{"meta":{"query_hash":"6e33b1a645cb","filters":{"topic":"Video Analysis and Summarization"},"cohort_total":486,"direct_labels_cover":0,"predictions_cover":486,"exported":486,"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/6e33b1a645cb","api":"https://metacan.xera.ac/api/v1/cohort?topic=Video+Analysis+and+Summarization"},"results":[{"id":"W1018054785","doi":"","title":"Video-4-Video: using video for searching, classifying and summarising video","year":2009,"lang":"en","type":"article","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Video Analysis and Summarization","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":"Universidad Rey Juan Carlos; Xi’an Jiaotong University; University of Glasgow; University of Ottawa; University of Central Florida; University of Southern California","keywords":"Metadata; Computer science; Information retrieval; Task (project management); World Wide Web; Online video; Video tracking; The Internet; Multimedia; Semantics (computer science); Shot (pellet); Benchmarking; Video processing; Artificial intelligence","score_opus":0.03023212877958934,"score_gpt":0.28945063749211847,"score_spread":0.2592185087125291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1018054785","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062786624,0.0154359,0.72016674,0.0023685822,0.0013570817,0.0031016949,0.06200519,0.07945165,0.053326514],"genre_scores_gemma":[0.16323376,0.0076238783,0.6878447,0.00081147393,0.00072094426,0.00090116187,0.10193028,0.0036635569,0.033270303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945146,0.000083523926,0.000039138176,0.00014449777,0.00021002739,0.00007141087],"domain_scores_gemma":[0.99938655,0.00013615408,0.000050727205,0.00009103049,0.0002735462,0.00006199274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069335895,0.0015397994,0.0006193742,0.0045632194,0.0006371706,0.0018065949,0.001244016,0.0013770118,0.011130344],"category_scores_gemma":[0.0021530369,0.00029550825,0.00055599696,0.0036906274,0.0004713372,0.0024626134,0.0009939489,0.0007348953,0.0072374325],"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":[0.000728231,0.00012841946,0.0015827266,0.0013063492,0.00014327359,0.00021896676,0.00019766686,0.0032038465,0.059221942,0.004135729,0.1385237,0.7906091],"study_design_scores_gemma":[0.00026561957,0.0018153741,0.022263063,0.00069588074,0.00045661983,0.0023034322,0.0013628096,0.16864684,0.26280338,0.01728853,0.5218079,0.00029057148],"about_ca_topic_score_codex":0.009786315,"about_ca_topic_score_gemma":0.011898597,"teacher_disagreement_score":0.011130344,"about_ca_system_score_codex":0.0007907121,"about_ca_system_score_gemma":0.0005886733,"threshold_uncertainty_score":0.037234664},"labels":[],"label_agreement":null},{"id":"W118321044","doi":"10.1007/978-3-642-40477-1_22","title":"Video Navigation with a Personal Viewing History","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":8,"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; Interface (matter); Popularity; Multimedia; Video processing; Turn-by-turn navigation; Video editing; Human–computer interaction; World Wide Web; Artificial intelligence; Psychology","score_opus":0.015885241012070582,"score_gpt":0.21004947457934886,"score_spread":0.19416423356727827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W118321044","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007112491,0.010569879,0.2617831,0.0014378537,0.0035649654,0.00032428518,0.0039835162,0.02124945,0.6899744],"genre_scores_gemma":[0.050990336,0.009303359,0.10501322,0.00086772675,0.0022244586,0.00021948002,0.0059170243,0.004247282,0.8212172],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99989676,0.000012567068,0.0000055835953,0.000029437666,0.000042906206,0.000012670985],"domain_scores_gemma":[0.9996238,0.00007348611,0.000013860561,0.00008083349,0.0001492637,0.000058786023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023060541,0.00082851684,0.00035508082,0.0015601729,0.000566839,0.0012965192,0.00070160755,0.00088619743,0.20223159],"category_scores_gemma":[0.0009089174,0.00028099134,0.00038550055,0.002026603,0.0002423465,0.0020615375,0.0010330151,0.0007063006,0.074884064],"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":[0.00015434458,0.000040319137,0.00021693666,0.00034779534,0.000009645855,0.00019241603,0.00025578067,0.0004015172,0.010174491,0.008098141,0.26696953,0.713139],"study_design_scores_gemma":[0.000013880511,0.00008116063,0.0008250224,0.0002611528,0.000026771746,0.00077788247,0.00013436303,0.002349452,0.004349725,0.0028354195,0.98832,0.000025258902],"about_ca_topic_score_codex":0.0017759275,"about_ca_topic_score_gemma":0.0032373215,"teacher_disagreement_score":0.20223159,"about_ca_system_score_codex":0.00026268023,"about_ca_system_score_gemma":0.00033927092,"threshold_uncertainty_score":0.67653203},"labels":[],"label_agreement":null},{"id":"W142560446","doi":"10.4018/978-1-59904-879-6.ch027","title":"Metadata and Metaphors in Visual Interfaces to Digital Libraries","year":2009,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Video Analysis and Summarization","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":"Metadata; Computer science; Digital library; Information retrieval; Interface (matter); World Wide Web; Visualization; User interface; Key (lock); Information visualization; Human–computer interaction; Artificial intelligence","score_opus":0.014446378323760348,"score_gpt":0.23946836695584797,"score_spread":0.2250219886320876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W142560446","genre_codex":"other","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031119993,0.1293283,0.25111845,0.010247258,0.0016644882,0.00021495209,0.00018269036,0.0017967586,0.5743271],"genre_scores_gemma":[0.5109863,0.09665389,0.17087144,0.0045201564,0.0017117216,0.00076157245,0.00038296307,0.0012290446,0.21288283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989347,0.00066777616,0.000053303997,0.000073954754,0.00021201743,0.000058279],"domain_scores_gemma":[0.9984871,0.00119167,0.00006984422,0.000073572286,0.00012597357,0.000051834322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009888451,0.0006390029,0.00035191586,0.0026129198,0.00087062747,0.0075083463,0.0007792254,0.0010507171,0.010121799],"category_scores_gemma":[0.004644058,0.0003280018,0.00042829989,0.0024354558,0.0039993054,0.00907676,0.0018615449,0.0015722997,0.0015332798],"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":[0.00004414583,0.00003426389,0.00022445695,0.0016043778,0.000016001512,0.00023755971,0.028313009,0.0009185057,0.0025513016,0.7883924,0.01807552,0.15958837],"study_design_scores_gemma":[0.000028997008,0.00009085591,0.0011656582,0.0020495767,0.00003049928,0.0012428922,0.012497896,0.0031483928,0.0018873231,0.30823827,0.6695477,0.00007205605],"about_ca_topic_score_codex":0.0010881076,"about_ca_topic_score_gemma":0.0012969658,"teacher_disagreement_score":0.010121799,"about_ca_system_score_codex":0.0014537638,"about_ca_system_score_gemma":0.0006457016,"threshold_uncertainty_score":0.033860743},"labels":[],"label_agreement":null},{"id":"W1491978853","doi":"","title":"A Simple 3D Visual Text Retrieval Interface","year":2002,"lang":"en","type":"article","venue":"Defense Technical Information Center (DTIC)","topic":"Video Analysis and Summarization","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":"Carleton University","funders":"","keywords":"Computer science; Simple (philosophy); Set (abstract data type); Information retrieval; Interface (matter); Metaphor; User interface; Human–computer interaction; Programming language","score_opus":0.018044728887026476,"score_gpt":0.2589672349201232,"score_spread":0.24092250603309673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491978853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01692835,0.00070028106,0.8611138,0.0014985189,0.00051155075,0.0010008293,0.003809418,0.071919225,0.042518087],"genre_scores_gemma":[0.17900357,0.0011588708,0.7065216,0.0028641708,0.00039636248,0.0017593025,0.005817362,0.0031424381,0.099336416],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999522,0.00009659605,0.00003962661,0.000088803645,0.00021438743,0.000038644594],"domain_scores_gemma":[0.9990627,0.00042817282,0.000045925306,0.00013383769,0.000233367,0.000095984025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007492643,0.0008295177,0.00055986294,0.00096041284,0.00039334397,0.00217567,0.0015584402,0.0017463481,0.060008112],"category_scores_gemma":[0.0035620925,0.000371605,0.0006457578,0.0005224718,0.00037122084,0.0025397504,0.0017696695,0.0005925075,0.016823266],"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":[0.001287115,0.00035690068,0.001053293,0.0015090165,0.00008030242,0.00082452776,0.001848874,0.0031374572,0.16607364,0.03294303,0.19225053,0.59863526],"study_design_scores_gemma":[0.0005602639,0.0013123838,0.0030378937,0.00040180568,0.00014911525,0.0035091916,0.0005543726,0.08046362,0.05512629,0.020943264,0.8335973,0.00034453438],"about_ca_topic_score_codex":0.0011515772,"about_ca_topic_score_gemma":0.0011206554,"teacher_disagreement_score":0.060008112,"about_ca_system_score_codex":0.000305776,"about_ca_system_score_gemma":0.0003639929,"threshold_uncertainty_score":0.20074713},"labels":[],"label_agreement":null},{"id":"W1510842536","doi":"","title":"Organizing Moving Image Collections for the Digital Era.","year":2002,"lang":"en","type":"article","venue":"Information outlook","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"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":"Endowment; Honor; Fund raising; Management; Investment fund; Library science; Special collections; Political science; Economics; Law; Finance; Computer science; Higher education","score_opus":0.010403279384661585,"score_gpt":0.19679531506144513,"score_spread":0.18639203567678353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1510842536","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03245724,0.066047005,0.7317056,0.013255086,0.006403262,0.0022187838,0.037041914,0.032214083,0.07865709],"genre_scores_gemma":[0.0830798,0.03215387,0.72059506,0.00077549386,0.0028005098,0.0007059403,0.0803556,0.0032895827,0.07624407],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993831,0.000104341634,0.00005090746,0.00012733015,0.00027618092,0.000058235924],"domain_scores_gemma":[0.9981263,0.00030323444,0.00020037853,0.00040993167,0.00074231194,0.00021780776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014126941,0.0008436365,0.00049550406,0.0072255493,0.0015968705,0.004169027,0.00090672076,0.00092153944,0.012774542],"category_scores_gemma":[0.004234098,0.00059089484,0.00056066667,0.006153989,0.0005670254,0.0063807494,0.0023361482,0.00095780066,0.00912165],"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":[0.00010389582,0.00004407742,0.0008690085,0.0005479242,0.000040622526,0.00012915205,0.00061305665,0.00074034627,0.009800561,0.007610349,0.27877977,0.7007213],"study_design_scores_gemma":[0.000036503156,0.00011474605,0.010052236,0.00036878485,0.00013317086,0.0006390159,0.0028462121,0.012425083,0.016303565,0.027470147,0.92952573,0.00008473844],"about_ca_topic_score_codex":0.0048433268,"about_ca_topic_score_gemma":0.013852941,"teacher_disagreement_score":0.012774542,"about_ca_system_score_codex":0.00084724795,"about_ca_system_score_gemma":0.001208449,"threshold_uncertainty_score":0.04273516},"labels":[],"label_agreement":null},{"id":"W1514911927","doi":"10.1007/978-1-84800-031-5_1","title":"What did I miss? Visualizing the Past through video traces","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Video Analysis and Summarization","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 Saskatchewan; University of Calgary","funders":"","keywords":"Computer graphics (images); Computer science","score_opus":0.049950467590561105,"score_gpt":0.29553831591930746,"score_spread":0.24558784832874636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514911927","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18788916,0.024823152,0.6734203,0.010138784,0.0018369097,0.0002941614,0.01020775,0.008469719,0.08292015],"genre_scores_gemma":[0.58630574,0.016816359,0.3551175,0.00064710336,0.00068920327,0.0001467012,0.007261659,0.001779961,0.031235702],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998221,0.000050960236,0.000012022597,0.000046205354,0.00004629626,0.000022521634],"domain_scores_gemma":[0.9990833,0.00052933156,0.000093077724,0.000059360307,0.00016159505,0.000073288524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006046771,0.00078404706,0.00037006984,0.002261296,0.00068544823,0.0033532383,0.00077027414,0.0007335323,0.0050692754],"category_scores_gemma":[0.0040471237,0.0002555275,0.00025757236,0.002744928,0.00041938457,0.003535735,0.00077488174,0.00092589203,0.0017433697],"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":[0.00039928895,0.000071864226,0.0100832125,0.0011768857,0.0001009913,0.0009148684,0.01973356,0.0051975385,0.017475542,0.03374487,0.08886366,0.8222376],"study_design_scores_gemma":[0.000059309263,0.00026934085,0.03548618,0.0024582245,0.00041478305,0.0028259729,0.051591933,0.13037454,0.03367189,0.15687917,0.5855963,0.0003723286],"about_ca_topic_score_codex":0.0061738263,"about_ca_topic_score_gemma":0.009706405,"teacher_disagreement_score":0.0061738263,"about_ca_system_score_codex":0.00047212638,"about_ca_system_score_gemma":0.00054038386,"threshold_uncertainty_score":0.016958475},"labels":[],"label_agreement":null},{"id":"W1517082020","doi":"10.1007/978-3-540-30473-9_25","title":"Information Capture Devices for Social Environments","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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","funders":"","keywords":"Computer science; Active listening; Ambient intelligence; Human–computer interaction; Multimedia; World Wide Web; Communication","score_opus":0.01159648926505227,"score_gpt":0.22554792129501208,"score_spread":0.2139514320299598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1517082020","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008966499,0.032383874,0.8315636,0.002135878,0.0015304751,0.0004002555,0.0018410964,0.0073452126,0.11383309],"genre_scores_gemma":[0.14409314,0.03161834,0.55598396,0.0011369003,0.0011420207,0.00076363183,0.0040879454,0.001249798,0.25992423],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966156,0.0000898281,0.000018154698,0.00006500041,0.00013343412,0.00003202755],"domain_scores_gemma":[0.99928635,0.00040512776,0.000021267673,0.00014017348,0.00011111277,0.00003585709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042858458,0.001096047,0.0004403434,0.00095325423,0.0005969284,0.0023230838,0.0012387176,0.001504051,0.029962575],"category_scores_gemma":[0.0013305494,0.00039467984,0.0003570018,0.0012899357,0.00047904742,0.0033345355,0.0013252951,0.0009236539,0.007979958],"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":[0.00017422451,0.000061547595,0.00032319076,0.0010094522,0.000035627232,0.00025604852,0.00075919204,0.001120181,0.024476942,0.07560284,0.10530875,0.790872],"study_design_scores_gemma":[0.000031468273,0.00018147015,0.0011647565,0.00049258256,0.000090369394,0.0010766962,0.00057663256,0.012639401,0.031205876,0.045776006,0.90670353,0.00006119203],"about_ca_topic_score_codex":0.000604833,"about_ca_topic_score_gemma":0.0013408873,"teacher_disagreement_score":0.029962575,"about_ca_system_score_codex":0.00043458163,"about_ca_system_score_gemma":0.00027557375,"threshold_uncertainty_score":0.10023481},"labels":[],"label_agreement":null},{"id":"W1528401192","doi":"10.1109/iscas.2015.7169270","title":"Real-time visual play-break detection in sport events using a context descriptor","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Université Laval; École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Context (archaeology); Computer vision; Artificial intelligence; Human–computer interaction; History","score_opus":0.027939228610210046,"score_gpt":0.27043145862377405,"score_spread":0.24249223001356401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528401192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22961244,0.0015265892,0.75789696,0.00011827341,0.00019722567,0.0004491163,0.0014229993,0.004467685,0.004308713],"genre_scores_gemma":[0.68628794,0.00070566597,0.30645406,0.000078410165,0.00011473477,0.00023065115,0.0025955755,0.00017829085,0.0033547962],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996923,0.000022245345,0.000014566138,0.000115337636,0.00009575414,0.000059827933],"domain_scores_gemma":[0.99970406,0.000054098575,0.00004552855,0.00004302942,0.00010290405,0.000050405175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023488485,0.000670292,0.0007320205,0.0025307983,0.00029900737,0.0006194072,0.0006957468,0.00045672603,0.0014366119],"category_scores_gemma":[0.0005636273,0.00021321583,0.0005105333,0.0009557574,0.00023408895,0.00071598426,0.0007041453,0.0005485137,0.00077810936],"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":[0.0008514056,0.0004077851,0.010998812,0.00033077577,0.0001240686,0.00032765296,0.00016401298,0.004803728,0.29651225,0.0011114127,0.0048028533,0.67956513],"study_design_scores_gemma":[0.00015346617,0.0014801216,0.142649,0.00012153856,0.00031498526,0.0019083235,0.00083828496,0.5883104,0.24097489,0.002463268,0.020598443,0.00018735534],"about_ca_topic_score_codex":0.0040110857,"about_ca_topic_score_gemma":0.009694491,"teacher_disagreement_score":0.0040110857,"about_ca_system_score_codex":0.0003034197,"about_ca_system_score_gemma":0.00042123103,"threshold_uncertainty_score":0.007975519},"labels":[],"label_agreement":null},{"id":"W1533986179","doi":"10.1007/978-3-642-14932-0_17","title":"A New Hierarchical Key Frame Tree-Based Video Representation Method Using Independent Component Analysis","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Key frame; Computer science; Artificial intelligence; Key (lock); Computer vision; Frame (networking); Tree structure; Tree (set theory); Video compression picture types; Reference frame; Representation (politics); Video tracking; Video processing; Component (thermodynamics); Pattern recognition (psychology); Feature (linguistics); Cluster analysis; Shot (pellet); Algorithm; Mathematics; Binary tree","score_opus":0.02443334668192619,"score_gpt":0.297231018180797,"score_spread":0.2727976714988708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533986179","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009395726,0.00015855665,0.99725217,0.000023116867,0.000054023156,0.000044971064,0.0000890191,0.00110336,0.00033514338],"genre_scores_gemma":[0.01515259,0.0003628577,0.98107874,0.00004607441,0.000065637614,0.0001629313,0.0006609091,0.00029628238,0.002173925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991048,0.00012175573,0.00005249349,0.0002175722,0.00043447886,0.00006878405],"domain_scores_gemma":[0.9991928,0.0001578708,0.000047715817,0.00008023671,0.00047585828,0.000045485438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000592149,0.0014463706,0.001694446,0.0028758405,0.0006455145,0.0012297485,0.0019401088,0.0009145514,0.004883512],"category_scores_gemma":[0.0015441754,0.0005843677,0.0013529602,0.003366427,0.00035129907,0.0018916567,0.0009742541,0.0014622409,0.0038309526],"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":[0.00019874034,0.000083343766,0.00017526082,0.00016060505,0.00008775405,0.000048260797,0.000048569873,0.00782246,0.04602065,0.002438329,0.0074718604,0.9354442],"study_design_scores_gemma":[0.00004942407,0.0001561242,0.0012575787,0.000032445972,0.00018759164,0.00026785908,0.00006211014,0.9310422,0.04719267,0.003521868,0.016145675,0.00008441728],"about_ca_topic_score_codex":0.0063041463,"about_ca_topic_score_gemma":0.0071604494,"teacher_disagreement_score":0.0063041463,"about_ca_system_score_codex":0.00047390175,"about_ca_system_score_gemma":0.0011338406,"threshold_uncertainty_score":0.016336918},"labels":[],"label_agreement":null},{"id":"W1541327667","doi":"","title":"Useful transcriptions of webcast lectures","year":2009,"lang":"en","type":"dissertation","venue":"TSpace","topic":"Video Analysis and Summarization","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":"Webcast; Computer science; Multimedia","score_opus":0.015287160850149927,"score_gpt":0.30291866366076403,"score_spread":0.2876315028106141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1541327667","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42748243,0.0037352617,0.29020557,0.0035461104,0.0080325315,0.0028321792,0.093875535,0.010303762,0.15998666],"genre_scores_gemma":[0.6518919,0.0027037703,0.19754471,0.00040833346,0.0012924004,0.0009779081,0.054609798,0.002038635,0.08853252],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945027,0.00011604188,0.00003357823,0.00008382074,0.000271354,0.000044904966],"domain_scores_gemma":[0.9969259,0.0012596595,0.00011711152,0.00040496193,0.0011876606,0.000104682666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004575998,0.00044043997,0.00027678313,0.001158494,0.0005962614,0.0007741687,0.00044175,0.00051200064,0.017427063],"category_scores_gemma":[0.0052399896,0.00013084202,0.00021953142,0.0014622346,0.00025878812,0.000425635,0.0005471772,0.00077671476,0.006668464],"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":[0.0015577722,0.00018860643,0.002803482,0.0023736784,0.000046230925,0.0026359078,0.008699724,0.004133019,0.11290506,0.008105513,0.13021779,0.7263332],"study_design_scores_gemma":[0.00026778824,0.00082306995,0.054516435,0.0009684852,0.00018542941,0.0031149224,0.012407422,0.03342824,0.14300121,0.009133346,0.7419388,0.0002148448],"about_ca_topic_score_codex":0.0018293721,"about_ca_topic_score_gemma":0.005076002,"teacher_disagreement_score":0.017427063,"about_ca_system_score_codex":0.0005035446,"about_ca_system_score_gemma":0.0004945233,"threshold_uncertainty_score":0.058299303},"labels":[],"label_agreement":null},{"id":"W1548271187","doi":"","title":"Proceedings of the international workshop on Educational multimedia and multimedia education","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Multimedia; Computer science; Exploit; Relevance (law); Curriculum; Mobile device; Field (mathematics); World Wide Web","score_opus":0.00954715382324736,"score_gpt":0.25920281435349557,"score_spread":0.2496556605302482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1548271187","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019609204,0.08963795,0.30238956,0.03578944,0.06541264,0.0007869895,0.0021894265,0.0060956185,0.47808915],"genre_scores_gemma":[0.10128082,0.04107668,0.12146973,0.0045781224,0.0124345785,0.0006065278,0.0072299214,0.0015979658,0.70972574],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988285,0.00038488122,0.00008610463,0.00019176285,0.0003430787,0.00016572395],"domain_scores_gemma":[0.9983973,0.00039776525,0.000054502932,0.00021687226,0.00048365485,0.00044980753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018636653,0.0009631729,0.00075387064,0.0013387435,0.00074971106,0.0044078194,0.0016409461,0.0019218836,0.071417905],"category_scores_gemma":[0.0029632628,0.00032394135,0.00067635096,0.0010727615,0.0007539672,0.0036400123,0.0026203191,0.0021903734,0.022697138],"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":[0.0002948251,0.00027891764,0.0004992638,0.00050068233,0.000041490828,0.0004323363,0.0006718925,0.0007744898,0.0060320115,0.018867593,0.42791486,0.5436917],"study_design_scores_gemma":[0.000018784287,0.000056268465,0.000646625,0.00025376547,0.000025841822,0.00032953257,0.0003484975,0.0021218727,0.001733505,0.0059472597,0.98850083,0.000017218963],"about_ca_topic_score_codex":0.001693221,"about_ca_topic_score_gemma":0.0032051618,"teacher_disagreement_score":0.071417905,"about_ca_system_score_codex":0.0008127406,"about_ca_system_score_gemma":0.0013690764,"threshold_uncertainty_score":0.2389167},"labels":[],"label_agreement":null},{"id":"W1565989234","doi":"10.1007/978-3-642-16638-9_35","title":"Automated Storytelling in Sports: A Rich Domain to Be Explored","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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 Alberta","funders":"","keywords":"Storytelling; Domain (mathematical analysis); Psychology; Computer science; Art; Narrative; Literature; Mathematics","score_opus":0.015744733898622864,"score_gpt":0.2390232506472739,"score_spread":0.22327851674865104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1565989234","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.100944474,0.013322648,0.8031288,0.0025403535,0.00028152487,0.0006204615,0.007205741,0.011034566,0.06092152],"genre_scores_gemma":[0.42199656,0.008348388,0.51645863,0.00033767647,0.0003879907,0.00031794567,0.020846542,0.0012411684,0.030065093],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992644,0.0003853207,0.000034516328,0.00014265627,0.0001283257,0.00004476045],"domain_scores_gemma":[0.99651945,0.0025695176,0.00015608898,0.00037085186,0.0002931202,0.00009103504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067064096,0.0010129571,0.00051268045,0.0012481457,0.0005091895,0.0023631735,0.0014869041,0.0010106676,0.011350858],"category_scores_gemma":[0.0034952194,0.0002647394,0.0005146234,0.0014076044,0.00046255637,0.0022495238,0.0007720558,0.00063381187,0.004427062],"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":[0.00023854185,0.00025831317,0.002269912,0.002051615,0.00008811146,0.0004727414,0.0023457175,0.00935399,0.023877263,0.013236777,0.030884027,0.914923],"study_design_scores_gemma":[0.000109784254,0.00064737606,0.021661824,0.0010813443,0.00020166839,0.0041265558,0.010128457,0.32563883,0.09260106,0.10439881,0.4392529,0.0001514003],"about_ca_topic_score_codex":0.0010077793,"about_ca_topic_score_gemma":0.0018736209,"teacher_disagreement_score":0.011350858,"about_ca_system_score_codex":0.00030602244,"about_ca_system_score_gemma":0.00038362847,"threshold_uncertainty_score":0.03797245},"labels":[],"label_agreement":null},{"id":"W1579878694","doi":"10.19173/irrodl.v13i5.1246","title":"A rapid auto-indexing