.00015005113,0.00028588838,0.00006855182,0.00017393085],"domain_scores_gemma":[0.99963546,0.000045327422,0.000014404275,0.0002598122,0.000021714184,0.000023311572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002086726,0.0000784224,0.000110640896,0.00022337558,0.000051512136,0.000051655094,0.00025747615,0.00003886808,9.549778e-7],"category_scores_gemma":[0.000027574804,0.00007667213,0.000017472803,0.00030771448,0.000024407198,0.00022602428,0.00015831387,0.00013727481,0.0000013597222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013798286,0.0002060896,0.0443775,0.00004041088,0.000009720062,0.00006188407,0.00023795855,0.04692926,0.005276945,0.42870015,0.000019342095,0.47412696],"study_design_scores_gemma":[0.00030776783,0.000032689546,0.029439528,0.00005568114,0.0000031554628,0.0000011563941,0.00007714999,0.96549016,0.0037054725,0.00074404554,0.000032520566,0.00011064954],"about_ca_topic_score_codex":0.00017527844,"about_ca_topic_score_gemma":0.00008099254,"teacher_disagreement_score":0.9185609,"about_ca_system_score_codex":0.00003720415,"about_ca_system_score_gemma":0.000030910378,"threshold_uncertainty_score":0.31265986},"labels":[],"label_agreement":null},{"id":"W4409736015","doi":"10.1145/3706598.3714294","title":"GazeNoter: Co-Piloted AR Note-Taking via Gaze Selection of LLM Suggestions to Match Users' Intentions","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Selection (genetic algorithm); Gaze; Human–computer interaction; Multimedia; Artificial intelligence","score_opus":0.015392827029950096,"score_gpt":0.30360066062768815,"score_spread":0.28820783359773805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409736015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04581805,0.000009019151,0.9437008,0.003632773,0.0003053094,0.00016667477,0.000003155548,0.00075285276,0.005611391],"genre_scores_gemma":[0.9212873,0.0000025932845,0.07745989,0.0002864425,0.00001230199,0.00002169669,0.0000027545339,0.0000069067823,0.0009201083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99878937,0.000052733783,0.00031090478,0.00038851114,0.00018251939,0.00027594928],"domain_scores_gemma":[0.99900156,0.00011499199,0.000113671354,0.00043909525,0.00027605114,0.000054613545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002078613,0.00013975558,0.00020345976,0.0005951241,0.0001880471,0.000063187545,0.00060060015,0.000113777554,0.00005262178],"category_scores_gemma":[0.00018824716,0.00013825222,0.00007926883,0.0015855534,0.0000660337,0.0001553761,0.00016372681,0.0002162035,0.00007294509],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029215773,0.0008533742,0.034850113,0.00007448944,0.00014239895,0.000008582603,0.00044529658,0.00086845213,0.36168495,0.45837635,0.009969801,0.13269697],"study_design_scores_gemma":[0.0011735895,0.00079497683,0.4725573,0.0005140981,0.000096657954,0.000059637958,0.0001745656,0.0800225,0.41001195,0.023813019,0.009975586,0.00080614124],"about_ca_topic_score_codex":0.00024086544,"about_ca_topic_score_gemma":0.00025119196,"teacher_disagreement_score":0.87546927,"about_ca_system_score_codex":0.000092648224,"about_ca_system_score_gemma":0.00008045472,"threshold_uncertainty_score":0.5637762},"labels":[],"label_agreement":null},{"id":"W4409769779","doi":"10.1007/s11548-025-03367-4","title":"The quiet eye phenomenon in minimally invasive surgery","year":2025,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"QUIET; Phenomenon; Invasive surgery; Medicine; Optometry; Computer science; Surgery; General surgery; Astronomy; Philosophy","score_opus":0.014492118000147394,"score_gpt":0.26130379389455055,"score_spread":0.24681167589440317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409769779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89510405,0.001774196,0.07156575,0.026184414,0.0051204637,0.000048466663,0.0000012923111,0.000039063296,0.00016231145],"genre_scores_gemma":[0.995628,0.00052022043,0.0026098886,0.0010233648,0.00016154867,0.0000035802384,0.0000013676533,0.000004033836,0.00004797663],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981878,0.00042357316,0.0007467616,0.00021529659,0.0001910212,0.0002355553],"domain_scores_gemma":[0.99203783,0.006991612,0.00041126524,0.00018499803,0.0003294549,0.00004486268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001979342,0.00013865513,0.00041878744,0.0007999089,0.00010497006,0.00013270561,0.0008775475,0.00012477496,0.0000018993262],"category_scores_gemma":[0.00041122394,0.00010055207,0.00017667453,0.00030109833,0.00023355694,0.00022589561,0.00019542027,0.00035239896,0.0000018134037],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006107189,0.00025697716,0.57433933,0.000017754699,0.00087545277,0.0014258653,0.00028740978,0.00015293896,0.00093486643,0.017168963,0.025437664,0.37849206],"study_design_scores_gemma":[0.0003948628,0.00006057797,0.9835902,0.0002101372,0.000009367741,0.00070994115,0.000033478602,0.0011523104,0.0002753488,0.004218299,0.009205079,0.00014041494],"about_ca_topic_score_codex":0.0000045608267,"about_ca_topic_score_gemma":0.000010955921,"teacher_disagreement_score":0.40925086,"about_ca_system_score_codex":0.00009126808,"about_ca_system_score_gemma":0.00040041242,"threshold_uncertainty_score":0.41003945},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":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","score_opus":0.021798993320181283,"score_gpt":0.333864495318591,"score_spread":0.31206550199840977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409884738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15700372,0.000044996275,0.8275562,0.009292967,0.00041192494,0.00022027761,0.0000014357639,0.0013017952,0.0041667093],"genre_scores_gemma":[0.9107985,8.4141965e-7,0.085413,0.0019159714,0.00006253822,0.000044674038,9.044124e-7,0.000008586711,0.0017550099],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986442,0.00004359336,0.0001989475,0.000519809,0.00015925891,0.0004342118],"domain_scores_gemma":[0.9994386,0.00007212798,0.00005892322,0.0002651262,0.000116167714,0.000049077513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003226896,0.00016110015,0.00021794043,0.00024557067,0.00047619262,0.00019267273,0.000858504,0.0001509933,0.0000126827],"category_scores_gemma":[0.00009071827,0.00015946671,0.00009828529,0.00081323285,0.00006850905,0.00018145332,0.00025155817,0.00023842705,0.00009611883],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007836932,0.00029932157,0.010388282,0.000069550835,0.00008885133,0.000023576888,0.009254601,0.00014167087,0.030499486,0.15655087,0.01574698,0.77692896],"study_design_scores_gemma":[0.006060016,0.0005814583,0.43079466,0.00094362145,0.0001593323,0.000018641285,0.028300261,0.13240424,0.24563062,0.013523434,0.13699259,0.004591121],"about_ca_topic_score_codex":0.000062649895,"about_ca_topic_score_gemma":0.000021029406,"teacher_disagreement_score":0.77233785,"about_ca_system_score_codex":0.00010064536,"about_ca_system_score_gemma":0.00007120679,"threshold_uncertainty_score":0.6502864},"labels":[],"label_agreement":null},{"id":"W4409884910","doi":"10.1145/3706598.3713777","title":"A Multimodal Approach for Targeting Error Detection in Virtual Reality Using Implicit User Behavior","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Virtual reality; Human–computer interaction; Augmented reality; Artificial intelligence; Computer vision","score_opus":0.03731748132287863,"score_gpt":0.3204038280260479,"score_spread":0.28308634670316923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409884910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3218151,0.0000052680284,0.6773684,0.000088533765,0.00009778022,0.00023715531,0.0000015055689,0.00021031173,0.00017592855],"genre_scores_gemma":[0.823345,2.0069963e-7,0.17639294,0.00005098806,0.000012407698,0.000095296185,0.0000020074726,0.0000046005093,0.00009654604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893725,0.000043317556,0.00023447258,0.0004310796,0.000074104435,0.0002797808],"domain_scores_gemma":[0.99952805,0.00006280308,0.000055621174,0.0002682793,0.00006215769,0.000023102755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003750561,0.00011186041,0.00015892168,0.00022120685,0.00013291619,0.000052435924,0.00036275026,0.00012833286,0.0000010823292],"category_scores_gemma":[0.0000824208,0.00010541984,0.00005700852,0.00046794227,0.00003708428,0.00016920481,0.0001596611,0.00016024729,7.7561606e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000088930145,0.0013998799,0.06477093,0.00013318956,0.00005235983,0.000012332612,0.00047362683,0.010620992,0.3578577,0.10911553,0.00023500563,0.45523953],"study_design_scores_gemma":[0.00056796655,0.000055303357,0.039738785,0.000010469097,0.000010850793,0.0000041479047,0.00014296979,0.9237925,0.03482665,0.0005522513,0.00013652418,0.0001615966],"about_ca_topic_score_codex":0.00053910236,"about_ca_topic_score_gemma":0.00010411458,"teacher_disagreement_score":0.91317147,"about_ca_system_score_codex":0.000109272274,"about_ca_system_score_gemma":0.00004169006,"threshold_uncertainty_score":0.42988962},"labels":[],"label_agreement":null},{"id":"W4409885299","doi":"10.1145/3706598.3713781","title":"There Is More to Dwell Than Meets the Eye: Toward Better Gaze-Based Text Entry Systems With Multi-Threshold Dwell","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Simon Fraser University","funders":"","keywords":"Dwell time; Computer science; Gaze; Eye tracking; Artificial intelligence; Computer vision; Psychology","score_opus":0.01663622418376365,"score_gpt":0.26027966771060734,"score_spread":0.2436434435268437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409885299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111409515,0.0007590942,0.7557315,0.1182892,0.00069602096,0.0010080552,0.000012605246,0.0012632214,0.010830753],"genre_scores_gemma":[0.97395533,0.0000048501943,0.009593507,0.0077520143,0.00003780599,0.000085411164,0.0000015149441,0.000017274026,0.008552284],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981603,0.00006817449,0.0002561358,0.0007014346,0.00031755836,0.00049636536],"domain_scores_gemma":[0.99828523,0.00013056242,0.0000799673,0.0012924911,0.0001234981,0.00008825134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022100627,0.00029846554,0.0002854514,0.00018405328,0.00019294427,0.00027183862,0.0017960523,0.00015113837,0.00003205952],"category_scores_gemma":[0.000017889823,0.00016854142,0.000089419926,0.00072535046,0.00014902934,0.000109336856,0.0003117778,0.00031314354,0.00019149706],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024861292,0.001850687,0.5194238,0.0007010898,0.0009948063,0.00052364613,0.004023573,0.010075604,0.0061753304,0.14119557,0.22748947,0.0872978],"study_design_scores_gemma":[0.0053280834,0.0009152267,0.21209632,0.0016183411,0.00017611563,0.000030455742,0.0020526217,0.3909893,0.09537534,0.00069668394,0.2884324,0.0022891497],"about_ca_topic_score_codex":0.00023665061,"about_ca_topic_score_gemma":0.00005966439,"teacher_disagreement_score":0.86254585,"about_ca_system_score_codex":0.000060492024,"about_ca_system_score_gemma":0.00009853081,"threshold_uncertainty_score":0.687292},"labels":[],"label_agreement":null},{"id":"W4410075904","doi":"10.1080/07370024.2025.2497236","title":"The effects of dynamic dwell time systems on the usability of eye-tracking technology: a systematic review and meta-analyses","year":2025,"lang":"en","type":"review","venue":"Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Usability; Dwell time; Eye tracking; Computer science; Tracking (education); Meta-analysis; Human–computer interaction; Psychology; Artificial intelligence; Medicine; Internal medicine","score_opus":0.06541416550578848,"score_gpt":0.40415362575315567,"score_spread":0.3387394602473672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410075904","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000017895456,0.98572403,0.010493721,0.00024335038,0.00044312194,0.0028366328,0.0000056918425,0.00017465805,0.00006089206],"genre_scores_gemma":[0.0051818932,0.99305946,0.00038977055,0.000064486005,0.000023067336,0.0008080724,0.000006595446,0.000026558386,0.0004400852],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.9955443,0.0014671375,0.001698424,0.0007150919,0.0003190917,0.00025593053],"domain_scores_gemma":[0.99025136,0.0050095054,0.0023420365,0.0020907205,0.00028198835,0.000024408439],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001599767,0.00053743634,0.004187889,0.00052051333,0.00025929467,0.00016190232,0.0020029217,0.0002741252,0.0000031454945],"category_scores_gemma":[0.0007078438,0.00025453098,0.00093808735,0.0009291036,0.00027602777,0.00014301058,0.00051651534,0.00075546355,0.000019401898],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[8.0217643e-7,0.00009592774,3.302722e-7,0.9447856,0.004757804,0.0000044788267,0.000028058497,0.000003146523,0.000014826025,0.00802029,0.00023639147,0.042052366],"study_design_scores_gemma":[0.00016147325,0.0006271446,0.000014489246,0.89446914,0.08939327,0.00015521767,0.000027990944,0.0039168745,0.00013482608,0.0010195405,0.009311778,0.00076822634],"about_ca_topic_score_codex":0.000014267925,"about_ca_topic_score_gemma":0.0000029722733,"teacher_disagreement_score":0.084635474,"about_ca_system_score_codex":0.00014357119,"about_ca_system_score_gemma":0.000059493614,"threshold_uncertainty_score":0.9999907},"labels":[],"label_agreement":null},{"id":"W4410191170","doi":"10.1145/3723010.3723018","title":"A Cookbook for Eye Tracking in Software Engineering","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Bundesministerium für Bildung und Forschung","keywords":"Computer science; Eye tracking; Software; Software engineering; Tracking (education); Artificial intelligence; Programming language","score_opus":0.010558228752490238,"score_gpt":0.27320704844849514,"score_spread":0.2626488196960049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410191170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016571093,0.000058666756,0.9812505,0.00081210834,0.00013667704,0.00010565348,3.2223352e-7,0.0005316195,0.00053335354],"genre_scores_gemma":[0.7998975,9.651455e-7,0.19906163,0.0001851424,0.0000062395675,0.00003709566,3.5720302e-7,0.0000032356147,0.00080785755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999461,0.0000044357985,0.000112129725,0.0002001589,0.00003676937,0.00018551953],"domain_scores_gemma":[0.9996249,0.00012827253,0.000014049679,0.00019098962,0.000028975155,0.000012778036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011257286,0.00006654483,0.00009823807,0.00020215215,0.000029190574,0.00003906494,0.0003652024,0.000052793133,0.0000018160656],"category_scores_gemma":[0.00016248062,0.000064324806,0.000031069303,0.00028286225,0.000010527519,0.00009676556,0.000071853254,0.0000833821,0.000003934018],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000040808095,0.000068225185,0.031946864,0.00007808689,0.00001695945,0.000012091019,0.00014791456,0.0010914039,0.0017741075,0.80196357,0.0009992078,0.16189751],"study_design_scores_gemma":[0.0024447555,0.00015134124,0.3973014,0.0005036241,0.000012509481,0.0000068348113,0.000069375536,0.4825682,0.03613714,0.030291183,0.04975792,0.0007556917],"about_ca_topic_score_codex":0.000007981529,"about_ca_topic_score_gemma":0.00001435939,"teacher_disagreement_score":0.7833264,"about_ca_system_score_codex":0.000033869823,"about_ca_system_score_gemma":0.000028286664,"threshold_uncertainty_score":0.26230893},"labels":[],"label_agreement":null},{"id":"W4410236849","doi":"10.1145/3723498.3723695","title":"Is the Jedi Force Pull Method Effective? Evaluating Eye-Tracked Object Grabbing in VR","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Computer vision; Object (grammar); Computer graphics (images); Artificial intelligence","score_opus":0.022144172131273614,"score_gpt":0.3738701120762566,"score_spread":0.35172593994498297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410236849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07105594,0.00016949681,0.9043214,0.0062412014,0.0003041208,0.0004385904,4.1858118e-7,0.0004273236,0.017041517],"genre_scores_gemma":[0.9011759,0.0000032851308,0.095598295,0.0015056565,0.000017064256,0.00008933847,3.4589675e-7,0.0000065686827,0.001603536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815357,0.00042758335,0.0002650558,0.00054374675,0.00021706978,0.0003929528],"domain_scores_gemma":[0.9979171,0.0012409927,0.00007974498,0.00065003993,0.0000886881,0.000023415292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024363038,0.00016854957,0.00023253754,0.00025635125,0.00022063547,0.0001211309,0.0011016252,0.000107067026,0.000008898815],"category_scores_gemma":[0.00050196075,0.000114821836,0.000094615505,0.0012807347,0.00006946151,0.00017495817,0.00039724968,0.00041669034,0.000027140295],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008025067,0.00005869349,0.01001787,0.000029682275,0.00004518151,0.00000852553,0.0007895807,0.00019771724,0.012261538,0.16636965,0.0005269594,0.8096866],"study_design_scores_gemma":[0.0012826722,0.00025032583,0.19741441,0.00022625102,0.000041834217,0.000010703817,0.0005128622,0.5128927,0.1163657,0.16890508,0.0016275581,0.0004698748],"about_ca_topic_score_codex":0.0001445776,"about_ca_topic_score_gemma":0.000060511808,"teacher_disagreement_score":0.83011997,"about_ca_system_score_codex":0.00008638819,"about_ca_system_score_gemma":0.00006929191,"threshold_uncertainty_score":0.46822986},"labels":[],"label_agreement":null},{"id":"W4410611083","doi":"10.1145/3725833","title":"EarEOG: Using Headphones and Around-the-Ear EOG Signals for Real-Time Wheelchair Control","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Headphones; Wheelchair; Computer science; Electrooculography; Audiology; Speech recognition; Physical medicine and rehabilitation; Medicine; Engineering; Artificial intelligence; Eye movement; Electrical engineering","score_opus":0.03749657495785841,"score_gpt":0.3230160874259,"score_spread":0.2855195124680416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410611083","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93759584,0.000027621667,0.05108172,0.00928169,0.0007602274,0.00058535056,0.0000030541216,0.000245869,0.0004186456],"genre_scores_gemma":[0.9868407,0.0000041507788,0.01224846,0.00045466694,0.00015701429,0.000033570293,4.3467688e-7,0.000011807368,0.0002491883],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880034,0.000020991352,0.00033360548,0.0004329642,0.0001707867,0.00024133912],"domain_scores_gemma":[0.99851906,0.00035181924,0.00034198345,0.0004541923,0.000307594,0.000025351352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041098503,0.000191995,0.00028247357,0.0002221865,0.0004304989,0.00025864702,0.0016799876,0.00009116701,0.0000020043622],"category_scores_gemma":[0.00016087024,0.0001313307,0.00013236552,0.0002724606,0.00012717494,0.00040165256,0.0005990065,0.00023421358,0.000003884172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002793109,0.00042199792,0.0038137524,0.00037153895,0.00045112794,0.000001153957,0.001877054,0.0006690823,0.81770796,0.10186841,0.016734919,0.055803705],"study_design_scores_gemma":[0.0029846362,0.0012339638,0.02761473,0.0018680491,0.00020305761,0.00009166457,0.0004323251,0.51878154,0.3139475,0.12863441,0.0034778458,0.0007302739],"about_ca_topic_score_codex":0.00004987962,"about_ca_topic_score_gemma":0.0000013491682,"teacher_disagreement_score":0.5181125,"about_ca_system_score_codex":0.00008062534,"about_ca_system_score_gemma":0.000020040754,"threshold_uncertainty_score":0.5355511},"labels":[],"label_agreement":null},{"id":"W4410611104","doi":"10.1145/3725842","title":"The Influence of Eye Gaze Interaction Technique Expertise and the Guided Evaluation Method on Text Entry Performance Evaluations","year":2025,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Simon Fraser University","funders":"","keywords":"Gaze; Eye tracking; Computer science; Human–computer interaction; Artificial intelligence","score_opus":0.045133733543855255,"score_gpt":0.39685029202287464,"score_spread":0.3517165584790194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410611104","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9653174,0.00004198663,0.021931108,0.00864756,0.00075410045,0.0011198272,6.7128974e-7,0.00012252503,0.0020647878],"genre_scores_gemma":[0.99219906,0.00003770696,0.0069779665,0.0002919905,0.00005179077,0.00030132185,5.512096e-7,0.000008154101,0.00013148278],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982102,0.00015165727,0.000557913,0.00041435915,0.0004860439,0.00017983915],"domain_scores_gemma":[0.9969241,0.00068927975,0.0006953815,0.0008060166,0.0008669792,0.000018214641],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023554603,0.00019646024,0.00022811494,0.000265117,0.0005589059,0.00017153371,0.0020182624,0.00009510852,0.0000027896267],"category_scores_gemma":[0.0010063131,0.000110120505,0.00011180942,0.0004944537,0.00024122451,0.0005675271,0.0007702689,0.00047017838,0.0000032820828],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005082901,0.0003793563,0.0022913485,0.00018458953,0.000239536,1.5947906e-7,0.003558699,0.007806046,0.14375691,0.16638468,0.0055110306,0.66937935],"study_design_scores_gemma":[0.0017101169,0.00053495285,0.09356592,0.0019971312,0.0001285725,0.00002462709,0.00031097035,0.32050505,0.5444184,0.035393078,0.001139582,0.0002716001],"about_ca_topic_score_codex":0.000029668274,"about_ca_topic_score_gemma":0.0000025297818,"teacher_disagreement_score":0.66910774,"about_ca_system_score_codex":0.00019299606,"about_ca_system_score_gemma":0.00004122854,"threshold_uncertainty_score":0.4490584},"labels":[],"label_agreement":null},{"id":"W4410701915","doi":"10.1145/3715669.3726785","title":"Learning Disorder Detection Using Eye Tracking: Are Large Language Models Better Than Machine Learning?","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Computer science; Artificial intelligence; Eye tracking; Machine learning; Tracking (education); Natural language processing; Psychology","score_opus":0.012325986925172581,"score_gpt":0.2681987318658679,"score_spread":0.25587274494069534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410701915","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45614064,0.00012656108,0.5417183,0.0004738689,0.00009816989,0.000050993593,4.692851e-7,0.0006793676,0.000711632],"genre_scores_gemma":[0.9911553,0.00000553773,0.006561531,0.00022703354,0.00002618946,0.0000071078853,0.0000028883678,0.000015133295,0.001999247],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861974,0.00013070385,0.00020084386,0.00048030395,0.00016076061,0.00040765814],"domain_scores_gemma":[0.9993932,0.000058125905,0.00011444231,0.0003278038,0.000069562644,0.000036867008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028820397,0.00019049831,0.00021143374,0.0003066626,0.0004288904,0.00014933912,0.00046210436,0.00014666219,0.000017936889],"category_scores_gemma":[0.00008688169,0.00017260273,0.000088662535,0.0006108164,0.00003830931,0.00037095122,0.00028555025,0.0006812958,0.00001716964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000191072,0.00031054116,0.668054,0.00007860546,0.00012336581,0.000072750125,0.0023269586,0.034819808,0.019280983,0.017584223,0.000034370074,0.2572953],"study_design_scores_gemma":[0.00041129522,0.00005604819,0.026492972,0.000047568286,0.000014441089,0.000006178029,0.00050897483,0.9632502,0.0061425297,0.0015038927,0.0013345265,0.00023134002],"about_ca_topic_score_codex":0.00027293956,"about_ca_topic_score_gemma":0.0003015461,"teacher_disagreement_score":0.92843044,"about_ca_system_score_codex":0.000054109692,"about_ca_system_score_gemma":0.000019732888,"threshold_uncertainty_score":0.7038535},"labels":[],"label_agreement":null},{"id":"W4410854979","doi":"10.1007/978-3-031-91760-8_16","title":"Experience 2.0: Mixed Realities and Beyond","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Human–computer interaction; Data