technology for designing readable e-learning content","year":2012,"lang":"en","type":"article","venue":"The International Review of Research in Open and Distributed Learning","topic":"Video Analysis and Summarization","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":"National Science Council","keywords":"Formative assessment; Computer science; Search engine indexing; Multimedia; Process (computing); Point (geometry); Instructional design; Distance education; E learning; Educational technology; World Wide Web; Mathematics education; The Internet","score_opus":0.15521817680966196,"score_gpt":0.4140959460130663,"score_spread":0.25887776920340433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579878694","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007142081,0.00040843844,0.9805797,0.000052698797,0.000096997,0.00042073868,0.00024752854,0.008940096,0.002111784],"genre_scores_gemma":[0.036663514,0.00046872822,0.95506066,0.000054370394,0.00007440133,0.00048077735,0.00081158924,0.000869595,0.0055164285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991949,0.0001520665,0.000098444965,0.00017568319,0.0003247769,0.000054097793],"domain_scores_gemma":[0.9974509,0.0009805479,0.00022512395,0.0004587341,0.00079015084,0.000094611794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009365706,0.0010517902,0.00090379204,0.0032233053,0.0004992304,0.0013202564,0.0014686483,0.00060828804,0.009372013],"category_scores_gemma":[0.0033361746,0.0005222865,0.000663673,0.0021024314,0.00043082846,0.002538958,0.00089004444,0.0006130826,0.005472292],"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":[0.00019049681,0.00010920153,0.00037452334,0.00057437667,0.000025962208,0.00013975428,0.00030459536,0.0008624908,0.2117625,0.003212288,0.0062086303,0.77623516],"study_design_scores_gemma":[0.00014708057,0.0012234433,0.0047299024,0.00012473414,0.0002687196,0.0021280015,0.0004775688,0.13402393,0.7292338,0.004666466,0.12273658,0.00023979135],"about_ca_topic_score_codex":0.0010451279,"about_ca_topic_score_gemma":0.0013658707,"teacher_disagreement_score":0.009372013,"about_ca_system_score_codex":0.0004262946,"about_ca_system_score_gemma":0.0006245206,"threshold_uncertainty_score":0.03135252},"labels":[],"label_agreement":null},{"id":"W1586356343","doi":"10.1007/11768012_55","title":"An Adaptive Hypermedia System Using a Constraint Satisfaction Approach for Information Personalization","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Dalhousie University","funders":"","keywords":"Personalization; Computer science; Adaptive hypermedia; Viewpoints; Adaptation (eye); Constraint satisfaction problem; Constraint (computer-aided design); Relevance (law); Information retrieval; Constraint satisfaction; Hypermedia; Information filtering system; World Wide Web; Artificial intelligence","score_opus":0.019589103544622382,"score_gpt":0.22975649540253446,"score_spread":0.21016739185791208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586356343","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017269067,0.00007402241,0.96681374,0.00014843664,0.000026556281,0.00023203425,0.0002537712,0.011768112,0.0034142428],"genre_scores_gemma":[0.15746878,0.0001561484,0.83112067,0.00017742325,0.00004203828,0.00040046943,0.0010883417,0.0007191109,0.008827052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991561,0.00019129817,0.00008590916,0.00019250144,0.00032328966,0.000050928185],"domain_scores_gemma":[0.99843186,0.0007112258,0.00006960007,0.00030851917,0.0003728016,0.00010596187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009893903,0.00070276315,0.00084241154,0.000851059,0.00094243954,0.0020617915,0.0024512762,0.0011213019,0.008210198],"category_scores_gemma":[0.0029831508,0.00053000916,0.00072507036,0.0013529178,0.0005073376,0.002392054,0.0019476375,0.0012353696,0.001576089],"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":[0.001036329,0.0010372505,0.0017933091,0.0005265109,0.00028554493,0.0006645868,0.0013933745,0.057132818,0.114984445,0.022183826,0.02582398,0.773138],"study_design_scores_gemma":[0.00014925111,0.00012493026,0.00068905065,0.000025823461,0.00014716944,0.00021366964,0.0001994551,0.9238871,0.04454592,0.0087120375,0.02121965,0.00008597507],"about_ca_topic_score_codex":0.009004259,"about_ca_topic_score_gemma":0.010223833,"teacher_disagreement_score":0.009004259,"about_ca_system_score_codex":0.00054878305,"about_ca_system_score_gemma":0.0010542957,"threshold_uncertainty_score":0.02746588},"labels":[],"label_agreement":null},{"id":"W1591513817","doi":"10.1007/11670834_6","title":"Developing AMIE: An Adaptive Multimedia Integrated Environment","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Concordia University","funders":"","keywords":"Computer science; Multimedia; Search engine indexing; Presentation (obstetrics); World Wide Web; Information retrieval","score_opus":0.01901763583519407,"score_gpt":0.22649627293544589,"score_spread":0.20747863710025183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591513817","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009810828,0.000090860216,0.95744485,0.00006746346,0.00005786067,0.00016693243,0.00017130769,0.027323218,0.004866657],"genre_scores_gemma":[0.084678575,0.00018845215,0.89382344,0.00019406616,0.000055355922,0.0002523989,0.0013525662,0.0029066983,0.016548535],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960774,0.000045523862,0.000026568905,0.00010187924,0.00017397458,0.0000442994],"domain_scores_gemma":[0.9996238,0.00010560808,0.000020663289,0.00007210724,0.000119637676,0.000058242505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069299166,0.0008146732,0.00052222656,0.00041681324,0.00026658233,0.0010637367,0.002250332,0.00090704917,0.009273856],"category_scores_gemma":[0.0013426738,0.00051791454,0.0005185617,0.00028514606,0.0002613126,0.001741035,0.0016004476,0.001139326,0.0038602434],"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":[0.0010133986,0.00061092945,0.0018668037,0.00041076023,0.00020831052,0.0007728128,0.0007135074,0.019954251,0.28792647,0.01493301,0.02882699,0.6427628],"study_design_scores_gemma":[0.00035286695,0.0010662947,0.0023300603,0.00011311028,0.00026388973,0.001791837,0.000310762,0.38197947,0.3799659,0.010434528,0.22123536,0.00015592643],"about_ca_topic_score_codex":0.00056117185,"about_ca_topic_score_gemma":0.0007586321,"teacher_disagreement_score":0.009273856,"about_ca_system_score_codex":0.00017995095,"about_ca_system_score_gemma":0.000338804,"threshold_uncertainty_score":0.031024158},"labels":[],"label_agreement":null},{"id":"W15976199","doi":"10.1213/01.ane.0000155260.93406.29","title":"Automatic camera control using unobtrusive vision and audio tracking","year":2010,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University; University of Toronto","funders":"National Heart, Lung, and Blood Institute","keywords":"Computer science; Video production; Video tracking; Variety (cybernetics); Video processing; Tracking (education); Post-production; Multimedia; Computer vision; Key (lock); Quality (philosophy); Artificial intelligence; Video quality; Video camera; Engineering","score_opus":0.0113881727990213,"score_gpt":0.2750508403293684,"score_spread":0.26366266753034706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W15976199","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.101993494,0.0009561459,0.8780388,0.00015395384,0.00039925653,0.0010172667,0.00042384045,0.006658714,0.010358576],"genre_scores_gemma":[0.6968576,0.0005605198,0.2914635,0.00021903067,0.00015503999,0.0009745829,0.00042505495,0.00033881533,0.009005879],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986248,0.00023149181,0.00006267365,0.00046104868,0.00050539075,0.00011470402],"domain_scores_gemma":[0.99832994,0.00067496824,0.0001718711,0.00023723414,0.0004996397,0.0000864589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012616928,0.0011804423,0.0006619488,0.001420681,0.00034258023,0.0008236403,0.001249776,0.00095176365,0.008886059],"category_scores_gemma":[0.003822778,0.00044651324,0.00041284433,0.0006415876,0.00043931557,0.00085255323,0.0012125993,0.0004663868,0.0019012422],"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":[0.0020479998,0.0004921606,0.00612583,0.00043153472,0.00008680749,0.00028667273,0.00036155997,0.007543154,0.16846868,0.0011966212,0.0042774566,0.8086814],"study_design_scores_gemma":[0.0016725303,0.006844698,0.06917274,0.0004094961,0.00058695633,0.0046345047,0.00043561045,0.5952991,0.2518613,0.0045958655,0.06393391,0.00055332424],"about_ca_topic_score_codex":0.0024348034,"about_ca_topic_score_gemma":0.0020314956,"teacher_disagreement_score":0.008886059,"about_ca_system_score_codex":0.00039087524,"about_ca_system_score_gemma":0.0006264801,"threshold_uncertainty_score":0.029726863},"labels":[],"label_agreement":null},{"id":"W1605316656","doi":"10.3233/978-1-60750-028-5-483","title":"Detecting Significant Events in Lecture Video using Supervised Machine Learning","year":2009,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Video Analysis and Summarization","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 Saskatchewan","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning","score_opus":0.033573481750832745,"score_gpt":0.2597355955254726,"score_spread":0.22616211377463985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605316656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05387022,0.0016772202,0.93372303,0.00022950233,0.00017015038,0.0002726835,0.0007671053,0.004190612,0.005099352],"genre_scores_gemma":[0.19952208,0.0013552927,0.78875744,0.000103248596,0.00026009275,0.0001846518,0.0032275084,0.00023511726,0.0063546016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934286,0.00015280707,0.00004973781,0.00020335597,0.00020587476,0.00004527634],"domain_scores_gemma":[0.9981261,0.00094420975,0.00021584987,0.00017151149,0.00046826366,0.00007418361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078660756,0.0009578723,0.0006945365,0.0025340528,0.00044511593,0.0010360305,0.0010435103,0.00059006806,0.002206215],"category_scores_gemma":[0.0019920783,0.00024519034,0.000806944,0.0014262322,0.000326052,0.0011826217,0.00042381426,0.0008540457,0.0018514488],"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":[0.0001221795,0.00020583303,0.0035316641,0.00039318364,0.00009378572,0.00012694266,0.00022372942,0.012848589,0.051000565,0.001585769,0.0071122325,0.9227556],"study_design_scores_gemma":[0.00004312737,0.00048038078,0.019295681,0.00012659421,0.00019447623,0.0006343301,0.00045166007,0.82960635,0.113859445,0.011071722,0.02412156,0.000114648996],"about_ca_topic_score_codex":0.0014070355,"about_ca_topic_score_gemma":0.004244281,"teacher_disagreement_score":0.0025340528,"about_ca_system_score_codex":0.0004094689,"about_ca_system_score_gemma":0.0003926067,"threshold_uncertainty_score":0.0073804855},"labels":[],"label_agreement":null},{"id":"W1608642888","doi":"10.1109/crv.2015.23","title":"Feature Ranking in Dynamic Texture Clustering","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Ranking (information retrieval); Feature (linguistics); Data mining; Artificial intelligence; Pattern recognition (psychology); Feature selection; Correlation clustering; Machine learning","score_opus":0.013457480199073602,"score_gpt":0.24442724673376962,"score_spread":0.230969766534696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1608642888","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03543601,0.00078449445,0.961374,0.000118684606,0.000045737273,0.00006640445,0.00019454374,0.0006013066,0.0013788212],"genre_scores_gemma":[0.62787324,0.00070916384,0.36610103,0.00013960295,0.00022168568,0.00021381883,0.0013764921,0.0002884869,0.0030764479],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897623,0.00026192484,0.00006499531,0.00024609527,0.00032016382,0.00013067205],"domain_scores_gemma":[0.99806136,0.00070418394,0.00025638554,0.00021652343,0.00064748444,0.00011405609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001056342,0.00082712766,0.0015577808,0.0031428614,0.0008200689,0.0011861912,0.0010638898,0.00086097565,0.0013500751],"category_scores_gemma":[0.004738413,0.00029212495,0.0007678699,0.002653992,0.0006050936,0.0013712288,0.000816022,0.0006920617,0.00058504596],"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":[0.00045520897,0.00018094,0.0037402038,0.00040278086,0.00016911881,0.00023863815,0.00027699466,0.31389865,0.024666049,0.020897293,0.009779935,0.62529415],"study_design_scores_gemma":[0.00001660561,0.0000873149,0.0016935364,0.00001390317,0.00003141739,0.00010581086,0.00007546536,0.98328215,0.0030913074,0.009680597,0.0018871318,0.000034750352],"about_ca_topic_score_codex":0.003999429,"about_ca_topic_score_gemma":0.004497274,"teacher_disagreement_score":0.003999429,"about_ca_system_score_codex":0.0007602043,"about_ca_system_score_gemma":0.0006818586,"threshold_uncertainty_score":0.0079523325},"labels":[],"label_agreement":null},{"id":"W16976956","doi":"10.1007/978-3-319-11782-9_9","title":"Scalable Video Genre Classification and Event Detection","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Event (particle physics); Conditional random field; Artificial intelligence; Probabilistic latent semantic analysis; Generality; Probabilistic logic; Histogram; Representation (politics); Scalability; Field (mathematics); Pattern recognition (psychology); Image (mathematics)","score_opus":0.016224597733133915,"score_gpt":0.21551114317702477,"score_spread":0.19928654544389085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W16976956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008604038,0.009295502,0.94659996,0.0008600402,0.0011874249,0.00025712166,0.00398771,0.013285245,0.015922958],"genre_scores_gemma":[0.09009723,0.009079461,0.8295834,0.00040055224,0.0019923218,0.00035307845,0.021625603,0.0012409174,0.045627423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992442,0.00006533507,0.000043646993,0.00025953146,0.00031965625,0.00006769656],"domain_scores_gemma":[0.9991277,0.0002223941,0.000053088872,0.0002219481,0.0003179493,0.00005695398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008249761,0.0017541193,0.0015859584,0.00315783,0.0005358485,0.0018570942,0.0018401046,0.0008619747,0.010509591],"category_scores_gemma":[0.0021346502,0.0005545547,0.001166354,0.0040454427,0.00028969703,0.002430834,0.0013583848,0.0012312505,0.010101109],"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":[0.000061836734,0.000072457005,0.00037533193,0.00017659765,0.000042436903,0.000049961232,0.000031515294,0.0032510147,0.0132442,0.0025637175,0.050399374,0.92973155],"study_design_scores_gemma":[0.00006577012,0.00024058214,0.0089871045,0.00024241544,0.00023864461,0.0010146818,0.00033502784,0.67177117,0.07169063,0.0672428,0.17805356,0.00011759696],"about_ca_topic_score_codex":0.0042566443,"about_ca_topic_score_gemma":0.0062040254,"teacher_disagreement_score":0.010509591,"about_ca_system_score_codex":0.00076553226,"about_ca_system_score_gemma":0.0007084409,"threshold_uncertainty_score":0.03515804},"labels":[],"label_agreement":null},{"id":"W1734873164","doi":"10.1109/hicss.1997.665705","title":"Supporting the retrieval process in multimedia information systems","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Set (abstract data type); Multimedia; Interface (matter); Process (computing); User interface; Information retrieval; Multimedia information retrieval; Interface design; World Wide Web; Human–computer interaction","score_opus":0.01297718968207566,"score_gpt":0.24623669964707454,"score_spread":0.2332595099649989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1734873164","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022758186,0.0021842627,0.95151496,0.0015326347,0.00008848057,0.00049442594,0.000073667885,0.005479667,0.01587387],"genre_scores_gemma":[0.21811011,0.002938541,0.76591945,0.00067725225,0.00030653575,0.0005882554,0.00033341435,0.00066034746,0.010466101],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9956285,0.0020794428,0.00033088314,0.00029481127,0.0013873823,0.00027906365],"domain_scores_gemma":[0.98525953,0.0110309515,0.00068974553,0.0016416503,0.0011083465,0.00026972187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005901642,0.0007511636,0.0011132106,0.0016294919,0.0017170928,0.005238173,0.0027198445,0.0023251583,0.0072758812],"category_scores_gemma":[0.026456181,0.0008953759,0.0006510749,0.0019456438,0.0016702651,0.011146232,0.003095065,0.001636324,0.003946998],"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":[0.00088228704,0.00048771402,0.0015377663,0.0023164086,0.000074132,0.0013033644,0.0076963,0.02294678,0.06561414,0.18973559,0.021850146,0.6855554],"study_design_scores_gemma":[0.0003601377,0.0010225304,0.0011285688,0.0009076001,0.00020049087,0.0020356195,0.0022198434,0.36211511,0.10475472,0.2440852,0.28088477,0.00028542307],"about_ca_topic_score_codex":0.0014297809,"about_ca_topic_score_gemma":0.0012526427,"teacher_disagreement_score":0.0072758812,"about_ca_system_score_codex":0.0006858377,"about_ca_system_score_gemma":0.0010083388,"threshold_uncertainty_score":0.031211257},"labels":[],"label_agreement":null},{"id":"W1750339067","doi":"10.1109/mmsp.2001.962777","title":"Local motion descriptors","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 British Columbia","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Motion (physics); Ranking (information retrieval); Object (grammar); Fourier domain; Boundary (topology); Domain (mathematical analysis); Fourier transform; Pattern recognition (psychology); Mathematics; Mathematical analysis","score_opus":0.020471852772740697,"score_gpt":0.19287972958881278,"score_spread":0.1724078768160721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1750339067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06315725,0.008606955,0.8915518,0.00040773832,0.00051759055,0.0010103013,0.01359102,0.0041140704,0.017043361],"genre_scores_gemma":[0.6453045,0.0046830117,0.29849675,0.00049949286,0.0007918467,0.0011722103,0.032550175,0.00046432923,0.016037645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999366,0.00006797077,0.00006233138,0.00014372269,0.00027608933,0.00008397537],"domain_scores_gemma":[0.99921954,0.00021095715,0.00015687743,0.00012686524,0.00024189078,0.00004377685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047649432,0.000758785,0.0010355203,0.0036919324,0.0003158787,0.0009916637,0.0008968872,0.00077000586,0.0065054493],"category_scores_gemma":[0.0027666118,0.00016119498,0.00059886475,0.0037747822,0.0003306195,0.0017460131,0.00057762,0.0005405608,0.003024841],"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":[0.0005187175,0.00022900272,0.006118897,0.00072999974,0.00017069904,0.0003168627,0.00009429261,0.026537359,0.07379525,0.02605849,0.03222245,0.83320796],"study_design_scores_gemma":[0.00035830864,0.001574691,0.08353365,0.00045799054,0.00053009606,0.0036490466,0.000664532,0.58010817,0.08526912,0.063872956,0.17955142,0.0004300409],"about_ca_topic_score_codex":0.0029123016,"about_ca_topic_score_gemma":0.0029446501,"teacher_disagreement_score":0.0065054493,"about_ca_system_score_codex":0.00071891607,"about_ca_system_score_gemma":0.00046919403,"threshold_uncertainty_score":0.021762908},"labels":[],"label_agreement":null},{"id":"W1781278756","doi":"10.1007/978-3-642-10520-3_42","title":"Online Video Textures Generation","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Concordia University","funders":"","keywords":"Isomap; Computer science; Artificial intelligence; Autoregressive model; Process (computing); Pattern recognition (psychology); Set (abstract data type); Computer vision; Dimensionality reduction; Nonlinear dimensionality reduction; Mathematics","score_opus":0.019012683711492027,"score_gpt":0.2483335604042227,"score_spread":0.22932087669273066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1781278756","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012929769,0.00061451574,0.9223925,0.00019324908,0.00048268816,0.0003764105,0.0022621278,0.025230788,0.03551797],"genre_scores_gemma":[0.20164542,0.0009989545,0.7164843,0.0002047146,0.00023867936,0.00040398596,0.009911334,0.0032207323,0.0668919],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997787,0.000017975643,0.0000089900295,0.000061030078,0.00010432222,0.000029074272],"domain_scores_gemma":[0.9997348,0.000050018178,0.00001187533,0.000084275256,0.000087061795,0.00003189833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022448444,0.0009868292,0.00055330945,0.0010936793,0.0003634875,0.00093112973,0.0010653619,0.00064413575,0.05108521],"category_scores_gemma":[0.0009174096,0.00030152942,0.00051992026,0.00083723076,0.00016651857,0.00082356937,0.0011077389,0.0005425683,0.014379659],"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":[0.00040379644,0.00011971403,0.00021362837,0.0001564394,0.00002050264,0.00020212737,0.00004654515,0.005186518,0.06092657,0.004721645,0.042573374,0.88542926],"study_design_scores_gemma":[0.00020008825,0.00030134502,0.0019149987,0.00010837139,0.00008013409,0.0012834276,0.00018892331,0.51778007,0.25738156,0.018739635,0.20194574,0.00007569461],"about_ca_topic_score_codex":0.0015487757,"about_ca_topic_score_gemma":0.0020376008,"teacher_disagreement_score":0.05108521,"about_ca_system_score_codex":0.00029143563,"about_ca_system_score_gemma":0.00029936308,"threshold_uncertainty_score":0.170897},"labels":[],"label_agreement":null},{"id":"W1820297294","doi":"10.1109/icip.2001.958134","title":"Scene break detection and classification using a block-wise difference method","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Artificial intelligence; Computer vision; Computer science; Motion compensation; Motion (physics); Block (permutation group theory); Quarter-pixel motion; Motion estimation; Block-matching algorithm; Motion detection; Matching (statistics); Shot (pellet); Pattern recognition (psychology); Mathematics; Video processing; Video tracking","score_opus":0.054329975964923054,"score_gpt":0.2740026022647557,"score_spread":0.21967262629983264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1820297294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019818045,0.00022363625,0.97777575,0.000067050896,0.000058433438,0.00011688722,0.00012973858,0.0011688166,0.0006417425],"genre_scores_gemma":[0.08612185,0.00020946263,0.9107538,0.00004556572,0.00006626979,0.0001358026,0.0005930808,0.0001348026,0.0019393949],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994479,0.00006391782,0.000042766256,0.00012789959,0.00024822043,0.00006936943],"domain_scores_gemma":[0.9991284,0.00020296441,0.00008343206,0.000103136306,0.00042011266,0.000062090374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006199187,0.0006401333,0.0011817596,0.002900637,0.00037652822,0.00085752393,0.0011056684,0.0008438343,0.0025219389],"category_scores_gemma":[0.0013537239,0.00037621733,0.0006655361,0.0011529042,0.00034408164,0.0010034159,0.0005054031,0.00083611306,0.0016391199],"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":[0.00032192498,0.00016576416,0.0017968775,0.00015498497,0.000061729945,0.00008559932,0.00008085189,0.010990856,0.16968876,0.0018539483,0.0019934722,0.8128052],"study_design_scores_gemma":[0.00005405685,0.00029546497,0.007240288,0.000019068912,0.00007734821,0.0003252538,0.00006773036,0.8978569,0.083829,0.0021875766,0.00799535,0.000051872557],"about_ca_topic_score_codex":0.002759359,"about_ca_topic_score_gemma":0.0035566268,"teacher_disagreement_score":0.002900637,"about_ca_system_score_codex":0.00041487973,"about_ca_system_score_gemma":0.00057568477,"threshold_uncertainty_score":0.00843668},"labels":[],"label_agreement":null},{"id":"W1862669148","doi":"10.1109/ccece.1996.548299","title":"Image/video indexing in the compressed domain","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; JPEG; Search engine indexing; Video compression picture types; Uncompressed video; Data compression; Computer vision; Artificial intelligence; Image compression; Transform coding; Multiview Video Coding; Database index; Smacker video; Video processing; Video tracking; Image processing; Image (mathematics); Discrete cosine transform","score_opus":0.017985725022776042,"score_gpt":0.22197284335271814,"score_spread":0.2039871183299421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1862669148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023138814,0.010015187,0.9311447,0.00091920217,0.0008550723,0.00053103286,0.00091062876,0.0055822874,0.026903095],"genre_scores_gemma":[0.14819711,0.008040336,0.81816953,0.00051508006,0.0014324216,0.0002974313,0.003369676,0.00038664948,0.019591687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993876,0.000048884016,0.000053002914,0.000074265336,0.0003810878,0.00005513218],"domain_scores_gemma":[0.99916875,0.00013403532,0.000086965876,0.00024649885,0.00032306195,0.00004070644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041985017,0.00056799984,0.0008457945,0.0026127186,0.00047363847,0.0017499336,0.0011768142,0.00066578446,0.0068187057],"category_scores_gemma":[0.0016336085,0.00019506499,0.00038825313,0.0031186887,0.0006905694,0.0026988434,0.00097051874,0.0006760232,0.004120971],"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":[0.000268753,0.00013807477,0.00029123246,0.00048626427,0.000039508486,0.00037457416,0.00016113537,0.002423603,0.15095882,0.03167135,0.019839117,0.79334766],"study_design_scores_gemma":[0.00023967904,0.0009074445,0.0028579195,0.0003207639,0.00018240759,0.006594469,0.0005521686,0.17675287,0.4879589,0.061913725,0.26154745,0.00017225214],"about_ca_topic_score_codex":0.0009593839,"about_ca_topic_score_gemma":0.00078811,"teacher_disagreement_score":0.0068187057,"about_ca_system_score_codex":0.00041202796,"about_ca_system_score_gemma":0.0005368471,"threshold_uncertainty_score":0.022810876},"labels":[],"label_agreement":null},{"id":"W1879939014","doi":"10.1109/enabl.2001.953431","title":"Suitability of MPEG4's BIFS for development of collaborative virtual environments","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Task (project management); Virtual