science","score_opus":0.014249541712676759,"score_gpt":0.24929313067702547,"score_spread":0.2350435889643487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410854979","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004007032,0.00070892205,0.9844852,0.001690017,0.0013224151,0.00018090667,0.000005466445,0.00033337084,0.0108729815],"genre_scores_gemma":[0.49463814,0.00019585066,0.4996652,0.002314333,0.00020618105,0.00002468604,0.00000420356,0.000027726212,0.0029236784],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969402,0.000027343089,0.0003946232,0.0015368793,0.0005269438,0.0005740284],"domain_scores_gemma":[0.9978122,0.00047052535,0.0001739325,0.0012664278,0.0001675437,0.00010937856],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004606867,0.00044844006,0.00049833464,0.0009091772,0.00028998542,0.00043058296,0.0026820465,0.00034900234,0.0000068424406],"category_scores_gemma":[0.00013296651,0.00041470106,0.000070363356,0.000528005,0.0016592834,0.00038112805,0.0018480893,0.0006970231,0.0000089023615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018832782,0.000011232018,0.00014294934,0.000029241823,0.000006305683,0.00006122925,0.0007500665,0.00016662551,0.00006505676,0.23869765,0.000068458416,0.7599993],"study_design_scores_gemma":[0.00043446105,0.00028184062,0.0017850583,0.00076044886,0.0000146749835,0.00017396854,0.000002888422,0.11029219,0.005058895,0.870253,0.009546815,0.0013957173],"about_ca_topic_score_codex":0.000030357865,"about_ca_topic_score_gemma":0.00006278417,"teacher_disagreement_score":0.7586036,"about_ca_system_score_codex":0.00014340656,"about_ca_system_score_gemma":0.00034066837,"threshold_uncertainty_score":0.9998305},"labels":[],"label_agreement":null},{"id":"W4410878274","doi":"10.1016/j.buildenv.2025.113244","title":"Consistency is key: Evaluation of and recommendations for thermostat usability testing","year":2025,"lang":"en","type":"article","venue":"Building and Environment","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Usability; Key (lock); Consistency (knowledge bases); Thermostat; Computer science; Engineering; Reliability engineering; Human–computer interaction; Computer security; Mechanical engineering; Artificial intelligence","score_opus":0.06742124919904682,"score_gpt":0.31642351654369444,"score_spread":0.24900226734464762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410878274","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.747233,0.0004501184,0.2432925,0.0083481455,0.00004818141,0.0002242969,0.000006057268,0.000035982688,0.00036177028],"genre_scores_gemma":[0.8880513,0.0000338328,0.111790664,0.00007278218,0.0000024190942,0.000026816571,6.113367e-7,0.0000014811365,0.000020144123],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9994696,0.000035546807,0.00012841981,0.00021470348,0.00007176897,0.00008001138],"domain_scores_gemma":[0.99952734,0.00021535142,0.000052365096,0.00016704797,0.00002341916,0.000014509766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000628271,0.000053224525,0.000081853155,0.000040608644,0.000120823795,0.000016076941,0.00007885981,0.000029051263,0.0000031833572],"category_scores_gemma":[0.00013631079,0.00004954999,0.000013258792,0.000059972055,0.00008744293,0.000043832544,0.00009083845,0.000034812154,2.2818695e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000015771393,0.000042024585,0.019370282,0.000024100216,0.000014911745,4.0981327e-8,0.00018590946,0.00006119129,0.003683773,0.019034525,0.00016259856,0.95741904],"study_design_scores_gemma":[0.0015372573,0.00025486478,0.46570623,0.00020886307,0.00015857402,0.0000054883017,0.00032025497,0.28235617,0.017581863,0.2213848,0.010195492,0.00029015992],"about_ca_topic_score_codex":0.000015430076,"about_ca_topic_score_gemma":6.3052136e-7,"teacher_disagreement_score":0.9571289,"about_ca_system_score_codex":0.000031889096,"about_ca_system_score_gemma":0.000017306515,"threshold_uncertainty_score":0.202059},"labels":[],"label_agreement":null},{"id":"W4411171967","doi":"10.1109/iccit64611.2024.11022078","title":"Low Latency Single-Cycle EOG Classification Using Cascaded ANN &amp; CNN","year":2024,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Latency (audio); Electrooculography; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Telecommunications; Eye movement","score_opus":0.07332535835734232,"score_gpt":0.2957951005062063,"score_spread":0.22246974214886395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411171967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23561087,0.00016697556,0.7540639,0.0026541543,0.00057400943,0.00007461951,0.0000012083445,0.0016731926,0.0051810737],"genre_scores_gemma":[0.94779646,0.0000053376084,0.050808482,0.000100805104,0.000057735808,0.0000043025775,0.0000027354786,0.0000114926825,0.0012126358],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988715,0.000034861798,0.00019396967,0.00047120167,0.00015740114,0.00027106132],"domain_scores_gemma":[0.9993182,0.000061565,0.00003812144,0.00047254702,0.000057841047,0.00005171701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001666194,0.0001259928,0.00011675518,0.00019693066,0.00011048734,0.00025324858,0.0004783715,0.00012109392,0.00003816237],"category_scores_gemma":[0.000035982193,0.00010845203,0.000058956713,0.00064745644,0.00006851731,0.00036180447,0.00011863294,0.0001930477,0.0004279601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000036244505,0.00029918348,0.001296978,0.000096333875,0.000058737496,0.000090268644,0.0006677826,0.00024908344,0.37616873,0.2888228,0.003877404,0.3283691],"study_design_scores_gemma":[0.00027025456,0.00011639335,0.012777041,0.0002344057,0.000030469451,0.00020296565,0.00007017154,0.88678306,0.0455922,0.031386416,0.021844788,0.0006918258],"about_ca_topic_score_codex":0.000046712576,"about_ca_topic_score_gemma":0.000020824187,"teacher_disagreement_score":0.886534,"about_ca_system_score_codex":0.00009802975,"about_ca_system_score_gemma":0.000057191803,"threshold_uncertainty_score":0.5500704},"labels":[],"label_agreement":null},{"id":"W4411400819","doi":"10.31234/osf.io/y5jmg_v1","title":"WITHDRAWN","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs; Max-Planck-Gesellschaft; University of Toronto","keywords":"Python (programming language); Computer science; Eye tracking; Preprocessor; Computer vision; Artificial intelligence; Eye movement; Computer graphics (images)","score_opus":0.01485314170935114,"score_gpt":0.26745204457696814,"score_spread":0.252598902867617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411400819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005573176,0.00008829536,0.8459425,0.0040354803,0.0007666695,0.000072823146,0.000002282862,0.0013723577,0.14716226],"genre_scores_gemma":[0.64547235,0.000017533548,0.32265535,0.00079897104,0.000046265395,0.000040529027,0.000004010069,0.0000045121924,0.030960457],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99912584,0.000020271402,0.00012390625,0.00047366475,0.00009674991,0.00015955693],"domain_scores_gemma":[0.9988235,0.000032089312,0.0000525557,0.0010091292,0.000058546495,0.000024156885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009085107,0.00013445751,0.00017253724,0.00015960317,0.000043642467,0.00011586906,0.00172984,0.00022544559,0.000014616494],"category_scores_gemma":[0.000023247267,0.00011591148,0.000065686654,0.00015640265,0.00004268787,0.000033621374,0.002710806,0.00045901744,0.00006404776],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[4.1497867e-7,0.00003201074,0.00054521416,0.00003847484,0.00002714676,0.000012323186,0.000037689566,0.00006802157,0.000014387541,0.90563136,0.01198458,0.08160838],"study_design_scores_gemma":[0.00042149384,0.000080244674,0.016476296,0.00048646584,0.0000355439,0.000018554998,0.000018959712,0.06342983,0.008672017,0.7962831,0.11284693,0.001230521],"about_ca_topic_score_codex":0.00004711492,"about_ca_topic_score_gemma":0.000009821806,"teacher_disagreement_score":0.64491504,"about_ca_system_score_codex":0.000027339505,"about_ca_system_score_gemma":0.00016753204,"threshold_uncertainty_score":0.47267333},"labels":[],"label_agreement":null},{"id":"W4411407833","doi":"10.57041/cf3geg55","title":"Integration of Hand Gesture and Voice Command Control for Smart Wheelchairs","year":2024,"lang":"en","type":"article","venue":"International Journal of Emerging Engineering and Technology","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Government College University, Lahore","keywords":"Wheelchair; Gesture; Gesture recognition; Computer science; Human–computer interaction; Control (management); Assistive technology; Independence (probability theory); Limit (mathematics); Architecture; Artificial intelligence","score_opus":0.006327302762683061,"score_gpt":0.24311639675827112,"score_spread":0.23678909399558806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411407833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1837042,0.0023529811,0.8053356,0.0077178976,0.00074282615,0.000038752805,0.000004381169,0.0000927225,0.000010648255],"genre_scores_gemma":[0.98223317,0.00010050522,0.017573757,0.00001448988,0.00005186838,0.0000034577242,5.3042595e-7,0.0000060502816,0.000016163554],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994869,0.000004903963,0.00021791433,0.000110402354,0.000094342024,0.00008551992],"domain_scores_gemma":[0.99947023,0.00012564893,0.00008484993,0.000069801055,0.00022957157,0.000019891975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019030331,0.00007776731,0.0001499657,0.0006336261,0.0000235994,0.000060088067,0.00027345933,0.00008753225,4.0879425e-7],"category_scores_gemma":[0.00014441955,0.00006555627,0.000033342916,0.00014398588,0.000065503045,0.00013876638,0.000049165756,0.000201853,1.6422084e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034759076,0.000053292344,0.0016983806,0.0001275156,0.00058478006,0.00009183446,0.00063239486,0.00086774374,0.2685382,0.4587438,0.00061520195,0.26801208],"study_design_scores_gemma":[0.0037475163,0.0013590165,0.007998601,0.0021613298,0.00015285722,0.0031031289,0.00032111045,0.76897085,0.08944113,0.049348008,0.07281848,0.0005779692],"about_ca_topic_score_codex":0.00000334746,"about_ca_topic_score_gemma":0.0000024130834,"teacher_disagreement_score":0.79852897,"about_ca_system_score_codex":0.000016823527,"about_ca_system_score_gemma":0.000018238268,"threshold_uncertainty_score":0.2673307},"labels":[],"label_agreement":null},{"id":"W4412353197","doi":"10.1109/icorr66766.2025.11063037","title":"Head Tracking for Confirming User Selection for Human-Machine Interaction","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Head (geology); Selection (genetic algorithm); Tracking (education); Human–computer interaction; Artificial intelligence; Geology","score_opus":0.04193764823084896,"score_gpt":0.37696200055716944,"score_spread":0.3350243523263205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412353197","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020494109,0.00001817218,0.97473186,0.0018166183,0.00044135356,0.00031098837,0.0000013187678,0.0005199062,0.0016656508],"genre_scores_gemma":[0.942267,6.117922e-7,0.05510539,0.00025647695,0.000034718112,0.00010621599,0.0000045353736,0.000005544966,0.0022194858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992843,0.000013552798,0.00017292137,0.0002952164,0.000044251752,0.0001897419],"domain_scores_gemma":[0.9994273,0.00020742908,0.000059854,0.00015145038,0.00013863052,0.000015289485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019776364,0.00009173522,0.00012378104,0.00019081359,0.00027711538,0.00010181632,0.0002485852,0.00007142519,0.0000051991447],"category_scores_gemma":[0.00007212314,0.000086146625,0.0000710536,0.00021744768,0.00001728861,0.00023961045,0.000039514674,0.000099469034,0.0000023738985],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001577387,0.00006994762,0.0022731768,0.00004199718,0.00003141301,1.7092765e-7,0.000044591547,0.000061930856,0.024149263,0.79522747,0.0015728123,0.17651145],"study_design_scores_gemma":[0.0028843528,0.00073485274,0.011487594,0.00021625943,0.00005891698,0.000019580712,0.00018014468,0.34419873,0.33145294,0.07731053,0.2308925,0.0005636042],"about_ca_topic_score_codex":0.00008408341,"about_ca_topic_score_gemma":0.00037952056,"teacher_disagreement_score":0.9217729,"about_ca_system_score_codex":0.000056800258,"about_ca_system_score_gemma":0.000023487113,"threshold_uncertainty_score":0.35129574},"labels":[],"label_agreement":null},{"id":"W4412439205","doi":"10.1167/jov.25.9.2476","title":"Emotional gaze increases target temporal processing","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Gaze; Psychology; Cognitive psychology; Computer science; Computer vision","score_opus":0.010632454066765119,"score_gpt":0.29242137123616513,"score_spread":0.2817889171694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412439205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24400194,0.0008664994,0.7489254,0.0048366995,0.0003570447,0.000030419633,7.182398e-7,0.000072271396,0.0009090269],"genre_scores_gemma":[0.9184434,0.000008421378,0.08123303,0.00015225922,0.000051561663,2.4481508e-7,3.678162e-7,0.0000020505954,0.00010866321],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999278,0.000039985694,0.0002647787,0.00010469332,0.00020567086,0.000106863234],"domain_scores_gemma":[0.9993593,0.00004930717,0.00021868174,0.0001137515,0.00022305302,0.000035882967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003594289,0.00006532973,0.00013592397,0.00027088675,0.00009379317,0.000079904436,0.0004432861,0.000050618502,0.000008192863],"category_scores_gemma":[0.00012573953,0.000049294846,0.0000630901,0.00032725697,0.000038000835,0.00036160045,0.00009749804,0.00019152899,0.000004593361],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000087465305,0.0012296133,0.10882406,0.00011909072,0.0000749218,0.00030027746,0.0001700544,0.0003711403,0.016305007,0.04605796,0.040476624,0.7859838],"study_design_scores_gemma":[0.0011795795,0.000627394,0.8850628,0.0014200204,0.000018859431,0.00036033298,0.00005299588,0.022159664,0.004091823,0.06394117,0.020873388,0.00021193946],"about_ca_topic_score_codex":0.00000433582,"about_ca_topic_score_gemma":6.12771e-7,"teacher_disagreement_score":0.78577185,"about_ca_system_score_codex":0.000036998328,"about_ca_system_score_gemma":0.00017131795,"threshold_uncertainty_score":0.20101856},"labels":[],"label_agreement":null},{"id":"W4412439264","doi":"10.1167/jov.25.9.2649","title":"Fading enhancement? Exploring the impact of touch timing on target enhancement in multiple-object tracking (MOT)","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Fading; Computer science; Tracking (education); Object (grammar); Computer vision; Psychology; Artificial intelligence; Telecommunications","score_opus":0.049813308935423795,"score_gpt":0.3384932096265209,"score_spread":0.2886799006910971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412439264","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7932449,0.00011405165,0.2055024,0.00038924374,0.00037274254,0.000079962214,2.4938956e-7,0.000013333376,0.00028309622],"genre_scores_gemma":[0.99211836,0.00007207004,0.0077059274,0.000032415424,0.000035517296,0.000003831358,1.6994173e-7,0.000005076232,0.000026626476],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985603,0.00009420906,0.0005971275,0.00018666282,0.0003089839,0.0002526834],"domain_scores_gemma":[0.99875003,0.00037766108,0.00042923063,0.0002832037,0.00012985528,0.000029999186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008227389,0.00013833109,0.00029271867,0.00053520064,0.00011231194,0.00006320221,0.0007070932,0.000045611592,0.000008412209],"category_scores_gemma":[0.00026415646,0.00008814454,0.00017243199,0.00057711994,0.00003522266,0.00041106474,0.00014123651,0.00040463955,0.0000025374484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017475455,0.0006870927,0.01818423,0.00004552653,0.00012795704,0.000057783895,0.002158075,0.010955991,0.46724674,0.0016209624,0.0003770902,0.49836382],"study_design_scores_gemma":[0.0016557862,0.0021728296,0.23749433,0.0028940483,0.000016885257,0.00002046757,0.0003294378,0.042869825,0.7096351,0.002478022,0.00020842288,0.00022488297],"about_ca_topic_score_codex":0.000026359334,"about_ca_topic_score_gemma":0.0000031406219,"teacher_disagreement_score":0.49813893,"about_ca_system_score_codex":0.00025216583,"about_ca_system_score_gemma":0.00008868687,"threshold_uncertainty_score":0.35944298},"labels":[],"label_agreement":null},{"id":"W4412445379","doi":"10.1109/tvcg.2025.3589333","title":"Head-EyeK: Head-Eye Coordination and Control Learned in Virtual Reality","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Virtual reality; Head (geology); Optical head-mounted display; Human–computer interaction; Computer graphics (images); Visualization; Artificial intelligence; Computer vision","score_opus":0.022258123491881728,"score_gpt":0.32299334915743244,"score_spread":0.3007352256655507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412445379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04268641,0.00004716402,0.955022,0.0013495015,0.00035391754,0.00019579417,0.0000054634843,0.0002942162,0.00004555103],"genre_scores_gemma":[0.9983475,0.000117234486,0.000327559,0.0010754637,0.000011441065,0.00002353457,0.0000031666225,0.0000075037046,0.000086591834],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986661,0.00019747143,0.0003043307,0.0004803722,0.0001496516,0.00020204314],"domain_scores_gemma":[0.9993726,0.00014649084,0.00006826019,0.00025682274,0.000097317636,0.00005853242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030807473,0.00017702661,0.00023623886,0.0006889852,0.00025115925,0.00014353562,0.00019553902,0.00016929151,0.0000017102831],"category_scores_gemma":[0.0000064541523,0.00018517592,0.00004551287,0.0010487997,0.00014416383,0.00024698384,0.000006677195,0.00025538562,0.0000014924739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036308113,0.0003093826,0.0017135,0.000030440346,0.00003652257,0.0000040410982,0.00031513936,0.0008599137,0.000087819375,0.927841,0.00008101294,0.06868494],"study_design_scores_gemma":[0.001817179,0.00029910522,0.027480954,0.00009422156,0.000016458282,0.0000052828896,0.000031151318,0.96377015,0.0005980131,0.0049047475,0.0007771962,0.00020553663],"about_ca_topic_score_codex":0.00006886729,"about_ca_topic_score_gemma":0.00021465741,"teacher_disagreement_score":0.96291023,"about_ca_system_score_codex":0.00003408586,"about_ca_system_score_gemma":0.00004413917,"threshold_uncertainty_score":0.75512546},"labels":[],"label_agreement":null},{"id":"W4412459047","doi":"10.1167/jov.25.9.1959","title":"Active manipulation promotes predictive gaze strategies during virtual object exploration","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Gaze; Object (grammar); Human–computer interaction; Computer science; Computer vision; Artificial intelligence; Psychology; Cognitive psychology; Communication","score_opus":0.016529973577223904,"score_gpt":0.2900472918823726,"score_spread":0.2735173183051487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412459047","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.56781757,0.00004690285,0.43060166,0.000702997,0.00030663583,0.00006462685,4.2501566e-7,0.00006162241,0.00039753388],"genre_scores_gemma":[0.9962748,0.00002281491,0.003575095,0.0000118388,0.000055520686,0.0000020178195,6.0306434e-7,0.0000034954348,0.000053762316],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991416,0.00007289716,0.00029234783,0.0001508717,0.0002252768,0.00011701884],"domain_scores_gemma":[0.9991919,0.000068556765,0.00030191545,0.00014889752,0.0002634416,0.000025279147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022715892,0.0000922562,0.00015206436,0.00035671258,0.00012342112,0.00013296751,0.00030822816,0.00007669173,0.0000021868311],"category_scores_gemma":[0.00009339962,0.000074965195,0.00006090803,0.00034474273,0.000033806828,0.0018927528,0.00008526013,0.000241942,0.0000032592707],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076743035,0.0008337736,0.0030051847,0.00010095642,0.0003044868,0.00017722507,0.0057796296,0.018000059,0.44361684,0.14578426,0.00062910403,0.38100106],"study_design_scores_gemma":[0.0012780275,0.0020368851,0.83084434,0.0007358295,0.000036089947,0.000073336065,0.0020059107,0.027295303,0.08095341,0.054440405,0.0001046809,0.00019574714],"about_ca_topic_score_codex":0.0000024598264,"about_ca_topic_score_gemma":0.0000030152225,"teacher_disagreement_score":0.8278392,"about_ca_system_score_codex":0.00009984441,"about_ca_system_score_gemma":0.00011328819,"threshold_uncertainty_score":0.3056992},"labels":[],"label_agreement":null},{"id":"W4412524184","doi":"10.1080/10447318.2025.2526581","title":"Gaze2Instruct (G2I): Towards a More Inclusive Language-Conditioned Robotic Assistance for Severe Speech and Motor Impairments","year":2025,"lang":"en","type":"article","venue":"International Journal of Human-Computer Interaction","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychology; Computer science; Speech recognition","score_opus":0.010644290502166876,"score_gpt":0.33436032150181283,"score_spread":0.32371603099964597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412524184","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42616138,0.000078205776,0.5661795,0.003577233,0.0034908873,0.00017005728,0.000012266015,0.00007131166,0.00025913393],"genre_scores_gemma":[0.94540924,0.000008021058,0.05333951,0.0005435867,0.0003741712,0.000011035456,0.000010282354,0.000008821333,0.000295339],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99862677,0.000048177637,0.000523085,0.00028673685,0.00034241015,0.00017282291],"domain_scores_gemma":[0.99840313,0.00013086172,0.0005011523,0.00018162007,0.00073109835,0.00005212017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023555805,0.00017531545,0.0002657863,0.0006077472,0.00013762196,0.0002817541,0.00086386496,0.000084948886,0.0000124587805],"category_scores_gemma":[0.00006768898,0.00016216135,0.00016069793,0.000135697,0.0000628518,0.0008350025,0.00026305998,0.00029110495,0.0000028785719],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000658019,0.000963374,0.003630905,0.00016600003,0.0021817735,0.0009580346,0.004101635,0.0011041651,0.041368473,0.035214804,0.01707569,0.8925771],"study_design_scores_gemma":[0.024032986,0.004815861,0.5643127,0.0051163095,0.0004914445,0.015869344,0.003139906,0.19028883,0.059777495,0.117809,0.011921633,0.0024244962],"about_ca_topic_score_codex":0.000020239268,"about_ca_topic_score_gemma":0.000011164673,"teacher_disagreement_score":0.89015263,"about_ca_system_score_codex":0.00034429386,"about_ca_system_score_gemma":0.00009079294,"threshold_uncertainty_score":0.6612748},"labels":[],"label_agreement":null},{"id":"W4412931375","doi":"10.3390/s25154737","title":"A Preprocessing Pipeline for Pupillometry Signal from Multimodal iMotion Data","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Manitoba","funders":"University of Manitoba","keywords":"Computer science; Pupillometry; Missing data; Artificial intelligence; Pipeline (software); Preprocessor; Data pre-processing; Data quality; Pattern recognition (psychology); Data mining; Computer vision; Pupil; Machine learning; Engineering; Metric (unit)","score_opus":0.03392988752176354,"score_gpt":0.3132383436291774,"score_spread":0.27930845610741384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412931375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15475574,0.000098232675,0.84270436,0.0013617967,0.0002409524,0.00011071917,0.000047014517,0.00035796897,0.00032325037],"genre_scores_gemma":[0.88161415,0.0000019412669,0.11778949,0.00012493701,0.00005783392,0.0000056313806,0.000050598268,0.0000052311098,0.00035021474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988991,0.000028699655,0.0001759591,0.0005942725,0.00009909182,0.00020287602],"domain_scores_gemma":[0.99878997,0.00019788356,0.000068158515,0.0008335441,0.000083733335,0.000026738986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022315505,0.000102884354,0.00014183443,0.0001645615,0.00012489423,0.00008589558,0.0009935752,0.00008976842,0.0000048918123],"category_scores_gemma":[0.0002521271,0.00009685345,0.000033367985,0.00040080122,0.000051591673,0.00018652996,0.00036210672,0.00012036226,0.000013363692],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030835014,0.00018003551,0.0056411913,0.00005409428,0.00005673723,0.000009939173,0.00016659955,0.00069621246,0.010846536,0.0061879307,0.0035018933,0.972628],"study_design_scores_gemma":[0.00048173772,0.000019184305,0.0072907726,0.00006143326,0.000016840675,0.0000016492746,0.000046403875,0.965163,0.009920299,0.014266207,0.0026002938,0.00013220645],"about_ca_topic_score_codex":0.000064293556,"about_ca_topic_score_gemma":0.000012057387,"teacher_disagreement_score":0.9724958,"about_ca_system_score_codex":0.000024114872,"about_ca_system_score_gemma":0.000051005747,"threshold_uncertainty_score":0.39495692},"labels":[],"label_agreement":null},{"id":"W4412947157","doi":"10.1111/jan.70110","title":"Conducting