reality; Object (grammar); Human–computer interaction; Joint (building); Order (exchange); Multimedia; Distributed computing; Systems engineering; Artificial intelligence; Engineering; Architectural engineering","score_opus":0.020805171685923288,"score_gpt":0.23151283055327257,"score_spread":0.21070765886734927,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1879939014","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06176902,0.0015832847,0.8609854,0.001287003,0.0005990825,0.0006695318,0.00085425196,0.005877017,0.0663754],"genre_scores_gemma":[0.33988407,0.0026783268,0.6269861,0.0003235529,0.00029827224,0.0004629853,0.002447127,0.0011305381,0.025788972],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991284,0.00016720986,0.0000775841,0.000043209413,0.0004853189,0.00009833211],"domain_scores_gemma":[0.998926,0.00017827182,0.00007451382,0.00020037983,0.0005637333,0.000057051522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017903782,0.0004620342,0.00018180287,0.0011593158,0.00037056018,0.0010975578,0.00093530567,0.0005291167,0.0023922361],"category_scores_gemma":[0.0039266935,0.00018907181,0.00025546903,0.000872733,0.0003197302,0.0012691239,0.00044274953,0.0004674127,0.0018674281],"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":[0.00074667967,0.00017057457,0.0028943317,0.00036355626,0.000032060863,0.0013660295,0.0004478901,0.019012252,0.20105954,0.08878533,0.018003438,0.6671184],"study_design_scores_gemma":[0.00013428551,0.00082015805,0.0054962416,0.0003766076,0.000110655725,0.0030579774,0.0006917174,0.17295755,0.3918649,0.026878133,0.3974348,0.00017687964],"about_ca_topic_score_codex":0.0036770655,"about_ca_topic_score_gemma":0.0021601224,"teacher_disagreement_score":0.0036770655,"about_ca_system_score_codex":0.0005867385,"about_ca_system_score_gemma":0.00085751485,"threshold_uncertainty_score":0.009468496},"labels":[],"label_agreement":null},{"id":"W1885015051","doi":"10.1109/icip.2001.958580","title":"Similarity matching of arbitrarily shaped video by still shape features and shape deformations","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Artificial intelligence; Similarity (geometry); Computer vision; Matching (statistics); Shape analysis (program analysis); Active shape model; Object (grammar); Video tracking; Pattern recognition (psychology); Domain (mathematical analysis); Similarity measure; Feature extraction; Image (mathematics); Mathematics; Segmentation","score_opus":0.014107179432923054,"score_gpt":0.2202403043932981,"score_spread":0.20613312496037503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1885015051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11819069,0.00058227783,0.8783523,0.0000829876,0.000061938794,0.00012363323,0.00020013205,0.0008423054,0.0015637294],"genre_scores_gemma":[0.55084324,0.0006416587,0.44409612,0.0001069878,0.00014569209,0.000118156764,0.0012420772,0.00024225775,0.002563742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994137,0.000083571525,0.000042368025,0.00012394704,0.0002824849,0.000053847718],"domain_scores_gemma":[0.9992306,0.00016741495,0.000138722,0.00019009899,0.00022745186,0.000045718534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040122622,0.00037435925,0.00080224755,0.0028011827,0.0002303446,0.0007488707,0.0007022671,0.0005908914,0.0011976281],"category_scores_gemma":[0.0027226394,0.00020868462,0.0005858224,0.0021409518,0.00044643387,0.0015404021,0.00060656114,0.00036619575,0.0007055663],"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":[0.0006581044,0.00012796625,0.002140414,0.00018566441,0.00010192517,0.000307911,0.00021015634,0.03144515,0.26691774,0.008078366,0.002760687,0.68706596],"study_design_scores_gemma":[0.00006826858,0.00042151668,0.01298182,0.000025595396,0.00007769291,0.0012442246,0.00026008073,0.8248204,0.13761647,0.014198071,0.008202174,0.000083624625],"about_ca_topic_score_codex":0.0013762842,"about_ca_topic_score_gemma":0.0014109751,"teacher_disagreement_score":0.0028011827,"about_ca_system_score_codex":0.00058367,"about_ca_system_score_gemma":0.00039915316,"threshold_uncertainty_score":0.004234791},"labels":[],"label_agreement":null},{"id":"W1918522533","doi":"10.1109/icde.2000.839445","title":"Mining recurrent items in multimedia with progressive resolution refinement","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":82,"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; University of Alberta","funders":"","keywords":"Computer science; Completeness (order theory); Resolution (logic); Data mining; Visualization; Information retrieval; Artificial intelligence","score_opus":0.022933252714577954,"score_gpt":0.2327635748267603,"score_spread":0.20983032211218233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1918522533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07404933,0.000692091,0.9234599,0.00014755985,0.000017512748,0.00020928727,0.00029845655,0.00065173244,0.00047416962],"genre_scores_gemma":[0.21865156,0.00043396588,0.7787693,0.000063665575,0.00004791936,0.00018142152,0.0012544814,0.00004753144,0.0005501293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979818,0.0005191173,0.0002820043,0.00038156088,0.00070892356,0.00012661144],"domain_scores_gemma":[0.9908993,0.0049995203,0.0012014478,0.0012206001,0.0015291355,0.00014994355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003007883,0.0007414642,0.0011858664,0.0053304657,0.00046868736,0.0014021058,0.0017281899,0.00088814425,0.0006711189],"category_scores_gemma":[0.017009895,0.0006170351,0.0014907952,0.003745202,0.000680002,0.002187068,0.001222072,0.00094605796,0.00055879087],"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":[0.00075196556,0.0004027819,0.018524362,0.0006561247,0.0004215543,0.0013024653,0.0011460787,0.11528237,0.05930386,0.009007686,0.0024090072,0.7907918],"study_design_scores_gemma":[0.00010283659,0.00050215365,0.007700746,0.00009402661,0.00032687307,0.0013671088,0.000617129,0.92533135,0.036324557,0.023375487,0.004178104,0.00007958191],"about_ca_topic_score_codex":0.002205988,"about_ca_topic_score_gemma":0.002759577,"teacher_disagreement_score":0.0053304657,"about_ca_system_score_codex":0.00033273146,"about_ca_system_score_gemma":0.00060236704,"threshold_uncertainty_score":0.015907407},"labels":[],"label_agreement":null},{"id":"W1918874199","doi":"10.1007/11424918_13","title":"A Bayesian Model to Smooth Telepointer Jitter","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Jitter; Cursor (databases); Bayesian network; Artificial intelligence; Bayesian probability; Gesture; Computer vision; Machine learning","score_opus":0.013909786075580128,"score_gpt":0.23830387570862302,"score_spread":0.2243940896330429,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1918874199","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0088954,0.00032598988,0.9884444,0.00033987057,0.000058552992,0.000026369056,0.00013197889,0.00032110367,0.001456403],"genre_scores_gemma":[0.72671384,0.0016786661,0.2318604,0.00046234668,0.00043374923,0.00029234964,0.00074782426,0.00063694286,0.037173886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874204,0.00038581312,0.000056038443,0.00036647156,0.00027256564,0.00017706574],"domain_scores_gemma":[0.9946496,0.0036190166,0.00045971686,0.00042921896,0.0006709618,0.00017147002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004131038,0.0011094048,0.0022606936,0.0014311689,0.0009125789,0.0022184027,0.003792598,0.0040467572,0.0057168743],"category_scores_gemma":[0.016584264,0.0023623754,0.0013403925,0.0019039996,0.0016512376,0.004580617,0.0017174287,0.0034087026,0.0015093733],"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":[0.00012876366,0.00005045218,0.0004785624,0.000054240918,0.00004681008,0.000061854524,0.000081957915,0.89907396,0.0008832756,0.07165229,0.0018188283,0.025669022],"study_design_scores_gemma":[0.000009247112,0.000009496609,0.00011766703,0.000006042622,0.000010867231,0.000015611025,0.0000044239637,0.98021,0.00011539774,0.019134136,0.00035377283,0.000013373541],"about_ca_topic_score_codex":0.014293485,"about_ca_topic_score_gemma":0.009808487,"teacher_disagreement_score":0.014293485,"about_ca_system_score_codex":0.0023869255,"about_ca_system_score_gemma":0.001358423,"threshold_uncertainty_score":0.028420568},"labels":[],"label_agreement":null},{"id":"W1921762010","doi":"10.1109/ccece.1995.528207","title":"An intelligent agent for multimedia","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Multimedia; Representation (politics); Newspaper; Intelligent agent; Interface (matter); Human–computer interaction; Boundary (topology); World Wide Web; Artificial intelligence","score_opus":0.040856582354597784,"score_gpt":0.2680815996809981,"score_spread":0.22722501732640032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1921762010","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004768568,0.0014829948,0.89619994,0.0027769462,0.000518761,0.00042443417,0.00017563967,0.0035761124,0.09007656],"genre_scores_gemma":[0.12865984,0.0016569424,0.78721666,0.0012718014,0.0003854313,0.00053518725,0.00050662825,0.0004176888,0.079349786],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902034,0.00020589058,0.00007398882,0.00017867777,0.00043982928,0.00008137576],"domain_scores_gemma":[0.99920636,0.00021163949,0.000066332526,0.00017763748,0.00019438716,0.00014363903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091285113,0.00072237296,0.00064766145,0.00081947126,0.0016646049,0.0037122353,0.0021339871,0.0025044254,0.008928051],"category_scores_gemma":[0.0019709114,0.0004042034,0.00087595964,0.0005498095,0.0011875757,0.0045626974,0.0025058137,0.0019796914,0.003794007],"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":[0.00022101088,0.00024043182,0.0007277348,0.00045545216,0.00009781536,0.00071087753,0.00087015016,0.012726536,0.014926761,0.7438621,0.030040992,0.19512019],"study_design_scores_gemma":[0.000094011244,0.00016856888,0.00026012823,0.00013190844,0.00012468347,0.00089711335,0.00025342824,0.15586391,0.010991915,0.13798885,0.693148,0.000077505494],"about_ca_topic_score_codex":0.0020763327,"about_ca_topic_score_gemma":0.0031984567,"teacher_disagreement_score":0.008928051,"about_ca_system_score_codex":0.0010015706,"about_ca_system_score_gemma":0.0011580851,"threshold_uncertainty_score":0.029867351},"labels":[],"label_agreement":null},{"id":"W1924814796","doi":"10.1109/tmm.2015.2485538","title":"Guest Editorial: Deep Learning for Multimedia Computing","year":2015,"lang":"en","type":"editorial","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","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 de Sherbrooke","funders":"","keywords":"Computer science; Multimedia; Deep learning; Artificial intelligence","score_opus":0.014566186611034149,"score_gpt":0.274321100272386,"score_spread":0.25975491366135184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1924814796","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009552756,0.008565164,0.0008013175,0.07202916,0.9159993,0.000024159832,0.00011339842,0.00016251972,0.0022095365],"genre_scores_gemma":[0.0011590219,0.006937373,0.00030895523,0.024848713,0.95093936,0.000033719552,0.000084388936,0.00007881255,0.01560965],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978787,0.0002997691,0.0001807414,0.00025360598,0.0011699434,0.00021739535],"domain_scores_gemma":[0.9921663,0.0025737886,0.0004891546,0.00017851379,0.0029913285,0.001600994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036487507,0.0016702708,0.0017147998,0.0021768338,0.001564566,0.0049232394,0.0021127537,0.0077986424,0.018521843],"category_scores_gemma":[0.011477271,0.00056614,0.0014626852,0.0007800077,0.0015249846,0.0033158707,0.0015183642,0.0116897365,0.0125154015],"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":[0.000028128461,0.00000764117,0.00002094958,0.00008938338,0.000009207846,0.00006066976,0.0000044987733,0.0000271245,0.000056395274,0.00041421247,0.99148273,0.007799101],"study_design_scores_gemma":[0.00004773712,0.000023881687,0.00015542474,0.00024655627,0.000024382454,0.0002174387,0.000017006982,0.0003276595,0.00020129766,0.0016317308,0.99709296,0.0000138640635],"about_ca_topic_score_codex":0.00051310554,"about_ca_topic_score_gemma":0.0015157596,"teacher_disagreement_score":0.018521843,"about_ca_system_score_codex":0.0016056831,"about_ca_system_score_gemma":0.0016368033,"threshold_uncertainty_score":0.06196177},"labels":[],"label_agreement":null},{"id":"W1944210268","doi":"10.5220/0004742206340640","title":"High Definition Visual Attention based Video Summarization","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Automatic summarization; Computer science; Artificial intelligence; Frame (networking); Key frame; Histogram; Feature (linguistics); Shot (pellet); Computer vision; Construct (python library); Block-matching algorithm; Visualization; Video tracking; Histogram of oriented gradients; Feature extraction; Reference frame; Pattern recognition (psychology); Key (lock); Video compression picture types; Video processing; Image (mathematics)","score_opus":0.01060302122194329,"score_gpt":0.21712206228883577,"score_spread":0.20651904106689248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1944210268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025884803,0.0012908682,0.9647867,0.00013328498,0.00016176299,0.00032706512,0.00060550036,0.0045975605,0.0022124257],"genre_scores_gemma":[0.2773701,0.00094329024,0.71029437,0.00013648949,0.00029939058,0.00035463567,0.0038398402,0.00045229745,0.006309501],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990183,0.00013615115,0.00007761582,0.00031881046,0.00034946276,0.0000997065],"domain_scores_gemma":[0.99826705,0.00033076393,0.00018016266,0.00015636181,0.0009877661,0.000077848075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088442175,0.0013297164,0.0010680228,0.0033417284,0.0004986751,0.0012385548,0.0011663747,0.00060052524,0.0029439575],"category_scores_gemma":[0.0032231398,0.00026665983,0.000745159,0.0017950578,0.00029748585,0.0015762397,0.0011027877,0.0007001194,0.001311338],"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":[0.00041990212,0.00011459458,0.00088036625,0.0004045811,0.00009102932,0.00015292346,0.00024557087,0.010182004,0.078244135,0.0022449319,0.007858481,0.89916146],"study_design_scores_gemma":[0.00012392603,0.0012055425,0.0150216445,0.0001022989,0.0003831701,0.0008836573,0.0005576456,0.7385305,0.20046258,0.009284357,0.033304565,0.0001400003],"about_ca_topic_score_codex":0.0030818633,"about_ca_topic_score_gemma":0.0033566183,"teacher_disagreement_score":0.0033417284,"about_ca_system_score_codex":0.00063232187,"about_ca_system_score_gemma":0.0005756653,"threshold_uncertainty_score":0.009848535},"labels":[],"label_agreement":null},{"id":"W1953951078","doi":"","title":"Use of the MPEG-7 standard as metadata framework for a location scouting system: an evaluation study","year":2005,"lang":"en","type":"article","venue":"International Conference on Dublin Core and Metadata Applications","topic":"Video Analysis and Summarization","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":"Computer Research Institute of Montréal","funders":"","keywords":"Metadata; Computer science; Information retrieval; Metadata repository; Metadata modeling; Vocabulary; Schema (genetic algorithms); Meta Data Services; Taxonomy (biology); Domain (mathematical analysis); Identification (biology); Annotation; World Wide Web; Artificial intelligence","score_opus":0.23858943370562707,"score_gpt":0.411428967821789,"score_spread":0.17283953411616193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1953951078","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97837204,0.00077030977,0.011223162,0.00016388146,0.00004811797,0.001395184,0.0006833156,0.0003363524,0.0070075095],"genre_scores_gemma":[0.97975314,0.00065852667,0.0141218025,0.00005711263,0.00003546312,0.000486625,0.00257676,0.00010925557,0.0022013488],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9708204,0.016325112,0.0021980267,0.001081936,0.008843348,0.0007310786],"domain_scores_gemma":[0.95438087,0.022377634,0.0025949006,0.002634803,0.016649488,0.0013623609],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02474157,0.0010035292,0.0007982008,0.003288536,0.0010006998,0.0016963521,0.0009894501,0.0010978271,0.0017245247],"category_scores_gemma":[0.044461142,0.00025807135,0.0006638729,0.0027922331,0.0010023974,0.002854484,0.0011008993,0.0006652996,0.0007194685],"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":[0.01829085,0.020149246,0.2239966,0.0047909664,0.0013403563,0.0013426086,0.009965472,0.027101707,0.08890622,0.0035066202,0.009922833,0.5906865],"study_design_scores_gemma":[0.002453414,0.118682325,0.4095182,0.00095241476,0.0028552886,0.003931569,0.021407925,0.19020078,0.20735954,0.0011974245,0.040928897,0.0005122065],"about_ca_topic_score_codex":0.009926488,"about_ca_topic_score_gemma":0.0069725,"teacher_disagreement_score":0.99830365,"about_ca_system_score_codex":0.0024074705,"about_ca_system_score_gemma":0.0010152613,"threshold_uncertainty_score":0.13084751},"labels":[],"label_agreement":null},{"id":"W1963636698","doi":"10.1145/1877850.1877855","title":"A novel video thumbnail extraction method using spatiotemporal vector quantization","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Metropolitan University","funders":"","keywords":"Codebook; Computer science; Vector quantization; Artificial intelligence; Thumbnail; Quantization (signal processing); Learning vector quantization; Pattern recognition (psychology); Gaussian; Computer vision; Feature vector; Image (mathematics)","score_opus":0.035193058872541545,"score_gpt":0.3276503988354235,"score_spread":0.292457339962882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963636698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032458266,0.00039540653,0.9944665,0.000053058433,0.00010709475,0.00006456063,0.00015947867,0.0010157409,0.0004923305],"genre_scores_gemma":[0.04322205,0.00057624094,0.95262617,0.00007837373,0.00010345766,0.00010948754,0.00071077415,0.00015779653,0.0024156251],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99934953,0.000052568073,0.000059019054,0.00015227101,0.00035303645,0.000033692693],"domain_scores_gemma":[0.9995117,0.00008785263,0.00005492909,0.0000739225,0.00024283835,0.000028787686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037533283,0.0008446886,0.00088165863,0.001938335,0.0003504015,0.0006525724,0.000953708,0.00046772978,0.0026803175],"category_scores_gemma":[0.0012690804,0.00033471864,0.00070383865,0.0017512601,0.0003018671,0.0015681754,0.0006388796,0.0007108018,0.0011659374],"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":[0.00012216427,0.00003603669,0.00031214338,0.00023732534,0.00004366711,0.000086375585,0.00007909988,0.0072895433,0.112848915,0.0034506836,0.0044285944,0.8710654],"study_design_scores_gemma":[0.00009523217,0.00041480034,0.0030228302,0.000077954355,0.00014746486,0.0014589467,0.00015388445,0.6936712,0.2451407,0.0072647976,0.04839769,0.00015447053],"about_ca_topic_score_codex":0.0018337064,"about_ca_topic_score_gemma":0.0021983457,"teacher_disagreement_score":0.0026803175,"about_ca_system_score_codex":0.00038703752,"about_ca_system_score_gemma":0.0004905433,"threshold_uncertainty_score":0.008966625},"labels":[],"label_agreement":null},{"id":"W1965599725","doi":"10.1145/2686873","title":"The Dream and the Cross","year":2015,"lang":"en","type":"article","venue":"Journal on Computing and Cultural Heritage","topic":"Video Analysis and Summarization","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 Lethbridge","funders":"","keywords":"Digitization; Dream; Computer science; XML; Context (archaeology); Poetry; Transcription (linguistics); World Wide Web; Multimedia; Art; Literature; History; Linguistics; Telecommunications","score_opus":0.021035987005459576,"score_gpt":0.2829418600249996,"score_spread":0.26190587301954005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965599725","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11049192,0.025518268,0.022411713,0.021454653,0.0023658262,0.000092251175,0.0003115194,0.00038089283,0.816973],"genre_scores_gemma":[0.73459846,0.010096577,0.012794242,0.004501102,0.000532356,0.00010537436,0.0003621165,0.0003746835,0.2366351],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982186,0.00090748473,0.000047864265,0.00030323298,0.00035511542,0.00016771897],"domain_scores_gemma":[0.99886644,0.00036948168,0.000077975244,0.00033901245,0.00012839574,0.00021868071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017302098,0.00037465824,0.00023616846,0.0013589426,0.006180793,0.010148939,0.00087467104,0.0015765497,0.018661646],"category_scores_gemma":[0.0030571567,0.0002457408,0.00024724158,0.0014554668,0.013948225,0.007837976,0.008282346,0.0025591296,0.0016142712],"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":[0.000044711163,0.000016306596,0.0007575541,0.00014931876,0.000008296226,0.00044986786,0.08828981,0.0001407701,0.00061450567,0.80319786,0.02220332,0.08412779],"study_design_scores_gemma":[0.000004158518,0.000034852037,0.0013529342,0.00031413866,0.0000060424204,0.00085028855,0.04730604,0.0001359456,0.0005662636,0.046114445,0.90329695,0.000017950833],"about_ca_topic_score_codex":0.005908895,"about_ca_topic_score_gemma":0.009266743,"teacher_disagreement_score":0.018661646,"about_ca_system_score_codex":0.0028596502,"about_ca_system_score_gemma":0.002218237,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1966971346","doi":"10.5539/mas.v3n4p95","title":"Study on the Intelligent Video Monitoring Technology and Its Applications","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Video Analysis and Summarization","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":"Video monitoring; Digitization; Computer science; Video processing; Digital video; Multimedia; Real-time computing; Artificial intelligence; Telecommunications","score_opus":0.030257461183847523,"score_gpt":0.27876632687360736,"score_spread":0.24850886568975983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966971346","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2667482,0.05599295,0.13122319,0.008348616,0.0005676482,0.00021611026,0.00019910606,0.00017667867,0.5365275],"genre_scores_gemma":[0.89025015,0.051420797,0.01813008,0.0008973765,0.00085694436,0.00010486927,0.00016100955,0.00003648262,0.0381424],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995505,0.00013291968,0.000023298904,0.00006908855,0.00017467313,0.00004944708],"domain_scores_gemma":[0.9985392,0.0006006715,0.00008264202,0.00006674414,0.0006396045,0.000071140974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006321926,0.00022266588,0.0001580106,0.0013712922,0.00043092007,0.0014516106,0.00044856462,0.0007231268,0.0049263705],"category_scores_gemma":[0.002842384,0.00009965854,0.00022618493,0.001961558,0.0005128285,0.0027170803,0.0003067486,0.00035112145,0.00084432814],"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":[0.00018419628,0.00023345827,0.02245019,0.0009896692,0.000040196403,0.0009609788,0.0027384157,0.0059038084,0.0135441115,0.3471511,0.0137376655,0.5920662],"study_design_scores_gemma":[0.00005346567,0.0009157599,0.06586219,0.0012898572,0.00017539012,0.0061366986,0.0051904614,0.069815814,0.024930894,0.18044998,0.6450693,0.00011013191],"about_ca_topic_score_codex":0.0015973443,"about_ca_topic_score_gemma":0.0006659187,"teacher_disagreement_score":0.0049263705,"about_ca_system_score_codex":0.00047896864,"about_ca_system_score_gemma":0.00062537886,"threshold_uncertainty_score":0.016480386},"labels":[],"label_agreement":null},{"id":"W1968090956","doi":"10.1007/s11042-010-0608-x","title":"CRIM’s content-based audio copy detection system for TRECVID 2009","year":2010,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","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":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Fingerprint (computing); Robustness (evolution); k-nearest neighbors algorithm; Frame (networking); Pattern recognition (psychology); Artificial intelligence; Task (project management); Graphics; Nearest neighbor search; Speech recognition; Computer graphics (images)","score_opus":0.030166033841618367,"score_gpt":0.24871566783433036,"score_spread":0.218549633992712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968090956","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11206025,0.0050646462,0.3593006,0.0015049762,0.0024806191,0.004484463,0.08164815,0.38490412,0.048552252],"genre_scores_gemma":[0.16373225,0.0014231659,0.60174817,0.0007635958,0.00058775506,0.0016232989,0.16495217,0.008339515,0.056830056],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984145,0.00016223213,0.00010553737,0.00039802276,0.00075592054,0.00016372986],"domain_scores_gemma":[0.998058,0.0001884405,0.00009930363,0.000451418,0.0010898401,0.00011291544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001730223,0.0020571053,0.0015653148,0.0057337903,0.0013945418,0.0013418358,0.002274613,0.0019412292,0.01610935],"category_scores_gemma":[0.0037787755,0.0007664538,0.00092873385,0.002667794,0.00031604766,0.0016796268,0.0012137092,0.0010875211,0.013743982],"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":[0.00089002185,0.00043669733,0.0024129176,0.00053082895,0.00030390755,0.00026274432,0.000065125416,0.0019279562,0.1003366,0.00067977246,0.33785427,0.5542991],"study_design_scores_gemma":[0.0005733558,0.0010905813,0.03335599,0.00014232974,0.0007509157,0.0020397515,0.00020218198,0.21424888,0.4861943,0.0019951945,0.2591087,0.00029778006],"about_ca_topic_score_codex":0.024516108,"about_ca_topic_score_gemma":0.036853906,"teacher_disagreement_score":0.024516108,"about_ca_system_score_codex":0.001277991,"about_ca_system_score_gemma":0.0016776784,"threshold_uncertainty_score":0.053891122},"labels":[],"label_agreement":null},{"id":"W1972619759","doi":"10.1145/1943403.1943476","title":"Analyzing sketch content using in-air packet information","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Sketch; Computer science; Matrix (chemical analysis); Expression (computer science); Construct (python library); Row; Row and column spaces; Network packet; Simple (philosophy); Task (project management); Content (measure theory); Outlier; Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics; Engineering; Computer network","score_opus":0.07937120914640468,"score_gpt":0.238619172389429,"score_spread":0.15924796324302432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1972619759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25438735,0.00040381652,0.72859657,0.00022367097,0.00023790992,0.00022067163,0.0021967248,0.0057728533,0.007960461],"genre_scores_gemma":[0.71102506,0.0010425254,0.27528965,0.00007073605,0.00017442946,0.00011671538,0.0030675149,0.0007850729,0.008428336],"study_design_codex":"design_other","study_design_gemma":"design_other","domain_scores_codex":[0.9996648,0.000023815008,0.000021747968,0.00005938619,0.00016940299,0.000060890347],"domain_scores_gemma":[0.9987835,0.00030570084,0.00013712868,0.00025510273,0.00044570718,0.00007283752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021766289,0.0006299558,0.00050743483,0.0031624294,0.00029149678,0.0014912601,0.00052303757,0.00042837035,0.0044536158],"category_scores_gemma":[0.0019189443,0.00020331626,0.00025898658,0.0020917263,0.00028906466,0.0017711688,0.0006363716,0.0005455558,0.0017716535],"study_design_candidate":"design_other","study_design_consensus":"design_other","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058161333,0.00012572293,0.0067861513,0.0003192601,0.000047231602,0.00052262074,0.00055120053,0.0092095705,0.15618527,0.004233315,0.005159791,0.8162782],"study_design_scores_gemma":[0.000042548905,0.0004531403,0.059207674,0.00009104408,0.00018669349,0.001174109,0.0018674398,0.530932,0.34584087,0.011043948,0.049044352,0.00011626606],"about_ca_topic_score_codex":0.001731069,"about_ca_topic_score_gemma":0.002515009,"teacher_disagreement_score":0.0044536158,"about_ca_system_score_codex":0.00039398586,"about_ca_system_score_gemma":0.00028735856,"threshold_uncertainty_score":0.014898896},"labels":[],"label_agreement":null},{"id":"W1975830978","doi":"10.5539/cis.v5n5p25","title":"Hybrid-Based Compressed Domain Video Fingerprinting Technique","year":2012,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Video Analysis and Summarization","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; Fingerprint (computing); Digital watermarking; Computer vision; Video processing; Artificial intelligence; Video compression picture types; Domain (mathematical analysis); Video tracking; Fingerprint recognition; Identification (biology); Pattern recognition (psychology); Image (mathematics)","score_opus":0.008557295047348286,"score_gpt":0.22745174773730817,"score_spread":0.21889445268995988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975830978","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05816305,0.0020922956,0.9329842,0.00022901173,0.00016655539,0.000123684,0.00018974225,0.001426249,0.0046251956],"genre_scores_gemma":[0.4934893,0.001642192,0.49567977,0.00026353393,0.00020919646,0.00011655734,0.00052220846,0.00007251532,0.008004661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999479,0.000056739005,0.000020720676,0.00009120028,0.00031933183,0.00003307733],"domain_scores_gemma":[0.99940467,0.00013187485,0.000097881784,0.00012413344,0.00021506996,0.000026369109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025167008,0.0005783574,0.0006213561,0.0016626378,0.0002749051,0.0005037594,0.00087741314,0.0007171842,0.00217017],"category_scores_gemma":[0.0011085037,0.00018941684,0.00033750932,0.0013499472,0.00029361376,0.0012733696,0.00041047216,0.00044516326,0.0007714528],"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":[0.00046551446,0.00015185306,0.0010469462,0.00020266187,0.00007831126,0.00026685197,0.00007966792,0.010813263,0.28683734,0.0041279555,0.0023776593,0.6935521],"study_design_scores_gemma":[0.000106793275,0.0010718304,0.0043792943,0.00008171142,0.00016490679,0.0057520154,0.00011988589,0.5553403,0.40688637,0.002922856,0.023064403,0.0001096906],"about_ca_topic_score_codex":0.000986903,"about_ca_topic_score_gemma":0.0010231691,"teacher_disagreement_score":0.00217017,"about_ca_system_score_codex":0.00033662945,"about_ca_system_score_gemma":0.00025480034,"threshold_uncertainty_score":0.007259965},"labels":[],"label_agreement":null},{"id":"W1977383060","doi":"10.1109/ism.2011.58","title":"Shot Boundary Detection Using Genetic Algorithm Optimization","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":13,"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":"University of Waterloo","keywords":"Computer science; Metric (unit); Genetic algorithm; Shot (pellet); Convergence (economics); Heuristic; Boundary (topology); Enhanced Data Rates for GSM Evolution; Precision and recall; Edge detection; Artificial intelligence; Algorithm; Pattern recognition (psychology); Computer vision; Image (mathematics); Machine learning; Image processing; Mathematics; Engineering","score_opus":0.0385976578761106,"score_gpt":0.23271651301387927,"score_spread":0.19411885513776866,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977383060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01585712,0.0001550495,0.9820984,0.00006511436,0.00002459105,0.000060610597,0.000018043742,0.0005676362,0.0011534203],"genre_scores_gemma":[0.23533192,0.00016505265,0.7619127,0.000111173336,0.000039453233,0.00022843441,0.00016094102,0.00017085581,0.0018794821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914694,0.00024331779,0.00003661982,0.00017654305,0.0003350422,0.000061540624],"domain_scores_gemma":[0.998965,0.00057880447,0.00012416653,0.000051412786,0.00025108058,0.000029501449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011744793,0.0011152113,0.0014189099,0.002176024,0.00048732682,0.0010443297,0.0013514657,0.001292618,0.0010242639],"category_scores_gemma":[0.0037773813,0.00047429194,0.00072360004,0.0011414287,0.0006717296,0.00083532947,0.00068902294,0.00079904357,0.00032685182],"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":[0.00008204378,0.00010820818,0.0011436492,0.000081011414,0.00010049261,0.000085723965,0.000108509514,0.7159912,0.011210409,0.0064769373,0.0011899534,0.26342183],"study_design_scores_gemma":[0.000009187655,0.000030937386,0.00015509952,0.0000050842996,0.000009677611,0.000019211842,0.000009703043,0.99689853,0.0012260913,0.0012944678,0.00033566912,0.000006376094],"about_ca_topic_score_codex":0.0063090497,"about_ca_topic_score_gemma":0.004625918,"teacher_disagreement_score":0.0063090497,"about_ca_system_score_codex":0.0011059506,"about_ca_system_score_gemma":0.0011880022,"threshold_uncertainty_score":0.012544692},"labels":[],"label_agreement":null},{"id":"W1977625330","doi":"10.1109/tip.2014.2300811","title":"An Unsupervised Feature Selection Dynamic Mixture Model for Motion Segmentation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Analysis and Summarization","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 Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Segmentation; Computer science; Robustness (evolution); Cluster analysis; Pattern recognition (psychology); Image segmentation; Feature selection; Scale-space segmentation; Feature (linguistics); Motion (physics); Segmentation-based object categorization; Computer vision","score_opus":0.00950619076647652,"score_gpt":0.2629760550621535,"score_spread":0.25346986429567697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977625330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043313857,0.00024391328,0.99470794,0.00007491847,0.000020500545,0.000023659997,0.00007267575,0.00025390214,0.00027096784],"genre_scores_gemma":[0.40323892,0.000978613,0.5870313,0.00024228562,0.00016626023,0.00051000575,0.0014598072,0.0003136267,0.006059195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952245,0.00011735946,0.00002499075,0.00015121697,0.00012663288,0.000057283498],"domain_scores_gemma":[0.99958044,0.00019912233,0.000053375705,0.00003792101,0.00010694761,0.000022083655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008019861,0.00076195836,0.0012516391,0.0014315827,0.0004904369,0.00073099433,0.001650326,0.0011438625,0.0012048216],"category_scores_gemma":[0.0017670131,0.00053601013,0.001421508,0.0016236958,0.00064999115,0.0011352159,0.0007261982,0.0010979081,0.0006067242],"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":[0.00024582737,0.00011877021,0.0016486128,0.00014277238,0.00015670389,0.00014776485,0.00020112064,0.7130344,0.017658727,0.020912282,0.0038806691,0.24185233],"study_design_scores_gemma":[0.0000041561875,0.000015279913,0.00020674798,0.0000040620475,0.000008657053,0.000021300015,0.0000050270883,0.9960641,0.00059167267,0.0024020988,0.0006679318,0.000008926407],"about_ca_topic_score_codex":0.006362488,"about_ca_topic_score_gemma":0.0060644164,"teacher_disagreement_score":0.006362488,"about_ca_system_score_codex":0.0007495036,"about_ca_system_score_gemma":0.0006506636,"threshold_uncertainty_score":0.012650907},"labels":[],"label_agreement":null},{"id":"W1979066240","doi":"10.1117/12.476352","title":"A real-time system for high-level video representation: application to video surveillance","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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":"Université du Québec; University of Ottawa; Concordia University","funders":"","keywords":"Computer science; Video tracking; Context (archaeology); Artificial intelligence; Object (grammar); Representation (politics); Feature extraction; Computer vision; Video content analysis; Noise (video); Video processing; Semantics (computer science); Image (mathematics)","score_opus":0.011579335029440937,"score_gpt":0.2329242114408034,"score_spread":0.22134487641136247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979066240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011258152,0.00027985938,0.97761565,0.00015656793,0.00005981643,0.0001502211,0.00015179992,0.009248157,0.0010797352],"genre_scores_gemma":[0.19406828,0.00045443268,0.8011873,0.00018332465,0.000114305774,0.00024898624,0.0005540301,0.00031838095,0.002870905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959844,0.000076002936,0.000030572555,0.00010961261,0.00015325294,0.00003208526],"domain_scores_gemma":[0.9993106,0.00015792255,0.000075669384,0.00013580319,0.00023127664,0.000088793495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001130995,0.00054063863,0.0005532293,0.0008383436,0.0003598982,0.0012606778,0.0013258866,0.00097273063,0.0035898807],"category_scores_gemma":[0.0020323116,0.0002640472,0.00032262286,0.0007338277,0.0004218877,0.001119696,0.00073124166,0.00085650274,0.0016008378],"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":[0.000708543,0.00023764811,0.0016817475,0.00031366022,0.00012128401,0.00037902687,0.000353275,0.017042259,0.2817434,0.008309774,0.009878788,0.67923063],"study_design_scores_gemma":[0.00013934288,0.00075747666,0.004246985,0.00008152733,0.0001418039,0.0013858991,0.000121596604,0.8017831,0.15344965,0.004872228,0.032885924,0.00013453407],"about_ca_topic_score_codex":0.0018011456,"about_ca_topic_score_gemma":0.001529637,"teacher_disagreement_score":0.0035898807,"about_ca_system_score_codex":0.0005378833,"about_ca_system_score_gemma":0.00037183377,"threshold_uncertainty_score":0.012009323},"labels":[],"label_agreement":null},{"id":"W1981143901","doi":"10.1109/wiamis.2012.6226748","title":"A variational statistical framework for clustering human action videos","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"","keywords":"Computer science; Cluster analysis; Correctness; Artificial intelligence; Mixture model; Statistical model; Latent Dirichlet allocation; Dirichlet distribution; Representation (politics); Machine learning; Pattern recognition (psychology); Topic model; Algorithm; Mathematics","score_opus":0.05472475757489776,"score_gpt":0.3447924576276955,"score_spread":0.29006770005279775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981143901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009954765,0.00012552048,0.9984707,0.000074368705,0.000011635629,0.000020849273,0.000040836472,0.00007089473,0.00018964651],"genre_scores_gemma":[0.16261874,0.0007480786,0.83071595,0.00024732522,0.0002526696,0.00040595388,0.0009833269,0.0002887247,0.0037391465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819106,0.00074936496,0.00008718024,0.0004896295,0.00039060556,0.00009205408],"domain_scores_gemma":[0.9983804,0.000918986,0.00015431248,0.00015754506,0.00030627506,0.000082425744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032424557,0.0011351249,0.0014632005,0.0024674512,0.0007070916,0.001283399,0.003465055,0.0016475134,0.0018587426],"category_scores_gemma":[0.00667967,0.0010306465,0.0017062495,0.0019588114,0.0018600302,0.0020579768,0.0016073281,0.0019754602,0.00063509983],"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":[0.000069412315,0.000059324524,0.00088373595,0.00019180568,0.0002161215,0.00011096796,0.00026231242,0.65698594,0.005068076,0.22330634,0.0036176995,0.109228194],"study_design_scores_gemma":[0.0000039220213,0.000015089877,0.0001428875,0.000007928007,0.000007232417,0.000025668523,0.000010954158,0.9597718,0.00028769366,0.03878224,0.0009307311,0.000013950211],"about_ca_topic_score_codex":0.009665998,"about_ca_topic_score_gemma":0.009307313,"teacher_disagreement_score":0.009665998,"about_ca_system_score_codex":0.0020252196,"about_ca_system_score_gemma":0.0017369278,"threshold_uncertainty_score":0.019219458},"labels":[],"label_agreement":null},{"id":"W1985020218","doi":"10.1145/2559206.2574795","title":"The CBC newsworld holodeck","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":true,"ca_institutions":"Ontario College of Art and Design; University of Toronto","funders":"","keywords":"Computer science; Visualization; Context (archaeology); Cultural heritage; Perspective (graphical); Multimedia; World Wide Web; Gesture; Key (lock); Data visualization; Artificial intelligence; Geography","score_opus":0.004368737696045667,"score_gpt":0.19153487499118574,"score_spread":0.18716613729514006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985020218","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016103515,0.010356617,0.0074593956,0.012241111,0.008018696,0.00039440766,0.048256144,0.007941844,0.8892283],"genre_scores_gemma":[0.042254213,0.005123199,0.012442134,0.0019020993,0.0015227543,0.00035643842,0.04399503,0.0048064794,0.8875976],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989361,0.00010418918,0.00003342865,0.00019678593,0.00059611624,0.00013327651],"domain_scores_gemma":[0.99740475,0.00034575225,0.000085268446,0.00040546298,0.0009126981,0.00084613723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013995789,0.0006685,0.00044178552,0.0046775984,0.0036882102,0.009256085,0.0011588554,0.0010673703,0.16902912],"category_scores_gemma":[0.0035829914,0.0004382319,0.0002578269,0.0052461876,0.00091724447,0.0031275398,0.003197008,0.0018383329,0.04590993],"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":[0.00014674288,0.000029877918,0.00043131036,0.00015999965,0.0000064836327,0.0002404148,0.0005756003,0.00007890543,0.0017898538,0.007958966,0.9005357,0.08804609],"study_design_scores_gemma":[0.000004982586,0.000005660663,0.00092206424,0.000055165743,0.0000016449576,0.000047074383,0.0002584658,0.000081940394,0.00037145035,0.00031448674,0.9979297,0.000007356513],"about_ca_topic_score_codex":0.061261665,"about_ca_topic_score_gemma":0.14806162,"teacher_disagreement_score":0.93873835,"about_ca_system_score_codex":0.0032898039,"about_ca_system_score_gemma":0.0036459814,"threshold_uncertainty_score":0.5654587},"labels":[],"label_agreement":null},{"id":"W1987964887","doi":"10.1109/iccit.2008.413","title":"Wireless Sensor Based Field Hockey Strategy System","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Wireless sensor network; Computer science; Cricket; Field hockey; Wireless; Tracking system; Node (physics); Field (mathematics); Real-time computing; Telecommunications; Engineering; Computer network; Artificial intelligence; Geography","score_opus":0.01820212897671132,"score_gpt":0.21243275461534947,"score_spread":0.19423062563863813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987964887","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19234452,0.0006368501,0.72446686,0.0004936763,0.00041640076,0.0009556656,0.0017264065,0.013744312,0.06521532],"genre_scores_gemma":[0.8578865,0.0006480327,0.09603635,0.00019298945,0.000054375512,0.0005497847,0.0014177762,0.00020844363,0.043005776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998293,0.000018151199,0.000010709505,0.00004399187,0.00008125801,0.000016490612],"domain_scores_gemma":[0.9998857,0.000019084275,0.000011243661,0.000013326047,0.000056712477,0.000013989038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017193938,0.00035050223,0.0002372158,0.00046737678,0.00017681037,0.00048138603,0.00055847544,0.00025713144,0.0064971983],"category_scores_gemma":[0.00030360464,0.00013082805,0.000110892746,0.0002610962,0.000109133296,0.00060621987,0.00025850724,0.00020908215,0.0015163544],"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":[0.0007708214,0.00042086525,0.0059069656,0.00049984513,0.000084836705,0.00064522726,0.0004679454,0.046308197,0.36325744,0.009975646,0.0210583,0.550604],"study_design_scores_gemma":[0.00017034278,0.0021222571,0.016088253,0.000101980324,0.00015865629,0.001462076,0.00062080147,0.48132244,0.33622536,0.0038550887,0.15773076,0.00014198644],"about_ca_topic_score_codex":0.0014789356,"about_ca_topic_score_gemma":0.0016240034,"teacher_disagreement_score":0.0064971983,"about_ca_system_score_codex":0.00030752076,"about_ca_system_score_gemma":0.0002662917,"threshold_uncertainty_score":0.02173531},"labels":[],"label_agreement":null},{"id":"W1988824236","doi":"10.1109/crv.2013.31","title":"I Remember Seeing This Video: Image Driven Search in Video Collections","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Ontario Tech University","funders":"","keywords":"Computer science; Artificial intelligence; Shot (pellet); Computer vision; Hidden Markov model; Pattern recognition (psychology); Support vector machine; Classifier (UML)","score_opus":0.012163983254149491,"score_gpt":0.24279296703043862,"score_spread":0.23062898377628913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988824236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1184862,0.0049029407,0.8582114,0.00079924957,0.00024370295,0.00079382124,0.0033795335,0.008724426,0.004458696],"genre_scores_gemma":[0.34220186,0.00159627,0.6386756,0.00042717828,0.00042759647,0.00033178582,0.008136211,0.00036421494,0.007839269],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990871,0.00012332453,0.000058705846,0.00025596094,0.00037616663,0.00009878521],"domain_scores_gemma":[0.9986553,0.00042287554,0.00018306103,0.0003256955,0.00030325458,0.000109749446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007530729,0.0009177178,0.0016091649,0.0042651976,0.0007540686,0.0011179844,0.0020442137,0.0012244893,0.0023153375],"category_scores_gemma":[0.0031386917,0.00040846053,0.00094932335,0.004124813,0.00053261523,0.002567818,0.0012685227,0.0009654799,0.0017025702],"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":[0.0008400467,0.00040680115,0.004608617,0.00082660903,0.00024912236,0.0006205107,0.00075191935,0.013891156,0.08207555,0.005159738,0.029022252,0.86154777],"study_design_scores_gemma":[0.00019545275,0.0013669347,0.015959563,0.00015805651,0.00035701718,0.004850371,0.0015857628,0.7921975,0.11016501,0.026320662,0.046617746,0.00022593034],"about_ca_topic_score_codex":0.005750102,"about_ca_topic_score_gemma":0.009703724,"teacher_disagreement_score":0.005750102,"about_ca_system_score_codex":0.0006279405,"about_ca_system_score_gemma":0.00090844906,"threshold_uncertainty_score":0.011433244},"labels":[],"label_agreement":null},{"id":"W1989699139","doi":"10.1007/s11042-010-0722-9","title":"Ice hockey shooting event modeling with mixture hidden Markov model","year":2011,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Hidden Markov model; Computer science; Mixture model; Ice hockey; Event (particle physics); Artificial intelligence; Pattern recognition (psychology); Frame (networking); Speech recognition","score_opus":0.03090408990025233,"score_gpt":0.23551724522525158,"score_spread":0.20461315532499924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989699139","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033829793,0.0004307108,0.9629771,0.00019006632,0.00010672508,0.000061360566,0.00053603796,0.0010571629,0.00081114727],"genre_scores_gemma":[0.82151324,0.00085240824,0.16514342,0.00014666145,0.00024101163,0.0003070659,0.0034148963,0.00022631753,0.008154956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999329,0.00016165576,0.0000496315,0.00026316653,0.0001189767,0.00007748868],"domain_scores_gemma":[0.99855584,0.0010061308,0.00013382146,0.00011078616,0.0001502817,0.000043274933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012834137,0.000973572,0.001356241,0.0012653858,0.0004947718,0.0011055329,0.0022022778,0.0014476721,0.0018587036],"category_scores_gemma":[0.0028720445,0.0009189939,0.0018415247,0.0011678966,0.00043544435,0.0015525416,0.0008002559,0.0020469723,0.0012390746],"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":[0.00041346217,0.00020926703,0.003757283,0.00014694738,0.0002628592,0.00020804485,0.00013792033,0.88811624,0.0038045433,0.00859244,0.0024478352,0.09190314],"study_design_scores_gemma":[0.00000417404,0.000012107485,0.00028620643,0.0000027505153,0.00001603351,0.000014854617,0.0000048345514,0.9975018,0.00031217487,0.0016772462,0.00016101217,0.0000067668443],"about_ca_topic_score_codex":0.011837599,"about_ca_topic_score_gemma":0.010666752,"teacher_disagreement_score":0.011837599,"about_ca_system_score_codex":0.0007090313,"about_ca_system_score_gemma":0.00071475445,"threshold_uncertainty_score":0.023537397},"labels":[],"label_agreement":null},{"id":"W1990160130","doi":"10.1117/12.528372","title":"&lt;title&gt;Automatic acquisition of motion trajectories: tracking hockey players&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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":"University of British Columbia","funders":"","keywords":"Tracking (education); Computer science; Motion (physics); Trajectory; Computer vision; Artificial intelligence; Psychology; Physics","score_opus":0.010208239507573902,"score_gpt":0.21764877538861696,"score_spread":0.20744053588104305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990160130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043988597,0.01103974,0.57363296,0.00806113,0.017308904,0.0031516955,0.031846207,0.0843607,0.22661015],"genre_scores_gemma":[0.15493105,0.011613003,0.21569149,0.0031542922,0.0056767454,0.0014319201,0.08292776,0.010883875,0.5136898],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923646,0.00007693567,0.000034679477,0.00026698137,0.0003064035,0.00007864891],"domain_scores_gemma":[0.99856645,0.00019310729,0.00007554326,0.00020589303,0.0008299278,0.0001291127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007398306,0.0016048807,0.0013317321,0.0016299309,0.0006439224,0.0029461388,0.001859286,0.0013364678,0.07706418],"category_scores_gemma":[0.0013680051,0.00050731655,0.00062395626,0.0018253026,0.000549901,0.002554674,0.0013378928,0.0014423365,0.07068679],"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":[0.0006830051,0.00022180368,0.0013371429,0.0004835254,0.000056529087,0.0002556754,0.00007538444,0.0039038067,0.035458382,0.0025185682,0.4761746,0.47883156],"study_design_scores_gemma":[0.00025800875,0.0008393654,0.015170706,0.0004302769,0.00013386658,0.0007176681,0.00025943364,0.20113932,0.06430681,0.005377795,0.71113515,0.0002315885],"about_ca_topic_score_codex":0.012689341,"about_ca_topic_score_gemma":0.013629135,"teacher_disagreement_score":0.07706418,"about_ca_system_score_codex":0.00095371844,"about_ca_system_score_gemma":0.000839318,"threshold_uncertainty_score":0.2578054},"labels":[],"label_agreement":null},{"id":"W1990724698","doi":"10.1007/s11042-007-0101-3","title":"METIS: a flexible foundation for the unified management of multimedia assets","year":2007,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":12,"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":"Computer science; Metis; Domain (mathematical analysis); Plug-in; Variety (cybernetics); Focus (optics); Abstraction; Abstraction layer; World Wide Web; Multimedia; Kernel (algebra); Artificial intelligence; Programming language; Software","score_opus":0.034789382767143155,"score_gpt":0.300991502509424,"score_spread":0.26620211974228086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990724698","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003774573,0.00030412278,0.9536588,0.00031021368,0.0001286096,0.00021120787,0.0008077395,0.034217488,0.0065872357],"genre_scores_gemma":[0.16827504,0.00094066985,0.80455166,0.00036437676,0.0002778477,0.0007396239,0.0052745612,0.0060218885,0.013554356],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975206,0.0003623343,0.0003253521,0.0003772439,0.0011475977,0.0002668047],"domain_scores_gemma":[0.9972729,0.00046333746,0.00024166486,0.0013192963,0.0004558627,0.00024680616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035559542,0.0011768697,0.0014996643,0.0028496508,0.0012699837,0.0076641007,0.004277747,0.0012163005,0.005774125],"category_scores_gemma":[0.006779797,0.0009137605,0.0011386621,0.0021725325,0.0013233627,0.008381829,0.0062289885,0.0027614383,0.0036476],"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":[0.0007353806,0.00022247492,0.002930155,0.0005874926,0.00028645378,0.00061462715,0.001283285,0.025424467,0.029651409,0.3703749,0.068851754,0.49903744],"study_design_scores_gemma":[0.000114284085,0.00019496614,0.0011286999,0.00030922514,0.00019564485,0.0004518984,0.00043392676,0.37747434,0.061705645,0.21636891,0.34144074,0.00018172982],"about_ca_topic_score_codex":0.0028541433,"about_ca_topic_score_gemma":0.0033452543,"teacher_disagreement_score":0.0076641007,"about_ca_system_score_codex":0.0012690725,"about_ca_system_score_gemma":0.0019200433,"threshold_uncertainty_score":0.019316316},"labels":[],"label_agreement":null},{"id":"W1990907002","doi":"10.1145/2072298.2071938","title":"A smart video player with content-based fast-forward playback","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Key (lock); Frame (networking); Key frame; Video tracking; Computer vision; Partition (number theory); Video processing; Quantization (signal processing); Set (abstract data type); Artificial intelligence; Real-time computing; Mathematics","score_opus":0.04391683854861232,"score_gpt":0.20466399901642504,"score_spread":0.16074716046781273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990907002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018434875,0.0002143695,0.9629497,0.00012660798,0.00013175346,0.0005617732,0.00040078384,0.012063341,0.0051169405],"genre_scores_gemma":[0.15311664,0.0003933425,0.8165959,0.00041303792,0.00015012737,0.0006978851,0.0014924323,0.00104183,0.026098877],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996921,0.000048287202,0.000019400066,0.00007462937,0.00013674388,0.000028921071],"domain_scores_gemma":[0.9996189,0.00011376074,0.000019799936,0.000064442196,0.00012570413,0.00005741663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005082132,0.00081999967,0.00063551124,0.0008255871,0.0002615574,0.0007985633,0.002423701,0.0008747565,0.0153964525],"category_scores_gemma":[0.0010756534,0.00041140744,0.0004014617,0.00037629085,0.00033496894,0.001543933,0.0008300146,0.0005343469,0.0046682726],"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":[0.0018362069,0.0005938792,0.0020177348,0.00065327925,0.00014449311,0.0014563361,0.00047962734,0.008219605,0.3629314,0.015181071,0.027333329,0.57915306],"study_design_scores_gemma":[0.00062630035,0.0022608016,0.0041253283,0.00012348073,0.00025570384,0.005300351,0.00023116411,0.51995444,0.31983265,0.0044897697,0.1425397,0.0002603409],"about_ca_topic_score_codex":0.0009380346,"about_ca_topic_score_gemma":0.00095152925,"teacher_disagreement_score":0.0153964525,"about_ca_system_score_codex":0.0003194575,"about_ca_system_score_gemma":0.0003340919,"threshold_uncertainty_score":0.05150622},"labels":[],"label_agreement":null},{"id":"W1992120605","doi":"10.1145/2614217.2630586","title":"How personal video navigation history can be visualized","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Computer vision; Computer graphics (images); Multimedia; Human–computer interaction; Artificial intelligence","score_opus":0.01841515805946739,"score_gpt":0.24060294529750023,"score_spread":0.22218778723803284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992120605","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023384325,0.009304079,0.18330671,0.008801972,0.007113911,0.0015587053,0.11923373,0.03941062,0.607886],"genre_scores_gemma":[0.25425765,0.01742774,0.16062178,0.0024037247,0.002692809,0.0011840385,0.07865291,0.009872611,0.4728868],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983954,0.000036837006,0.000012079397,0.000033416214,0.0000569913,0.000021186017],"domain_scores_gemma":[0.9989279,0.00038157913,0.000054663265,0.00018062937,0.00033560413,0.00011967348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038545846,0.0005856697,0.00033480165,0.0021921666,0.00047280037,0.0018916524,0.00048559302,0.00086132606,0.23935473],"category_scores_gemma":[0.0037805052,0.00024725575,0.0002804034,0.0021581303,0.00019392486,0.0020203998,0.00079969293,0.00063888053,0.06512502],"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":[0.00060015405,0.000053720578,0.0014214773,0.0013436769,0.00002625979,0.0006457681,0.0006212195,0.0005973207,0.013166768,0.0048480392,0.5026352,0.47404042],"study_design_scores_gemma":[0.00005916217,0.00012800386,0.008266561,0.00079386495,0.00007254482,0.0010156897,0.0007052153,0.0038712823,0.013443659,0.0077067805,0.9638587,0.0000785076],"about_ca_topic_score_codex":0.0039957343,"about_ca_topic_score_gemma":0.007681012,"teacher_disagreement_score":0.23935473,"about_ca_system_score_codex":0.00025157363,"about_ca_system_score_gemma":0.0005311511,"threshold_uncertainty_score":0.80072135},"labels":[],"label_agreement":null},{"id":"W1993774321","doi":"10.1007/s00779-006-0088-1","title":"Collaborative capturing, interpreting, and sharing of experiences","year":2006,"lang":"en","type":"article","venue":"Personal and Ubiquitous Computing","topic":"Video Analysis and Summarization","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":"University of British Columbia","funders":"National Institute of Information and Communications Technology","keywords":"Computer science; Viewpoints; Human–computer interaction; Wearable computer; Ubiquitous computing; Multimedia; Human interaction; Augmented reality; Data science; World Wide Web","score_opus":0.004153534173890885,"score_gpt":0.213654792496252,"score_spread":0.2095012583223611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1993774321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15707445,0.0014257793,0.80699646,0.0009005919,0.0001743757,0.0007385451,0.00046895116,0.0021625762,0.030058274],"genre_scores_gemma":[0.62528527,0.0011333223,0.3629701,0.00018547695,0.00015109124,0.00028027213,0.00077531504,0.00038954816,0.008829535],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9961816,0.0016872543,0.00018318667,0.00085323176,0.00085315,0.00024161555],"domain_scores_gemma":[0.99222744,0.003604218,0.0006800368,0.0020944113,0.0010399008,0.0003540727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028928348,0.00087198574,0.0007913547,0.002491733,0.001205061,0.005090708,0.0019311659,0.0014278812,0.004458794],"category_scores_gemma":[0.016253084,0.0005077813,0.00070561667,0.0023053181,0.0013659982,0.004242824,0.0043894807,0.0011842072,0.0013409224],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005354379,0.00039184233,0.008384144,0.0009902002,0.00026398603,0.0008312471,0.047432497,0.005425242,0.13344261,0.02073297,0.010926214,0.7706436],"study_design_scores_gemma":[0.00021534109,0.0015171771,0.09111857,0.0014309838,0.0014577818,0.0038412982,0.11587414,0.20005187,0.20096469,0.15729158,0.22527938,0.0009571761],"about_ca_topic_score_codex":0.0029617639,"about_ca_topic_score_gemma":0.0043894323,"teacher_disagreement_score":0.005090708,"about_ca_system_score_codex":0.0005550468,"about_ca_system_score_gemma":0.0013138414,"threshold_uncertainty_score":0.015298963},"labels":[],"label_agreement":null},{"id":"W1996034511","doi":"10.1109/mmul.2009.11","title":"Interactive Multimedia for Adaptive Online Education","year":2009,"lang":"en","type":"article","venue":"IEEE Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":43,"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 science; Multimedia; Blank; Representation (politics); Human–computer interaction; World Wide Web","score_opus":0.020845113869752307,"score_gpt":0.2976635127079863,"score_spread":0.276818398838234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996034511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009766613,0.055675343,0.5056602,0.0114483,0.0049285204,0.0004866012,0.0007812514,0.0044161994,0.40683687],"genre_scores_gemma":[0.26374897,0.04090243,0.45693842,0.005365021,0.006325915,0.0013231662,0.0013544222,0.0009106989,0.22313097],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995596,0.00014083296,0.000029645029,0.00006168523,0.0001674062,0.000040773113],"domain_scores_gemma":[0.9991818,0.00043038413,0.000057462064,0.00014138059,0.00012523562,0.00006376188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005127435,0.0004851223,0.00024921828,0.00088396977,0.00042343244,0.0023052457,0.0007916726,0.0017110223,0.03480714],"category_scores_gemma":[0.001972653,0.00014656804,0.00032222507,0.0009274729,0.00083419756,0.002300027,0.0011769952,0.0010664348,0.005608252],"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":[0.00008927006,0.000075639495,0.0003794591,0.00081622443,0.000019861083,0.00033126315,0.0002528437,0.0013587435,0.008520948,0.31073987,0.05185225,0.6255636],"study_design_scores_gemma":[0.00003244588,0.00009609172,0.0012302168,0.00048716282,0.000035582838,0.0010764651,0.00017355596,0.011045964,0.004403442,0.10126717,0.8801113,0.000040681236],"about_ca_topic_score_codex":0.0006560576,"about_ca_topic_score_gemma":0.0009982402,"teacher_disagreement_score":0.03480714,"about_ca_system_score_codex":0.0006055676,"about_ca_system_score_gemma":0.0005235925,"threshold_uncertainty_score":0.11644149},"labels":[],"label_agreement":null},{"id":"W1999722489","doi":"","title":"K-grid: A Structure for Storage and Retrieval of Affective Knowledge.","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Grid; Computer science; Dissemination; Cluster analysis; Grid computing; Information retrieval; Process (computing); Knowledge extraction; Interpolation (computer graphics); Data grid; Data mining; Mechanism (biology); Artificial intelligence; Mathematics; Physics","score_opus":0.007332157117331871,"score_gpt":0.24819031053151105,"score_spread":0.24085815341417918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999722489","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005650235,0.0011371161,0.94936574,0.00077893236,0.0004068227,0.00055980554,0.0063422243,0.024337726,0.011421416],"genre_scores_gemma":[0.10799823,0.0014241443,0.8516637,0.00062952854,0.00019442543,0.0011769383,0.015709506,0.0024254732,0.018778073],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985342,0.0003161665,0.0002926252,0.00031563538,0.00036982776,0.00017158377],"domain_scores_gemma":[0.9948048,0.0009833232,0.00033197395,0.0023152577,0.0010604203,0.0005042922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018031751,0.0009400428,0.0014390984,0.0030124406,0.0021612253,0.005850862,0.0034516302,0.001411119,0.016300948],"category_scores_gemma":[0.010224934,0.0008116118,0.0013158965,0.004807135,0.001426766,0.010280079,0.0065641156,0.0016038051,0.015561294],"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":[0.0016184252,0.0002808286,0.003121559,0.0016813143,0.0002159192,0.0008273399,0.003947112,0.010302142,0.015937138,0.17441791,0.16449744,0.62315285],"study_design_scores_gemma":[0.00019170983,0.00036224478,0.0024329661,0.0004349915,0.00016342108,0.0012080446,0.0017488395,0.12057819,0.031032939,0.31312862,0.52839154,0.00032646724],"about_ca_topic_score_codex":0.0053638965,"about_ca_topic_score_gemma":0.0055625974,"teacher_disagreement_score":0.016300948,"about_ca_system_score_codex":0.0013366898,"about_ca_system_score_gemma":0.0020330332,"threshold_uncertainty_score":0.05453211},"labels":[],"label_agreement":null},{"id":"W2001429429","doi":"10.1109/iccnc.2012.6167488","title":"Trends and opportunities in consumer video content navigation and analysis","year":2012,"lang":"en","type":"article","venue":"2012 International Conference on Computing, Networking and Communications (ICNC)","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Publication; Digital video; Multimedia; Video processing; The Internet; Digital content; Video tracking; World Wide Web; Telecommunications; Artificial intelligence; Advertising","score_opus":0.17019496641544096,"score_gpt":0.324239046526992,"score_spread":0.15404408011155102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001429429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3736022,0.2710341,0.25337756,0.032302786,0.0008246763,0.000268982,0.0015511118,0.0012153529,0.06582326],"genre_scores_gemma":[0.6402383,0.15743555,0.18442695,0.0020464235,0.002124117,0.00021370049,0.00242067,0.00028521658,0.010809054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858093,0.00027114304,0.000107227126,0.0002534693,0.0007142203,0.0000729327],"domain_scores_gemma":[0.9933616,0.00212788,0.0008070058,0.00019756952,0.0033362154,0.0001697723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023237406,0.0004016041,0.00042419013,0.0042341524,0.00037038975,0.002348568,0.0006981033,0.0010298701,0.001087332],"category_scores_gemma":[0.0052825166,0.00026777497,0.00035397965,0.0068748253,0.00072931044,0.004329084,0.00048628807,0.0008254841,0.00051138387],"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":[0.0002186772,0.00016157526,0.056421872,0.0014191224,0.00006122647,0.00040818332,0.002044051,0.0041387947,0.009675595,0.05372856,0.018338265,0.853384],"study_design_scores_gemma":[0.000042657182,0.0005494428,0.16709536,0.0018858236,0.00032555734,0.004195572,0.013130752,0.1632587,0.033573993,0.076593235,0.5390616,0.00028723595],"about_ca_topic_score_codex":0.0040156753,"about_ca_topic_score_gemma":0.004599547,"teacher_disagreement_score":0.0042341524,"about_ca_system_score_codex":0.001506944,"about_ca_system_score_gemma":0.0008423955,"threshold_uncertainty_score":0.012289286},"labels":[],"label_agreement":null},{"id":"W2001441590","doi":"10.1109/tip.2011.2143421","title":"Video Keyframe Analysis Using a Segment-Based Statistical Metric in a Visually Sensitive Parametric Space","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Metric (unit); Parametric statistics; Feature vector; Feature extraction; Gaussian; Similarity (geometry); Kullback–Leibler divergence; Computer vision; Wavelet transform; Wavelet; Mathematics; Image (mathematics); Statistics","score_opus":0.027966620599179915,"score_gpt":0.2858468418775313,"score_spread":0.25788022127835136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001441590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005947117,0.00011681206,0.9935017,0.000022632548,0.000017140703,0.000023437326,0.000026904616,0.00016560704,0.00017858349],"genre_scores_gemma":[0.2410842,0.0008547512,0.75561,0.000040900988,0.00014701899,0.000112873546,0.00037624227,0.00020590067,0.0015681525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993587,0.00010815952,0.000045881032,0.00014229074,0.00030890925,0.000036043904],"domain_scores_gemma":[0.9990941,0.00025572046,0.00016335811,0.00013781751,0.00030042403,0.000048569324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064570986,0.00070451235,0.0008668581,0.0022887352,0.00029133254,0.0012985484,0.00069145916,0.0005664061,0.00089548464],"category_scores_gemma":[0.002632599,0.00025784044,0.0005655166,0.0017337649,0.0005786055,0.001932868,0.000816435,0.0006097613,0.00057769543],"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":[0.00038235798,0.000073485746,0.0017364108,0.00036255323,0.00013523872,0.00017414747,0.00025776948,0.06455653,0.17625563,0.018778881,0.0021533053,0.73513377],"study_design_scores_gemma":[0.00001883708,0.0003974171,0.003998566,0.000032122105,0.00010993237,0.0005588691,0.00021544803,0.90389293,0.07108622,0.010708947,0.00891457,0.00006611886],"about_ca_topic_score_codex":0.0013828435,"about_ca_topic_score_gemma":0.0014953178,"teacher_disagreement_score":0.0022887352,"about_ca_system_score_codex":0.0005172027,"about_ca_system_score_gemma":0.00053087703,"threshold_uncertainty_score":0.0037525892},"labels":[],"label_agreement":null},{"id":"W2002027782","doi":"10.1109/ism.2012.95","title":"Thin and Light Video Editing Extensions for Education with Opencast Matterhorn","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Saskatchewan","funders":"","keywords":"French horn; Clipping (morphology); Computer science; Plan (archaeology); Software; Video editing; Web application; Multimedia; State (computer science); Human–computer interaction; World Wide Web; Operating system; Geology; Programming language; Acoustics","score_opus":0.011004650025791592,"score_gpt":0.24241766436596965,"score_spread":0.23141301434017805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002027782","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058997933,0.0003987824,0.83354354,0.0004320774,0.00029057421,0.0009943814,0.0007666255,0.08062501,0.02395111],"genre_scores_gemma":[0.27439046,0.00057388097,0.6732915,0.00053444726,0.000326942,0.0008609704,0.0027022832,0.010508082,0.036811456],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99917114,0.00020412149,0.00006939932,0.00013800358,0.00031819913,0.00009918956],"domain_scores_gemma":[0.995948,0.001855342,0.00018208972,0.0012026199,0.00038749937,0.0004243487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014657709,0.00075537176,0.0004037736,0.0007545926,0.00046250084,0.0020657424,0.0017866299,0.0007695569,0.0191161],"category_scores_gemma":[0.0059945704,0.00045894308,0.0006009449,0.0003941322,0.0005673984,0.0030714294,0.0027781215,0.0015379083,0.0025676652],"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":[0.002822432,0.00088005274,0.0025432517,0.0010559863,0.000092200244,0.00094477175,0.00454791,0.0041699754,0.17121117,0.02257391,0.032689977,0.7564683],"study_design_scores_gemma":[0.00079003215,0.0015145664,0.0126775745,0.00066629477,0.00022162168,0.0017194797,0.0017149467,0.13013686,0.1990588,0.022699023,0.62842,0.00038084254],"about_ca_topic_score_codex":0.0012310194,"about_ca_topic_score_gemma":0.0031731306,"teacher_disagreement_score":0.0191161,"about_ca_system_score_codex":0.00041712227,"about_ca_system_score_gemma":0.00051902776,"threshold_uncertainty_score":0.063949764},"labels":[],"label_agreement":null},{"id":"W2004978749","doi":"10.1145/1878061.1878080","title":"A content-based rapid video playback method using motion-based video time density function and temporal quantization","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Video tracking; Artificial intelligence; Computer vision; Quantization (signal processing); Motion compensation; Block-matching algorithm; Codebook; Video post-processing; Video denoising; Video compression picture types; Video processing; Multiview Video Coding","score_opus":0.03168881430816845,"score_gpt":0.25709185064738316,"score_spread":0.2254030363392147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004978749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003383951,0.00017859647,0.99566346,0.000035426485,0.000034602628,0.000049596383,0.000028957827,0.0002876945,0.00033771657],"genre_scores_gemma":[0.11863855,0.0006362259,0.8772013,0.00008239583,0.00011369356,0.00016796122,0.0002737331,0.00014588605,0.002740187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947625,0.000082621336,0.000032921966,0.00008499513,0.00029110626,0.000032170217],"domain_scores_gemma":[0.99927956,0.0002543018,0.0000612491,0.00008279301,0.00028543532,0.00003673476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067887624,0.0008414801,0.0007520151,0.0016451252,0.0003542874,0.00074428925,0.0012256444,0.00054779457,0.002391887],"category_scores_gemma":[0.0021279491,0.00037131304,0.0005408754,0.0009587411,0.00043619136,0.0018724032,0.00061388686,0.0007472336,0.00067286886],"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":[0.00038308272,0.00012391432,0.0007065824,0.00024179708,0.000058590445,0.00016987338,0.0002101152,0.035201598,0.09726469,0.013307975,0.0027075456,0.84962434],"study_design_scores_gemma":[0.0000669658,0.00029965286,0.0011036053,0.000035931276,0.00007013814,0.0007331746,0.00014060503,0.9073592,0.07526565,0.005116939,0.009725494,0.00008274403],"about_ca_topic_score_codex":0.002617193,"about_ca_topic_score_gemma":0.001994483,"teacher_disagreement_score":0.002617193,"about_ca_system_score_codex":0.0006144245,"about_ca_system_score_gemma":0.00048206357,"threshold_uncertainty_score":0.008001626},"labels":[],"label_agreement":null},{"id":"W2004981726","doi":"10.1145/1979742.1979711","title":"MediaDiver","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 British Columbia","funders":"","keywords":"Computer science; Timeline; Multimedia; Viewpoints; Context (archaeology); Annotation; Selection (genetic algorithm); Video production; Quality (philosophy); Video tracking; Post-production; Human–computer interaction; Video processing; World Wide Web; Artificial intelligence","score_opus":0.03651079778722984,"score_gpt":0.1869516425392326,"score_spread":0.15044084475200276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004981726","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01700358,0.0029324181,0.62649703,0.0034004245,0.003276682,0.00079627824,0.0064536594,0.17389172,0.16574813],"genre_scores_gemma":[0.113410234,0.0028178173,0.57934767,0.0050951918,0.0026285718,0.0012082168,0.01833142,0.021462655,0.2556983],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999027,0.00018494816,0.000054372897,0.00025750292,0.00034193753,0.00013424005],"domain_scores_gemma":[0.9980958,0.0005849062,0.00007938858,0.00052146486,0.0003329366,0.00038553274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017798854,0.0012041315,0.000672638,0.0014859911,0.000906681,0.0038344539,0.0026933376,0.0019788558,0.1077625],"category_scores_gemma":[0.003877328,0.00066541275,0.00086763327,0.0006395784,0.00061856926,0.008973437,0.0050154044,0.0021134294,0.027361913],"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":[0.0011987926,0.00032361477,0.0013763973,0.0011368042,0.00009227533,0.0010840667,0.0016782086,0.0009889163,0.065988585,0.049995758,0.27582687,0.6003097],"study_design_scores_gemma":[0.000097863514,0.00032889537,0.00079504115,0.00014641682,0.000044666525,0.0011653188,0.00022847865,0.005153508,0.020612154,0.008887784,0.96244,0.00009985615],"about_ca_topic_score_codex":0.0007754686,"about_ca_topic_score_gemma":0.0014475686,"teacher_disagreement_score":0.1077625,"about_ca_system_score_codex":0.00046772935,"about_ca_system_score_gemma":0.00052807026,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2005695412","doi":"10.1142/s0219878911002501","title":"STORYBOARD OF WCE VIDEO EXTRACTION BASED ON FRAME DIFFERENCE","year":2011,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Video Analysis and Summarization","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":"Qilu Hospital of Shandong University","keywords":"Storyboard; Computer science; Frame (networking); Shot (pellet); Artificial intelligence; Computer vision; Volume (thermodynamics); Multimedia; Telecommunications","score_opus":0.01337402082957837,"score_gpt":0.24538868504804448,"score_spread":0.23201466421846612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005695412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16202256,0.0014871344,0.8245632,0.0003447321,0.00041705108,0.00054893753,0.0023652893,0.0035930797,0.0046579554],"genre_scores_gemma":[0.3252053,0.001698724,0.66076577,0.000110029505,0.00025462726,0.00032420296,0.005139474,0.0002582975,0.0062435293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969864,0.00003717369,0.000023449356,0.00009180995,0.00010990487,0.000039003564],"domain_scores_gemma":[0.99935204,0.00017311242,0.00007877002,0.0000670236,0.00027175978,0.000057278754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002994587,0.0009113885,0.0005244626,0.0028957205,0.00032298663,0.0007361075,0.00048767787,0.0005462554,0.0025333175],"category_scores_gemma":[0.002037055,0.00022793614,0.00042172906,0.0012998037,0.00027441882,0.0007393066,0.00064517563,0.0003588736,0.0012347931],"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":[0.00078500324,0.000059731883,0.001990685,0.00037200327,0.0000489581,0.0009318127,0.00037294248,0.0031844308,0.1700293,0.0013877,0.00585242,0.8149851],"study_design_scores_gemma":[0.00009888262,0.00093558745,0.057385538,0.00021627714,0.00031990983,0.0044227517,0.0011998261,0.4398457,0.42570832,0.0037472064,0.06593092,0.0001890691],"about_ca_topic_score_codex":0.0016175252,"about_ca_topic_score_gemma":0.00204346,"teacher_disagreement_score":0.0028957205,"about_ca_system_score_codex":0.00022648899,"about_ca_system_score_gemma":0.00026846724,"threshold_uncertainty_score":0.008474767},"labels":[],"label_agreement":null},{"id":"W2006956654","doi":"10.1117/12.544438","title":"&lt;title&gt;Fuzzy-logic-based multitarget tracker&lt;/title&gt;","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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":"Royal Military College of Canada","funders":"","keywords":"Fuzzy logic; Computer science; Artificial intelligence; Computer vision","score_opus":0.010736290621385317,"score_gpt":0.22137853667724813,"score_spread":0.21064224605586282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006956654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016725978,0.0006288769,0.9703593,0.00031649738,0.00027871644,0.000065788336,0.00015300464,0.0011881851,0.010283636],"genre_scores_gemma":[0.55759656,0.00089609553,0.40855244,0.00047520295,0.00017075521,0.000092485854,0.000713407,0.00020107106,0.031301975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978167,0.000026965115,0.000013054582,0.000048823324,0.00011283258,0.000016523767],"domain_scores_gemma":[0.9996728,0.000083817984,0.000027008244,0.000049094586,0.00014924895,0.00001805989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004909081,0.00023542701,0.00037140294,0.00045155137,0.0003333245,0.0006832262,0.0005553439,0.00074707303,0.007244949],"category_scores_gemma":[0.000833915,0.00011319432,0.00026763408,0.00038872354,0.00031687226,0.0007740684,0.00027328645,0.00044000102,0.0023042096],"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":[0.0005124356,0.00009213498,0.0019307956,0.00020700184,0.000051446154,0.00046629834,0.000086592925,0.13445374,0.13486938,0.016839627,0.013557116,0.69693345],"study_design_scores_gemma":[0.000033026594,0.00015571428,0.0016230491,0.000024598723,0.000021261638,0.0003773085,0.000025401605,0.90655065,0.0678129,0.0045293,0.018810457,0.000036388825],"about_ca_topic_score_codex":0.002255246,"about_ca_topic_score_gemma":0.0024285954,"teacher_disagreement_score":0.007244949,"about_ca_system_score_codex":0.0005687296,"about_ca_system_score_gemma":0.00044155066,"threshold_uncertainty_score":0.024236739},"labels":[],"label_agreement":null},{"id":"W2008985566","doi":"10.1049/cp.2012.0403","title":"Video analytics: past, present, and future","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Software deployment; Computer science; Analytics; Data science; Quarter (Canadian coin); Software