Eye‐Tracking Research in Acute Care: A Scoping Review of Ethical, Feasibility and Acceptability Challenges","year":2025,"lang":"en","type":"review","venue":"Journal of Advanced Nursing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Université de Montréal; Montreal Heart Institute","funders":"Réseau de recherche portant sur les interventions en sciences infirmières du Québec","keywords":"CINAHL; PsycINFO; MEDLINE; Confidentiality; Acute care; Context (archaeology); Informed consent; Psychology; Medicine; Medical education; Nursing; Health care; Psychological intervention; Alternative medicine; Computer science","score_opus":0.3048739437361392,"score_gpt":0.5422027082808548,"score_spread":0.23732876454471563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412947157","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00014361553,0.9952903,0.0012359648,0.0018502054,0.00047162772,0.00083044794,0.000002149699,0.00003040864,0.00014529905],"genre_scores_gemma":[0.0011098941,0.9695557,0.029231066,0.000026160913,0.000049386763,0.000011258141,7.103772e-7,0.000014688954,0.0000011434437],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9946699,0.0016110023,0.0019019613,0.000692533,0.0006373505,0.00048723866],"domain_scores_gemma":[0.9942962,0.0021789616,0.0015669563,0.00078300515,0.0010770954,0.00009776774],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.006503981,0.00032965175,0.0027872652,0.0009224382,0.0001536268,0.00006421942,0.0012458616,0.0005128727,0.000001803747],"category_scores_gemma":[0.0032026747,0.00027773454,0.00037240103,0.0014968724,0.0004869789,0.00046201624,0.00032400983,0.004038046,2.7447894e-7],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000050901476,0.000051083207,0.0000115181265,0.195544,0.000023580522,0.00002518338,0.00033948367,0.0000010944843,0.0000045742845,0.00034389462,0.0000031210604,0.8036474],"study_design_scores_gemma":[0.00025631138,0.00020135927,0.00007550186,0.99377793,0.00014997089,0.00014071434,0.000540918,0.0000027223834,0.000049350372,0.0017744574,0.002821876,0.00020890468],"about_ca_topic_score_codex":0.0000017872574,"about_ca_topic_score_gemma":0.000004970227,"teacher_disagreement_score":0.8034385,"about_ca_system_score_codex":0.00074133294,"about_ca_system_score_gemma":0.0012967876,"threshold_uncertainty_score":0.99996746},"labels":[],"label_agreement":null},{"id":"W4413227771","doi":"10.18280/isi.300613","title":"Gaze-Controlled Arabic Virtual Keyboard: Design and Evaluation of a Novel Layout","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Gaze; Arabic; Computer science; Human–computer interaction; Computer graphics (images); Artificial intelligence; Linguistics","score_opus":0.025778066563495235,"score_gpt":0.2668096289650196,"score_spread":0.24103156240152435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413227771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21800756,0.00010057337,0.7789244,0.000105185325,0.00016611679,0.0004541738,0.00000238255,0.00014445712,0.0020951098],"genre_scores_gemma":[0.9734648,0.0000065141703,0.02630586,0.00007974563,0.0000068376066,0.00009889779,0.0000056104805,0.0000026772911,0.000029045264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998743,0.000121657984,0.0005096961,0.00014062451,0.00031131934,0.00017370438],"domain_scores_gemma":[0.9986568,0.00018412575,0.0002937312,0.0002814137,0.0005570146,0.000026902297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001758814,0.0001291907,0.00027321436,0.00046019474,0.00013232336,0.00013500576,0.00030560145,0.0001118721,0.0000030228919],"category_scores_gemma":[0.0006870301,0.000115727984,0.00004818651,0.0004902361,0.00013535132,0.0014060899,0.00009915838,0.00009780785,0.000006939415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017308771,0.00011552309,0.0015814108,0.00016215295,0.00013248321,5.079828e-7,0.004191621,0.018349327,0.00294112,0.26418445,0.00027529677,0.707893],"study_design_scores_gemma":[0.004868048,0.00015604196,0.01577474,0.00020174624,0.00006249821,0.000010576878,0.00028594464,0.9630718,0.0028269358,0.012475006,0.00011348435,0.00015312782],"about_ca_topic_score_codex":0.000019411133,"about_ca_topic_score_gemma":0.0000020036546,"teacher_disagreement_score":0.94472253,"about_ca_system_score_codex":0.00013065415,"about_ca_system_score_gemma":0.0002010229,"threshold_uncertainty_score":0.47192502},"labels":[],"label_agreement":null},{"id":"W4413277779","doi":"10.1109/taffc.2025.3599859","title":"Leveraging Eye Movement for Instructing Robust Video-Based Facial Expression Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Hubei Key Laboratory of Intelligent Geo-Information Processing; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Facial expression; Computer science; Facial expression recognition; Computer vision; Eye movement; Artificial intelligence; Emotion recognition; Expression (computer science); Speech recognition; Human–computer interaction; Facial recognition system; Feature extraction","score_opus":0.028814937899736638,"score_gpt":0.27376315371822685,"score_spread":0.24494821581849022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413277779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1716171,0.000009026844,0.8252823,0.00035485643,0.0013177925,0.00047022672,0.0000074235772,0.00065805344,0.0002832614],"genre_scores_gemma":[0.8924426,7.9417146e-7,0.10703292,0.00035756113,0.00004142911,0.00007617528,0.0000025816585,0.000014111474,0.000031850475],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982797,0.00013646753,0.00030106225,0.00071161,0.00017653733,0.00039464058],"domain_scores_gemma":[0.9986632,0.0006161085,0.00015054471,0.00033005563,0.00019164332,0.00004844723],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041305128,0.00024883394,0.00025489557,0.00051996566,0.00091102865,0.00012972974,0.00038523323,0.0001332328,0.000004003178],"category_scores_gemma":[0.00004704811,0.00026365975,0.0001775384,0.00062014913,0.00006035443,0.00020510035,0.000010036092,0.00040201165,0.000007621618],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004740819,0.00021239175,0.00020480818,0.00006941373,0.000049400132,0.0000028639329,0.00023146394,0.12703116,0.023539359,0.00026447623,0.00005614995,0.8482911],"study_design_scores_gemma":[0.0014629664,0.00017934533,0.001436514,0.0005490541,0.000025108775,0.0000012849938,0.00013026771,0.4636919,0.529653,0.0025166275,0.00006306125,0.00029085364],"about_ca_topic_score_codex":0.000015618085,"about_ca_topic_score_gemma":0.000006244941,"teacher_disagreement_score":0.8480002,"about_ca_system_score_codex":0.00026676283,"about_ca_system_score_gemma":0.00008198287,"threshold_uncertainty_score":0.9999816},"labels":[],"label_agreement":null},{"id":"W4413288893","doi":"10.1007/s00221-025-07142-4","title":"Exercise intensity improves performance on a spatial memory task","year":2025,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Intensity (physics); Task (project management); Neuroscience; Psychology; Physical medicine and rehabilitation; Medicine; Physics; Optics","score_opus":0.03486824571890028,"score_gpt":0.3602894651753503,"score_spread":0.32542121945645003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413288893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97546315,0.0003627263,0.0033213852,0.006104203,0.00051076023,0.00039188526,0.000001350628,0.0003574482,0.013487092],"genre_scores_gemma":[0.9950581,0.000010352855,0.0011772643,0.0004776051,0.00003659101,0.000095434574,0.0000017605869,0.00000767568,0.0031351964],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981489,0.00011448204,0.00016387604,0.0005793833,0.0004674513,0.00052590104],"domain_scores_gemma":[0.99884146,0.00020160661,0.000026001964,0.00072848727,0.00012693951,0.000075475364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000771193,0.00014232263,0.00018343613,0.0004349177,0.00039386403,0.00012988795,0.0011774328,0.00009915508,0.00002003378],"category_scores_gemma":[0.00014259193,0.00012866993,0.00005397467,0.00063695165,0.0003606487,0.00016671982,0.0009675397,0.0005733486,0.00020951654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020387046,0.0005922161,0.00031580112,0.000032383992,0.000023042,0.00005161451,0.0009080323,0.000004415721,0.6511578,0.017313436,0.028570365,0.30082703],"study_design_scores_gemma":[0.0005342417,0.0005829749,0.020014117,0.00014945009,8.3960555e-7,0.0000043571795,0.00042375136,0.0069983546,0.9682379,0.00058393733,0.0023090485,0.00016104881],"about_ca_topic_score_codex":0.00018518296,"about_ca_topic_score_gemma":0.0000040586333,"teacher_disagreement_score":0.31708008,"about_ca_system_score_codex":0.00017631328,"about_ca_system_score_gemma":0.00011017342,"threshold_uncertainty_score":0.52470076},"labels":[],"label_agreement":null},{"id":"W4413308885","doi":"10.18280/mmep.120717","title":"Intelligent Wheelchair Control System Using Real-Time Head Movement Detection","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Wheelchair; Movement (music); Head (geology); Computer science; Movement control; Real-time computing; Physical medicine and rehabilitation; Medicine; Acoustics; Geology; Physics","score_opus":0.01776448449396455,"score_gpt":0.2219221652897523,"score_spread":0.20415768079578775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413308885","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042001575,0.00010974843,0.95638305,0.000108404754,0.00011227357,0.00019499112,6.1916904e-7,0.00081983686,0.00026950223],"genre_scores_gemma":[0.8987541,0.000011582855,0.10109951,0.000012838997,0.000013324607,0.00002978575,2.2432111e-7,0.000011116058,0.0000675266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989518,0.000017475502,0.00031733408,0.00030728185,0.000121242454,0.00028486096],"domain_scores_gemma":[0.99947506,0.00009582091,0.00004546524,0.0002797468,0.000043987417,0.000059906502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034079235,0.00017142092,0.00027241814,0.00017428077,0.00009988307,0.00009979067,0.00020831663,0.00010018037,8.165773e-7],"category_scores_gemma":[0.000015280068,0.00015454764,0.00004841881,0.00021611605,0.000022988383,0.00007941268,0.0000775821,0.00015852078,0.000010691588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018656535,0.000033955952,0.0000067142846,0.00054243533,0.000036199224,0.0000022935283,0.00010142518,0.84209436,0.008152039,0.14668576,0.0000014523196,0.00234151],"study_design_scores_gemma":[0.00017038299,0.000033001874,0.0000073583146,0.00063822593,0.000018036706,0.00000951129,0.000010260739,0.98467135,0.0025008947,0.011773906,0.000029936537,0.00013711363],"about_ca_topic_score_codex":0.000028124481,"about_ca_topic_score_gemma":3.5249528e-7,"teacher_disagreement_score":0.8567525,"about_ca_system_score_codex":0.00010010437,"about_ca_system_score_gemma":0.0000124510725,"threshold_uncertainty_score":0.63022697},"labels":[],"label_agreement":null},{"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Gaze; Transformer; Artificial intelligence; Computer vision; Theoretical computer science; Voltage","score_opus":0.021644526259896057,"score_gpt":0.2545988458130528,"score_spread":0.23295431955315676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.118697956,0.00012079681,0.8775311,0.000035883535,0.00039712488,0.0003957657,0.0000136135195,0.00019768244,0.0026100662],"genre_scores_gemma":[0.9809583,0.00001296174,0.018678457,0.000079528356,0.0000051067323,0.00010100342,0.00000249518,0.000007989616,0.00015419758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876547,0.00006372371,0.00027733724,0.00044918837,0.00018725303,0.00025703665],"domain_scores_gemma":[0.9995977,0.00010543144,0.000051231335,0.000101369165,0.00008510186,0.000059120757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017990076,0.00020329778,0.00021706895,0.00043168647,0.00042071592,0.00011950723,0.000156784,0.00011195454,0.0000032703274],"category_scores_gemma":[0.000004161233,0.00018805549,0.000058696285,0.00069655536,0.00011276037,0.00011017177,0.0000024887452,0.00018646113,0.000016085627],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025176496,0.0023509297,0.00056718645,0.00087175047,0.0009941072,0.00004903048,0.0062010153,0.08505695,0.015127354,0.01396119,0.00012878672,0.87443995],"study_design_scores_gemma":[0.0026411638,0.00018492785,0.0018705165,0.0012024052,0.00012090982,0.0002803937,0.0037120872,0.951098,0.03756401,0.00042090632,0.00012468583,0.00077997183],"about_ca_topic_score_codex":0.000058656635,"about_ca_topic_score_gemma":0.000004632884,"teacher_disagreement_score":0.87365997,"about_ca_system_score_codex":0.00009806764,"about_ca_system_score_gemma":0.00009927904,"threshold_uncertainty_score":0.766868},"labels":[],"label_agreement":null},{"id":"W4413580735","doi":"10.1101/2025.08.20.671334","title":"Gaze-related functions driving gaze anchoring in reaching","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Deutsche Forschungsgemeinschaft","keywords":"Gaze; Anchoring; Psychology; Cognitive psychology; Computer science; Computer vision; Communication; Cognitive science","score_opus":0.012729559785550076,"score_gpt":0.22285687849478017,"score_spread":0.2101273187092301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413580735","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6700351,0.0017262624,0.31549522,0.0013083745,0.0056514773,0.000841492,0.000052201733,0.004584818,0.00030502482],"genre_scores_gemma":[0.972943,0.00010667329,0.026433883,0.000084845735,0.00016038233,0.00017234535,2.1563083e-7,0.000053977776,0.000044651657],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99582773,0.0002493914,0.00084927055,0.0017855027,0.00037061473,0.00091751496],"domain_scores_gemma":[0.9965185,0.00019271193,0.000439858,0.0023800319,0.00028656435,0.00018232188],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001139199,0.00066158915,0.00074134726,0.0013138701,0.00031116628,0.00041790796,0.0020902231,0.0008797102,0.000010165041],"category_scores_gemma":[0.0004897454,0.00077694026,0.0002033953,0.0019359848,0.00012347955,0.00039050783,0.0019797853,0.0026134038,0.00006844105],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028018905,0.001387751,0.30743283,0.0014163891,0.00094834605,0.0010820561,0.00026307866,0.01005792,0.5234111,0.15116243,0.0019462628,0.0008638515],"study_design_scores_gemma":[0.0019742686,0.0001317889,0.85537905,0.008326819,0.00020351903,1.9133336e-7,0.000017474034,0.052480467,0.06795645,0.00027771926,0.009459313,0.0037929707],"about_ca_topic_score_codex":0.00013078781,"about_ca_topic_score_gemma":0.000018791396,"teacher_disagreement_score":0.5479462,"about_ca_system_score_codex":0.0006690235,"about_ca_system_score_gemma":0.0006508535,"threshold_uncertainty_score":0.9996876},"labels":[],"label_agreement":null},{"id":"W4413949908","doi":"10.1016/j.mex.2025.103607","title":"EyeMap: A fusion-based method for eye movement-based visual attention maps as predictive markers of parkinsonism","year":2025,"lang":"en","type":"article","venue":"MethodsX","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network","funders":"National Institute of Mental Health and Neurosciences","keywords":"Eye movement; Parkinsonism; Artificial intelligence; Fusion; Computer science; Computer vision; Movement (music); Neuroscience; Psychology; Pattern recognition (psychology); Medicine; Art; Pathology; Philosophy","score_opus":0.013302777756703849,"score_gpt":0.35093629433011087,"score_spread":0.337633516573407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413949908","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007139043,0.0000652959,0.9884234,0.0023709235,0.000497918,0.0005800943,0.000028243567,0.00031151724,0.0005835307],"genre_scores_gemma":[0.09687671,0.0000016165869,0.90144855,0.0009318278,0.000016970263,0.00022168276,0.000014709559,0.000013128717,0.00047483246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975664,0.0007245475,0.000428274,0.00064712536,0.0002731936,0.00036046052],"domain_scores_gemma":[0.9973513,0.0015018301,0.00026628634,0.0005603326,0.00026161422,0.000058676695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025886223,0.0002235304,0.00040139264,0.00048681654,0.00016945762,0.000043785516,0.00068753824,0.00018865652,0.000010420173],"category_scores_gemma":[0.00067721587,0.00021253152,0.00025020138,0.000756094,0.00014356447,0.00008962965,0.00015029531,0.00018461642,0.0000038038788],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080107874,0.0006912539,0.004577799,0.00032562963,0.00024282555,0.000010261045,0.00007980245,0.00092783716,0.093090184,0.05504902,0.0011646317,0.8430397],"study_design_scores_gemma":[0.002613109,0.0015241212,0.021175995,0.00023749881,0.000119196746,4.869904e-7,0.00009007473,0.67194456,0.23983812,0.055486247,0.0066401083,0.00033045874],"about_ca_topic_score_codex":0.00009492806,"about_ca_topic_score_gemma":0.0000046314317,"teacher_disagreement_score":0.84270924,"about_ca_system_score_codex":0.000096285774,"about_ca_system_score_gemma":0.0002655341,"threshold_uncertainty_score":0.8666784},"labels":[],"label_agreement":null},{"id":"W4413979871","doi":"10.1109/tim.2025.3606068","title":"Regular RGB-Video-Based Eye Movement Assessment for Parkinson’s Disease","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Parkinson's disease; Computer science; Computer vision; Movement (music); Eye movement; RGB color model; Artificial intelligence; Physical medicine and rehabilitation; Medicine; Disease; Physics","score_opus":0.0261559008261403,"score_gpt":0.28865818899196005,"score_spread":0.2625022881658198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413979871","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010487837,0.000041086954,0.9793302,0.008287489,0.00074424385,0.00067900994,0.00001775862,0.00021877614,0.00019362642],"genre_scores_gemma":[0.96537936,0.000022022614,0.031834483,0.0018825249,0.000008578959,0.00067460525,0.0000037520729,0.00000784901,0.00018681595],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854165,0.0000649764,0.00028435708,0.00046193157,0.00041764602,0.0002294558],"domain_scores_gemma":[0.99923915,0.000036110356,0.000081602135,0.00037030003,0.00016592619,0.00010690486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004221259,0.00018049791,0.00015790095,0.00028872487,0.00037117163,0.000116043215,0.0002243809,0.000052514108,0.000009888583],"category_scores_gemma":[0.0000068697473,0.0001785164,0.00009483895,0.00024405186,0.000056615194,0.00015180447,0.0000027382068,0.000118776676,0.000002772527],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024011472,0.0015372635,0.0016424013,0.00026440946,0.00028011107,0.0000056309937,0.00013363446,0.007694815,0.013183904,0.049216274,0.0011768019,0.9246246],"study_design_scores_gemma":[0.019066844,0.0017309862,0.12076998,0.0011001395,0.00059955614,0.00000175106,0.0005603781,0.28474548,0.46987143,0.02802315,0.07191487,0.0016154282],"about_ca_topic_score_codex":0.000015062471,"about_ca_topic_score_gemma":0.000028787123,"teacher_disagreement_score":0.9548915,"about_ca_system_score_codex":0.00036502135,"about_ca_system_score_gemma":0.00023709559,"threshold_uncertainty_score":0.72796875},"labels":[],"label_agreement":null},{"id":"W4413989568","doi":"10.1515/auto-2024-0108","title":"Advancements in intelligent wheelchairs: a scoping review","year":2025,"lang":"en","type":"article","venue":"at - Automatisierungstechnik","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Computer science","score_opus":0.012203451425737939,"score_gpt":0.3220853311019943,"score_spread":0.30988187967625636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413989568","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00866922,0.08090458,0.87934417,0.01026572,0.00091448915,0.0028510857,0.000003924809,0.004072492,0.012974341],"genre_scores_gemma":[0.8346699,0.019288966,0.13641511,0.006188045,0.000021675372,0.0010425755,0.000011593843,0.000040731422,0.0023213974],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.99807656,0.00007126599,0.0005964245,0.0005861526,0.00023222204,0.00043740138],"domain_scores_gemma":[0.9986881,0.000110977904,0.00015933275,0.0009424334,0.000053958862,0.000045184668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048442997,0.00021746838,0.00042342814,0.00040652804,0.00010876363,0.000044887263,0.0011753571,0.00011617574,0.000033575674],"category_scores_gemma":[0.00016993654,0.00021456211,0.000076320095,0.0014650547,0.00006975524,0.0002711982,0.0008450024,0.00022796681,0.00014192128],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006186915,0.0002669431,0.00729836,0.0067967065,0.000050793395,0.0001375639,0.0001587905,0.000029394236,0.0014793379,0.13799216,0.009316526,0.8364672],"study_design_scores_gemma":[0.0034292105,0.00058439584,0.049980313,0.74328524,0.00011720518,0.00019760244,0.000084879335,0.034815956,0.06713296,0.045650072,0.051746693,0.0029754827],"about_ca_topic_score_codex":0.000020767293,"about_ca_topic_score_gemma":0.00008681627,"teacher_disagreement_score":0.83349174,"about_ca_system_score_codex":0.0003057168,"about_ca_system_score_gemma":0.000106165884,"threshold_uncertainty_score":0.8749589},"labels":[],"label_agreement":null},{"id":"W4414798925","doi":"10.1109/tvcg.2025.3616824","title":"Exploring and Modeling the Effects of Eye-Tracking Accuracy and Precision on Gaze-Based Steering in Virtual Environments","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Robustness (evolution); Virtual reality; Gaze; Predictability; Modalities; Eye tracking; Path (computing)","score_opus":0.0370683069848125,"score_gpt":0.290311802451816,"score_spread":0.2532434954670035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414798925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42571786,0.00004413224,0.57390535,0.000037429705,0.00014859057,0.000107081294,3.930681e-7,0.000037701822,0.0000014596046],"genre_scores_gemma":[0.99881655,0.0005235315,0.00044712698,0.00017132131,0.0000051875995,0.000025327185,4.5750764e-7,0.000006981351,0.000003521435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909085,0.000097441814,0.00021944863,0.00032000893,0.00014456935,0.0001276841],"domain_scores_gemma":[0.9992263,0.0004667354,0.00005501175,0.00020332348,0.000020118587,0.000028511171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018338897,0.00013879618,0.00015125214,0.0004565707,0.00019280106,0.00008120587,0.00015404378,0.00006604819,1.5628083e-7],"category_scores_gemma":[0.000011485486,0.000119153345,0.000027541724,0.00043269395,0.000075119984,0.0002474066,0.000010936909,0.0001903662,1.495869e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007874731,0.000549989,0.0018571226,0.00027202055,0.00007304754,0.000007146577,0.0022900077,0.13722725,0.002175332,0.39942524,0.0000025353256,0.45604154],"study_design_scores_gemma":[0.00060899387,0.00022425428,0.0067445333,0.00046212645,0.000011653318,9.572819e-7,0.000034010398,0.9812451,0.0101822335,0.0003636135,0.000019646752,0.000102848164],"about_ca_topic_score_codex":0.00000880653,"about_ca_topic_score_gemma":0.0000061731184,"teacher_disagreement_score":0.84401786,"about_ca_system_score_codex":0.000015160985,"about_ca_system_score_gemma":0.000011467117,"threshold_uncertainty_score":0.48589322},"labels":[],"label_agreement":null},{"id":"W4415000209","doi":"10.1145/3744335.3758474","title":"Exploring a Novel Ball‐Shaped Steering Mechanism System Architecture and Preliminary User Feedback","year":2025,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Mechanism (biology); Architecture; Systems architecture; Control system; Feedback