engineering","score_opus":0.012035353091536222,"score_gpt":0.23377017592128985,"score_spread":0.22173482282975363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008985566","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006988901,0.8362367,0.014296038,0.10845201,0.00330132,0.000047193236,0.00023483159,0.00040987553,0.030033145],"genre_scores_gemma":[0.13115965,0.8235839,0.021371612,0.008455132,0.0063023595,0.00008007211,0.00043659497,0.00010268695,0.008507926],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978796,0.000700613,0.00018342996,0.0002800571,0.0007356691,0.0002206237],"domain_scores_gemma":[0.98973036,0.0036596663,0.0007882318,0.00045718506,0.0032600942,0.0021044426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008016266,0.00081883935,0.0005712977,0.0030700937,0.0011414563,0.0065393182,0.0013762513,0.003933978,0.0070767305],"category_scores_gemma":[0.0065532015,0.0004297204,0.00045899337,0.0034923689,0.0040327557,0.01705605,0.0022319453,0.00370331,0.0025485791],"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":[0.00032803696,0.00020557456,0.004516563,0.0022762138,0.00004404647,0.00018053902,0.0006906645,0.0006365033,0.0015136226,0.04549678,0.06280005,0.8813115],"study_design_scores_gemma":[0.000053229025,0.0005930385,0.0071812742,0.00810305,0.00011157724,0.0015380465,0.008874979,0.007449814,0.0025772238,0.08933506,0.8740453,0.00013734029],"about_ca_topic_score_codex":0.0024018018,"about_ca_topic_score_gemma":0.004134722,"teacher_disagreement_score":0.008016266,"about_ca_system_score_codex":0.0022025167,"about_ca_system_score_gemma":0.0024053096,"threshold_uncertainty_score":0.042394638},"labels":[],"label_agreement":null},{"id":"W2009924454","doi":"10.3166/isi.8.5-6.173-196","title":"Un modèle d'indexation de la vidéo","year":2003,"lang":"fr","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Analysis and Summarization","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":"Humanities; Art","score_opus":0.015605160958790928,"score_gpt":0.22711225833116255,"score_spread":0.2115070973723716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009924454","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015534594,0.00049919036,0.9652464,0.0008259661,0.00011307117,0.00017712296,0.0022305988,0.0028729022,0.012500188],"genre_scores_gemma":[0.24973874,0.002140065,0.70918274,0.00030889065,0.00009794693,0.00079158245,0.0055781403,0.0007023292,0.031459607],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882084,0.00026293617,0.00010846605,0.0003339989,0.00040567748,0.00006812326],"domain_scores_gemma":[0.9986802,0.0005101661,0.00007587926,0.00026206882,0.00041732442,0.00005451656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013095994,0.00077038567,0.0004932769,0.0020581323,0.0008442835,0.005354903,0.0015017284,0.0016818903,0.0067432653],"category_scores_gemma":[0.0042062984,0.00058194273,0.0011853876,0.0019784227,0.0013467945,0.004807861,0.0012331817,0.0011423788,0.00210494],"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":[0.00047936008,0.00017413853,0.0053308737,0.0010990907,0.00012236787,0.0009014511,0.004893905,0.09365593,0.035495646,0.681176,0.012764733,0.16390665],"study_design_scores_gemma":[0.000113041315,0.0002851061,0.0023546459,0.000533246,0.0001872523,0.0010036643,0.0013228573,0.5448642,0.038686577,0.12571715,0.28477615,0.00015614733],"about_ca_topic_score_codex":0.021837136,"about_ca_topic_score_gemma":0.01428229,"teacher_disagreement_score":0.021837136,"about_ca_system_score_codex":0.0022704687,"about_ca_system_score_gemma":0.0018799523,"threshold_uncertainty_score":0.043420076},"labels":[],"label_agreement":null},{"id":"W2012584471","doi":"10.1109/icmew.2012.102","title":"A Textural Based Hidden Markov Model for Animation Genre Discrimination","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Hidden Markov model; Computer science; Categorization; Animation; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Feature vector; Feature extraction; Markov chain; Computer vision; Computer graphics (images); Machine learning","score_opus":0.029948163886868166,"score_gpt":0.2672181499239211,"score_spread":0.23726998603705296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012584471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04269522,0.0007657605,0.9509015,0.00035753977,0.00019272185,0.00012650271,0.0010667841,0.0016116217,0.0022822395],"genre_scores_gemma":[0.7146543,0.000991008,0.2701951,0.00024268276,0.0002150258,0.00038779722,0.0028966842,0.00017612686,0.0102413045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996424,0.000075843214,0.000028558785,0.0001260187,0.000081928054,0.000045210163],"domain_scores_gemma":[0.9992361,0.00049272284,0.000062781255,0.000055883604,0.00012437756,0.000028147835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009206741,0.00057649496,0.0006125123,0.0011162163,0.0003759185,0.00065668597,0.0011408753,0.00076175,0.0029612095],"category_scores_gemma":[0.0022068343,0.0003519001,0.0009455979,0.00071286777,0.0002972205,0.0009889662,0.0003691282,0.0011036699,0.0014606293],"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":[0.00088535703,0.00054820953,0.013731454,0.00030892243,0.00033458078,0.00046679133,0.00037640217,0.36928958,0.031038303,0.028009046,0.0094238715,0.5455875],"study_design_scores_gemma":[0.00000948829,0.00003422888,0.0011272858,0.000010316251,0.00002599515,0.000046781097,0.000011104091,0.9941241,0.0012364093,0.0026205692,0.00073989196,0.000013816879],"about_ca_topic_score_codex":0.013064328,"about_ca_topic_score_gemma":0.013803203,"teacher_disagreement_score":0.013064328,"about_ca_system_score_codex":0.00082757825,"about_ca_system_score_gemma":0.00072087615,"threshold_uncertainty_score":0.025976539},"labels":[],"label_agreement":null},{"id":"W2012777944","doi":"10.1117/12.526891","title":"&lt;title&gt;Internet broadcast of hockey: a scale prototype&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Server; Computer science; The Internet; XML; Video game; Process (computing); Scale (ratio); Multimedia; Computer network; World Wide Web; Operating system","score_opus":0.009057364135122,"score_gpt":0.21318770987095617,"score_spread":0.20413034573583416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012777944","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6730316,0.00039623198,0.20844574,0.001231923,0.00056886475,0.0049714674,0.0027715622,0.0571772,0.05140532],"genre_scores_gemma":[0.80603194,0.0003111457,0.122046605,0.000526898,0.00014617805,0.0012745239,0.008508859,0.0027801255,0.058373664],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933285,0.000116234136,0.00003054176,0.00013975377,0.00028838354,0.00009226825],"domain_scores_gemma":[0.99904174,0.00011201931,0.000022677788,0.00019981868,0.00033344098,0.00029033216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010269484,0.00060182816,0.0005690787,0.0003960493,0.00053231453,0.0011731448,0.0025394522,0.0007625104,0.020247752],"category_scores_gemma":[0.0014314675,0.00033588943,0.0003534784,0.0002923721,0.000573466,0.0016504751,0.0008102603,0.00082923315,0.0072666155],"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":[0.0031063876,0.0087220175,0.012373918,0.00068431476,0.00022515186,0.002018621,0.0020094742,0.006980416,0.52685577,0.0027605828,0.080536835,0.35372645],"study_design_scores_gemma":[0.0039101806,0.028597238,0.050203335,0.0001622439,0.00056219357,0.003758855,0.0020394446,0.21786968,0.48834705,0.0017982809,0.20218848,0.00056304183],"about_ca_topic_score_codex":0.008682874,"about_ca_topic_score_gemma":0.0063975835,"teacher_disagreement_score":0.020247752,"about_ca_system_score_codex":0.0006533191,"about_ca_system_score_gemma":0.0006043458,"threshold_uncertainty_score":0.06773549},"labels":[],"label_agreement":null},{"id":"W2013792503","doi":"10.1145/2457450.2457452","title":"A reward-and-punishment-based approach for concept detection using adaptive ontology rules","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Analysis and Summarization","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 Winnipeg","funders":"","keywords":"Ontology; Computer science; Exploit; Punishment (psychology); Artificial intelligence; Machine learning; Data mining; Computer security; Psychology; Social psychology","score_opus":0.03949509969133991,"score_gpt":0.2842619872846184,"score_spread":0.24476688759327847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013792503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024205348,0.00033775717,0.9695659,0.0003936381,0.00014932927,0.0003952666,0.00012394291,0.0028833435,0.0019454443],"genre_scores_gemma":[0.40442866,0.00017724963,0.59090126,0.0003520763,0.00009851823,0.000351258,0.00028512906,0.00017070722,0.003235192],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676204,0.0004926234,0.00023203094,0.00084564683,0.0014553891,0.00021225736],"domain_scores_gemma":[0.99538946,0.0017508705,0.0005591751,0.0006248064,0.0013811081,0.00029460277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024903552,0.0013295612,0.0016705355,0.0019434902,0.00094813027,0.0013802391,0.0041128444,0.0017686875,0.0018080635],"category_scores_gemma":[0.009080784,0.0005031075,0.0008302658,0.001049602,0.0009343334,0.002538562,0.0016774319,0.002347535,0.0007054718],"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":[0.00050062675,0.001293306,0.0051905103,0.0002231324,0.00021267192,0.00038313508,0.00031362305,0.071576916,0.035428528,0.011531169,0.0071726795,0.8661737],"study_design_scores_gemma":[0.000042627373,0.0001147211,0.0009812579,0.0000131283705,0.000056070367,0.0001929612,0.00004569335,0.9787365,0.010502842,0.007189968,0.0020778954,0.000046374862],"about_ca_topic_score_codex":0.0064782323,"about_ca_topic_score_gemma":0.009124784,"teacher_disagreement_score":0.0064782323,"about_ca_system_score_codex":0.001253745,"about_ca_system_score_gemma":0.0028840553,"threshold_uncertainty_score":0.013170421},"labels":[],"label_agreement":null},{"id":"W2015596402","doi":"10.1007/s11760-011-0268-y","title":"Group-based spatio-temporal video analysis and abstraction using wavelet parameters","year":2011,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Wavelet; Computer vision; Wavelet transform; Centroid; Set (abstract data type); Abstraction; Algorithm","score_opus":0.030734577197058216,"score_gpt":0.25544099498484274,"score_spread":0.22470641778778452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015596402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014039725,0.00016891226,0.9845776,0.000049259936,0.00003711409,0.000038272465,0.000083622894,0.00049192895,0.00051359483],"genre_scores_gemma":[0.28759903,0.0005819354,0.7084478,0.00004452521,0.000113722956,0.00012850836,0.0005415037,0.00025073046,0.002292273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996772,0.00006621996,0.000026987715,0.00007592068,0.00011262353,0.00004118174],"domain_scores_gemma":[0.9993357,0.00017958353,0.00008435416,0.00016316543,0.00018967691,0.000047541937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056968955,0.00074008305,0.0008348115,0.0017711703,0.00037257644,0.0011306707,0.00064262276,0.00048053375,0.0022201966],"category_scores_gemma":[0.0016965144,0.0002513969,0.0007530327,0.0020761003,0.0004010006,0.0015200967,0.000989915,0.00074701355,0.0009327871],"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":[0.0008703979,0.00013514802,0.0014740889,0.00022399066,0.00015980405,0.0001768684,0.0003027517,0.049163844,0.1615598,0.013591398,0.0031253174,0.76921654],"study_design_scores_gemma":[0.00003503352,0.00017633822,0.0029568842,0.000026453494,0.00013661623,0.00018635846,0.00021020975,0.9214811,0.057563007,0.011826352,0.0053650155,0.000036625148],"about_ca_topic_score_codex":0.0013231314,"about_ca_topic_score_gemma":0.0012053725,"teacher_disagreement_score":0.0022201966,"about_ca_system_score_codex":0.0003713171,"about_ca_system_score_gemma":0.00033881096,"threshold_uncertainty_score":0.007427275},"labels":[],"label_agreement":null},{"id":"W2018088206","doi":"10.1109/oceans.2012.6404996","title":"Detection of salient events in large datasets of underwater video","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":true,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"Salient; Computer science; Observatory; Underwater; Artificial intelligence; Computer vision; Remote sensing; Real-time computing; Geology","score_opus":0.013452010614262362,"score_gpt":0.24949267464363703,"score_spread":0.23604066402937468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2018088206","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69049144,0.003864476,0.24918064,0.0007352334,0.0002887652,0.0010719846,0.041034065,0.010884004,0.0024493553],"genre_scores_gemma":[0.6313694,0.001519743,0.2727003,0.00012038884,0.0005094863,0.00043902433,0.09198179,0.0002629123,0.0010968791],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988932,0.00011345743,0.000103294115,0.00029407153,0.0004653296,0.00013072914],"domain_scores_gemma":[0.9973381,0.00095307064,0.0005524589,0.0003371863,0.0006229559,0.00019616241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009173156,0.0015312459,0.0015273105,0.00797866,0.00065083295,0.0011716874,0.0011754503,0.0013277592,0.0008330664],"category_scores_gemma":[0.003939656,0.00041731657,0.0005987518,0.0053794626,0.0003097851,0.0019467486,0.0009993874,0.0007769405,0.0007392932],"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":[0.0017106703,0.0012390276,0.038109228,0.002201342,0.00051660056,0.0027218848,0.0006508756,0.02060645,0.21562123,0.0009850345,0.024014894,0.69162273],"study_design_scores_gemma":[0.00017582634,0.0013204513,0.29118392,0.00029720168,0.000543956,0.0030172595,0.0026391828,0.5524982,0.11847127,0.005133638,0.024528084,0.00019098341],"about_ca_topic_score_codex":0.0050150696,"about_ca_topic_score_gemma":0.009941163,"teacher_disagreement_score":0.00797866,"about_ca_system_score_codex":0.00053072575,"about_ca_system_score_gemma":0.0005938332,"threshold_uncertainty_score":0.0099717975},"labels":[],"label_agreement":null},{"id":"W2019001263","doi":"10.1109/ccece.2014.6901095","title":"Interactive user oriented visual attention based video summarization and exploration framework","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Automatic summarization; Relation (database); Feature (linguistics); Cluster analysis; Artificial intelligence; Node (physics); Frame (networking); Visualization; Information retrieval; Feature extraction; Table (database); Data mining; Computer vision","score_opus":0.008286585857214908,"score_gpt":0.2516586462665807,"score_spread":0.2433720604093658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019001263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008945426,0.00030931272,0.98441666,0.0000879634,0.00001622263,0.0001947673,0.00015918289,0.0035498086,0.002320797],"genre_scores_gemma":[0.20357907,0.00045925265,0.78547084,0.00013125701,0.00007659462,0.0005937823,0.0009379848,0.00030517322,0.008446076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994381,0.0001277093,0.000028262099,0.00014094361,0.00019390414,0.00007104426],"domain_scores_gemma":[0.9995819,0.000118607866,0.00003803287,0.000045494475,0.00014261165,0.000073403884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069978787,0.0011547024,0.00069541566,0.0015623695,0.0004795024,0.0011322761,0.002475756,0.00086800166,0.0050270515],"category_scores_gemma":[0.0010139308,0.00031012436,0.0009778725,0.0006102072,0.00052690104,0.0017189444,0.001738031,0.00072346634,0.0010145815],"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":[0.0010019256,0.00089600927,0.0017224703,0.0009708134,0.00027254023,0.0010401986,0.0022749342,0.05666611,0.22526367,0.035887875,0.019286122,0.6547173],"study_design_scores_gemma":[0.000097368174,0.00071251945,0.0031306653,0.000086498614,0.00020993613,0.0010073935,0.00059432647,0.86515135,0.06994823,0.018904705,0.039990246,0.00016666336],"about_ca_topic_score_codex":0.0035475988,"about_ca_topic_score_gemma":0.0044755423,"teacher_disagreement_score":0.0050270515,"about_ca_system_score_codex":0.00061533606,"about_ca_system_score_gemma":0.00066138443,"threshold_uncertainty_score":0.016817153},"labels":[],"label_agreement":null},{"id":"W2022330695","doi":"10.1117/12.410966","title":"&lt;title&gt;Video retrieval by spatial and temporal structure of trajectories&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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 British Columbia","funders":"","keywords":"Computer science; Trajectory; Computer vision; Artificial intelligence; Dynamic time warping; Representation (politics); Position (finance); Video tracking; Motion estimation; Path (computing); Focus (optics); Object (grammar)","score_opus":0.00692940845273451,"score_gpt":0.20916641302778446,"score_spread":0.20223700457504995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022330695","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04348889,0.0047709765,0.90422904,0.001885195,0.00079224247,0.0003360656,0.0047904532,0.009577577,0.030129582],"genre_scores_gemma":[0.33786118,0.007145312,0.5281389,0.00048490742,0.0009673846,0.00032471938,0.021710701,0.0022484984,0.10111842],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99973124,0.000033704306,0.000025693758,0.00006588721,0.00010472576,0.000038648668],"domain_scores_gemma":[0.99933916,0.000116919495,0.00008952449,0.00018058298,0.00022499671,0.00004877166],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005175472,0.0005245706,0.000704263,0.0030794104,0.0004593501,0.0022004596,0.00094367587,0.00076837715,0.015371936],"category_scores_gemma":[0.0020646967,0.00024735194,0.000301969,0.0040116343,0.00058451365,0.0031414798,0.00072017784,0.00047728576,0.011050681],"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":[0.00042096607,0.00006610049,0.0012553248,0.00028074253,0.000028706376,0.00032329498,0.0001617171,0.017571213,0.06546951,0.05056579,0.08131409,0.78254265],"study_design_scores_gemma":[0.000082296465,0.00026447952,0.0060226666,0.00018798403,0.000063366875,0.0010515469,0.00026067096,0.6364961,0.07511008,0.057526045,0.22280885,0.00012591305],"about_ca_topic_score_codex":0.0061713955,"about_ca_topic_score_gemma":0.0059793605,"teacher_disagreement_score":0.9846281,"about_ca_system_score_codex":0.0010763473,"about_ca_system_score_gemma":0.00054253393,"threshold_uncertainty_score":0.051424265},"labels":[],"label_agreement":null},{"id":"W2026174573","doi":"10.1145/1631024.1631031","title":"Ice hockey shot event modeling with mixture hidden Markov model","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Hidden Markov model; Ice hockey; Computer science; Event (particle physics); Mixture model; Shot (pellet); Artificial intelligence; Frame (networking); Pattern recognition (psychology); Speech recognition","score_opus":0.015187518709923773,"score_gpt":0.23633146473245187,"score_spread":0.2211439460225281,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026174573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009294824,0.00026284208,0.9890431,0.000076867575,0.00005245508,0.000029823403,0.00013943724,0.0005415718,0.00055894547],"genre_scores_gemma":[0.67572427,0.0010009301,0.3162364,0.00014291484,0.0001900574,0.00020473404,0.001466202,0.00021548157,0.004819006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991266,0.0002485198,0.00005324153,0.00027636427,0.0002169926,0.00007831456],"domain_scores_gemma":[0.9989619,0.00067579467,0.0001020509,0.000095019896,0.00012592763,0.000039324583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00118237,0.0008544172,0.001059202,0.0011358703,0.00035817208,0.0010219543,0.0015423894,0.0009132913,0.0015259498],"category_scores_gemma":[0.0033498663,0.00051149895,0.0013848711,0.0008345764,0.0004538495,0.0018013702,0.0006107449,0.0014441919,0.0007151667],"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":[0.0005045697,0.00019916163,0.008343443,0.00030649544,0.00049307884,0.0005175006,0.00038955914,0.7343103,0.0125852795,0.029659905,0.004152213,0.20853832],"study_design_scores_gemma":[0.000004975364,0.000019211511,0.00065192214,0.0000054521556,0.000031182793,0.000037130212,0.000014871267,0.9932592,0.0008947754,0.004416278,0.00065005413,0.00001496548],"about_ca_topic_score_codex":0.008525331,"about_ca_topic_score_gemma":0.0061914376,"teacher_disagreement_score":0.008525331,"about_ca_system_score_codex":0.0007298548,"about_ca_system_score_gemma":0.00055510947,"threshold_uncertainty_score":0.016951442},"labels":[],"label_agreement":null},{"id":"W2027038623","doi":"10.1145/1111449.1111480","title":"Interactive multimedia summaries of evaluative text","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":63,"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; Multimedia; Interactive media; World Wide Web; Human–computer interaction","score_opus":0.008962131532321867,"score_gpt":0.25622267357838086,"score_spread":0.247260542046059,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027038623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11706096,0.0027192459,0.76115793,0.0011605446,0.00046488305,0.001007522,0.012384681,0.08027192,0.02377227],"genre_scores_gemma":[0.29412785,0.0015077455,0.66578496,0.00046092857,0.0007797339,0.0011063484,0.015878217,0.0025715004,0.017782714],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996239,0.00014605612,0.000031957814,0.000069513306,0.000107352025,0.000021197491],"domain_scores_gemma":[0.99610597,0.0025393274,0.00032628299,0.00026614597,0.00059980643,0.00016247842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075091555,0.0010634155,0.00040270688,0.0018762829,0.0002756941,0.0011323149,0.0007364275,0.0004664146,0.018677669],"category_scores_gemma":[0.005551833,0.00017581096,0.00028048013,0.0010433478,0.0001609827,0.0014216683,0.00072515704,0.0003611002,0.0033093009],"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":[0.001456735,0.00030129158,0.00138499,0.0017872519,0.00009892231,0.0007961798,0.0028770235,0.0038551332,0.071711,0.003870757,0.09123534,0.82062536],"study_design_scores_gemma":[0.0006055467,0.0025421535,0.016294822,0.00086349255,0.00047848263,0.0021611725,0.0037192851,0.13731094,0.15827167,0.022222368,0.6551748,0.00035522325],"about_ca_topic_score_codex":0.00034291894,"about_ca_topic_score_gemma":0.0007923337,"teacher_disagreement_score":0.018677669,"about_ca_system_score_codex":0.00021170705,"about_ca_system_score_gemma":0.00017362293,"threshold_uncertainty_score":0.062483072},"labels":[],"label_agreement":null},{"id":"W2031000129","doi":"10.1002/asi.21445","title":"Understanding how webcasts are used as sources of information","year":2010,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Video Analysis and Summarization","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; McGill University; Université de Montréal","funders":"","keywords":"Webcast; Computer science; Event (particle physics); Set (abstract data type); World Wide Web","score_opus":0.022383158680271218,"score_gpt":0.2532603049608587,"score_spread":0.2308771462805875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031000129","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67614126,0.004776755,0.18197758,0.007245432,0.00015624898,0.0005415008,0.00026776543,0.00054912484,0.12834437],"genre_scores_gemma":[0.96167177,0.0031795448,0.030081788,0.0002383197,0.0000314842,0.00012540072,0.00011575197,0.000075246724,0.004480735],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.99643,0.0024671531,0.00016331997,0.00025912697,0.00047996233,0.0002003958],"domain_scores_gemma":[0.9897139,0.0073129456,0.0009813982,0.00063291285,0.0010872663,0.00027166083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037426585,0.00039190854,0.00022797784,0.0021938798,0.0014022953,0.00853835,0.0009035097,0.0015053973,0.0016908378],"category_scores_gemma":[0.020297965,0.0004610788,0.0002966611,0.0015504232,0.003288875,0.014408971,0.0012047488,0.001389178,0.00044526724],"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":[0.00029772345,0.0002932346,0.0409133,0.0013161266,0.000113413436,0.0008301633,0.48733237,0.0030983402,0.019178538,0.19797309,0.003984003,0.24466977],"study_design_scores_gemma":[0.00012741667,0.00046004518,0.10103855,0.0019241426,0.00032132215,0.0017172453,0.41377628,0.023777032,0.020948745,0.16538773,0.2702628,0.00025862257],"about_ca_topic_score_codex":0.008878665,"about_ca_topic_score_gemma":0.007596654,"teacher_disagreement_score":0.008878665,"about_ca_system_score_codex":0.0014755678,"about_ca_system_score_gemma":0.0015571454,"threshold_uncertainty_score":0.019793332},"labels":[],"label_agreement":null},{"id":"W2032189893","doi":"10.5539/cis.v2n3p71","title":"Text Emotion Computing under Cognition Vision","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Video Analysis and Summarization","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; Cognition; Emotion recognition; Computation; Emotion detection; Cognitive computing; Affective computing; Cognitive psychology; Artificial intelligence; Cognitive science; Emotional intelligence; Natural language processing; Psychology; Algorithm; Social psychology","score_opus":0.00964154893826375,"score_gpt":0.251901892938125,"score_spread":0.24226034399986124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032189893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10804005,0.004327897,0.82037634,0.005533232,0.0007766777,0.00013630863,0.00039093193,0.0023263884,0.05809228],"genre_scores_gemma":[0.88562137,0.0014320955,0.102707855,0.0006480761,0.00045525766,0.00008958256,0.00032082756,0.000108479595,0.008616562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972993,0.00005508168,0.00001473994,0.000101910155,0.00006392415,0.000034400735],"domain_scores_gemma":[0.9997712,0.000055784843,0.000029199315,0.000032458927,0.00007567539,0.00003570257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030317684,0.0003088167,0.00023512596,0.0005888248,0.000334741,0.0021074433,0.0004036183,0.0004941132,0.0027637938],"category_scores_gemma":[0.0014490134,0.00008359459,0.00048603828,0.00043206458,0.0006936788,0.0031467075,0.0006739374,0.0007086772,0.00055545394],"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":[0.0004537009,0.00013616467,0.0024826464,0.00035574796,0.00010250146,0.00023624048,0.0016783286,0.009445736,0.057717036,0.42401496,0.022231357,0.4811456],"study_design_scores_gemma":[0.00006458539,0.00019830848,0.009043625,0.000085905216,0.00014747496,0.00033364355,0.0010196579,0.3052221,0.024288403,0.6111534,0.04838382,0.00005903154],"about_ca_topic_score_codex":0.0015149896,"about_ca_topic_score_gemma":0.0007614804,"teacher_disagreement_score":0.0027637938,"about_ca_system_score_codex":0.0008832099,"about_ca_system_score_gemma":0.00026249173,"threshold_uncertainty_score":0.0092458725},"labels":[],"label_agreement":null},{"id":"W2034367898","doi":"10.1155/s1110865703210076","title":"Retrieval by Local Motion","year":2003,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Video Analysis and Summarization","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":"Artificial intelligence; Computer vision; Computer science; Motion (physics); Video retrieval; Ranking (information retrieval); Invariant (physics); Quarter-pixel motion; Minimum bounding box; Bounding overwatch; Mathematics; Image (mathematics)","score_opus":0.009932568818001657,"score_gpt":0.266163763396072,"score_spread":0.25623119457807036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034367898","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.119789,0.012837062,0.85002744,0.0006431152,0.00037870125,0.0004235525,0.001900496,0.0031016814,0.010898965],"genre_scores_gemma":[0.7636029,0.005705017,0.20795742,0.00048324437,0.0008067337,0.0003164335,0.0058996803,0.00030927933,0.014919438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999119,0.00014056252,0.00007099036,0.00021697376,0.0003423844,0.00011004906],"domain_scores_gemma":[0.99910116,0.00023053677,0.00012830069,0.00025962302,0.0002362533,0.00004414237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000644428,0.0006668647,0.0016525066,0.0030499888,0.00041452036,0.0013423682,0.00080052787,0.0007388562,0.0038076434],"category_scores_gemma":[0.003525706,0.00021249393,0.0006094899,0.0035725185,0.000460212,0.0027171217,0.00092667964,0.0005088014,0.0025476785],"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":[0.0005702979,0.00016115641,0.0029221298,0.0004939376,0.00013897811,0.00020152371,0.00013038902,0.018236041,0.093610786,0.0142403245,0.01453212,0.8547624],"study_design_scores_gemma":[0.00027862296,0.0015977215,0.024086926,0.00020265032,0.00045802514,0.0026010533,0.0005273371,0.7198169,0.13470648,0.04984642,0.06558871,0.00028918733],"about_ca_topic_score_codex":0.0025416913,"about_ca_topic_score_gemma":0.0026557497,"teacher_disagreement_score":0.0038076434,"about_ca_system_score_codex":0.0007220746,"about_ca_system_score_gemma":0.00054652937,"threshold_uncertainty_score":0.012737811},"labels":[],"label_agreement":null},{"id":"W2035969696","doi":"10.1145/1496984.1497038","title":"Camera selection using SCSPs","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Saskatchewan","funders":"","keywords":"Computer science; Selection (genetic algorithm); Artificial intelligence; Computer vision; Computer graphics (images)","score_opus":0.030992143608573088,"score_gpt":0.23951254375945705,"score_spread":0.20852040015088397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035969696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028411977,0.000370784,0.94907725,0.00023962729,0.00006818678,0.00025571763,0.0005727014,0.0044962377,0.016507538],"genre_scores_gemma":[0.63226056,0.0004210849,0.35113963,0.0001897562,0.00009567719,0.00034695351,0.001271713,0.0005536696,0.0137209445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990067,0.00025121297,0.000073888106,0.00023157416,0.00032766722,0.000109132285],"domain_scores_gemma":[0.99876595,0.00051337725,0.00010688636,0.0002365629,0.00030064338,0.00007663794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005990692,0.0007101244,0.00073406653,0.0009017044,0.00068102503,0.0010935819,0.0007024505,0.00046969677,0.007502126],"category_scores_gemma":[0.002583429,0.00029512253,0.0005001563,0.0011755263,0.0004040096,0.0012277008,0.0010637384,0.00056903105,0.0021221936],"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":[0.00085286103,0.00012345766,0.0021340665,0.0004674715,0.00010486743,0.00081029773,0.0004338407,0.090491824,0.075871274,0.04006068,0.021850638,0.76679873],"study_design_scores_gemma":[0.00011466906,0.00022056756,0.0016972349,0.00008407758,0.00010788416,0.00058582844,0.00046142165,0.7656966,0.10289297,0.06618101,0.061876025,0.00008177206],"about_ca_topic_score_codex":0.0029721826,"about_ca_topic_score_gemma":0.005836163,"teacher_disagreement_score":0.007502126,"about_ca_system_score_codex":0.0006511956,"about_ca_system_score_gemma":0.000683674,"threshold_uncertainty_score":0.025097132},"labels":[],"label_agreement":null},{"id":"W2041100838","doi":"10.1155/2008/547923","title":"Hierarchical Fuzzy Feature Similarity Combination for Presentation Slide Retrieval","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Video Analysis and Summarization","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":"Computer science; Information retrieval; Feature (linguistics); Relevance (law); Disk formatting; Presentation (obstetrics); Relevance feedback; XML; Fuzzy logic; Scheme (mathematics); Data mining; Similarity (geometry); Artificial intelligence; Image retrieval; World Wide Web","score_opus":0.01656152424879052,"score_gpt":0.314791954584857,"score_spread":0.29823043033606644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041100838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05309245,0.001221304,0.9416357,0.00010736763,0.000078834695,0.00018020195,0.00019504389,0.0016413272,0.0018476847],"genre_scores_gemma":[0.5359628,0.0005177707,0.4592127,0.000100740675,0.00018694114,0.00019400983,0.0006960978,0.000084984305,0.0030438926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986185,0.00019524159,0.00011798905,0.00023579218,0.0007340328,0.00009847003],"domain_scores_gemma":[0.99934286,0.00015697356,0.000083033585,0.000104418956,0.0002780016,0.0000346513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090374297,0.0005345881,0.0009722132,0.0027890734,0.0004146743,0.00082702015,0.0010794587,0.0006329275,0.0023802766],"category_scores_gemma":[0.0025230462,0.00024679457,0.0009632068,0.0020654732,0.00031009773,0.0014067334,0.0007642112,0.0005080974,0.0010766068],"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":[0.0005193523,0.00014340313,0.0013758267,0.00022708254,0.00013623711,0.00016757243,0.00013744777,0.020426719,0.08061429,0.0031288534,0.0034580594,0.8896651],"study_design_scores_gemma":[0.00012116218,0.0006987086,0.009549231,0.00003951698,0.00031937024,0.0009396101,0.00018568503,0.86957157,0.10143308,0.007209444,0.009803061,0.00012953313],"about_ca_topic_score_codex":0.0026536589,"about_ca_topic_score_gemma":0.002572151,"teacher_disagreement_score":0.0027890734,"about_ca_system_score_codex":0.00064267387,"about_ca_system_score_gemma":0.00052644697,"threshold_uncertainty_score":0.007962823},"labels":[],"label_agreement":null},{"id":"W2042614532","doi":"10.1117/12.641924","title":"Key-text spotting in documentary videos using Adaboost","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Spotting; Artificial intelligence; AdaBoost; Key (lock); Grayscale; Key frame; Frame (networking); Pattern recognition (psychology); Computer vision; Image (mathematics); Support vector machine","score_opus":0.008898259019334276,"score_gpt":0.221112113397501,"score_spread":0.21221385437816673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042614532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04726263,0.0008730984,0.9449305,0.00011598226,0.00020423287,0.00022540793,0.00011390885,0.004818816,0.0014554749],"genre_scores_gemma":[0.30409336,0.00051810365,0.68800616,0.00017596483,0.00018034397,0.00023623835,0.0005541097,0.00039504623,0.0058406387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999445,0.000076060875,0.000035316916,0.00016079943,0.00019418565,0.00008864197],"domain_scores_gemma":[0.9991646,0.00029681638,0.000093953975,0.000047897884,0.00034759278,0.00004912877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011571528,0.0012392811,0.0016050189,0.0020967228,0.0005592811,0.0008543961,0.001464815,0.0011636823,0.0022843839],"category_scores_gemma":[0.0016001505,0.0005005582,0.0008747956,0.0012528301,0.00041920168,0.0011317391,0.00042201517,0.001055842,0.0013257944],"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":[0.00064789515,0.0003610715,0.001046664,0.00026648102,0.00015422047,0.000085203974,0.000101703474,0.059358325,0.059680726,0.0007915328,0.0051392266,0.8723669],"study_design_scores_gemma":[0.000028295397,0.00015057347,0.0012837894,0.00001883861,0.0000392056,0.00007992718,0.000049111473,0.9671026,0.028061533,0.0009592776,0.0022042217,0.000022695218],"about_ca_topic_score_codex":0.00524833,"about_ca_topic_score_gemma":0.0043141902,"teacher_disagreement_score":0.00524833,"about_ca_system_score_codex":0.0007458271,"about_ca_system_score_gemma":0.0005517277,"threshold_uncertainty_score":0.010435581},"labels":[],"label_agreement":null},{"id":"W2043885557","doi":"10.1080/13614560601051141","title":"A structural computing approach to the production of multimedia document series","year":2006,"lang":"en","type":"article","venue":"New Review of Hypermedia and Multimedia","topic":"Video Analysis and Summarization","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":"Computer science; XML; Formalism (music); Document Structure Description; Information retrieval; World Wide Web; Programming language; Multimedia","score_opus":0.010998802132568089,"score_gpt":0.2408176444395552,"score_spread":0.2298188423069871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043885557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045152484,0.0006851337,0.9876178,0.0003984434,0.000058371388,0.000058204514,0.000064132335,0.0003091031,0.006293652],"genre_scores_gemma":[0.18779188,0.002286179,0.7981521,0.00012815981,0.00042735218,0.00022871135,0.00033408502,0.00029123077,0.010360364],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922514,0.00023171671,0.000053995,0.00013460101,0.00029889125,0.000055708617],"domain_scores_gemma":[0.9974897,0.0016140548,0.00016041033,0.000283206,0.00038741544,0.00006521899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008613146,0.0005897948,0.0006487398,0.0028402843,0.0010921583,0.0032573768,0.0018871445,0.0007529258,0.006976882],"category_scores_gemma":[0.0047292085,0.00055171957,0.0009826608,0.0034692725,0.0027543434,0.00326583,0.0011665412,0.0011872952,0.0013522981],"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":[0.00008214663,0.000087789565,0.0006436155,0.00040624695,0.000036712256,0.0001953208,0.00070918124,0.0643139,0.011852173,0.68217796,0.0034231925,0.23607166],"study_design_scores_gemma":[0.000022385348,0.00010924736,0.0005735305,0.000069472255,0.000050169096,0.00021929362,0.0002798025,0.49713552,0.009263628,0.46328798,0.028957486,0.000031527456],"about_ca_topic_score_codex":0.0032638654,"about_ca_topic_score_gemma":0.0031626113,"teacher_disagreement_score":0.006976882,"about_ca_system_score_codex":0.0013891568,"about_ca_system_score_gemma":0.0013540663,"threshold_uncertainty_score":0.023339987},"labels":[],"label_agreement":null},{"id":"W2047495560","doi":"10.1007/s00138-006-0058-7","title":"Scout: a game speed analysis and tracking system","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Life Prediction Technologies (Canada)","funders":"","keywords":"Computer vision; Tracking (education); Background subtraction; Computer science; Artificial intelligence; CLIPS; Tracking system; Football; Field (mathematics); Subtraction; Computer graphics (images); Pixel; Kalman filter; Mathematics; Geography","score_opus":0.006951556687361913,"score_gpt":0.27145830158449524,"score_spread":0.26450674489713333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047495560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14751229,0.0010620338,0.46480113,0.00021798867,0.00066074595,0.0028297899,0.0330701,0.32230845,0.027537463],"genre_scores_gemma":[0.48363608,0.00063515874,0.39876732,0.0007435254,0.0004024038,0.0025182012,0.046719976,0.008636211,0.05794105],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996172,0.000037286118,0.000025835372,0.00012740455,0.00014475596,0.000047378966],"domain_scores_gemma":[0.9988809,0.00014866993,0.000095721225,0.00009506404,0.00053238566,0.00024719612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005683811,0.0018097493,0.0013700383,0.004229735,0.00055918226,0.00080983306,0.0014933619,0.0007911643,0.01947807],"category_scores_gemma":[0.0012891429,0.00057393435,0.00037760893,0.001272624,0.0002163313,0.00085357623,0.000956016,0.0005271713,0.009826946],"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":[0.005811142,0.00091659644,0.018424315,0.00063876796,0.00041808453,0.00027879665,0.00031586195,0.005432112,0.11568335,0.0010423798,0.14823785,0.70280075],"study_design_scores_gemma":[0.002493466,0.002712006,0.12476643,0.0001557126,0.0007285262,0.001431438,0.0003561826,0.62256986,0.14303744,0.0034634047,0.09772874,0.0005567273],"about_ca_topic_score_codex":0.010767793,"about_ca_topic_score_gemma":0.021028878,"teacher_disagreement_score":0.01947807,"about_ca_system_score_codex":0.0006759926,"about_ca_system_score_gemma":0.00073869387,"threshold_uncertainty_score":0.06516069},"labels":[],"label_agreement":null},{"id":"W2047815757","doi":"10.1145/2642918.2647366","title":"Video lens","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Computer science; Metadata; Set (abstract data type); Through-the-lens metering; Lens (geology); Domain (mathematical analysis); Multimedia; Field (mathematics); World Wide Web; Information retrieval; Human–computer interaction","score_opus":0.009433938488803164,"score_gpt":0.2028284825895042,"score_spread":0.19339454410070103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047815757","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017749708,0.0031382279,0.8196268,0.0012218826,0.00051298045,0.0013457525,0.014881993,0.055554684,0.08596807],"genre_scores_gemma":[0.12849776,0.003713265,0.77630234,0.0011228445,0.00043775933,0.0013856576,0.024931211,0.005659915,0.057949226],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994691,0.00011156933,0.000034910434,0.00011348495,0.00018073242,0.00009024886],"domain_scores_gemma":[0.9987571,0.000377491,0.000088163186,0.00024602652,0.00028190005,0.00024936383],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00083759456,0.0009872881,0.00043034472,0.0021452531,0.0006956877,0.0031605149,0.0016526035,0.00080080784,0.036822136],"category_scores_gemma":[0.0035191786,0.00033324063,0.0005861019,0.0013613746,0.00059477804,0.0036291971,0.0027967335,0.0008236721,0.009522724],"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":[0.00078487647,0.00019607229,0.0024077029,0.0018401167,0.00007417689,0.00069450407,0.002941076,0.002706919,0.03231549,0.070716605,0.20390932,0.68141305],"study_design_scores_gemma":[0.000069829075,0.00020289632,0.0020927638,0.0003602111,0.00003729634,0.0011587562,0.0012451145,0.0135915615,0.015356671,0.015868165,0.94990474,0.0001118895],"about_ca_topic_score_codex":0.0058521964,"about_ca_topic_score_gemma":0.010723395,"teacher_disagreement_score":0.96317786,"about_ca_system_score_codex":0.0007354877,"about_ca_system_score_gemma":0.0010865937,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2049051812","doi":"10.3166/isi.10.1.81-99","title":"A Video Content Independent Mining Algorithm for Evolved Rule-based Detection of Scene Boundaries","year":2005,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":3,"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":"Content (measure theory); Computer science; Artificial intelligence; Algorithm; Computer vision; Pattern recognition (psychology); Data mining; Mathematics","score_opus":0.019673371089606935,"score_gpt":0.22652960568799002,"score_spread":0.2068562345983831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049051812","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020869507,0.00006449387,0.9775934,0.00006783846,0.000017303135,0.000087182096,0.00005955102,0.00048467,0.0007559736],"genre_scores_gemma":[0.13101716,0.0000704795,0.86679965,0.00010805181,0.000014924815,0.00023167486,0.0002971999,0.000075588956,0.001385345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994653,0.00008750298,0.000038024664,0.0001821301,0.0001853578,0.000041655083],"domain_scores_gemma":[0.9989128,0.0005393994,0.000111164394,0.00007515392,0.00032615435,0.000035279736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009347617,0.0005661881,0.0007027495,0.001236429,0.00043315455,0.0007226635,0.001653233,0.0011151211,0.00092792773],"category_scores_gemma":[0.0036182029,0.00032756024,0.00062245445,0.00092377764,0.0005556017,0.00077542017,0.00058926665,0.0009513135,0.00030022682],"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":[0.00015383509,0.00031620904,0.0057067536,0.00012316293,0.00014630982,0.0004592654,0.0003150709,0.23287737,0.033185814,0.008723559,0.0023820698,0.71561056],"study_design_scores_gemma":[0.000017399761,0.00006119141,0.00063247216,0.000013233141,0.00002475185,0.00014019066,0.000024108294,0.9888182,0.0073637757,0.0019239249,0.0009710302,0.000009664691],"about_ca_topic_score_codex":0.0029633585,"about_ca_topic_score_gemma":0.0025180194,"teacher_disagreement_score":0.0029633585,"about_ca_system_score_codex":0.00061563967,"about_ca_system_score_gemma":0.000808955,"threshold_uncertainty_score":0.005892217},"labels":[],"label_agreement":null},{"id":"W2050563473","doi":"10.1145/604045.604106","title":"EduNuggets","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Alberta","funders":"Government of Alberta","keywords":"Computer science; Leverage (statistics); World Wide Web; Multimedia; Artificial intelligence","score_opus":0.006952546326383278,"score_gpt":0.2023671715094025,"score_spread":0.19541462518301922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050563473","genre_codex":"software","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012675328,0.0052301865,0.13220358,0.002019595,0.0015397212,0.0012020873,0.17180856,0.3737454,0.29957557],"genre_scores_gemma":[0.066303894,0.005003365,0.11728951,0.0017165411,0.00074810983,0.0011431528,0.4561042,0.05692762,0.29476362],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99880195,0.00021699307,0.00011365389,0.00019246992,0.0005513796,0.00012346548],"domain_scores_gemma":[0.99758816,0.0004294699,0.0001522301,0.0009713562,0.00054212345,0.00031668105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011016374,0.0015881418,0.00087149855,0.005962948,0.0013095121,0.004531898,0.0028600213,0.0015333483,0.100507975],"category_scores_gemma":[0.0066084275,0.00063640164,0.0007750154,0.0058890893,0.00050160417,0.0056210174,0.0048684035,0.0011990794,0.0824762],"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":[0.0004822175,0.00010371418,0.000950343,0.0005963912,0.000034397042,0.00028343985,0.0005134366,0.0010381918,0.0028417115,0.015963659,0.66463864,0.31255382],"study_design_scores_gemma":[0.000038089067,0.000038452326,0.0005874658,0.00010602508,0.000014695906,0.00021230461,0.00013295999,0.0020210382,0.0030065503,0.0056771194,0.98813254,0.000032726595],"about_ca_topic_score_codex":0.0049276776,"about_ca_topic_score_gemma":0.0069750724,"teacher_disagreement_score":0.100507975,"about_ca_system_score_codex":0.00086611795,"about_ca_system_score_gemma":0.0012861033,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2052285567","doi":"10.1109/icmew.2013.6618333","title":"Still visualization of object motion in compressed video","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Sprite (computer graphics); Computer vision; Computer science; Artificial intelligence; Motion vector; Pixel; Visualization; Quarter-pixel motion; Automatic summarization; Motion compensation; Computer graphics (images); Image (mathematics)","score_opus":0.011764411114004015,"score_gpt":0.24326948520971453,"score_spread":0.23150507409571053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052285567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11691986,0.00081292633,0.87547904,0.00030605044,0.00007379474,0.00008217416,0.00082549814,0.0032517132,0.0022489466],"genre_scores_gemma":[0.5275631,0.0016048907,0.46616346,0.000089827365,0.00012998567,0.000070404916,0.0015462046,0.0003498637,0.0024822778],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998807,0.000019243802,0.000004970356,0.000020959913,0.000059417715,0.000014803785],"domain_scores_gemma":[0.99969804,0.000086876455,0.000044817643,0.00003881652,0.000102045,0.000029470322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017206927,0.0006563406,0.00032203837,0.0014267518,0.00017703002,0.00065702747,0.0003978084,0.00033057193,0.0020247367],"category_scores_gemma":[0.0008392472,0.00018103965,0.00023097554,0.00082484673,0.00022075218,0.000902365,0.0004826657,0.00047124736,0.00033169106],"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":[0.0006945781,0.000070113805,0.001431954,0.00039361336,0.00007353437,0.00058233005,0.00041534545,0.0405128,0.6197315,0.005354353,0.004069184,0.32667068],"study_design_scores_gemma":[0.000057581336,0.00029882594,0.008761165,0.00007099932,0.00006992591,0.0011157735,0.00029293264,0.7654809,0.2062747,0.0046193306,0.01288712,0.000070755974],"about_ca_topic_score_codex":0.002134507,"about_ca_topic_score_gemma":0.0022907886,"teacher_disagreement_score":0.002134507,"about_ca_system_score_codex":0.00028400734,"about_ca_system_score_gemma":0.00024715674,"threshold_uncertainty_score":0.006773412},"labels":[],"label_agreement":null},{"id":"W2053329155","doi":"10.1145/2207676.2207766","title":"Swift","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Autodesk (Canada)","funders":"","keywords":"Swift; Computer science; Data scrubbing; Latency (audio); Real-time computing; Overlay; Multimedia; Telecommunications; Operating system","score_opus":0.010872546366494009,"score_gpt":0.21985940088614292,"score_spread":0.2089868545196489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053329155","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21637963,0.0018407485,0.5403365,0.0008449325,0.0014404429,0.0020473886,0.011398986,0.1606768,0.0650345],"genre_scores_gemma":[0.4913363,0.001178244,0.40040448,0.00084164564,0.00039017692,0.0012133475,0.02695923,0.010510409,0.0671661],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900085,0.00007743709,0.0000754776,0.0002011218,0.0005194962,0.00012558461],"domain_scores_gemma":[0.9968322,0.00062439404,0.00022148421,0.0010660785,0.0010834846,0.0001722851],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00082532596,0.0009991123,0.00061475456,0.0012094767,0.000562094,0.0011505,0.0017461726,0.00065419014,0.016906409],"category_scores_gemma":[0.0050724703,0.0003696467,0.00038312923,0.0011012343,0.00026986198,0.0024506338,0.0010985673,0.00074708625,0.0094730025],"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":[0.0018107588,0.00080745656,0.0043921256,0.0008801819,0.00018839643,0.00054215675,0.00046154336,0.0094703445,0.089085154,0.0065973434,0.13001566,0.7557489],"study_design_scores_gemma":[0.0005387552,0.0027567237,0.013846742,0.00015992235,0.00020841145,0.002750138,0.0006654218,0.2736946,0.28634956,0.009913928,0.4088221,0.00029373323],"about_ca_topic_score_codex":0.002925779,"about_ca_topic_score_gemma":0.0040159747,"teacher_disagreement_score":0.9830936,"about_ca_system_score_codex":0.00035481364,"about_ca_system_score_gemma":0.00084127404,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2054987869","doi":"10.1145/1186415.1186505","title":"Managing parameter spaces for multimedia composition","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Regina","funders":"","keywords":"Composition (language); Computer science; Multimedia; Art","score_opus":0.014241189500658417,"score_gpt":0.24601386593982927,"score_spread":0.23177267643917085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054987869","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014954298,0.0006410795,0.9671829,0.00020456676,0.00005932817,0.00012276329,0.00013230805,0.0077795326,0.008923209],"genre_scores_gemma":[0.36926296,0.000981445,0.610283,0.00011121091,0.0001619074,0.00033578242,0.0009913945,0.002683298,0.015188935],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99815696,0.0005175271,0.00016738026,0.00029532076,0.00065189064,0.00021081121],"domain_scores_gemma":[0.9954277,0.0018939899,0.00027334332,0.0016259061,0.0004857662,0.00029309603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018508354,0.0010812532,0.0009944229,0.0017106143,0.0017343271,0.003773312,0.0020636742,0.0011359205,0.016530015],"category_scores_gemma":[0.008342032,0.00067297404,0.0007205465,0.0018694327,0.0014258111,0.008471321,0.005884663,0.0014089737,0.004846716],"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":[0.00094350654,0.00021770237,0.0011858582,0.00055012107,0.00008549561,0.00070100144,0.001666799,0.039463572,0.055123374,0.16749497,0.012328298,0.7202393],"study_design_scores_gemma":[0.00014677345,0.00023673523,0.00052909466,0.00021646493,0.00017428349,0.0006926898,0.0010318429,0.33111876,0.10194167,0.42865238,0.13512474,0.00013450833],"about_ca_topic_score_codex":0.0010026705,"about_ca_topic_score_gemma":0.0011047802,"teacher_disagreement_score":0.016530015,"about_ca_system_score_codex":0.0007726475,"about_ca_system_score_gemma":0.00066023227,"threshold_uncertainty_score":0.055298388},"labels":[],"label_agreement":null},{"id":"W2056573693","doi":"10.1007/s10209-008-0141-0","title":"Towards computer-vision software tools to increase production and accessibility of video description for people with vision loss","year":2009,"lang":"en","type":"article","venue":"Universal Access in the Information Society","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":31,"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 