control","score_opus":0.03823377885829942,"score_gpt":0.22460198065273607,"score_spread":0.18636820179443664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415000209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13429655,0.000054939228,0.8609338,0.0010411321,0.00029966,0.00013899026,8.993933e-7,0.0010256825,0.0022083824],"genre_scores_gemma":[0.9112138,0.0000053529197,0.08792011,0.00010678732,0.000016518536,0.000051076822,3.2415937e-7,0.000008004645,0.00067799503],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989178,0.00002119699,0.00017908242,0.00047259947,0.00012259399,0.00028669785],"domain_scores_gemma":[0.99934596,0.0000882307,0.000041644587,0.0004328088,0.00004038124,0.000050967672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015515735,0.00016836815,0.00019631781,0.00023499844,0.00013828622,0.000119644435,0.0005633518,0.00007804467,0.0000017607431],"category_scores_gemma":[0.000022527889,0.0001442183,0.00003923178,0.00035650382,0.000034027078,0.00023626265,0.0006105685,0.00021113575,0.000009165259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009140537,0.000034458335,0.00017848024,0.00014846463,0.000036599817,0.000014875173,0.00022482518,0.00005894874,0.018123224,0.953749,0.000028787073,0.027393218],"study_design_scores_gemma":[0.007704777,0.0016537803,0.20372562,0.004858,0.00021764654,0.0008945991,0.0055283895,0.53257746,0.20192425,0.026856698,0.010599101,0.0034596904],"about_ca_topic_score_codex":0.000026874113,"about_ca_topic_score_gemma":0.0000066688326,"teacher_disagreement_score":0.9268923,"about_ca_system_score_codex":0.000044812554,"about_ca_system_score_gemma":0.000027498218,"threshold_uncertainty_score":0.58810514},"labels":[],"label_agreement":null},{"id":"W4415221997","doi":"10.1109/access.2025.3621645","title":"Gaze Analysis in Early Warning Visual Feedback System for Hand Tracking Failures in Virtual Reality","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Google","keywords":"Visual feedback; Gaze; Virtual reality; Focus (optics); Eye tracking; Visualization; Warning system; Haptic technology; Corrective feedback","score_opus":0.029749366582829517,"score_gpt":0.33848366024181353,"score_spread":0.308734293658984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415221997","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54231966,0.000032066928,0.45677525,0.00031942522,0.00019299863,0.00012955238,0.0000024675294,0.00012530107,0.000103283106],"genre_scores_gemma":[0.9991507,0.0000018428757,0.0006428001,0.000042543794,0.0000280039,0.00006650536,0.0000028507982,0.000006752454,0.000058019024],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9982732,0.00013123224,0.00044093817,0.0005969614,0.00016295382,0.00039470335],"domain_scores_gemma":[0.9990135,0.0003218363,0.00014100112,0.00038959403,0.000099025805,0.0000350751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007109728,0.00016724094,0.00043487447,0.0011051898,0.00013428912,0.0005328534,0.0012477153,0.0001569058,8.491959e-7],"category_scores_gemma":[0.000111436464,0.00016506616,0.00011471028,0.0028569195,0.000073655254,0.00060813245,0.00017874222,0.00026172513,0.00000272311],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009648429,0.00028490814,0.84510416,0.00027885946,0.00034000745,0.0000918727,0.0014842536,0.02614183,0.0033038286,0.027876392,0.00021294592,0.09478447],"study_design_scores_gemma":[0.0011540925,0.0000809331,0.83377814,0.00033212753,0.000072207375,0.0000017341258,0.00021854679,0.14535858,0.017623223,0.0009446212,0.00013704375,0.00029876904],"about_ca_topic_score_codex":0.0014209804,"about_ca_topic_score_gemma":0.0027862976,"teacher_disagreement_score":0.45683104,"about_ca_system_score_codex":0.00015912,"about_ca_system_score_gemma":0.0000705227,"threshold_uncertainty_score":0.67312026},"labels":[],"label_agreement":null},{"id":"W4415822142","doi":"10.1109/ro-man63969.2025.11217715","title":"Gaze to Grasp: Shared Autonomy in VR Robot Teleoperation <sup>*</sup>","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council","keywords":"Teleoperation; Headset; Task (project management); Virtual reality; Gaze; Robot; Task analysis; Telerobotics; Trajectory; Control (management)","score_opus":0.015312809542917619,"score_gpt":0.2667261118485313,"score_spread":0.25141330230561365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415822142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05424675,0.00022661076,0.8934856,0.0266699,0.00063130294,0.0008507707,0.0000061710084,0.0006961882,0.023186687],"genre_scores_gemma":[0.9369422,0.000013044862,0.049804907,0.0022173496,0.000051992105,0.00011076542,0.0000068944573,0.000014758208,0.010838086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963745,0.0001763433,0.0008551006,0.0014225963,0.00027604145,0.0008954424],"domain_scores_gemma":[0.9981767,0.00014635126,0.00009099329,0.0011848512,0.00023834764,0.00016277892],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000560421,0.00045087442,0.0005590081,0.0011758045,0.00027975885,0.0006313345,0.0017500097,0.00037329042,0.0003119895],"category_scores_gemma":[0.00024708672,0.0004604636,0.0001271505,0.0031843327,0.00012885338,0.00059217063,0.0008759174,0.0006067261,0.00067530514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034011675,0.00067634997,0.022832418,0.00007525222,0.00007572964,0.00007124705,0.0027900082,0.040957082,0.0014997093,0.22737159,0.005916716,0.6976999],"study_design_scores_gemma":[0.0013236661,0.00041512804,0.19009444,0.0005034147,0.000029258885,0.00001212659,0.0003958174,0.77131057,0.0062619722,0.003810394,0.024962876,0.0008803432],"about_ca_topic_score_codex":0.0004353963,"about_ca_topic_score_gemma":0.00032476275,"teacher_disagreement_score":0.88269544,"about_ca_system_score_codex":0.00048557748,"about_ca_system_score_gemma":0.00080734765,"threshold_uncertainty_score":0.9997847},"labels":[],"label_agreement":null},{"id":"W4416183730","doi":"10.1109/ismar67309.2025.00170","title":"Exploring Pointing and Confirmation Techniques for Teleportation Across Varying Elevations in Virtual Reality","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of British Columbia, Okanagan Campus; Kelowna General Hospital","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Teleportation; Virtual reality; Pointer (user interface); Leverage (statistics); Gaze; Wearable computer","score_opus":0.11916001910201199,"score_gpt":0.3577026765737226,"score_spread":0.23854265747171063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416183730","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28446606,0.000027621196,0.7108597,0.002847092,0.00021321827,0.00048403087,0.000005783883,0.00033007906,0.00076637],"genre_scores_gemma":[0.9686401,0.00006608426,0.030652057,0.00014459429,0.000027506185,0.00030310603,0.000009960878,0.000007820703,0.0001487767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795556,0.00007526423,0.0007954463,0.0006112473,0.0001333395,0.0004291136],"domain_scores_gemma":[0.9986425,0.00044259208,0.00026995444,0.00033120762,0.00027969765,0.000034026605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014709929,0.00020047372,0.00028543017,0.00040319085,0.00049476797,0.00031737445,0.00027722638,0.00015544519,0.0000019295853],"category_scores_gemma":[0.0006395083,0.00022567766,0.00005058332,0.00096748816,0.00013529473,0.0013154346,0.00020204332,0.0002692156,9.975633e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013232269,0.00007399468,0.0054636816,0.00011415175,0.000017075734,0.000001974912,0.0017342387,0.0001278931,0.0038335884,0.27466688,0.000036868434,0.7139164],"study_design_scores_gemma":[0.0015794813,0.00035157756,0.20315504,0.0012724346,0.00004145395,0.000013874426,0.0041815736,0.58521664,0.1639447,0.037990425,0.0014829051,0.0007698819],"about_ca_topic_score_codex":0.00032311052,"about_ca_topic_score_gemma":0.00022041635,"teacher_disagreement_score":0.71314657,"about_ca_system_score_codex":0.00011162128,"about_ca_system_score_gemma":0.00010426358,"threshold_uncertainty_score":0.92028683},"labels":[],"label_agreement":null},{"id":"W4416832645","doi":"10.2196/78339","title":"Assistive Robotic Arm to Support Activities of Daily Living in Individuals With Tetraplegia: Protocol for a Real-World Convergent Parallel Mixed Methods Feasibility Study","year":2025,"lang":"en","type":"article","venue":"JMIR Research Protocols","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Protocol (science); Activities of daily living; Assistive technology; Robotic arm; Rehabilitation; Rehabilitation robotics; Robotics","score_opus":0.26782404435826546,"score_gpt":0.5717499213663466,"score_spread":0.30392587700808116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416832645","genre_codex":"protocol","genre_gemma":"protocol","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"protocol","genre_consensus":"protocol","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030582885,1.6107933e-7,0.117458604,0.0003937642,0.000011347118,0.8781012,0.000007764009,0.00016581264,0.0008030158],"genre_scores_gemma":[0.06390513,2.2046176e-8,0.10309772,0.000018603225,0.000011618089,0.83217996,8.443632e-7,0.000018879182,0.000767227],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.993743,0.002334876,0.00079498463,0.0012253969,0.00089161005,0.0010101164],"domain_scores_gemma":[0.9950175,0.0025495193,0.00025415036,0.0013956059,0.00061630545,0.00016687062],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00906429,0.00033037754,0.0007961509,0.0016446761,0.00025439454,0.00022374281,0.0019527379,0.00014086124,0.000025122585],"category_scores_gemma":[0.0009786447,0.0002679352,0.000103060425,0.003321069,0.00035978833,0.00034731344,0.0012101943,0.0007999128,0.0000037448942],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011048819,0.012994749,0.93274206,0.0017034928,0.00018583359,0.000024909117,0.00264641,0.00018179181,0.0007268048,0.014825819,0.0040406743,0.028822558],"study_design_scores_gemma":[0.0043109795,0.011116755,0.9685536,0.002311749,0.000005044998,0.0000014783471,0.0012450381,0.000422238,0.006147155,0.0023668825,0.0031850953,0.00033398677],"about_ca_topic_score_codex":0.00015868939,"about_ca_topic_score_gemma":0.0010741426,"teacher_disagreement_score":0.060846843,"about_ca_system_score_codex":0.00044548657,"about_ca_system_score_gemma":0.0012517665,"threshold_uncertainty_score":0.9999773},"labels":[],"label_agreement":null},{"id":"W4417025562","doi":"10.1145/3756884.3766051","title":"Saccaidance: Saccade-Aware Pattern Embedding for Gaze Guidance on High-Speed Displays","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Innovation Cluster (Canada)","funders":"","keywords":"Gaze; Embedding; Eye tracking; Feature (linguistics)","score_opus":0.017471276115761106,"score_gpt":0.30520112289231094,"score_spread":0.28772984677654984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417025562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028143482,0.0005815384,0.9472495,0.014165562,0.004234607,0.00097507186,0.000100848614,0.0008923601,0.0036570665],"genre_scores_gemma":[0.97172654,0.00008439233,0.011621408,0.003193692,0.00025564712,0.00011978362,0.000012851872,0.000049728136,0.012935938],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9945035,0.0001477888,0.0010960154,0.0022100678,0.000501307,0.0015413339],"domain_scores_gemma":[0.9960725,0.000886535,0.00042580743,0.001982371,0.00044553337,0.00018730774],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000718303,0.00084204477,0.0009606928,0.0006245289,0.0008480487,0.0006123401,0.0030411468,0.00062943023,0.0001348871],"category_scores_gemma":[0.0003348869,0.00080532563,0.00039432067,0.0014234243,0.00033305574,0.00048041722,0.00082130614,0.0008547747,0.00025236743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013198044,0.0006609086,0.0085259555,0.00056644663,0.0002725227,0.000054526496,0.00027073128,0.0019877471,0.002389502,0.2870919,0.03945831,0.6585895],"study_design_scores_gemma":[0.0054027587,0.0015624036,0.072096325,0.0043314868,0.0002078214,0.000020315596,0.00026839325,0.8105571,0.055334512,0.02189342,0.025964266,0.0023611723],"about_ca_topic_score_codex":0.00012913538,"about_ca_topic_score_gemma":0.00006370047,"teacher_disagreement_score":0.9435831,"about_ca_system_score_codex":0.00035946403,"about_ca_system_score_gemma":0.00029689536,"threshold_uncertainty_score":0.9994398},"labels":[],"label_agreement":null},{"id":"W4417195853","doi":"10.1121/10.0041807","title":"Mobile eye-tracking glasses capture ocular and head markers of listening effort","year":2025,"lang":"en","type":"article","venue":"JASA Express Letters","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Baycrest Hospital; McMaster University; University of Toronto","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Active listening; Pupil; Head (geology); Gaze; Eye movement; Eye tracking","score_opus":0.0071217180761943034,"score_gpt":0.26674342977095544,"score_spread":0.2596217116947611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417195853","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8486441,0.00061234296,0.14814344,0.001388717,0.000257265,0.00015419333,0.0000021738506,0.0002240336,0.0005737679],"genre_scores_gemma":[0.97919464,0.00001567622,0.01958631,0.0010036229,0.000017742044,0.000032489832,0.000001718098,0.000008711049,0.00013908348],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99882305,0.000046461584,0.00024413134,0.00043183792,0.00016599141,0.00028851032],"domain_scores_gemma":[0.9991951,0.00009924698,0.0001127292,0.00050266477,0.00005031632,0.000039963263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018674329,0.0001686185,0.0002664775,0.00020515827,0.00010593363,0.00009048552,0.00060072466,0.000107208514,0.000002249062],"category_scores_gemma":[0.00003981684,0.00016021346,0.00007654458,0.00029780474,0.0001788622,0.00023446305,0.00028541606,0.00023342777,0.0000014893682],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005499607,0.00013520925,0.2569443,0.00047778187,0.00024058897,0.00019279882,0.0019590037,0.0008596013,0.66843855,0.0087972265,0.009687328,0.05221262],"study_design_scores_gemma":[0.0034647437,0.00047976515,0.5986672,0.0024192291,0.00017307325,0.000070591515,0.0013429126,0.017085174,0.2834823,0.0017606218,0.08929479,0.001759588],"about_ca_topic_score_codex":0.00009348862,"about_ca_topic_score_gemma":0.0000043454356,"teacher_disagreement_score":0.38495624,"about_ca_system_score_codex":0.000028081475,"about_ca_system_score_gemma":0.000021435022,"threshold_uncertainty_score":0.6533315},"labels":[],"label_agreement":null},{"id":"W4417430458","doi":"10.1186/s12984-025-01844-0","title":"REAsmash-ET: a methodological framework for combined cognitive and motor assessment through eye-tracking and kinematic metrics in immersive VR search-and-reach task","year":2025,"lang":"en","type":"article","venue":"Journal of NeuroEngineering and Rehabilitation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Centre for Interdisciplinary Research in Rehabilitation","funders":"","keywords":"Task (project management); Kinematics; Cognition; Cognitive Assessment System; Task analysis; Virtual reality; Motor imagery; Motor control","score_opus":0.03921258658478753,"score_gpt":0.38043211687818407,"score_spread":0.3412195302933965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417430458","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47564322,0.00028029465,0.5210142,0.0028471367,0.00006630584,0.0001330284,9.090078e-7,0.000011282965,0.0000036175559],"genre_scores_gemma":[0.66061366,0.00013993433,0.33917072,0.000056414327,0.000006297906,0.0000071859977,2.3344899e-7,0.0000037105447,0.00000180413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99900436,0.00020477522,0.00031413863,0.00021839468,0.00011616494,0.00014216016],"domain_scores_gemma":[0.9935959,0.006024331,0.000106931344,0.0000729537,0.00015673124,0.000043162774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010004578,0.00011366622,0.00030586854,0.00039677517,0.00006249353,0.00007706376,0.00008582391,0.000074880045,8.868283e-8],"category_scores_gemma":[0.003508327,0.00009278807,0.00003805776,0.00035764053,0.000100962876,0.0002294441,0.00007752957,0.00038022883,1.868552e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075698114,0.00095957785,0.12321933,0.0045306184,0.0005214084,0.00015733183,0.021255024,0.0070204884,0.07652583,0.5015667,0.000044032808,0.26344267],"study_design_scores_gemma":[0.0013469026,0.0021665033,0.8798334,0.00092232664,0.000056523248,0.000052698037,0.0015392106,0.080889076,0.00020384634,0.032829538,0.000018696925,0.00014126259],"about_ca_topic_score_codex":0.0000052750956,"about_ca_topic_score_gemma":5.1451866e-7,"teacher_disagreement_score":0.7566141,"about_ca_system_score_codex":0.00002830362,"about_ca_system_score_gemma":0.00002524356,"threshold_uncertainty_score":0.42000487},"labels":[],"label_agreement":null},{"id":"W51795594","doi":"","title":"Eye-tracker data filtering using pulse coupled neural network","year":2006,"lang":"en","type":"article","venue":"international conference on Modelling and simulation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais; Institut national de psychiatrie légale Philippe-Pinel; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Filter (signal processing); Computer vision; Noise (video); Artificial neural network; Median filter; Pulse (music); Nonlinear filter; Eye tracking; Signal-to-noise ratio (imaging); SIGNAL (programming language); Pattern recognition (psychology); Filter design; Image (mathematics); Image processing; Detector; Telecommunications","score_opus":0.14067106191744772,"score_gpt":0.3430623449662049,"score_spread":0.2023912830487572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W51795594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32301116,0.000020123927,0.67580485,0.00041138125,0.00023942406,0.00004099111,0.0000044024837,0.00010546622,0.0003622149],"genre_scores_gemma":[0.96748686,0.000008034562,0.032113787,0.00007145749,0.00018580971,0.0000015440564,0.000062783874,0.0000072087664,0.00006251086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893767,0.00002854222,0.00021834951,0.00041889728,0.00021853215,0.00017802474],"domain_scores_gemma":[0.99929243,0.00007648739,0.00010307528,0.00035136155,0.00014238135,0.00003425283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018959556,0.00012299222,0.00011089883,0.000092062146,0.00013977778,0.00030243816,0.000567514,0.00006872068,0.000012449937],"category_scores_gemma":[0.000020341693,0.00012188253,0.000018361432,0.00009405821,0.00003631297,0.00056650507,0.0001759512,0.00015089216,0.000005428517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009628142,0.00001912695,0.00083515106,0.0000017883874,0.0000067757296,0.0000042024353,0.00001397379,0.95602137,0.00034794674,0.039039947,0.000008984584,0.0036911264],"study_design_scores_gemma":[0.00018151735,0.000018361048,0.0010790719,0.000038206897,0.0000045459633,0.0000029846615,0.000004516757,0.9842143,0.0000328332,0.014208518,0.000087967615,0.00012718872],"about_ca_topic_score_codex":0.00013412164,"about_ca_topic_score_gemma":0.000008674053,"teacher_disagreement_score":0.6444757,"about_ca_system_score_codex":0.000026695152,"about_ca_system_score_gemma":0.000025362388,"threshold_uncertainty_score":0.4970225},"labels":[],"label_agreement":null},{"id":"W6892611453","doi":"10.5281/zenodo.11368509","title":"HeadShift: Head Pointing with Dynamic Control-Display Gain","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Head (geology); Noise (video); Field (mathematics); Human head","score_opus":0.01718898908827891,"score_gpt":0.24995957488008852,"score_spread":0.23277058579180962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892611453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020522768,0.00028567153,0.93538314,0.00820315,0.00016452247,0.00030272844,0.00005805427,0.0057305237,0.02934943],"genre_scores_gemma":[0.9961429,0.000011183944,0.002599776,0.0001428525,0.00003925525,4.4167336e-8,0.00008805104,0.0004911877,0.00048470352],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984544,0.00017698901,0.00017430156,0.00052892696,0.00027030258,0.00039508604],"domain_scores_gemma":[0.99913245,0.000044944318,0.00004669916,0.0004717637,0.0002045398,0.0000995778],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058220077,0.00013936518,0.0001351151,0.0002711266,0.0010686533,0.0013854706,0.0012459374,0.00005619763,0.0003824999],"category_scores_gemma":[0.00016683408,0.00012168646,0.00004060779,0.00081079657,0.00014640582,0.00036512283,0.0005599442,0.00035192017,0.0040293606],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005312502,0.00017465021,0.000018948325,0.00016611522,0.00013722567,0.00034086395,0.0014256872,0.00034515475,0.010021663,0.38883173,0.027663017,0.5708218],"study_design_scores_gemma":[0.00072431756,0.00056648627,0.001769097,0.00019694156,0.000015999667,0.0006139499,0.000090363705,0.09195102,0.00042150763,0.0014105899,0.901911,0.0003287234],"about_ca_topic_score_codex":0.000011677878,"about_ca_topic_score_gemma":0.000001151901,"teacher_disagreement_score":0.97562015,"about_ca_system_score_codex":0.00011177913,"about_ca_system_score_gemma":0.000006461713,"threshold_uncertainty_score":0.9996512},"labels":[],"label_agreement":null},{"id":"W6893256151","doi":"10.5281/zenodo.14597164","title":"Gahanopsis Ogloblin 1946","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Ovipositor; Sulcus; Subgenus; Foramen; Petiole (insect anatomy)","score_opus":0.03184682707470425,"score_gpt":0.25154586658750167,"score_spread":0.2196990395127974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893256151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0113746505,0.00071424176,0.7154011,0.01050595,0.0006991899,0.00035869578,0.00008697444,0.014560795,0.24629839],"genre_scores_gemma":[0.9945499,0.000047394817,0.0030547434,0.00011843631,0.00009853529,2.7521615e-8,0.000110219014,0.00052570185,0.0014950804],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866736,0.00013902428,0.00015665713,0.00047658005,0.0002460527,0.0003143376],"domain_scores_gemma":[0.9991271,0.000026437767,0.000029699646,0.0005183729,0.00020259595,0.00009577533],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00047693873,0.000107516746,0.00009703066,0.0003203381,0.0009852042,0.0015334608,0.001705807,0.000061242536,0.0018413553],"category_scores_gemma":[0.00020970494,0.00010577991,0.000049388957,0.0010294205,0.00011183195,0.0003302872,0.0012773644,0.0002668692,0.016304094],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003794504,0.000055639393,0.0000018645229,0.00004341924,0.000030329305,0.00007090943,0.00041245462,0.000012911708,0.0043282676,0.21611154,0.21626201,0.56266683],"study_design_scores_gemma":[0.00010299746,0.00010998185,0.0003137785,0.000035861183,0.000004776059,0.00016634731,0.000032915297,0.004643978,0.0011389254,0.0022481077,0.9910732,0.00012913803],"about_ca_topic_score_codex":0.0000056431886,"about_ca_topic_score_gemma":9.125516e-8,"teacher_disagreement_score":0.9831752,"about_ca_system_score_codex":0.00009003321,"about_ca_system_score_gemma":0.0000041028193,"threshold_uncertainty_score":0.999503},"labels":[],"label_agreement":null},{"id":"W6893650518","doi":"10.5281/zenodo.3793066","title":"Bisnius cephalotes","year":2008,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Canadian Forest Service; Natural Resources Canada; Nova Scotia Hospital","funders":"","keywords":"Nova scotia; Fauna; Fish <Actinopterygii>; Historical record; Vegetation (pathology); China","score_opus":0.04554989678786297,"score_gpt":0.23515774794774813,"score_spread":0.18960785115988515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893650518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14029147,0.00025379815,0.5563033,0.005884753,0.00036059148,0.0004568664,0.000053282776,0.011427826,0.28496808],"genre_scores_gemma":[0.99519855,0.000045079967,0.003567836,0.00013335675,0.00005165879,1.3484568e-8,0.0000649923,0.00035135588,0.0005871379],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988185,0.00013007798,0.00014270583,0.00036752078,0.00023410039,0.00030708322],"domain_scores_gemma":[0.99900913,0.000019020963,0.000057347013,0.000538625,0.0002772242,0.000098670185],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00025298505,0.00009847635,0.00010088081,0.00021340718,0.00201874,0.00028789495,0.0017382568,0.00005277315,0.0009804226],"category_scores_gemma":[0.00027445317,0.00009996201,0.00003764207,0.0006288426,0.00019705157,0.00026280287,0.0011006393,0.00020414489,0.007774185],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000173381,0.00034330718,0.00007632027,0.000027426786,0.000049372647,0.00021835364,0.0017268118,0.000048676095,0.012101647,0.15835463,0.31255385,0.51448226],"study_design_scores_gemma":[0.00027719015,0.00017155656,0.0057157315,0.0000089824125,0.0000023599096,0.0006265376,0.000031254986,0.0008459607,0.0020214256,0.0008201109,0.98932123,0.00015767424],"about_ca_topic_score_codex":0.0000072335565,"about_ca_topic_score_gemma":5.8328585e-8,"teacher_disagreement_score":0.8549071,"about_ca_system_score_codex":0.000054473072,"about_ca_system_score_gemma":0.0000029066628,"threshold_uncertainty_score":0.9999328},"labels":[],"label_agreement":null},{"id":"W6906435859","doi":"10.17605/osf.io/wbzg5","title":"Contrapposto posture captures visual attention: an online gaze tracking experiment","year":2023,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Eye tracking; Tracking (education); Gaze; Visualization; Tracking system","score_opus":0.03200336678537717,"score_gpt":0.3035325981894626,"score_spread":0.2715292314040854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6906435859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94576347,0.0000066309785,0.030296968,0.0025697125,0.0007010394,0.0005799627,0.0000126797695,0.0022398545,0.017829686],"genre_scores_gemma":[0.9769282,0.00003154979,0.0041779787,0.00025218815,0.00012118715,0.000113136884,0.000052352607,0.000037137364,0.018286306],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99543375,0.0004345466,0.00053690304,0.0023830764,0.00051646505,0.000695248],"domain_scores_gemma":[0.99602413,0.00022738033,0.00022431917,0.0030617781,0.00021113797,0.00025127092],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002014392,0.00037323884,0.00041546836,0.00033339628,0.00032699943,0.00029308264,0.0023039228,0.00030759064,0.014167339],"category_scores_gemma":[0.00056963693,0.00039115638,0.000199883,0.00067454495,0.00018784907,0.0005670577,0.0015688757,0.0006078938,0.18785122],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019249461,0.004457325,0.07487046,0.00014630037,0.00057089014,0.0008109714,0.008714951,0.0026072103,0.45397222,0.049855765,0.029155552,0.37464586],"study_design_scores_gemma":[0.0018805359,0.000026996755,0.8643888,0.00019762795,0.000064926244,0.00022361962,0.0013159796,0.024120973,0.06747011,0.0074951095,0.031475775,0.0013395381],"about_ca_topic_score_codex":0.00010465194,"about_ca_topic_score_gemma":0.000055108085,"teacher_disagreement_score":0.78951836,"about_ca_system_score_codex":0.00014310425,"about_ca_system_score_gemma":0.00011314339,"threshold_uncertainty_score":0.999854},"labels":[],"label_agreement":null},{"id":"W6925471933","doi":"10.17605/osf.io/s26yb","title":"IWELL (Inuit well-being and learning from the land)","year":2023,"lang":"en","type":"article","venue":"OSF Preprints (OSF Preprints)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Experiential learning; Wildlife; Social learning; Active learning (machine learning); Learning sciences","score_opus":0.0133557674602253,"score_gpt":0.2349358195084367,"score_spread":0.2215800520482114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925471933","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.81286025,0.0000032111939,0.02104554,0.0031085433,0.00030332807,0.00029793556,0.0000020370842,0.0013631704,0.161016],"genre_scores_gemma":[0.954995,0.00007719551,0.0019383655,0.00018255478,0.000058193156,0.000043653155,0.0000047077156,0.000019092467,0.04268126],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9969946,0.0005022916,0.0002783993,0.0015317413,0.00026970223,0.00042327124],"domain_scores_gemma":[0.99613965,0.0013088484,0.00013175311,0.0022656177,0.000055469514,0.000098654986],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0030925756,0.00020440448,0.00023304058,0.000112463626,0.0004477358,0.00026163936,0.0018323143,0.00015366977,0.015103853],"category_scores_gemma":[0.0014559216,0.00017559854,0.00008451067,0.00046574188,0.00016751133,0.00021080968,0.002964011,0.00076749106,0.4704654],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026186506,0.00010307706,0.7143651,0.000033693756,0.00018864257,0.00014453109,0.012463981,0.0025555461,0.004847443,0.022542875,0.014509779,0.22821912],"study_design_scores_gemma":[0.0006865795,0.0000028831346,0.7819328,0.00011961445,0.000041385392,0.000047288915,0.00044876774,0.040927686,0.0064030383,0.071049586,0.09776484,0.00057554705],"about_ca_topic_score_codex":0.0004980309,"about_ca_topic_score_gemma":0.00008384908,"teacher_disagreement_score":0.45536155,"about_ca_system_score_codex":0.000056768913,"about_ca_system_score_gemma":0.000053190113,"threshold_uncertainty_score":0.98579645},"labels":[],"label_agreement":null},{"id":"W6926445107","doi":"10.25316/ir-11330","title":"The Nanaimo Free Press [Saturday, January 9, 1875]","year":2019,"lang":"en","type":"other","venue":"VIUSpace (Vancouver Island University Library)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.0064386511526357245,"score_gpt":0.17749983054040092,"score_spread":0.1710611793877652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6926445107","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000004622206,0.0011071094,0.009631794,0.0016131784,0.0021468995,0.00036421488,0.00009766958,0.0015948208,0.9834397],"genre_scores_gemma":[0.0006303186,0.0009384523,0.0037552451,0.00008602763,0.00012606765,0.0000013393312,0.0000030238566,0.00014795833,0.9943116],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99798363,0.00017669173,0.00013448462,0.0007918047,0.00034594827,0.00056744943],"domain_scores_gemma":[0.99709356,0.00020876573,0.00029761047,0.002259708,0.000025777334,0.000114605624],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000066376255,0.00044092603,0.0004111576,0.0003284175,0.0003033513,0.00021026809,0.0042751958,0.00055570947,0.00006398033],"category_scores_gemma":[0.00003000369,0.00034534134,0.00019471649,0.00049350096,0.00024410519,0.00057738635,0.0015394118,0.00067356,0.00017776266],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010841699,0.000031611355,0.00003369067,0.000027977116,0.000103664366,0.00010775994,0.00006229361,0.000004008217,0.0000013302916,0.057531644,0.9408346,0.0012506022],"study_design_scores_gemma":[0.00069351326,0.00006427348,0.00005649452,0.00011412515,0.00003245312,0.0000032483658,0.0001084619,0.00010184918,0.00008537917,0.00093632104,0.99734294,0.0004609528],"about_ca_topic_score_codex":0.0003969195,"about_ca_topic_score_gemma":0.071115114,"teacher_disagreement_score":0.07071819,"about_ca_system_score_codex":0.000049563732,"about_ca_system_score_gemma":0.00022452576,"threshold_uncertainty_score":0.99989986},"labels":[],"label_agreement":null},{"id":"W6931351420","doi":"10.5281/zenodo.4373407","title":"Sedum acre Linnaeus 1753","year":2007,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Sedum; Acre; Taxon; Genus; Type (biology)","score_opus":0.03158897691912673,"score_gpt":0.2544166324717728,"score_spread":0.22282765555264608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931351420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02912795,0.00008330233,0.77994543,0.0031172836,0.00027667944,0.00025363825,0.00002480132,0.0047731185,0.18239777],"genre_scores_gemma":[0.99276865,0.00001443072,0.0054969625,0.0002261265,0.0001097451,9.094168e-9,0.00008715766,0.0004342701,0.0008626573],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985565,0.00010499357,0.00019580561,0.0004164091,0.00027795517,0.00044832935],"domain_scores_gemma":[0.9987688,0.000028773144,0.000077229604,0.0006195399,0.00036607095,0.00013960592],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009410313,0.00011406961,0.00010856916,0.00026576265,0.0013557156,0.0005335548,0.0020222731,0.00008102002,0.0010151946],"category_scores_gemma":[0.0003681974,0.00011733704,0.00004173527,0.00077927357,0.00013735698,0.00025365208,0.0014015454,0.00030484944,0.0069614993],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018544692,0.00016151887,0.00002408127,0.000021156533,0.000027620199,0.000083684026,0.00071419514,0.0000128656775,0.007563793,0.12988067,0.08950939,0.7719825],"study_design_scores_gemma":[0.0002727188,0.00017160541,0.0039546737,0.000013075242,0.0000032135872,0.00014688133,0.000093879855,0.0005240164,0.0029817338,0.0010284181,0.99065065,0.00015911116],"about_ca_topic_score_codex":0.000006641815,"about_ca_topic_score_gemma":3.0757542e-7,"teacher_disagreement_score":0.9636407,"about_ca_system_score_codex":0.00008900038,"about_ca_system_score_gemma":0.0000029747157,"threshold_uncertainty_score":0.9999444},"labels":[],"label_agreement":null},{"id":"W6931496671","doi":"10.5281/zenodo.7003405","title":"Liopterus haemorrhoidalis","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Instar; Margin (machine learning); Table (database); Seta; Head and neck","score_opus":0.03224118539328549,"score_gpt":0.23364026215641095,"score_spread":0.20139907676312546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931496671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06879964,0.00029452442,0.47523537,0.017081022,0.00096389576,0.00083063584,0.00031131518,0.013679375,0.42280424],"genre_scores_gemma":[0.9960107,0.0000075780267,0.002246382,0.0002981937,0.000043165728,6.5628164e-8,0.00017034561,0.00039299336,0.00083057187],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856037,0.00027058285,0.0001453347,0.00039851727,0.0003209422,0.0003042454],"domain_scores_gemma":[0.9990631,0.0000157503,0.0000701141,0.0006006222,0.00016764834,0.000082780745],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00053837075,0.000090995876,0.00009547365,0.0002646278,0.0033614393,0.0005186159,0.002551493,0.000026065925,0.004854028],"category_scores_gemma":[0.00015450963,0.00010287073,0.00004074727,0.00079995394,0.00008766151,0.00019779676,0.0038680313,0.00033311115,0.002661841],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015380176,0.00021059101,0.00000986212,0.000014239126,0.00002671803,0.000089546986,0.0010242545,0.0001944533,0.0034046746,0.14940245,0.22099218,0.62461567],"study_design_scores_gemma":[0.00021963315,0.00021883663,0.00048027682,0.0000027443984,0.000002621949,0.00030873227,0.00011330902,0.0018655877,0.00047531023,0.0011219127,0.9950648,0.00012622177],"about_ca_topic_score_codex":0.000010916795,"about_ca_topic_score_gemma":6.327241e-8,"teacher_disagreement_score":0.92721105,"about_ca_system_score_codex":0.000126406,"about_ca_system_score_gemma":0.0000029188216,"threshold_uncertainty_score":0.9981147},"labels":[],"label_agreement":null},{"id":"W6931975640","doi":"10.5683/sp3/jjszzw","title":"UNI-CEN Boundaries (CBF-Harmonized Shorelines) - Census Tract (CT) - 1971 - geojson format (WGS84 / EPSG:4326)","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Shapefile; North American Datum of 1927; Census; File format; Documentation; Geocoding; Boundary (topology)","score_opus":0.0312840322429156,"score_gpt":0.27654264519486277,"score_spread":0.24525861295194717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931975640","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010728613,0.00085542543,0.0016471448,0.002334621,0.0014078441,0.0004610771,0.99171036,0.0011039036,0.00037236165],"genre_scores_gemma":[0.00041692966,0.00061159587,0.0018082958,0.00067360396,0.0002413236,0.00018324984,0.99586946,0.000064317894,0.00013121805],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99521834,0.00028283522,0.00094288215,0.0013479111,0.0010183479,0.001189658],"domain_scores_gemma":[0.9955985,0.00026046927,0.0006867965,0.003006061,0.00019423597,0.00025394064],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007542835,0.0008926556,0.0010897323,0.00068136194,0.0012262781,0.0011127286,0.004818588,0.0004395573,0.0007374156],"category_scores_gemma":[0.0003083813,0.000844122,0.00038695618,0.00087248493,0.0005687631,0.0004886148,0.0016453842,0.0017952565,0.0000511272],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024407806,0.00026266932,0.000022731763,0.00008751353,0.000092847506,0.0008486617,0.00007593779,0.00000916685,0.0000032483645,0.0021920966,0.98547095,0.010909764],"study_design_scores_gemma":[0.0007848117,0.00019844572,0.00045432543,0.000052201696,0.00012117631,0.0004267945,0.00006760311,0.00037162015,0.00005105081,0.0012618867,0.9952975,0.0009126087],"about_ca_topic_score_codex":0.030870732,"about_ca_topic_score_gemma":0.0029893615,"teacher_disagreement_score":0.02788137,"about_ca_system_score_codex":0.00037218575,"about_ca_system_score_gemma":0.00062634866,"threshold_uncertainty_score":0.9999242},"labels":[],"label_agreement":null},{"id":"W6939520881","doi":"10.6084/m9.figshare.12965906.v1","title":"Additional file 1 of The role of cognitive load in modulating social looking: a mobile eye tracking study","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"","keywords":"Cognitive load; Eye tracking; Cognition; Mobile device; Window (computing); Key (lock)","score_opus":0.025687395171642296,"score_gpt":0.26957144600011035,"score_spread":0.24388405082846806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6939520881","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008723973,0.000013224563,0.000009784391,0.000039775645,0.000006943494,0.0002528173,0.98950213,0.00006344036,0.0013878993],"genre_scores_gemma":[0.9792818,1.074202e-8,0.00018488498,0.000035924575,0.000029362947,0.0003085319,0.02014662,0.0000054315005,0.000007455827],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9991724,0.00005588212,0.00019093289,0.00021655139,0.00023571962,0.00012851576],"domain_scores_gemma":[0.9988848,0.0006211456,0.00020419304,0.000104630795,0.00016803629,0.000017189097],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000026844446,0.00008023729,0.00015188962,0.000034911758,0.00006407153,0.0000151516415,0.00048352766,0.00005127571,0.39214668],"category_scores_gemma":[0.0023234915,0.000069615155,0.000063643434,0.00045300924,0.000017514805,0.00009588874,0.00030385505,0.00018519157,0.0000536446],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019681027,0.0009086347,0.0027208494,0.000112166126,0.00008090255,0.000027987258,0.020443253,0.00023717029,0.0003490712,0.0001536611,0.7349864,0.23996022],"study_design_scores_gemma":[0.0015704053,0.0008197217,0.86415493,0.006521238,0.000018605966,0.000004872886,0.010139681,0.07418411,0.0059830095,0.0010531807,0.034914583,0.00063563086],"about_ca_topic_score_codex":0.0000040681825,"about_ca_topic_score_gemma":0.000009286728,"teacher_disagreement_score":0.9705578,"about_ca_system_score_codex":0.000020811021,"about_ca_system_score_gemma":0.000111062705,"threshold_uncertainty_score":0.608409},"labels":[],"label_agreement":null},{"id":"W6957865023","doi":"10.60692/xrcb4-12b31","title":"FRCNN-GNB: Cascade Faster R-CNN With Gabor Filters and Naïve Bayes for Enhanced Eye Detection","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Humber Polytechnic","funders":"","keywords":"Pattern recognition (psychology); Biometrics; Convolutional neural network; Bayes' theorem; Iris recognition; Gabor filter; Identification (biology); Naive Bayes classifier; Cascade","score_opus":0.015476579194225903,"score_gpt":0.20386692974630144,"score_spread":0.18839035055207554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6957865023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38509145,0.0000031704362,0.6139229,0.00011171094,0.0001755503,0.00015969794,0.000016194666,0.0002530201,0.00026634207],"genre_scores_gemma":[0.9907838,1.4262069e-7,0.008779744,0.00013273834,0.000031799053,0.000106461055,0.0000052048613,0.0000070253313,0.00015305643],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989934,0.0000348677,0.00033331694,0.00024043699,0.00016010736,0.00023789346],"domain_scores_gemma":[0.99913114,0.00001510182,0.0002077165,0.00034025297,0.0002480769,0.00005770087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001549481,0.00015745529,0.00019401865,0.00016795295,0.00017255252,0.00028772687,0.00017984552,0.00010946469,0.0000019660467],"category_scores_gemma":[0.00002008713,0.00012264738,0.00004137265,0.0002543325,0.00003478244,0.0007856272,0.000072444,0.00008845293,0.000049817616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008787459,0.000060321894,0.14668123,0.006881277,0.0012257313,0.00013395918,0.46327344,0.001556986,0.008792117,0.013362198,0.0006711692,0.3564828],"study_design_scores_gemma":[0.005517384,0.00076957885,0.15012158,0.00080082804,0.00009795849,0.0005155581,0.024107225,0.06288824,0.7509755,0.000033358756,0.0029183128,0.0012544971],"about_ca_topic_score_codex":0.0000027258986,"about_ca_topic_score_gemma":0.0000015408168,"teacher_disagreement_score":0.7421834,"about_ca_system_score_codex":0.00005235248,"about_ca_system_score_gemma":0.00003311312,"threshold_uncertainty_score":0.5001415},"labels":[],"label_agreement":null},{"id":"W6958545183","doi":"10.6084/m9.figshare.12195522","title":"Additional file 2 of Improving the treatment of pre-operative anemia in hepato-pancreato-biliary patients: a quality improvement initiative","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital; London Health Sciences Centre","funders":"","keywords":"Quality management; Anemia; Quality (philosophy); MEDLINE; Performance improvement","score_opus":0.04008524013312414,"score_gpt":0.2677882112126293,"score_spread":0.22770297107950516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6958545183","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009946218,0.000018912138,0.000022595641,0.00013646734,0.000008584179,0.00034680593,0.9891759,0.00004836136,0.00029615275],"genre_scores_gemma":[0.70344615,0.0000014911899,0.0015411527,0.00026600613,0.000027834472,0.0014769783,0.29321107,0.0000085621505,0.000020737683],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99894226,0.00009078957,0.00032150987,0.00030560093,0.00018453748,0.00015532022],"domain_scores_gemma":[0.99813807,0.0010776162,0.00028472985,0.00026456817,0.00019685293,0.000038157217],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002379536,0.00013368225,0.00023077561,0.000042308868,0.00004665296,0.000014207101,0.00038845636,0.000059596572,0.3127988],"category_scores_gemma":[0.0013036042,0.00009438957,0.0000748088,0.00029322863,0.000029473253,0.0001490292,0.00014669531,0.00010256677,0.000050435217],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046733774,0.0010418087,0.0007627398,0.00016497765,0.000053894713,0.000009160556,0.004380021,0.000012608281,0.00015823645,0.00015500143,0.88942623,0.10378859],"study_design_scores_gemma":[0.004262931,0.010124464,0.7941895,0.002753901,0.00002574937,0.0000024713818,0.0012250895,0.019830296,0.041325763,0.0004227578,0.12487436,0.00096272305],"about_ca_topic_score_codex":0.00003816215,"about_ca_topic_score_gemma":0.000023586801,"teacher_disagreement_score":0.79342675,"about_ca_system_score_codex":0.00008422306,"about_ca_system_score_gemma":0.00014419352,"threshold_uncertainty_score":0.6878294},"labels":[],"label_agreement":null},{"id":"W6966804948","doi":"10.4230/lipics.cosit.2024.30","title":"Wheelchair Users Navigational Behavior: Insights from Eye Movement Data and Environment Legibility (Short Paper)","year":2024,"lang":"en","type":"article","venue":"DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Centre de Géomatique du Québec","funders":"","keywords":"Legibility; Eye movement; Wheelchair; Cognition; Process (computing); Fixation (population genetics)","score_opus":0.022895423866908904,"score_gpt":0.2724276601275259,"score_spread":0.249532236260617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6966804948","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7476028,0.00048015424,0.24528883,0.0013704363,0.0010777463,0.001022894,0.0020725187,0.0006425361,0.00044206166],"genre_scores_gemma":[0.9689644,0.000095546886,0.028407304,0.00059593347,0.000093636474,0.00014043423,0.0016040026,0.00002736758,0.00007138638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973818,0.000033934182,0.000837365,0.0007188396,0.0005514983,0.000476548],"domain_scores_gemma":[0.99779445,0.00013518568,0.00012805329,0.0017094717,0.000058526224,0.0001743335],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037391545,0.0003790315,0.00034347046,0.00017272444,0.00028674369,0.000565508,0.0016516285,0.0002219481,0.000028622608],"category_scores_gemma":[0.000027547016,0.00033188175,0.000106941494,0.00018081043,0.00025320423,0.0023744947,0.0016053993,0.00047639763,0.0000911956],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012988523,0.0027866683,0.2251142,0.0013708973,0.0015971687,0.00020225711,0.024846694,0.00027238778,0.004978608,0.17640048,0.0154190725,0.5468817],"study_design_scores_gemma":[0.0027021694,0.0006079681,0.18285702,0.000590922,0.00027660574,0.000047892005,0.0016775925,0.42211512,0.003458819,0.022053692,0.36162892,0.0019833036],"about_ca_topic_score_codex":0.00004193793,"about_ca_topic_score_gemma":0.000028420145,"teacher_disagreement_score":0.5448984,"about_ca_system_score_codex":0.00017944617,"about_ca_system_score_gemma":0.00007354348,"threshold_uncertainty_score":0.99991333},"labels":[],"label_agreement":null},{"id":"W6967610318","doi":"10.5281/zenodo.11368508","title":"HeadShift: Head Pointing with Dynamic Control-Display Gain","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Head (geology); Noise (video); Field (mathematics); Human head","score_opus":0.01718898908827891,"score_gpt":0.24995957488008852,"score_spread":0.23277058579180962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6967610318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020522768,0.00028567153,0.93538314,0.00820315,0.00016452247,0.00030272844,0.00005805427,0.0057305237,0.02934943],"genre_scores_gemma":[0.9961429,0.000011183944,0.002599776,0.0001428525,0.00003925525,4.4167336e-8,0.00008805104,0.0004911877,0.00048470352],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984544,0.00017698901,0.00017430156,0.00052892696,0.00027030258,0.00039508604],"domain_scores_gemma":[0.99913245,0.000044944318,0.00004669916,0.0004717637,0.0002045398,0.0000995778],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00058220077,0.00013936518,0.0001351151,0.0002711266,0.0010686533,0.0013854706,0.0012459374,0.00005619763,0.0003824999],"category_scores_gemma":[0.00016683408,0.00012168646,0.00004060779,0.00081079657,0.00014640582,0.00036512283,0.0005599442,0.00035192017,0.0040293606],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005312502,0.00017465021,0.000018948325,0.00016611522,0.00013722567,0.00034086395,0.0014256872,0.00034515475,0.010021663,0.38883173,0.027663017,0.5708218],"study_design_scores_gemma":[0.00072431756,0.00056648627,0.001769097,0.00019694156,0.000015999667,0.0006139499,0.000090363705,0.09195102,0.00042150763,0.0014105899,0.901911,0.0003287234],"about_ca_topic_score_codex":0.000011677878,"about_ca_topic_score_gemma":0.000001151901,"teacher_disagreement_score":0.97562015,"about_ca_system_score_codex":0.00011177913,"about_ca_system_score_gemma":0.000006461713,"threshold_uncertainty_score":0.9996512},"labels":[],"label_agreement":null},{"id":"W6977257805","doi":"10.6084/m9.figshare.21228843","title":"Additional file 4 of Dapagliflozin reduces the vulnerability of rats with pulmonary arterial hypertension-induced right heart failure to ventricular arrhythmia by restoring calcium handling","year":2022,"lang":"en","type":"other","venue":"Figshare","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Montreal Heart Institute","funders":"","keywords":"TUNEL assay; Dapagliflozin; DAPI; Calcium; Heart failure; Apoptosis","score_opus":0.02197028870056937,"score_gpt":0.22687845311282204,"score_spread":0.20490816441225268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977257805","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00012399675,0.0000977228,0.0000142600675,0.0003516594,0.00012564148,0.00035879968,0.9950931,0.00017813583,0.0036566856],"genre_scores_gemma":[0.027000919,5.384375e-7,0.008199418,0.00018676335,0.0011712718,0.0029110026,0.9464834,0.00026407235,0.013782621],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982906,0.00013422314,0.00025842024,0.00058377057,0.0004746497,0.0002583434],"domain_scores_gemma":[0.99809206,0.0005964649,0.00033696208,0.00077946775,0.0001290202,0.00006601641],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006439736,0.00025055453,0.00041726837,0.00017640652,0.00015413489,0.000030718147,0.00080561946,0.00019009427,0.82538205],"category_scores_gemma":[0.00073937257,0.00017907222,0.000105067855,0.00050243747,0.000030989933,0.00005879277,0.0003737797,0.0004105895,0.00010698674],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008759508,0.00004496656,7.814072e-7,0.000049365255,0.000030972617,0.000071708026,0.000010109039,0.000005679737,0.001467347,0.0000049638224,0.99772716,0.0005781769],"study_design_scores_gemma":[0.0000846389,0.00011064236,0.00017687406,0.0019865267,0.0000078249095,0.000088911045,0.000015629252,0.00005748656,0.006842811,0.000010903751,0.9904012,0.0002165595],"about_ca_topic_score_codex":0.00002007392,"about_ca_topic_score_gemma":0.000007304566,"teacher_disagreement_score":0.82527506,"about_ca_system_score_codex":0.000057388967,"about_ca_system_score_gemma":0.0001810991,"threshold_uncertainty_score":0.7302353},"labels":[],"label_agreement":null},{"id":"W6977834467","doi":"10.6084/m9.figshare.c.7315071","title":"Understanding the epidemiology and perceived efficacy of cannabis use in patients with chronic musculoskeletal pain","year":2024,"lang":"en","type":"other","venue":"Figshare","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto General Hospital; Sunnybrook Health Science Centre; University Health Network","funders":"","keywords":"Cannabis; Chronic pain; Epidemiology; Effects of cannabis; Logistic regression; Population; Cross-sectional study","score_opus":0.06990411985559515,"score_gpt":0.2788390346597565,"score_spread":0.20893491480416138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977834467","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038522523,0.054457862,0.050323185,0.03255507,0.0045283455,0.028235989,0.57906204,0.016224131,0.19609088],"genre_scores_gemma":[0.9590543,0.000021336024,0.0013017883,0.00019676346,0.00014553903,0.00021944859,0.008197891,0.0004204552,0.030442469],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9988281,0.00022132066,0.00016994576,0.0004287518,0.00009496851,0.00025687632],"domain_scores_gemma":[0.99897826,0.00038803133,0.00016072602,0.0004213122,0.000020257838,0.000031388427],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00016294337,0.0001921573,0.00030898023,0.00023327243,0.00002465755,0.000027089507,0.0004490864,0.00021600202,0.00194389],"category_scores_gemma":[0.0009639938,0.0001208002,0.00004974348,0.0002510553,0.00006304696,0.0000461387,0.00022060305,0.0003415245,0.00006461492],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003953923,0.000036658374,0.0037209045,0.0005251659,0.00007110042,0.000013913889,0.0001302318,0.000011553002,3.3443544e-7,0.0010830263,0.9874247,0.006978486],"study_design_scores_gemma":[0.0022284526,0.0019194924,0.69339705,0.024378238,0.00006272598,0.000008027104,0.00009138068,0.0024884425,0.0000014309551,0.0014752755,0.27282953,0.0011199674],"about_ca_topic_score_codex":0.00010055751,"about_ca_topic_score_gemma":0.00078946096,"teacher_disagreement_score":0.9205318,"about_ca_system_score_codex":0.00012850546,"about_ca_system_score_gemma":0.000065657325,"threshold_uncertainty_score":0.9989685},"labels":[],"label_agreement":null},{"id":"W6989489728","doi":"","title":"Benefit of \"rysis\" : A Wheelchair Seated Posture Measurement Based on ISO 16840-1","year":2016,"lang":"","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wheelchair; Manual wheelchair; Units of measurement; Software; Work (physics); International standard","score_opus":0.02613488807120797,"score_gpt":0.24166126456636455,"score_spread":0.21552637649515657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989489728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12398921,0.0026857855,0.8159326,0.024371425,0.018121114,0.0017440489,0.0049782665,0.0013457445,0.0068318574],"genre_scores_gemma":[0.9919715,0.000066942426,0.0065866695,0.00030227262,0.0005614795,0.000073209674,0.00010042972,0.000033523647,0.0003039879],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99274284,0.00022893593,0.0013142979,0.0017639728,0.003015233,0.00093470485],"domain_scores_gemma":[0.99325866,0.0004180314,0.00079598586,0.0026841413,0.0024721562,0.00037102087],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011068541,0.0008111666,0.00081834575,0.00059561036,0.0009708134,0.00021016588,0.0019194947,0.00045508117,0.000080772596],"category_scores_gemma":[0.0016120094,0.00061614654,0.00036776558,0.0013662433,0.0015163514,0.0011240093,0.0007234851,0.0005865573,0.00012435697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015865602,0.004080398,0.022547727,0.0005940663,0.0008562917,0.001308363,0.00029247958,0.0020768181,0.26895002,0.640901,0.008014634,0.04879159],"study_design_scores_gemma":[0.011473083,0.0048703454,0.14866471,0.021130357,0.00073316135,0.00067418884,0.00012396564,0.022577664,0.6211145,0.003350182,0.16103975,0.0042480817],"about_ca_topic_score_codex":0.0004067149,"about_ca_topic_score_gemma":0.0001450481,"teacher_disagreement_score":0.86798227,"about_ca_system_score_codex":0.0011968514,"about_ca_system_score_gemma":0.002278798,"threshold_uncertainty_score":0.99962896},"labels":[],"label_agreement":null},{"id":"W6990386893","doi":"","title":"Design of miniaturized coil system using MEMS technology for eye movement measurement","year":2009,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"McGill University","keywords":"Electromagnetic coil; Voice coil; Inductive sensor; Microelectromechanical systems; Search coil; Eye movement; Gyroscope; Eye tracking on the ISS; Eye tracking","score_opus":0.039651671753132015,"score_gpt":0.26056185369382384,"score_spread":0.22091018194069184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990386893","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9379832,0.0032769737,0.024289854,0.00027850905,0.007894978,0.009908471,0.00085655693,0.0060770265,0.009434414],"genre_scores_gemma":[0.8797096,0.00003742639,0.11927551,0.00007589393,0.000025936964,0.0003350512,0.00006383314,0.000113338996,0.00036338854],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9945081,0.00026902053,0.0014314356,0.0015712732,0.0012354952,0.0009847058],"domain_scores_gemma":[0.99492234,0.00015935452,0.0015729836,0.0015524767,0.0016275964,0.00016527472],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002209405,0.00088920916,0.0014014202,0.0013277453,0.0007920009,0.00008346699,0.0024591705,0.0014321029,0.0000038521684],"category_scores_gemma":[0.0005548831,0.0009239896,0.000390857,0.001239019,0.00009157766,0.0003629759,0.00019818742,0.0010118838,0.000018203102],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019228006,0.0003712202,0.0000056334175,0.00079479936,0.00040076044,0.000040312178,0.0000073123097,0.00023631644,0.6329437,0.20764792,0.00000514807,0.15735462],"study_design_scores_gemma":[0.002477596,0.00096380117,0.00024589672,0.0021782082,0.00040438474,0.000024170686,0.00028480563,0.002930823,0.9506339,0.035927158,0.0024987343,0.0014305648],"about_ca_topic_score_codex":0.00006771647,"about_ca_topic_score_gemma":0.00008055835,"teacher_disagreement_score":0.3176902,"about_ca_system_score_codex":0.0016141474,"about_ca_system_score_gemma":0.00020569419,"threshold_uncertainty_score":0.9998642},"labels":[],"label_agreement":null},{"id":"W6991688795","doi":"","title":"Human scanpaths during active vision","year":2024,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Electronic Science and Technology of China; McGill University","keywords":"Salient; Salience (neuroscience); Eye movement; Fixation (population genetics); Microsaccade; Perception; Active vision; Visual search; Satisficing","score_opus":0.011607467638252876,"score_gpt":0.26361706798766726,"score_spread":0.2520096003494144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991688795","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92107934,0.00024657004,0.0000024080769,0.00005063345,0.0023803783,0.00042245717,0.00021680175,0.00228221,0.0733192],"genre_scores_gemma":[0.983775,0.00004499004,0.0012331941,0.000063720596,0.00006541876,0.00012639847,0.0002030301,0.00017743163,0.014310771],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9949648,0.00022451654,0.0007946129,0.002157284,0.00089588494,0.00096293486],"domain_scores_gemma":[0.99725705,0.0000966882,0.0005009544,0.0015190494,0.00035243435,0.00027382403],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005123315,0.0009211165,0.000796858,0.0010913885,0.0017020024,0.0003618268,0.0024350376,0.0011330406,0.000072027855],"category_scores_gemma":[0.00021030243,0.00095603336,0.000460775,0.0012251384,0.00008926787,0.0011348412,0.0006440408,0.0029405665,0.0011159932],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000042105275,0.00024785107,0.000010225592,0.00055299996,0.0002543372,0.00059933943,0.000032094693,0.000011540204,0.23396206,0.46978107,0.000018833864,0.29448754],"study_design_scores_gemma":[0.0014228976,0.00066562806,0.02093037,0.0031929843,0.00032521973,0.00019474122,0.0003949571,0.00022050295,0.7274606,0.21474403,0.027039437,0.0034086378],"about_ca_topic_score_codex":0.00018064429,"about_ca_topic_score_gemma":0.0007231319,"teacher_disagreement_score":0.49349853,"about_ca_system_score_codex":0.0009005433,"about_ca_system_score_gemma":0.00006509458,"threshold_uncertainty_score":0.99966174},"labels":[],"label_agreement":null},{"id":"W6997085240","doi":"","title":"Über das Nationale in der lettischen Musik des 20. Jahrhunderts","year":2004,"lang":"de","type":"other","venue":"Qucosa (Saxon State and University Library Dresden)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Quebec Population Health Research Network","funders":"","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Diafiltration; Liquation; Emperipolesis; Triacetin; Durvalumab","score_opus":0.015272558327978192,"score_gpt":0.21406413160942206,"score_spread":0.19879157328144387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997085240","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21202973,0.018181864,0.04459609,0.017780708,0.0019108974,0.002128714,0.0010390108,0.0018387676,0.7004942],"genre_scores_gemma":[0.14496441,0.0038353452,0.017123656,0.0008788014,0.00020994732,0.000003220854,0.00025995108,0.00019273335,0.8325319],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971921,0.0002412015,0.00032119482,0.0011584,0.0003608374,0.0007262925],"domain_scores_gemma":[0.9987114,0.00014234085,0.00027128172,0.0005780107,0.000050366794,0.00024658465],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001277935,0.00055668224,0.0005841447,0.0011537166,0.00036912147,0.00027088475,0.0011219397,0.00052898657,0.0015076788],"category_scores_gemma":[0.000014731951,0.0006321896,0.0001397791,0.0009411346,0.0007664647,0.0018504822,0.00095680146,0.0007590889,0.0006996982],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00063481997,0.0026350939,0.1404245,0.0017454852,0.0021083828,0.015245816,0.013584721,0.000721275,0.00032572474,0.27894285,0.38679212,0.15683919],"study_design_scores_gemma":[0.0023566198,0.00018501659,0.034819867,0.0007771511,0.00009196428,0.000043026135,0.00039839078,0.00034145062,0.00018584759,0.017147154,0.9425842,0.0010692951],"about_ca_topic_score_codex":0.00036704267,"about_ca_topic_score_gemma":0.0010743177,"teacher_disagreement_score":0.5557921,"about_ca_system_score_codex":0.00018091303,"about_ca_system_score_gemma":0.00059891655,"threshold_uncertainty_score":0.9996129},"labels":[],"label_agreement":null},{"id":"W6998581995","doi":"","title":"Affective interpretations of assisted driving interventions on a smart-wheelchair : an exploratory study","year":2018,"lang":"en","type":"other","venue":"cIRcle (University of British Columbia)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nucleofection; TSG101; Hyporeflexia; Gestational period; Dysgeusia; Diafiltration; Fusible alloy; Proteogenomics","score_opus":0.018386110212650235,"score_gpt":0.2307630338091516,"score_spread":0.21237692359650137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6998581995","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8081376,0.0000980098,0.13935861,0.000051688687,0.00079047546,0.0011148545,0.00021059075,0.0012200631,0.04901809],"genre_scores_gemma":[0.98227406,0.000007278542,0.0023389522,0.0000059732447,0.000019311265,0.0000033614335,0.00001127909,0.000057857033,0.015281939],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99848604,0.0002629966,0.00017561128,0.00064172567,0.00024029214,0.00019335966],"domain_scores_gemma":[0.9984074,0.00006249325,0.00045854526,0.00078039477,0.00021183303,0.00007931923],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023417613,0.00009018593,0.00045135553,0.00036036677,0.00014647834,0.00007237022,0.0011612838,0.00020352568,0.00016882551],"category_scores_gemma":[0.000052149255,0.00028463107,0.00022743792,0.00049238966,0.00040183676,0.00023594413,0.00038533565,0.00021123029,0.000045182165],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000089748555,0.004936459,0.02232867,0.00029468385,0.00076846156,0.00027270825,0.0035310655,0.0000028428617,0.00008460947,0.00007401413,0.06793407,0.89976346],"study_design_scores_gemma":[0.0007619427,0.0015383072,0.99042654,0.0022047793,0.00009489392,0.000012899234,0.003705168,0.00026231023,9.448442e-7,0.00017059219,0.00052985456,0.0002917917],"about_ca_topic_score_codex":0.01916421,"about_ca_topic_score_gemma":0.6501836,"teacher_disagreement_score":0.96809787,"about_ca_system_score_codex":0.00009221094,"about_ca_system_score_gemma":0.00008300162,"threshold_uncertainty_score":0.9999606},"labels":[],"label_agreement":null},{"id":"W7019030590","doi":"","title":"Explorative Data Analysis of Eye-tracking Datafor Cognitive Assessments","year":2025,"lang":"en","type":"article","venue":"Örebro University Library (Örebro University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Test (biology); Cognition; Random forest; Montreal Cognitive Assessment; Variety (cybernetics); Set (abstract data type); Feature selection; Selection (genetic algorithm)","score_opus":0.038605898360260654,"score_gpt":0.28649947075248056,"score_spread":0.2478935723922199,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019030590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12909049,0.000051293126,0.8036821,0.0015391636,0.00035974008,0.0004146668,0.0011489653,0.0011112092,0.06260239],"genre_scores_gemma":[0.96330243,0.00014251657,0.020629086,0.0001622294,0.000014325192,1.4781813e-7,0.00087642885,0.000014041211,0.01485877],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99745905,0.0003244396,0.0002486265,0.0012271799,0.00029844177,0.0004422403],"domain_scores_gemma":[0.9971829,0.00040226924,0.00032834226,0.0017539063,0.00019380142,0.00013880336],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015807695,0.0003145432,0.00062553334,0.0035228697,0.0004112899,0.00011709341,0.0050630746,0.00021456569,0.000079547055],"category_scores_gemma":[0.00005554346,0.00035159322,0.00021745625,0.00944523,0.00041410656,0.007248555,0.0046442663,0.00039746112,0.000017903018],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052392884,0.001345668,0.32577294,0.00015573065,0.010422784,0.001129356,0.0016626439,0.00032594326,0.0007036255,0.62022376,0.011783802,0.025949799],"study_design_scores_gemma":[0.009612405,0.000876071,0.55742633,0.0011455389,0.010916781,0.0000049590276,0.043885127,0.09011889,0.01303844,0.0042131455,0.26552188,0.0032404358],"about_ca_topic_score_codex":0.0001040064,"about_ca_topic_score_gemma":0.00008795643,"teacher_disagreement_score":0.83421195,"about_ca_system_score_codex":0.00012144976,"about_ca_system_score_gemma":0.00043594916,"threshold_uncertainty_score":0.9998936},"labels":[],"label_agreement":null},{"id":"W7034353587","doi":"","title":"Tendances pédagogiques de la formation professionnelle : étude réalisée pour le ministère de l'éducation du Québec /","year":2015,"lang":"fr","type":"other","venue":"Bibliothèque et Archives nationales du Québec (Québec government)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Data collection; Context (archaeology); Work (physics); Qualitative research","score_opus":0.02309984114845134,"score_gpt":0.2718310045293424,"score_spread":0.24873116338089105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034353587","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09897883,0.016801372,0.15761459,0.24747443,0.0007387597,0.0012264304,0.00027080867,0.0012691016,0.47562566],"genre_scores_gemma":[0.7379027,0.00089705235,0.018019928,0.0013832647,0.0006522756,0.00036753342,0.000044529304,0.00018922478,0.24054348],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9949222,0.0014138189,0.00087707373,0.00097535586,0.001052128,0.0007594023],"domain_scores_gemma":[0.99046266,0.0070290393,0.0011824814,0.0007318609,0.00025134443,0.00034263887],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001487795,0.00077788933,0.0006567505,0.0011961249,0.0005809775,0.0007497048,0.00208709,0.00043146353,0.001358389],"category_scores_gemma":[0.005391093,0.00075970305,0.00029484846,0.00094193645,0.0009535026,0.0012659632,0.0007753161,0.00090667815,0.00048838684],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00010413941,0.0014897101,0.03804685,0.00045501077,0.00026312927,0.00006137732,0.039811637,0.00030128215,0.0014500117,0.09249475,0.81934184,0.006180245],"study_design_scores_gemma":[0.0013441682,0.00025168195,0.13889086,0.001028298,0.00010277921,0.00036966102,0.0021450052,0.013239855,0.0016458279,0.0055893036,0.8343623,0.0010302222],"about_ca_topic_score_codex":0.45562702,"about_ca_topic_score_gemma":0.9746072,"teacher_disagreement_score":0.6389239,"about_ca_system_score_codex":0.012021357,"about_ca_system_score_gemma":0.20377591,"threshold_uncertainty_score":0.9995545},"labels":[],"label_agreement":null},{"id":"W7034398554","doi":"","title":"Toronto air view, waterfront","year":2008,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Shore; Skyline; Work (physics); Vegetation (pathology); Human life","score_opus":0.007818663062461146,"score_gpt":0.15024975069361193,"score_spread":0.14243108763115078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034398554","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00010198439,0.0006060615,0.010392438,0.00038061268,0.0004384319,0.00025550037,0.00044801895,0.0046932003,0.9826838],"genre_scores_gemma":[0.009533051,0.0006681423,0.005312581,0.00012330743,0.00012801107,1.0770515e-7,0.00022248377,0.00018724459,0.9838251],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99763525,0.00009034002,0.0001605494,0.0011253898,0.0003199869,0.00066846656],"domain_scores_gemma":[0.9981248,0.000054385153,0.000274781,0.0011928002,0.000025414975,0.00032781658],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000017933626,0.0005990413,0.0006042603,0.000824663,0.00030568417,0.00018484879,0.0039538057,0.00066885434,0.0005764798],"category_scores_gemma":[0.000004700705,0.0007027358,0.00034383504,0.0009954523,0.0004110903,0.0028418112,0.0017584708,0.0004252895,0.00083057315],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035159686,0.00018970197,0.00082045305,0.000037705177,0.00019046421,0.0019625672,0.00010836327,0.0000041138956,0.0000020658215,0.04837055,0.93911844,0.009160416],"study_design_scores_gemma":[0.00059064315,0.00012424108,0.00023057191,0.0001557002,0.00003342936,0.000037818634,0.00040870876,0.000023602128,0.000026063628,0.00009463,0.99749714,0.00077743514],"about_ca_topic_score_codex":0.00032126074,"about_ca_topic_score_gemma":0.0001509938,"teacher_disagreement_score":0.05837872,"about_ca_system_score_codex":0.0003229438,"about_ca_system_score_gemma":0.0002990301,"threshold_uncertainty_score":0.99994737},"labels":[],"label_agreement":null},{"id":"W7036706740","doi":"","title":"The Convenience of War: Transboundary River Development in North America 1939-1945","year":2010,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Event (particle physics); Citation; River valley; Niche; Yangtze river","score_opus":0.004751618489202513,"score_gpt":0.17609442072492873,"score_spread":0.1713428022357262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7036706740","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00058113964,0.00022439248,0.0000987867,0.00020777209,0.00026195808,0.0002553762,0.000020910644,0.00014736694,0.9982023],"genre_scores_gemma":[0.0101323305,0.0002544786,0.018298764,0.00011190095,0.000023080938,0.000020502897,0.000023074164,0.000039127335,0.97109675],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984351,0.000051894192,0.00062579964,0.00021819877,0.00037296882,0.0002960345],"domain_scores_gemma":[0.99858826,0.0001422139,0.0005887387,0.00051382463,0.00011366975,0.000053312055],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00020263223,0.0002424967,0.00034892806,0.0001085974,0.00009162557,0.000026104874,0.0011472135,0.000259617,0.007149309],"category_scores_gemma":[0.000064217085,0.00019426426,0.000082349674,0.00003016063,0.0006021632,3.8664433e-7,0.00014263042,0.0004412129,0.0011043745],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013501739,0.0000399384,0.000036565056,0.00010607756,0.000023658526,0.000011111725,0.00069676567,0.000036808848,6.043461e-7,0.000113367714,0.9409021,0.058019508],"study_design_scores_gemma":[0.0002569197,0.00006390601,0.0003914005,0.000093932096,0.000006365636,0.0000184499,0.000035773435,0.000046299072,0.000038221264,0.000012026639,0.9988319,0.00020479022],"about_ca_topic_score_codex":0.0017652023,"about_ca_topic_score_gemma":0.007979698,"teacher_disagreement_score":0.05792982,"about_ca_system_score_codex":0.00003286898,"about_ca_system_score_gemma":0.00011915366,"threshold_uncertainty_score":0.99967337},"labels":[],"label_agreement":null},{"id":"W7038911569","doi":"","title":"L'aérospatiale numérique au Québec : un écosystème innovant au cœur des enjeux de la société","year":2020,"lang":"fr","type":"other","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Gloom; Context (archaeology); Period (music)","score_opus":0.014719922170724471,"score_gpt":0.24716381710861474,"score_spread":0.23244389493789028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7038911569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01751944,0.004583971,0.9094012,0.03148891,0.0007146133,0.001271839,0.0001649464,0.0035633736,0.03129175],"genre_scores_gemma":[0.7262671,0.0005798818,0.23422517,0.0030708981,0.0011938503,0.0008663828,0.000039749488,0.00063244015,0.033124544],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9918182,0.0013511867,0.0013518163,0.0019967675,0.0007249468,0.0027570748],"domain_scores_gemma":[0.9942687,0.00092117646,0.0011490321,0.0023032227,0.00037826394,0.0009796079],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0018077758,0.0016028418,0.0017129004,0.0010227694,0.0010781565,0.00076176034,0.0038894834,0.002718341,0.0006782429],"category_scores_gemma":[0.0018107647,0.0017326813,0.00070306787,0.0020449099,0.0017596,0.00046509915,0.001703717,0.0029592598,0.00035559526],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":true,"study_design_scores_codex":[0.00009182174,0.0011439734,0.12370112,0.0007244725,0.0006015309,0.0021142864,0.006578295,0.00049400545,0.008473834,0.35579765,0.009834758,0.49044424],"study_design_scores_gemma":[0.0033617206,0.0016481724,0.31739962,0.0030826295,0.0003585467,0.0038148358,0.0009236394,0.10954306,0.035948206,0.0920582,0.42603728,0.0058240807],"about_ca_topic_score_codex":0.5387571,"about_ca_topic_score_gemma":0.44726858,"teacher_disagreement_score":0.7087476,"about_ca_system_score_codex":0.009026937,"about_ca_system_score_gemma":0.012897451,"threshold_uncertainty_score":0.99967194},"labels":[],"label_agreement":null},{"id":"W7044116614","doi":"","title":"Using a 3D hand motion controller for reaching tasks in a powered wheelchair simulator","year":2015,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Centre for Interdisciplinary Research in Rehabilitation","keywords":"Joystick; Wheelchair; Virtual reality; Task (project management); Interface (matter); Controller (irrigation); Cursor (databases); Lift (data mining); Motion (physics)","score_opus":0.04172526919074632,"score_gpt":0.29585572593024406,"score_spread":0.25413045673949775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7044116614","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98894894,0.00042027875,0.0018737974,0.000040593022,0.0019693072,0.0017303489,0.0004145295,0.0007484384,0.0038537828],"genre_scores_gemma":[0.98251534,0.0000102130225,0.01609698,0.00011370784,0.00004766949,0.00016320481,0.00022610807,0.00013121695,0.0006955676],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953793,0.00036584918,0.0010262226,0.0015802833,0.00067898666,0.0009693341],"domain_scores_gemma":[0.99694896,0.00033623553,0.0008109178,0.0009259313,0.0007220284,0.0002559203],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018333796,0.0007894901,0.0011170122,0.0010445437,0.0009050286,0.00028440065,0.0013554916,0.0011939117,0.000006997464],"category_scores_gemma":[0.0016013881,0.00083945855,0.0003296743,0.00078692555,0.00007127254,0.0010346352,0.00019956587,0.001548381,0.00003440462],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007117291,0.00073485274,0.00017294461,0.00047065018,0.0003476086,0.00015425963,0.00013495753,0.0030777252,0.12193806,0.15023167,0.000010673177,0.72201484],"study_design_scores_gemma":[0.030059574,0.0018455571,0.006652284,0.0047272597,0.0007476975,0.00017550318,0.0009762253,0.3580763,0.06732381,0.47784376,0.04356521,0.00800683],"about_ca_topic_score_codex":0.00037992783,"about_ca_topic_score_gemma":0.00086900615,"teacher_disagreement_score":0.71400803,"about_ca_system_score_codex":0.0012226911,"about_ca_system_score_gemma":0.00013382368,"threshold_uncertainty_score":0.9994056},"labels":[],"label_agreement":null},{"id":"W7071125964","doi":"","title":"Second Order Change Detection, and its Application to Blink-Controlled Perceptual Interfaces","year":2003,"lang":"en","type":"article","venue":"NPARC","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Perception; Analogy; Motion (physics); Base (topology); Set (abstract data type)","score_opus":0.019301981349518695,"score_gpt":0.2485588011349873,"score_spread":0.2292568197854686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071125964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.70025766,0.00008662837,0.29342347,0.0014038499,0.00013681821,0.00039171474,0.0000010343899,0.00023338967,0.0040654102],"genre_scores_gemma":[0.99247724,0.000004807013,0.006646275,0.0003253785,0.000026225487,0.00016832954,2.0427348e-7,0.000005337147,0.0003461902],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993047,0.00004137921,0.00011397722,0.0002986151,0.000077988596,0.00016333608],"domain_scores_gemma":[0.99957305,0.000036245852,0.00003908568,0.0002014154,0.000096166266,0.000054005977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017143147,0.000089858615,0.00013721228,0.00011727831,0.00009159281,0.000045783225,0.0002152025,0.00006521049,0.000076673066],"category_scores_gemma":[0.000085503736,0.0000796086,0.000015904134,0.00031999947,0.0000226789,0.00013347848,0.000059229114,0.000098703196,0.00010305993],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035238296,0.0000966375,0.0005654291,0.000017737875,0.000033159016,0.0000027750302,0.0023761084,0.000004241626,0.5430824,0.08745973,0.00024965202,0.36607686],"study_design_scores_gemma":[0.011836194,0.001809521,0.06826296,0.00007460479,0.000066617315,0.00023766374,0.0009630706,0.072819814,0.6497281,0.052868262,0.139464,0.0018691632],"about_ca_topic_score_codex":0.000002034439,"about_ca_topic_score_gemma":0.000048465427,"teacher_disagreement_score":0.36420768,"about_ca_system_score_codex":0.000016999425,"about_ca_system_score_gemma":0.0000105677545,"threshold_uncertainty_score":0.32463446},"labels":[],"label_agreement":null},{"id":"W7097283221","doi":"","title":"Tracking the Line of Primary Gaze in a Walking Simulator: Modeling and Calibration","year":2007,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"BitTorrent tracker; Tracking (education); Eye tracking; Calibration; Tracking system; Gaze; Line (geometry); Point (geometry)","score_opus":0.02666372302074166,"score_gpt":0.25887646347680365,"score_spread":0.232212740456062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097283221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4136615,0.00007762363,0.5855745,0.00024788774,0.000021466909,0.00003715896,6.913398e-8,0.000056569854,0.0003231921],"genre_scores_gemma":[0.98419374,0.0000063889893,0.015646758,0.00012556485,0.000013164793,7.9434085e-7,2.9148262e-7,0.000003275732,0.000009991182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993614,0.000020553287,0.00021337856,0.00016047835,0.00010013798,0.00014403637],"domain_scores_gemma":[0.99957037,0.00015336038,0.000046585417,0.0001846861,0.000029260467,0.000015719586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006631545,0.00005995874,0.00010181316,0.00010820179,0.000042159452,0.000027769333,0.00022248366,0.000051136853,8.6741056e-7],"category_scores_gemma":[0.000038764534,0.000041650026,0.000016527527,0.00024572219,0.00003426415,0.0001934406,0.00009653968,0.00012279936,2.7785563e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020752575,0.00013637959,0.029936083,0.000053039923,0.000015466389,0.000022910419,0.0025128962,0.06966831,0.027317146,0.17314565,0.000009503853,0.69716185],"study_design_scores_gemma":[0.00016256032,0.000023152073,0.012245815,0.000026354539,0.0000015510576,0.000005233083,0.000066713576,0.9785012,0.0051372387,0.0037631146,0.000010811126,0.000056277535],"about_ca_topic_score_codex":0.000046170044,"about_ca_topic_score_gemma":0.00005674488,"teacher_disagreement_score":0.90883285,"about_ca_system_score_codex":0.000018259872,"about_ca_system_score_gemma":0.00001641867,"threshold_uncertainty_score":0.16984387},"labels":[],"label_agreement":null},{"id":"W7097582148","doi":"","title":"mann @ eecg. toronto, edu","year":2008,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Point (geometry); The Internet; Function (biology); Wireless; Field (mathematics); Class (philosophy)","score_opus":0.017083646307348852,"score_gpt":0.2244913633401983,"score_spread":0.20740771703284946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097582148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053815447,0.00027379213,0.69300133,0.0025420566,0.00041671595,0.00005965796,3.671753e-7,0.0015988336,0.24829184],"genre_scores_gemma":[0.943754,0.00002131352,0.049989324,0.0003337459,0.000029185809,0.00000384831,2.6378444e-7,0.0000029630648,0.0058653518],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99944407,0.000011083572,0.00007726354,0.00020081301,0.00009465753,0.0001721077],"domain_scores_gemma":[0.999535,0.00002059908,0.000018949344,0.00036073555,0.000028092343,0.00003660199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050210256,0.00006321522,0.00007755021,0.000021405807,0.000093688635,0.000015207008,0.00053948857,0.00004388422,0.000103540835],"category_scores_gemma":[0.000011825263,0.000051976946,0.000031201143,0.00008357342,0.000050346483,0.00020033137,0.000119824705,0.000055256904,0.0002799243],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000016618378,0.0001230412,0.010081197,0.0000031263464,0.000015611871,0.00012539611,0.00023984507,0.0000041593375,0.0009776314,0.82403636,0.044794228,0.11959777],"study_design_scores_gemma":[0.0011127184,0.00047804698,0.5828414,0.00002101689,0.0000081594435,0.0011199405,0.000119894474,0.01101652,0.03484574,0.011558535,0.35583943,0.0010385775],"about_ca_topic_score_codex":0.00017166417,"about_ca_topic_score_gemma":0.000093208684,"teacher_disagreement_score":0.88993853,"about_ca_system_score_codex":0.000028832821,"about_ca_system_score_gemma":0.000021072452,"threshold_uncertainty_score":0.3597954},"labels":[],"label_agreement":null},{"id":"W7100750037","doi":"","title":"Pupil Dilation and Eye-tracking","year":2014,"lang":"en","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pupillary response; Pupil; Dilation (metric space); Cornea; Eye tracking; Position (finance)","score_opus":0.010518823144321645,"score_gpt":0.2499743961850237,"score_spread":0.23945557304070206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100750037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1658628,0.000013094267,0.82267165,0.0017224441,0.00007121955,0.000019391337,3.7103593e-8,0.00035804973,0.009281313],"genre_scores_gemma":[0.96763706,0.0000016974594,0.031912122,0.0001617769,0.000018432502,0.0000013609883,2.125939e-7,0.000002070019,0.00026525132],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9995968,0.000016482369,0.00006207745,0.00016614787,0.00005710091,0.000101387355],"domain_scores_gemma":[0.99971455,0.000039198196,0.000022727394,0.00017801326,0.000022840708,0.000022666552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014473012,0.000045436314,0.000053783755,0.000047406324,0.00006191417,0.00006600352,0.00017138848,0.000034106164,0.000004289553],"category_scores_gemma":[0.000040478943,0.000037786616,0.000009722562,0.00009218243,0.000028946932,0.00016294529,0.0000768755,0.0000537814,0.000026425854],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.34399e-7,0.000010445963,0.031434365,0.0000029350533,0.0000022470144,7.2128137e-7,0.00006525287,0.0000063390116,0.0023607786,0.5942535,0.00018045769,0.37168264],"study_design_scores_gemma":[0.0002336076,0.00007795188,0.8387153,0.000012149571,0.0000029140842,0.0000099166145,0.000012845716,0.099453494,0.007932921,0.041079815,0.012287726,0.00018137619],"about_ca_topic_score_codex":0.00000765741,"about_ca_topic_score_gemma":0.000004717636,"teacher_disagreement_score":0.8072809,"about_ca_system_score_codex":0.0000047662957,"about_ca_system_score_gemma":0.0000033189376,"threshold_uncertainty_score":0.15408935},"labels":[],"label_agreement":null},{"id":"W7117879937","doi":"10.5220/0013916100004919","title":"Gesture-Based Virtual Control Interface: Enhancing Interaction with Eye Tracking and Voice Commands","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Eye tracking; Control (management); Control system; Tracking (education); Identification (biology); Tracking system","score_opus":0.008372791340171961,"score_gpt":0.2751928869349759,"score_spread":0.26682009559480396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117879937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2378164,0.0003671442,0.75354,0.0053217513,0.0007728247,0.00029199917,0.000002433026,0.00032753652,0.0015599242],"genre_scores_gemma":[0.99395066,0.000017111499,0.0036251915,0.0013942525,0.00005193994,0.000026802481,0.0000015240149,0.000020634503,0.00091187534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971793,0.00023838966,0.00061352976,0.0010596387,0.00028176035,0.00062733056],"domain_scores_gemma":[0.9978247,0.0007230351,0.00029607117,0.0007066941,0.00033009434,0.00011939594],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063209183,0.00050936535,0.000625987,0.0005525273,0.00051568734,0.0007108087,0.00074383756,0.00031612391,0.000029807836],"category_scores_gemma":[0.00015314562,0.0004310117,0.0000899334,0.00091613637,0.0004394902,0.0006500951,0.000247144,0.0010962425,0.000023814131],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022007856,0.0017274971,0.12786333,0.00061267184,0.0012407401,0.0001866973,0.0039282693,0.013621247,0.10957103,0.058333054,0.0010576507,0.67965704],"study_design_scores_gemma":[0.009063465,0.0031605265,0.11055284,0.0029763053,0.00038000048,0.000056998746,0.0020500878,0.6330158,0.23397446,0.00042962586,0.003090179,0.0012496895],"about_ca_topic_score_codex":0.00012782439,"about_ca_topic_score_gemma":0.00059310754,"teacher_disagreement_score":0.7561343,"about_ca_system_score_codex":0.00017346354,"about_ca_system_score_gemma":0.00023354642,"threshold_uncertainty_score":0.99981415},"labels":[],"label_agreement":null},{"id":"W7117974948","doi":"10.1007/978-3-032-00815-2_1","title":"How Generative AI Review Summaries Disrupt Users’ Evaluative Processes in Online Purchase Environments","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in information systems and organisation","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Eye tracking; Gaze; Generative grammar; Eye movement; Generative model","score_opus":0.017253441806032225,"score_gpt":0.25782309783531365,"score_spread":0.24056965602928143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117974948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00027396012,0.0153463995,0.971583,0.008621188,0.0004053603,0.0019010693,0.00017339321,0.00013639583,0.001559249],"genre_scores_gemma":[0.93904245,0.027496181,0.012436517,0.008861442,0.000310015,0.0004565242,0.0036203258,0.00007747027,0.007699075],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985321,0.0000639946,0.00058946427,0.0003044931,0.00033992395,0.00017002744],"domain_scores_gemma":[0.9988596,0.00016887923,0.00049533753,0.00030471833,0.0001410613,0.000030418583],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032752065,0.00031600925,0.00047017128,0.00044069582,0.000091637325,0.00028351115,0.00026634408,0.0003496329,0.0000060818834],"category_scores_gemma":[0.0005103821,0.00027192276,0.000029683742,0.0002101135,0.00008209484,0.0011453658,0.00013068451,0.00043445508,0.000008647008],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000057613855,0.0001823801,0.005643223,0.022076797,0.00029130405,0.000034779267,0.03149764,0.002951026,0.0001770153,0.24193817,0.001263374,0.6938867],"study_design_scores_gemma":[0.0067634727,0.0012969228,0.011035732,0.061764568,0.00043303645,0.0001866361,0.0011726058,0.065911055,0.0046840026,0.09972757,0.7413798,0.0056446176],"about_ca_topic_score_codex":0.000034232806,"about_ca_topic_score_gemma":0.00014985623,"teacher_disagreement_score":0.95914644,"about_ca_system_score_codex":0.00026413414,"about_ca_system_score_gemma":0.00020377964,"threshold_uncertainty_score":0.9999733},"labels":[],"label_agreement":null},{"id":"W7118750637","doi":"10.1016/j.ifacol.2025.12.465","title":"Human Variability in Human-Robot Locomotion","year":2025,"lang":"en","type":"article","venue":"IFAC-PapersOnLine","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Context (archaeology); Focus (optics); Motor control; Natural (archaeology); Control (management); Variation (astronomy); Human behavior","score_opus":0.01470433522060089,"score_gpt":0.3039022146488477,"score_spread":0.2891978794282468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118750637","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5959295,0.000060687275,0.38243476,0.010109311,0.0004248733,0.00032184224,0.0000058514624,0.00091580255,0.009797398],"genre_scores_gemma":[0.86140645,0.0000015368105,0.13743992,0.0002719541,0.00003332832,0.000016486552,0.000008655592,0.000005568729,0.0008161309],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.998504,0.00015280813,0.00031542528,0.00056362204,0.00014124454,0.00032294594],"domain_scores_gemma":[0.99905723,0.00008284494,0.00007209785,0.0006852358,0.0000637219,0.00003887565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071282935,0.00016960538,0.0002486631,0.00026211838,0.00017913646,0.000057576395,0.0008176915,0.00016136917,0.00003198156],"category_scores_gemma":[0.00011520257,0.00016592458,0.000068303845,0.00072404905,0.00011784073,0.00016152482,0.00024206014,0.0003649574,0.000023529345],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007702095,0.0016549904,0.07230841,0.000120746336,0.00005646865,0.00007861017,0.00045554125,0.0004342742,0.14282265,0.633733,0.00009106272,0.14823657],"study_design_scores_gemma":[0.0021373187,0.00025757754,0.88364905,0.00024164755,0.000024482399,0.00001149541,0.00009075787,0.021297242,0.012232483,0.07814985,0.0012371081,0.000671002],"about_ca_topic_score_codex":0.00014398369,"about_ca_topic_score_gemma":0.00022371607,"teacher_disagreement_score":0.81134063,"about_ca_system_score_codex":0.0001329541,"about_ca_system_score_gemma":0.000047169884,"threshold_uncertainty_score":0.6766208},"labels":[],"label_agreement":null},{"id":"W7127917559","doi":"10.22260/crc-csce-2025/0201","title":"Eye-Tracking-Based Risk Perception Prediction for Adaptive Hazard Recognition Training","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta; WorkSafeBC","keywords":"Training (meteorology); Perception; Hazard; Feature (linguistics); Risk assessment","score_opus":0.050900015379430695,"score_gpt":0.2991054721936157,"score_spread":0.24820545681418502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127917559","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086868085,0.00008550706,0.9038788,0.0024491856,0.0021191488,0.001016475,0.00018139904,0.0010871788,0.002314194],"genre_scores_gemma":[0.8955225,0.000050283805,0.10265926,0.00043061757,0.0001868898,0.00024111984,0.000065940214,0.00002450731,0.0008189079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643815,0.00030502022,0.00078685593,0.0013838533,0.00031501247,0.00077109016],"domain_scores_gemma":[0.997517,0.00048084755,0.00043779117,0.00063458213,0.0008218296,0.00010794787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013190431,0.00046515162,0.00049550884,0.0007867853,0.0009901389,0.0003510574,0.0006519211,0.0006085775,0.000096029165],"category_scores_gemma":[0.00058649,0.0004963839,0.0003755252,0.0011038953,0.00032271672,0.0006393137,0.000109944434,0.00071242783,0.000082276856],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020073507,0.00027944482,0.0035366528,0.000074418705,0.00012709697,0.0000024387348,0.0012219867,0.001099794,0.0012497456,0.0032197777,0.0013531562,0.9876348],"study_design_scores_gemma":[0.0021784767,0.0013079734,0.11381251,0.0006144978,0.00030004335,0.0000028468116,0.0014476834,0.85923874,0.0030442225,0.015815642,0.0017658346,0.0004715034],"about_ca_topic_score_codex":0.000068178975,"about_ca_topic_score_gemma":0.000084128864,"teacher_disagreement_score":0.98716325,"about_ca_system_score_codex":0.00036875656,"about_ca_system_score_gemma":0.0004943802,"threshold_uncertainty_score":0.99974877},"labels":[],"label_agreement":null},{"id":"W7132873266","doi":"","title":"Design and Development of a Chatbot as an Alternative Web-browser for those with Severe Motor Impairments","year":2025,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute","funders":"","keywords":"Chatbot; Selection (genetic algorithm); Interface (matter); User interface; Function (biology); Data collection; Web application","score_opus":0.038698443688869696,"score_gpt":0.3394348238227837,"score_spread":0.300736380133914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132873266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.556365,0.00016915669,0.4410794,0.00013385352,0.0002714003,0.0016305353,0.000010257959,0.000106413965,0.00023396233],"genre_scores_gemma":[0.6321179,0.000056522782,0.36066628,0.000047246274,0.000022689665,0.0004292493,0.000055339686,0.000036173893,0.006568571],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9971602,0.00015859377,0.0005105808,0.0011905871,0.00042616256,0.00055385643],"domain_scores_gemma":[0.9976464,0.00025764614,0.0007156256,0.00058397633,0.0006389573,0.00015740136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005316612,0.0006139199,0.00071732776,0.00046430575,0.00036519696,0.00012812353,0.0009452542,0.00032028678,0.000015410802],"category_scores_gemma":[0.00006964179,0.000550767,0.00006624801,0.00039743734,0.00012762495,0.00026533534,0.00016813062,0.00032727842,0.0000064690594],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.007577596,0.003101644,0.0016798037,0.0037535601,0.0027474188,0.00015129909,0.2621792,0.00085467164,0.028886754,0.009548334,0.00031531657,0.6792044],"study_design_scores_gemma":[0.02476,0.031957593,0.06015624,0.015874008,0.0011950416,0.0002994257,0.06309302,0.24405737,0.5381024,0.007913715,0.0050924667,0.0074986992],"about_ca_topic_score_codex":0.000094660485,"about_ca_topic_score_gemma":0.00010774437,"teacher_disagreement_score":0.67170566,"about_ca_system_score_codex":0.00014116423,"about_ca_system_score_gemma":0.0018743043,"threshold_uncertainty_score":0.9996944},"labels":[],"label_agreement":null},{"id":"W7132906059","doi":"","title":"A new approach for eye detection in remote gaze-estimation systems","year":2007,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Face (sociological concept); Identification (biology); Constant false alarm rate; Pattern recognition (psychology); Eye tracking; Facial recognition system; Artificial neural network","score_opus":0.03185011146115159,"score_gpt":0.35925080396755377,"score_spread":0.3274006925064022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7132906059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02325647,0.00066055066,0.96850353,0.00015228149,0.0021158785,0.0017830261,0.000002319235,0.00040708945,0.0031188533],"genre_scores_gemma":[0.7402528,0.000046570345,0.23526043,0.000018015047,0.00026347887,0.0000817598,0.00016757706,0.000072304676,0.023837045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962476,0.0001453944,0.0008515017,0.0014211613,0.00049496075,0.00083936926],"domain_scores_gemma":[0.9975539,0.00021289197,0.0008339335,0.00089892896,0.00033135945,0.00016900667],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013369703,0.00060685835,0.00075131934,0.0012430974,0.0002735024,0.000340935,0.00092925195,0.0012056917,0.00000779879],"category_scores_gemma":[0.00038015694,0.0006928942,0.00020073696,0.0016250707,0.000054998687,0.00029313174,0.000066912435,0.0008748346,0.00006334293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030171877,0.00014629273,0.0000775271,0.0013435795,0.00006977617,0.000015625652,0.0103996135,0.024262749,0.0045825955,0.004675356,0.00032458312,0.95380056],"study_design_scores_gemma":[0.0010810429,0.00028957552,0.0026909732,0.0006439463,0.00006781611,0.000023647332,0.003147162,0.983621,0.0063219676,0.00053886324,0.00090304704,0.00067096355],"about_ca_topic_score_codex":0.0031552748,"about_ca_topic_score_gemma":0.000501864,"teacher_disagreement_score":0.9593583,"about_ca_system_score_codex":0.0004855982,"about_ca_system_score_gemma":0.000366719,"threshold_uncertainty_score":0.99955225},"labels":[],"label_agreement":null},{"id":"W7133022541","doi":"","title":"Eye Biometrics Signal Analysis and Potential Applications in User Authentication and Affective Computing","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Identification (biology); Usability; Feature extraction; Iris recognition; Eye movement; Feature (linguistics); Key (lock)","score_opus":0.014342831231255626,"score_gpt":0.33215269231114575,"score_spread":0.3178098610798901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133022541","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60677034,0.0002772816,0.3920085,0.0002280743,0.00011122252,0.0004043694,0.000006300216,0.00016092457,0.00003300294],"genre_scores_gemma":[0.9932998,0.00016284343,0.005285441,0.000008983119,0.00004082311,0.00005542742,0.00019381606,0.000029858162,0.0009230021],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.997173,0.00018585009,0.00047911025,0.0013120628,0.00037300432,0.00047698047],"domain_scores_gemma":[0.998082,0.0005116778,0.000520826,0.0004897772,0.0002686262,0.00012708595],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006631707,0.00041417754,0.0006562113,0.004802858,0.00043418782,0.0003613656,0.0004893901,0.00047231387,0.00000824408],"category_scores_gemma":[0.00011163702,0.00045838964,0.00013478647,0.012645845,0.00023254438,0.00015109607,0.00029295956,0.0005626938,0.000021155704],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007450333,0.0010303551,0.4993316,0.0008580312,0.0027993415,0.00007004793,0.058156252,0.0020439473,0.019587245,0.01926479,0.000045233788,0.39673865],"study_design_scores_gemma":[0.00030775406,0.0000810183,0.8413978,0.000073207266,0.0006785863,0.0000020862478,0.0046645193,0.15098462,0.00081239996,0.0005816684,0.000023103326,0.00039321755],"about_ca_topic_score_codex":0.0007071745,"about_ca_topic_score_gemma":0.00031948218,"teacher_disagreement_score":0.39634544,"about_ca_system_score_codex":0.000107579915,"about_ca_system_score_gemma":0.000083681814,"threshold_uncertainty_score":0.9997868},"labels":[],"label_agreement":null},{"id":"W7151384608","doi":"10.1109/icosec67334.2025.11459777","title":"Gyroscope and Accelerometer based Assistive Spoon for Motor Disorder Patients","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Accelerometer; Gyroscope; Motor activity; Motor control; Electromyography","score_opus":0.011796938750401416,"score_gpt":0.26210457447478325,"score_spread":0.2503076357243818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7151384608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19254853,0.00020853171,0.7993686,0.0030027009,0.0011763915,0.0010591339,0.00008959452,0.00023032048,0.0023161964],"genre_scores_gemma":[0.9480194,0.000017440818,0.04541882,0.0011297432,0.000021648886,0.00015589212,0.000012585874,0.000018103772,0.005206375],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99740005,0.000083934254,0.00047708262,0.0011699083,0.0002147828,0.00065426],"domain_scores_gemma":[0.99817264,0.00044739485,0.00017314023,0.000679392,0.00041427324,0.00011317809],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026941427,0.000416036,0.0004868255,0.00050182873,0.0004348559,0.00037354522,0.00084003137,0.0003018783,0.000093236245],"category_scores_gemma":[0.00034794037,0.00037305488,0.00015379908,0.00076104124,0.00033038962,0.00031178858,0.0005048935,0.00025996933,0.000024991075],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019997706,0.0012924073,0.28974715,0.00027447217,0.00016208139,0.0000025381237,0.00005950803,0.00000602449,0.00040809193,0.020655438,0.005474262,0.68171805],"study_design_scores_gemma":[0.0038369356,0.0012348815,0.89881825,0.0002014415,0.00009687341,2.7285017e-7,0.000023002778,0.07350386,0.0027816107,0.0016236687,0.01735928,0.0005199219],"about_ca_topic_score_codex":0.00004772482,"about_ca_topic_score_gemma":0.00002081714,"teacher_disagreement_score":0.7554709,"about_ca_system_score_codex":0.00009016652,"about_ca_system_score_gemma":0.00017356154,"threshold_uncertainty_score":0.99987215},"labels":[],"label_agreement":null},{"id":"W7154095046","doi":"10.1145/3772318.3808984","title":"10.1145/3772318.3808984","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"","score_opus":0.00562275369650243,"score_gpt":0.17593656397960447,"score_spread":0.17031381028310205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7154095046","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013476693,0.00003138881,0.00072553987,0.001871123,0.0000056184545,0.00008502931,0.0000022697807,0.00090627535,0.9950251],"genre_scores_gemma":[0.0055607725,1.6431706e-7,0.004366348,0.00012528937,0.000040528084,0.000011129014,0.0000014669592,0.000009229391,0.9898851],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990774,0.000027531923,0.0001271376,0.00032850317,0.00014098077,0.00029848836],"domain_scores_gemma":[0.9992556,0.00003601976,0.000022029886,0.0005568455,0.000035248522,0.00009426376],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000117816875,0.00011679278,0.00013466596,0.000099603494,0.000084438194,0.000067046756,0.00086967833,0.00006641306,0.87238586],"category_scores_gemma":[0.000022059829,0.00011145914,0.000043758922,0.00038589968,0.00003919775,0.00012369812,0.00011143233,0.000111436624,0.97327864],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000004543463,0.000032749256,6.7453067e-7,0.0000011590297,0.0000055997834,0.000011947195,0.000009064511,0.00002967361,0.000085810214,0.00018139023,0.06072561,0.9389118],"study_design_scores_gemma":[0.0001147598,0.00012416685,0.00032547917,0.000008128662,0.0000028464967,0.000017205593,1.8671643e-7,0.0014624636,0.00031145374,0.00013736609,0.99734956,0.00014638448],"about_ca_topic_score_codex":0.000009772717,"about_ca_topic_score_gemma":8.803847e-8,"teacher_disagreement_score":0.9387654,"about_ca_system_score_codex":0.000023491826,"about_ca_system_score_gemma":0.000021960457,"threshold_uncertainty_score":0.4545172},"labels":[],"label_agreement":null},{"id":"W7160109835","doi":"10.1109/iccv51701.2025.02426","title":"From Gaze to Movement: Predicting Visual Attention for Autonomous Driving Human-Machine Interaction based on Programmatic Imitation Learning","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Gaze; Visual attention; Imitation; Eye tracking; Joint attention; Visual search; Eye movement","score_opus":0.015996897249076228,"score_gpt":0.32256856921401683,"score_spread":0.3065716719649406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160109835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30415282,0.000011523596,0.69011176,0.002252863,0.0009630741,0.0010454444,0.0000035424096,0.0007156239,0.00074334076],"genre_scores_gemma":[0.96762496,9.418709e-7,0.030317841,0.00040961505,0.00013417254,0.00034602592,0.00008897639,0.000028612803,0.0010488462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99678916,0.00023536773,0.0008903077,0.0011450689,0.00034079648,0.00059927534],"domain_scores_gemma":[0.99789083,0.00082114106,0.00047424118,0.00042939343,0.00028143305,0.00010296898],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008493867,0.00040658592,0.00040583592,0.00086794485,0.00095400325,0.0007072978,0.0005176735,0.000213088,0.000040005994],"category_scores_gemma":[0.0006768998,0.000436033,0.00021141708,0.0007880718,0.00005381585,0.00045068926,0.00023766501,0.0005973101,0.00003620986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092333605,0.0009380835,0.033869453,0.00020584535,0.00016643312,0.0000031436346,0.0008312288,0.04374794,0.029833455,0.010578557,0.00008720935,0.8796463],"study_design_scores_gemma":[0.0011697654,0.0017251112,0.0312727,0.0013901136,0.0000985473,2.409381e-7,0.0006008884,0.9553342,0.005406171,0.0024405748,0.00022328968,0.00033844015],"about_ca_topic_score_codex":0.00031765926,"about_ca_topic_score_gemma":0.00017752848,"teacher_disagreement_score":0.9115862,"about_ca_system_score_codex":0.00050567713,"about_ca_system_score_gemma":0.00010275412,"threshold_uncertainty_score":0.99980915},"labels":[],"label_agreement":null},{"id":"W7161968720","doi":"10.82308/26146","title":"Using a 3D hand motion controller for reaching tasks in a powered wheelchair simulator","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Joystick; Wheelchair; Virtual reality; Task (project management); Interface (matter); Controller (irrigation); Cursor (databases); Lift (data mining); Motion (physics)","score_opus":0.05086002412210546,"score_gpt":0.335820706769527,"score_spread":0.2849606826474216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161968720","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16534553,0.00023618524,0.8310522,0.000116618,0.0008152624,0.00074810773,0.00000967508,0.00033074841,0.0013457101],"genre_scores_gemma":[0.9600474,0.0000015287662,0.038541205,0.000050319257,0.000051793864,0.000046672776,0.000080139114,0.00002645484,0.0011544639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983931,0.00006009378,0.00038462845,0.0005935352,0.00021989731,0.00034873685],"domain_scores_gemma":[0.99895954,0.0001088681,0.00025579377,0.00034554346,0.0002691906,0.000061085295],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005119475,0.00026939678,0.00047377165,0.00047453097,0.00012447996,0.00017679605,0.0004972888,0.00044508796,0.0000033249523],"category_scores_gemma":[0.0002626677,0.00024873673,0.000101858655,0.00026172755,0.00002468083,0.00022852377,0.000043794393,0.00032584267,0.00000633256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010514416,0.0011726505,0.0026381346,0.00071622356,0.00043513085,0.000098769626,0.012801427,0.014589656,0.05665639,0.0907701,0.0022271485,0.8168429],"study_design_scores_gemma":[0.0031872287,0.00015545585,0.00222189,0.00027443143,0.0000343487,0.00000542113,0.00033361532,0.9757206,0.0010804487,0.015627692,0.00083579903,0.00052307337],"about_ca_topic_score_codex":0.00025738575,"about_ca_topic_score_gemma":0.00046948672,"teacher_disagreement_score":0.9611309,"about_ca_system_score_codex":0.0001983902,"about_ca_system_score_gemma":0.00016372577,"threshold_uncertainty_score":0.9999965},"labels":[],"label_agreement":null},{"id":"W7162026095","doi":"10.82308/20423","title":"Design of miniaturized coil system using MEMS technology for eye movement measurement","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electromagnetic coil; Voice coil; Inductive sensor; Microelectromechanical systems; Search coil; Eye movement; Gyroscope; Eye tracking on the ISS; Eye tracking","score_opus":0.04784423454039076,"score_gpt":0.28384570953421556,"score_spread":0.2360014749938248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162026095","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00780027,0.000554244,0.9880386,0.00020960979,0.0007834606,0.0011396424,0.000004327643,0.000768966,0.00070090673],"genre_scores_gemma":[0.487447,0.000012956504,0.5115025,0.000044691966,0.000034593795,0.00018602923,0.000024061741,0.000033602984,0.00071453827],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9977525,0.000046109762,0.0006434817,0.0006455305,0.000518419,0.00039398836],"domain_scores_gemma":[0.9977326,0.000045954766,0.0006421444,0.00072855107,0.0008113415,0.0000394466],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00072119245,0.00034558785,0.0006871446,0.0007008583,0.00011587809,0.000040894665,0.0011731337,0.00061507936,0.0000017387205],"category_scores_gemma":[0.00007548806,0.00031224996,0.00013878228,0.0005174043,0.0000378717,0.000066071916,0.000048800954,0.00021217666,0.0000033033407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020102363,0.00051180203,0.000034344015,0.0014435276,0.0005345364,0.000019987549,0.00029088947,0.0005098975,0.7361861,0.15363884,0.0008358609,0.105793215],"study_design_scores_gemma":[0.0020231637,0.001024476,0.00047727258,0.0015613191,0.0002454277,0.000007335029,0.0010378885,0.047661275,0.936028,0.008625052,0.0003865283,0.00092224136],"about_ca_topic_score_codex":0.00003033996,"about_ca_topic_score_gemma":0.000025814861,"teacher_disagreement_score":0.4796467,"about_ca_system_score_codex":0.0003279244,"about_ca_system_score_gemma":0.0003041944,"threshold_uncertainty_score":0.99993294},"labels":[],"label_agreement":null},{"id":"W7162537833","doi":"10.1109/wiecon-ece69386.2025.11526422","title":"Towards Single-Channel &amp; Single-Cycle EOG Based Multi-Directional Eye Tracking in Wearable System","year":2025,"lang":"","type":"article","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Eye tracking; Wearable computer; Tracking system; Noise (video); Tracking (education)","score_opus":0.04574331055487664,"score_gpt":0.29073655444861185,"score_spread":0.24499324389373522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7162537833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045804195,0.0012007831,0.92139155,0.0037503731,0.003923709,0.0005871772,0.000013359598,0.0017541054,0.02157476],"genre_scores_gemma":[0.9278563,0.000008872374,0.06617001,0.00038522528,0.00009475655,0.0000680004,0.0000057319417,0.00004275318,0.0053683403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940453,0.0004745054,0.001357638,0.001967118,0.00067519373,0.0014801989],"domain_scores_gemma":[0.99719965,0.00040643744,0.00036959557,0.0012993078,0.00051908055,0.00020590783],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0013968988,0.000767955,0.0009955189,0.0016796172,0.0006358025,0.00074416655,0.0016793087,0.00074624305,0.0001040049],"category_scores_gemma":[0.00049676804,0.00082226156,0.00036521337,0.0034943274,0.00039577842,0.0006495187,0.00055231655,0.0010620811,0.00021559594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004666801,0.019871864,0.04200915,0.0033995705,0.0007772933,0.0006242945,0.0028694882,0.09500222,0.07180388,0.061747115,0.0020595868,0.69936883],"study_design_scores_gemma":[0.0036950347,0.0003127582,0.02675265,0.0040734755,0.000081558595,0.000038006354,0.0006034472,0.92462593,0.034808,0.00089375436,0.0028777448,0.0012376528],"about_ca_topic_score_codex":0.0011709089,"about_ca_topic_score_gemma":0.0013298703,"teacher_disagreement_score":0.8820521,"about_ca_system_score_codex":0.0014591943,"about_ca_system_score_gemma":0.00069010205,"threshold_uncertainty_score":0.99942285},"labels":[],"label_agreement":null},{"id":"W805185953","doi":"10.1167/15.12.1269","title":"Quantifying the variance in eye movements while watching intact versus scrambled movies.","year":2015,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Eye movement; Fixation (population genetics); Gaze; Artificial intelligence; Computer vision; Eye tracking; Ellipse; Computer science; Normality; Sample (material); Statistics; Mathematics; Pattern recognition (psychology)","score_opus":0.07786883039080905,"score_gpt":0.3480566620170959,"score_spread":0.27018783162628685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W805185953","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88854146,0.00037167568,0.10378033,0.0048294254,0.001727717,0.000069753325,2.844617e-7,0.000039954997,0.00063938956],"genre_scores_gemma":[0.9883957,0.0000266772,0.011379808,0.00011223647,0.000057430483,6.5913747e-7,1.1623793e-7,0.0000046887235,0.000022715603],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99889636,0.00010281922,0.00033457947,0.0001315164,0.0003543152,0.0001804088],"domain_scores_gemma":[0.9991356,0.00011872063,0.00030197637,0.00025127534,0.00013652533,0.00005588704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013806317,0.000086238484,0.00016790064,0.00015300472,0.0000757248,0.00011209356,0.0008344365,0.000052782947,0.0000022591478],"category_scores_gemma":[0.00022025866,0.000054272772,0.000051973006,0.00034282645,0.000025946583,0.00052544236,0.00019767486,0.00039986605,0.000013144561],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011126932,0.0013413433,0.07735595,0.000052627944,0.00022024581,0.00092143,0.009561494,0.011177828,0.05093858,0.049321212,0.007463202,0.79053336],"study_design_scores_gemma":[0.012342445,0.0032178096,0.82225525,0.0013209174,0.000031967113,0.00012184038,0.0017901711,0.117076196,0.0044345176,0.01946371,0.017294183,0.0006510094],"about_ca_topic_score_codex":0.000032469237,"about_ca_topic_score_gemma":0.000016135711,"teacher_disagreement_score":0.78988236,"about_ca_system_score_codex":0.00011000313,"about_ca_system_score_gemma":0.0000782744,"threshold_uncertainty_score":0.22131793},"labels":[],"label_agreement":null},{"id":"W839644000","doi":"","title":"Un outil d'evaluation neurocognitive des interactions humain-machine","year":2013,"lang":"fr","type":"dissertation","venue":"","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Human–computer interaction; Computer science; Supervisor; Inference; User experience design; Artificial intelligence; Perspective (graphical)","score_opus":0.05519036899213176,"score_gpt":0.3294654663049339,"score_spread":0.27427509731280214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W839644000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4175096,0.0013781904,0.4809613,0.003570693,0.00590408,0.0016764823,0.00003883745,0.0011048702,0.08785596],"genre_scores_gemma":[0.95174104,0.00007500714,0.02384389,0.00025249552,0.00015920063,0.00029903033,0.000448085,0.000047101,0.023134165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967562,0.0004602434,0.00062492094,0.0010272643,0.0005512689,0.0005801106],"domain_scores_gemma":[0.99622715,0.0004411449,0.00047210115,0.00058981066,0.0021230548,0.00014676758],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000503769,0.0005524347,0.00044222752,0.0004950073,0.0007994653,0.00043340796,0.0008688545,0.00036208445,0.00247582],"category_scores_gemma":[0.000762657,0.00054028153,0.00022425441,0.00073670933,0.00030900657,0.0008090164,0.00016873624,0.0010089732,0.0022497186],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011332607,0.00028830607,0.0010622995,0.00006230674,0.00014478041,0.0000150798,0.0027800975,0.000055712902,0.0027799816,0.06890816,0.0004001633,0.9234918],"study_design_scores_gemma":[0.0015927654,0.000714024,0.41507915,0.00089046464,0.0007602548,0.00017540927,0.0039885174,0.42886865,0.03769575,0.102997415,0.0055400655,0.0016975435],"about_ca_topic_score_codex":0.0014188376,"about_ca_topic_score_gemma":0.0019625174,"teacher_disagreement_score":0.92179424,"about_ca_system_score_codex":0.00022983926,"about_ca_system_score_gemma":0.00026752678,"threshold_uncertainty_score":0.9997049},"labels":[],"label_agreement":null},{"id":"W893708899","doi":"","title":"Reaching And Grasping In Glaucoma: The Effect Of Visual Field Defect Location","year":2011,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Glaucoma; Visual field; Optometry; Visual field loss; Ophthalmology; Medicine","score_opus":0.030683499075349476,"score_gpt":0.31262986063091736,"score_spread":0.2819463615555679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W893708899","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99695843,0.00010745351,0.001697282,0.00024776653,0.00014280422,0.00020080913,1.3200139e-7,0.00005594705,0.0005893569],"genre_scores_gemma":[0.99770373,0.0000012265524,0.0021773325,0.00007061309,0.000010345107,0.00002661521,2.0458981e-7,0.000004777867,0.000005145943],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9981539,0.00041041616,0.00026869413,0.0005604348,0.00022497085,0.00038158215],"domain_scores_gemma":[0.9986378,0.0007091354,0.00018889114,0.00029594955,0.00008725424,0.0000809942],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0020114735,0.00017022091,0.0002488158,0.0002948781,0.00030402406,0.000036520185,0.0009984177,0.00010191434,0.0000029484688],"category_scores_gemma":[0.0014050223,0.00011803303,0.000037138274,0.0015698639,0.0029515948,0.0004472306,0.0004807395,0.00035143032,0.00000483189],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000140147,0.000059309314,0.8349979,0.000022854969,0.000010173097,0.00006155774,0.0025029585,0.000003133582,0.14849535,0.008420247,0.0000027798476,0.005409725],"study_design_scores_gemma":[0.00017149064,0.0017017078,0.58937067,0.00006014934,0.000005717943,0.00014010268,0.000060399336,0.0033322705,0.39609557,0.00895355,6.6512683e-7,0.00010768757],"about_ca_topic_score_codex":0.00054721464,"about_ca_topic_score_gemma":0.0000108765025,"teacher_disagreement_score":0.24760024,"about_ca_system_score_codex":0.0000484379,"about_ca_system_score_gemma":0.000106675805,"threshold_uncertainty_score":0.9997618},"labels":[],"label_agreement":null},{"id":"W917912440","doi":"","title":"Effect of Sleeping Position on Iop in Progressive Glaucoma","year":2009,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Glaucoma; Ophthalmology; Optometry; Medicine; Position (finance); Economics","score_opus":0.01846827142334518,"score_gpt":0.333687607170872,"score_spread":0.3152193357475268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W917912440","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9967827,0.000043103828,0.00014468818,0.0012157885,0.00015707695,0.00029158202,8.906496e-7,0.00010962254,0.001254502],"genre_scores_gemma":[0.9965512,4.5976412e-7,0.0031680372,0.00022770329,0.000016835946,0.000023321705,9.216262e-7,0.0000044367202,0.000007044242],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9975824,0.00037936348,0.00032201293,0.0007643829,0.00040607317,0.00054581824],"domain_scores_gemma":[0.9987574,0.000340641,0.00025922124,0.00037623273,0.0001373817,0.00012911073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013533785,0.00022307619,0.00035234375,0.00059015496,0.00019313468,0.000041539806,0.0012668151,0.00017692894,0.000004883558],"category_scores_gemma":[0.0007209892,0.00018742552,0.000052341275,0.0022040491,0.0024897712,0.00048493347,0.00018540492,0.00046542197,0.000022764454],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050064125,0.00026611247,0.06951122,0.000013803387,0.000006351413,0.00060791447,0.00049537735,0.00002832437,0.8866503,0.021888083,0.000006569703,0.020475836],"study_design_scores_gemma":[0.00026314816,0.0048989803,0.38930768,0.000112330315,0.000002476891,0.00015863586,0.0000067959354,0.0019471311,0.59280604,0.010373668,6.5749316e-7,0.00012247043],"about_ca_topic_score_codex":0.000019059657,"about_ca_topic_score_gemma":3.7582382e-7,"teacher_disagreement_score":0.31979644,"about_ca_system_score_codex":0.00014336656,"about_ca_system_score_gemma":0.00015010333,"threshold_uncertainty_score":0.91736656},"labels":[],"label_agreement":null},{"id":"W93135273","doi":"10.1007/978-3-642-23623-5_9","title":"3D Ocular Ultrasound Using Gaze Tracking on the Contralateral Eye: A Feasibility Study","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"British Columbia Innovation Council","keywords":"Computer science; Gaze; Eye tracking; Computer vision; Artificial intelligence; Tracking (education); Optometry; Computer graphics (images); Medicine; Psychology","score_opus":0.06033861956752868,"score_gpt":0.29579288438610923,"score_spread":0.23545426481858056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W93135273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49092922,0.00001610558,0.5080721,0.00017889058,0.00035170224,0.0002631526,4.061414e-7,0.00017767608,0.000010774725],"genre_scores_gemma":[0.88810813,5.729328e-7,0.111248136,0.00056956947,0.000054250148,0.0000083001305,1.08065215e-7,0.000010684947,2.2314457e-7],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99704707,0.00024985505,0.00033730693,0.0011085155,0.00054837717,0.00070885336],"domain_scores_gemma":[0.9977527,0.00050187507,0.00013359573,0.0013808487,0.00014685672,0.000084090454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017956009,0.00028412603,0.00029039045,0.00027855017,0.00048675822,0.00038702742,0.002862209,0.00009271301,0.0000074280415],"category_scores_gemma":[0.00036538328,0.00019100067,0.000072581184,0.0017741794,0.00068487733,0.00048401006,0.00043812857,0.00057939184,0.000011161337],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037075763,0.0020730486,0.7491725,0.00001534714,0.00004513699,0.0003252552,0.016465465,0.017678091,0.011519329,0.0052467105,0.0000036665554,0.19741833],"study_design_scores_gemma":[0.0007034284,0.0006561234,0.62141836,0.000080448866,0.000009607072,0.0000746272,0.000010139869,0.33822766,0.023627976,0.014738495,0.000003625898,0.00044948806],"about_ca_topic_score_codex":0.0001343607,"about_ca_topic_score_gemma":0.000049378847,"teacher_disagreement_score":0.39717895,"about_ca_system_score_codex":0.00017397274,"about_ca_system_score_gemma":0.00013387337,"threshold_uncertainty_score":0.77887815},"labels":[],"label_agreement":null},{"id":"W998232048","doi":"","title":"Binocular Remote Gaze Estimation System for Infants","year":2008,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaze; Computer science; Optometry; Estimation; Computer vision; Artificial intelligence; Medicine; Engineering; Systems engineering","score_opus":0.04883827553066423,"score_gpt":0.3263392600888926,"score_spread":0.27750098455822836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W998232048","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9298284,0.000047489117,0.067334525,0.0005711485,0.0005352852,0.00038225573,0.000004122951,0.00046574214,0.0008310493],"genre_scores_gemma":[0.85161376,0.0000013124564,0.14811969,0.000098425495,0.00003327395,0.000036660338,0.0000025533097,0.000011000088,0.00008335401],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9972119,0.00013311507,0.00038866332,0.0010423444,0.00047663006,0.0007473978],"domain_scores_gemma":[0.998164,0.0002486629,0.00027862444,0.0006066159,0.00044213206,0.00025995428],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0010221445,0.00026915164,0.00034747203,0.0004347568,0.0010592361,0.0000902744,0.0017663668,0.00017197874,0.0000035812918],"category_scores_gemma":[0.0010525045,0.00024641052,0.00008209303,0.0018359607,0.004412063,0.00094135734,0.00040099662,0.00023949116,0.00009091977],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003283687,0.00020181356,0.018939042,0.000100871664,0.000053050244,0.0010026994,0.0023419622,0.0008362961,0.90869504,0.05159807,0.00024191593,0.015956419],"study_design_scores_gemma":[0.0006615095,0.0009200275,0.07440832,0.00013404828,0.0000127280755,0.0033776043,0.0000913186,0.29988152,0.59894675,0.020931104,0.000087754896,0.0005473196],"about_ca_topic_score_codex":0.00006090843,"about_ca_topic_score_gemma":6.2530285e-7,"teacher_disagreement_score":0.30974826,"about_ca_system_score_codex":0.0002139009,"about_ca_system_score_gemma":0.0005318985,"threshold_uncertainty_score":0.9999988},"labels":[],"label_agreement":null}]}