Laval; Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Key (lock); Multimedia; Identification (biology); Human–computer interaction; Software; Artificial intelligence; Computer vision; Computer security","score_opus":0.014912735843091633,"score_gpt":0.2717112958282652,"score_spread":0.2567985599851736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056573693","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031902913,0.0006092113,0.9417496,0.0010097413,0.00010849118,0.00043729765,0.0003127982,0.018791024,0.0050790003],"genre_scores_gemma":[0.13560802,0.0005033448,0.8566771,0.00030104973,0.00009461412,0.00038988388,0.0007095024,0.0009780047,0.004738522],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99858487,0.00054673693,0.00013039757,0.0001839604,0.00048455462,0.00006963603],"domain_scores_gemma":[0.9859411,0.007436884,0.000633002,0.0010148088,0.0045504807,0.00042362895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034821315,0.0008718341,0.00055701775,0.00258018,0.00036501852,0.00200078,0.0018240772,0.0014741754,0.006648629],"category_scores_gemma":[0.016122416,0.00043169488,0.0005030843,0.0013360147,0.00052611186,0.0030534253,0.0013566867,0.0009893186,0.0023698402],"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":[0.000692278,0.00065494946,0.0034296727,0.0005950203,0.000066980014,0.000317336,0.0013113695,0.0036357206,0.077570215,0.0062854253,0.017446253,0.88799477],"study_design_scores_gemma":[0.00072961755,0.0021527465,0.017095182,0.0007988814,0.0006524634,0.0022845017,0.0029342256,0.41846666,0.3904859,0.03176526,0.13231054,0.0003239627],"about_ca_topic_score_codex":0.0021204967,"about_ca_topic_score_gemma":0.0018673962,"teacher_disagreement_score":0.006648629,"about_ca_system_score_codex":0.0005080308,"about_ca_system_score_gemma":0.00092990836,"threshold_uncertainty_score":0.02224189},"labels":[],"label_agreement":null},{"id":"W2058234427","doi":"10.1145/2661714.2661729","title":"Automatic Video Intro and Outro Detection on Internet Television","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Histogram; Bandwidth (computing); Reliability (semiconductor); Multimedia; Key (lock); The Internet; Real-time computing; Computer network; Artificial intelligence; World Wide Web; Power (physics); Image (mathematics); Computer security","score_opus":0.005523855563204349,"score_gpt":0.20481192881447205,"score_spread":0.1992880732512677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058234427","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7266602,0.0010798571,0.26319262,0.000115645955,0.00007035378,0.0001423524,0.00043041486,0.0041226894,0.004185985],"genre_scores_gemma":[0.89898556,0.00036933136,0.09683092,0.00006627944,0.00006483064,0.00004780859,0.0011242565,0.00015062439,0.0023603472],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962854,0.000060674716,0.00001771189,0.000100830184,0.0001240113,0.00006812278],"domain_scores_gemma":[0.9991628,0.0003147642,0.0001590161,0.00007477005,0.00019996129,0.00008865743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033893436,0.0006035359,0.0006353796,0.0024538704,0.00041686848,0.00061336457,0.0005408133,0.0005055732,0.00047708128],"category_scores_gemma":[0.0013287953,0.00021015143,0.00033268545,0.0007901841,0.00028005635,0.0004058194,0.0005677511,0.00042719854,0.00038637803],"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":[0.0010887319,0.00029736955,0.020734694,0.00033243163,0.00010289633,0.0010984562,0.0005514006,0.021232136,0.28711787,0.0010183164,0.004400589,0.66202515],"study_design_scores_gemma":[0.000035314053,0.0006583138,0.10150201,0.000052775486,0.0001283425,0.0017035917,0.0006670756,0.7101963,0.17867646,0.0011198476,0.005191595,0.000068364585],"about_ca_topic_score_codex":0.0026720401,"about_ca_topic_score_gemma":0.004432959,"teacher_disagreement_score":0.0026720401,"about_ca_system_score_codex":0.00024875868,"about_ca_system_score_gemma":0.00019770066,"threshold_uncertainty_score":0.0053129196},"labels":[],"label_agreement":null},{"id":"W2059724456","doi":"10.1145/500141.500217","title":"Classification of summarized videos using hidden markov models on compressed chromaticity signatures","year":2001,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Automatic summarization; Hidden Markov model; Artificial intelligence; Frame (networking); Chromaticity; Cluster analysis; Pattern recognition (psychology); Feature (linguistics); Feature extraction; Computer vision","score_opus":0.04872186445408002,"score_gpt":0.2724287717261104,"score_spread":0.22370690727203038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059724456","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22473995,0.00050017395,0.7711764,0.0004118808,0.00006877913,0.00016665002,0.0005690005,0.0012173305,0.0011497743],"genre_scores_gemma":[0.8313385,0.0004485271,0.16281089,0.00010273356,0.00014236724,0.0001317144,0.0021933126,0.00006411526,0.0027677817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996741,0.000065191365,0.000028945991,0.00008977202,0.000081092665,0.000060948667],"domain_scores_gemma":[0.9987907,0.0005062937,0.00019690483,0.000110108,0.00033260393,0.000063465675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068103574,0.0005671714,0.0005817658,0.0017691517,0.00032241314,0.00076781755,0.000638844,0.00054083986,0.0007403691],"category_scores_gemma":[0.0025321709,0.00019818872,0.00060304377,0.0008274063,0.00033458116,0.0010145019,0.00030934537,0.0006857896,0.00034195767],"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":[0.0008716413,0.00039894893,0.013547815,0.00018735738,0.00016807087,0.0002667141,0.0004833326,0.32456043,0.03843385,0.009082625,0.0045120134,0.60748714],"study_design_scores_gemma":[0.000007589596,0.0000440977,0.0017666471,0.0000076257124,0.000018348203,0.000019886458,0.000041497722,0.99266887,0.002931417,0.0022127666,0.0002707646,0.00001051974],"about_ca_topic_score_codex":0.0084969895,"about_ca_topic_score_gemma":0.009390847,"teacher_disagreement_score":0.0084969895,"about_ca_system_score_codex":0.0008775737,"about_ca_system_score_gemma":0.00049196754,"threshold_uncertainty_score":0.016895056},"labels":[],"label_agreement":null},{"id":"W2060287945","doi":"10.1145/985921.986178","title":"From cookies to puppies to athletes","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Dalhousie University","funders":"","keywords":"Voting; Ranking (information retrieval); Computer science; Context (archaeology); Focus (optics); Multimedia; Process (computing); Target audience; Athletes; Audience response; Advertising; Human–computer interaction; Information retrieval; Political science; Business","score_opus":0.009855550555465836,"score_gpt":0.22667009769005542,"score_spread":0.21681454713458959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060287945","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09712593,0.0016377665,0.6822662,0.0018523315,0.0005674941,0.0037398322,0.0023877406,0.053891405,0.1565313],"genre_scores_gemma":[0.25350034,0.0020623268,0.4797016,0.0016250783,0.00016199201,0.002167641,0.002791511,0.007526205,0.25046325],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995914,0.00009363052,0.000026692001,0.00012393553,0.000103479055,0.000060897266],"domain_scores_gemma":[0.99904364,0.000371315,0.000039778104,0.00015336565,0.00022591004,0.00016598063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082043465,0.00088986923,0.0003702385,0.000773183,0.00087662,0.0019230713,0.0013558283,0.0011297081,0.03142719],"category_scores_gemma":[0.0021499575,0.0006280338,0.00049699505,0.00041909004,0.00066219113,0.0027068688,0.0013706823,0.0009873948,0.009471176],"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":[0.0010932157,0.00080443546,0.0056712497,0.0035127755,0.00013256217,0.002863549,0.008508067,0.0047462583,0.16728,0.03574653,0.07341932,0.6962219],"study_design_scores_gemma":[0.00008281962,0.00092312926,0.006486604,0.00042935004,0.00009964073,0.0021304695,0.0013341646,0.010062642,0.049390372,0.0071434723,0.92179,0.00012742761],"about_ca_topic_score_codex":0.0012361988,"about_ca_topic_score_gemma":0.0035200415,"teacher_disagreement_score":0.03142719,"about_ca_system_score_codex":0.00030821757,"about_ca_system_score_gemma":0.00041972852,"threshold_uncertainty_score":0.10513437},"labels":[],"label_agreement":null},{"id":"W2064090491","doi":"10.1109/wifs.2011.6123134","title":"Fast matching for video/audio fingerprinting algorithms","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Matching (statistics); Scheme (mathematics); Blossom algorithm; Window (computing); Pattern matching; Algorithm; Pattern recognition (psychology); Artificial intelligence","score_opus":0.032905253166096396,"score_gpt":0.2390505247462004,"score_spread":0.20614527158010398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064090491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007046312,0.00068713154,0.99015725,0.00006837966,0.00005375269,0.000051115094,0.00003742004,0.00096109306,0.00093761156],"genre_scores_gemma":[0.14499725,0.0006599303,0.8499245,0.0000976715,0.00009966422,0.00016835037,0.00024108132,0.00014830989,0.0036631862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987116,0.0002501358,0.00009904462,0.00027235027,0.00053460815,0.00013236229],"domain_scores_gemma":[0.9979722,0.00090306625,0.00019114203,0.0004068459,0.0004649584,0.00006177443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016889031,0.0007161901,0.00081775786,0.0016340114,0.00060619763,0.0012652103,0.0013766892,0.0011218014,0.0043942467],"category_scores_gemma":[0.004989816,0.00045597114,0.0005986194,0.0015984259,0.0005167567,0.0023037854,0.0010387097,0.0013506224,0.0018602401],"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":[0.00050058996,0.00011478091,0.0008823387,0.00019332547,0.00006136671,0.00009202399,0.000081109654,0.03528997,0.05911338,0.033880934,0.003852398,0.8659378],"study_design_scores_gemma":[0.000085559834,0.0003208474,0.0016112095,0.00003892302,0.000047769365,0.0004755054,0.00004944221,0.861377,0.092370905,0.028764585,0.0147859175,0.00007230135],"about_ca_topic_score_codex":0.0012868991,"about_ca_topic_score_gemma":0.0011737993,"teacher_disagreement_score":0.0043942467,"about_ca_system_score_codex":0.0008042407,"about_ca_system_score_gemma":0.0008536112,"threshold_uncertainty_score":0.014700174},"labels":[],"label_agreement":null},{"id":"W2064458142","doi":"10.1007/s00138-013-0525-x","title":"Multimedia event detection with multimodal feature fusion and temporal concept localization","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":50,"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":"Interior Business Center; Georgia Institute of Technology; Seoul National University; U.S. Department of the Interior","keywords":"Computer science; Discriminative model; Event (particle physics); Artificial intelligence; Feature (linguistics); Feature learning; Machine learning","score_opus":0.0032254395443092284,"score_gpt":0.22903081286930013,"score_spread":0.2258053733249909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064458142","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024056468,0.00079209724,0.9716635,0.0001429993,0.00009787351,0.00009288634,0.0003057426,0.001277801,0.0015706621],"genre_scores_gemma":[0.36216706,0.0009831964,0.63144326,0.0001322631,0.00025871815,0.00021483106,0.001238291,0.00012561951,0.003436851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952364,0.000060786955,0.000030211057,0.00015355556,0.00015216006,0.000079556674],"domain_scores_gemma":[0.9995517,0.00011743543,0.00006727383,0.00006761459,0.00016257871,0.000033336702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008693545,0.0010153851,0.0008421439,0.002793274,0.000414997,0.001053533,0.0007530517,0.0008842208,0.0024309335],"category_scores_gemma":[0.0019447856,0.00027149994,0.0009316915,0.002125228,0.00031702121,0.0015340325,0.0012633535,0.0007212819,0.0013013664],"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":[0.0005887281,0.00023775177,0.002227703,0.00018705147,0.00012127479,0.00023261689,0.000095902775,0.008637447,0.13989268,0.0035597354,0.0033041432,0.84091496],"study_design_scores_gemma":[0.000053743268,0.00056054135,0.012908076,0.000058459842,0.000311279,0.0012630422,0.00021684353,0.81189644,0.1474144,0.015684113,0.009530166,0.00010285249],"about_ca_topic_score_codex":0.001279369,"about_ca_topic_score_gemma":0.0016970793,"teacher_disagreement_score":0.002793274,"about_ca_system_score_codex":0.00037558287,"about_ca_system_score_gemma":0.0005076874,"threshold_uncertainty_score":0.0081323385},"labels":[],"label_agreement":null},{"id":"W2064652814","doi":"10.1145/1101149.1101265","title":"Automatic identification of digital video based on shot-level sequence matching","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Similarity measure; Dynamic programming; Normalization (sociology); Histogram; Pattern recognition (psychology); Algorithm","score_opus":0.03960774583001928,"score_gpt":0.2732007551381429,"score_spread":0.23359300930812363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064652814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040583268,0.00026044442,0.9564146,0.000045798584,0.00003868127,0.00009701619,0.00010363341,0.0010609344,0.0013956509],"genre_scores_gemma":[0.20594072,0.00029382226,0.7917629,0.00004324564,0.000038601273,0.00009031665,0.00040685406,0.00011257683,0.0013108883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947876,0.00007589244,0.000032937434,0.00014186527,0.00023561974,0.000034994267],"domain_scores_gemma":[0.99915934,0.00023435286,0.00014580612,0.0001007941,0.00031436415,0.00004544585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040062692,0.00035459825,0.000689955,0.0027558263,0.00032057596,0.0007257274,0.0007901287,0.0005276476,0.0012938005],"category_scores_gemma":[0.002215619,0.00019635088,0.00028129693,0.0013901447,0.0003913864,0.0017335346,0.0005780052,0.0005051481,0.0007114321],"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":[0.0002564086,0.000101590194,0.0015869838,0.00018143585,0.000048704293,0.00011951042,0.00009503662,0.0069879615,0.16521566,0.0054424508,0.001420503,0.8185439],"study_design_scores_gemma":[0.000043200373,0.0004889027,0.010397181,0.000058507547,0.000080191654,0.0016491169,0.00027188976,0.65928525,0.3061106,0.011986493,0.009529704,0.0000990726],"about_ca_topic_score_codex":0.0007404449,"about_ca_topic_score_gemma":0.00084929785,"teacher_disagreement_score":0.0027558263,"about_ca_system_score_codex":0.00035328648,"about_ca_system_score_gemma":0.0003440567,"threshold_uncertainty_score":0.0043281913},"labels":[],"label_agreement":null},{"id":"W2065928200","doi":"10.1109/icce.2010.5418777","title":"Robust video hashing based on temporally informative representative images","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of British Columbia","funders":"","keywords":"Computer science; Hash function; Artificial intelligence; Computer vision; Frame (networking); Video tracking; Range (aeronautics); Noise (video); Video processing; Pattern recognition (psychology); Image (mathematics); Computer security; Computer network","score_opus":0.018747480080678756,"score_gpt":0.2497253435971386,"score_spread":0.23097786351645985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2065928200","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034386568,0.0006455722,0.9625814,0.00008933149,0.000085692154,0.00006752567,0.000077432516,0.00081066083,0.0012558071],"genre_scores_gemma":[0.4173203,0.0006641628,0.57822937,0.00010347725,0.00021581363,0.000091813905,0.00039429535,0.0001065376,0.0028742899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993513,0.00012866699,0.000033301934,0.00013730442,0.00029879104,0.000050508388],"domain_scores_gemma":[0.99896777,0.00023963254,0.00020401261,0.0002986715,0.00024095755,0.00004901133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061006006,0.00044035644,0.00070890866,0.0009003897,0.00035104365,0.00056329375,0.0008362094,0.0005122796,0.0011257869],"category_scores_gemma":[0.002826943,0.00026890656,0.00039725078,0.0007980163,0.00048458364,0.001507249,0.0007670453,0.00057243067,0.0007489053],"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":[0.00080727605,0.00009179136,0.0013737391,0.00025079612,0.0000942841,0.0002660652,0.00020241637,0.04237498,0.24866307,0.0176771,0.003374784,0.6848237],"study_design_scores_gemma":[0.00010442348,0.0009377594,0.0036811775,0.000041759733,0.00009830535,0.0022852744,0.0001783038,0.7186552,0.2500256,0.009945167,0.013916414,0.00013058963],"about_ca_topic_score_codex":0.00040979966,"about_ca_topic_score_gemma":0.0003807458,"teacher_disagreement_score":0.0011257869,"about_ca_system_score_codex":0.0003052069,"about_ca_system_score_gemma":0.00032116545,"threshold_uncertainty_score":0.0037661195},"labels":[],"label_agreement":null},{"id":"W2066272345","doi":"10.1109/mmsp.2010.5662069","title":"An efficient framework on large-scale video genre classification","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Codebook; Artificial intelligence; Feature extraction; Histogram; Pattern recognition (psychology); Search engine indexing; Scale-invariant feature transform; Bag-of-words model; Latent Dirichlet allocation; Classifier (UML); Scalability; Categorization; Cluster analysis; Data mining; Topic model; Image (mathematics); Database","score_opus":0.010631744367505589,"score_gpt":0.2651127059455956,"score_spread":0.25448096157809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066272345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002797915,0.00026446642,0.9934296,0.00008215485,0.000043932872,0.0001338109,0.00029068431,0.0023919353,0.00056539517],"genre_scores_gemma":[0.06659712,0.00039865338,0.92710704,0.00007879307,0.0001670316,0.0004152271,0.0027875316,0.00016306245,0.0022856176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987822,0.00016830233,0.00011029077,0.0002986582,0.00050194503,0.000138591],"domain_scores_gemma":[0.9988839,0.0001906491,0.000110890745,0.0002558713,0.00045603988,0.00010261137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013153515,0.0011947642,0.001913815,0.004625567,0.0009594955,0.0016904315,0.0024620243,0.0011445818,0.0026469547],"category_scores_gemma":[0.0036491095,0.0004519407,0.0015622303,0.0044721337,0.0005342708,0.0025710748,0.0017552627,0.0014730507,0.0028226506],"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":[0.0001670596,0.0002748768,0.0017061135,0.00019578537,0.00007893609,0.0002001288,0.00012454708,0.031151287,0.029633228,0.017913196,0.018557062,0.8999977],"study_design_scores_gemma":[0.000036630383,0.00010908678,0.0013049599,0.00002098843,0.00004056509,0.00027754882,0.00010817769,0.9602019,0.007881674,0.019489964,0.010489558,0.000038883518],"about_ca_topic_score_codex":0.008855655,"about_ca_topic_score_gemma":0.010051823,"teacher_disagreement_score":0.008855655,"about_ca_system_score_codex":0.0011450154,"about_ca_system_score_gemma":0.0017864166,"threshold_uncertainty_score":0.017608166},"labels":[],"label_agreement":null},{"id":"W2071249697","doi":"10.1145/358916.361973","title":"CSCW 2000 video program","year":2000,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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-supported cooperative work; Computer science; Multimedia; Computer graphics (images); Human–computer interaction; Engineering","score_opus":0.006987564808481028,"score_gpt":0.23221372611655353,"score_spread":0.2252261613080725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071249697","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015744559,0.0008292646,0.012458683,0.002175184,0.0021072465,0.0008914631,0.03278963,0.021514293,0.9256597],"genre_scores_gemma":[0.0026109011,0.0007522824,0.0026912144,0.00041772978,0.00030765793,0.0003786972,0.026301155,0.0016388117,0.96490157],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99981636,0.000022221218,0.00000863781,0.000032120424,0.00009088588,0.000029765264],"domain_scores_gemma":[0.99883205,0.00016467828,0.00003631064,0.00014515436,0.00049564784,0.00032613546],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047354927,0.0017414769,0.00085882004,0.002358866,0.0010794536,0.0032941427,0.0013035876,0.0013702648,0.80117553],"category_scores_gemma":[0.0020532333,0.000520617,0.0003251158,0.0031282324,0.00026008004,0.0020823604,0.0012653569,0.001459508,0.72032255],"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":[0.00004392451,0.00003957749,0.00002563641,0.000072791794,0.0000018252287,0.000019415094,0.000016251586,0.00004712654,0.00044434753,0.0007702216,0.95132726,0.04719164],"study_design_scores_gemma":[0.00007575794,0.000045229233,0.0007608285,0.00013176205,0.0000058401592,0.00004259271,0.000070204085,0.0007709889,0.00065822084,0.0013639564,0.9960598,0.000014850001],"about_ca_topic_score_codex":0.012750325,"about_ca_topic_score_gemma":0.021171533,"teacher_disagreement_score":0.80117553,"about_ca_system_score_codex":0.0008298806,"about_ca_system_score_gemma":0.0010201917,"threshold_uncertainty_score":0.28359896},"labels":[],"label_agreement":null},{"id":"W2072416027","doi":"10.1145/636593.636617","title":"MVIP-II","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Communications Research Centre Canada","funders":"","keywords":"Virtual world; Computer science; Metaverse; Multicast; Human–computer interaction; Protocol (science); Multimedia; World Wide Web; Virtual reality; Distributed computing","score_opus":0.008358774004886738,"score_gpt":0.20296328174313022,"score_spread":0.19460450773824348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072416027","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021393204,0.002404012,0.6021135,0.0020149087,0.0045274654,0.002902661,0.009593798,0.056201126,0.2988494],"genre_scores_gemma":[0.292936,0.0030980762,0.2881544,0.0034638303,0.001748025,0.0037309714,0.046192057,0.013300329,0.34737635],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983437,0.00025776742,0.00015598345,0.00019404605,0.00072640216,0.00032201302],"domain_scores_gemma":[0.99802125,0.00020433973,0.00006111087,0.00078189466,0.0007420933,0.00018932503],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014754443,0.0006994235,0.00074942753,0.00084752095,0.0010554292,0.0028711066,0.0023901078,0.001212022,0.03607846],"category_scores_gemma":[0.00440464,0.00049547997,0.00047091057,0.00066634343,0.00041277037,0.002482846,0.003166423,0.0023501185,0.022361038],"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":[0.0022583613,0.00035181886,0.0014478598,0.00095568737,0.0001260749,0.0003545728,0.00045254728,0.006743253,0.040739298,0.15450406,0.29847145,0.49359503],"study_design_scores_gemma":[0.00018408768,0.0004265046,0.0007381197,0.00022742289,0.000064211294,0.00064388604,0.00013981765,0.035306625,0.04292026,0.025214065,0.89402455,0.00011048532],"about_ca_topic_score_codex":0.0013791327,"about_ca_topic_score_gemma":0.00095728715,"teacher_disagreement_score":0.96392155,"about_ca_system_score_codex":0.0008252957,"about_ca_system_score_gemma":0.0012786491,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2076143524","doi":"10.1117/12.476176","title":"&lt;title&gt;CaML: Camera Markup Language for Network Interaction&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Computer science; Markup language; XML; Video processing; Uncompressed video; Video camera; Video capture; Video tracking; Multimedia; Computer network; Computer hardware; Artificial intelligence; Operating system","score_opus":0.008519977010309094,"score_gpt":0.2257988219642867,"score_spread":0.2172788449539776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076143524","genre_codex":"other","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024543486,0.0029419996,0.39566913,0.0065283254,0.010122624,0.0021492245,0.019712243,0.07566533,0.4847567],"genre_scores_gemma":[0.020849096,0.0036460399,0.12720536,0.0044409744,0.0030702294,0.0015386491,0.03919296,0.040943213,0.75911343],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99894327,0.00020772083,0.00013929648,0.0001873047,0.0004251255,0.0000972883],"domain_scores_gemma":[0.9973411,0.0009830006,0.00017236285,0.0004890533,0.00083066855,0.00018380156],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014411055,0.0014256381,0.0010811008,0.0013436102,0.0007231974,0.00375748,0.0021917683,0.0021653716,0.31962404],"category_scores_gemma":[0.0037352305,0.00066390453,0.0006440418,0.0016617696,0.0011025828,0.0044168034,0.0011693671,0.0021304828,0.38433817],"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":[0.00017696181,0.00007519034,0.00021494465,0.0005030576,0.000011670683,0.00030750784,0.00014634096,0.00041650826,0.015240666,0.026966343,0.74146444,0.21447638],"study_design_scores_gemma":[0.00003690038,0.00005113504,0.00030752676,0.00009560592,0.00000483109,0.00023525977,0.000023214654,0.0015839629,0.00490891,0.0031715576,0.9895494,0.000031660118],"about_ca_topic_score_codex":0.0027577744,"about_ca_topic_score_gemma":0.002649917,"teacher_disagreement_score":0.31962404,"about_ca_system_score_codex":0.0013440697,"about_ca_system_score_gemma":0.0008731634,"threshold_uncertainty_score":0.97047377},"labels":[],"label_agreement":null},{"id":"W2076424778","doi":"10.1145/2168752.2168760","title":"A Generic Approach for Systematic Analysis of Sports Videos","year":2012,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Computer science; Probabilistic latent semantic analysis; Conditional random field; Artificial intelligence; Topic model; Bag-of-words model; Support vector machine; Pattern recognition (psychology); Categorization; Representation (politics); Field (mathematics); Probabilistic logic; Classifier (UML); Video content analysis; Histogram; Machine learning; Object (grammar); Video tracking; Image (mathematics)","score_opus":0.023500772465597664,"score_gpt":0.25227753013392207,"score_spread":0.2287767576683244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076424778","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002099005,0.00045015532,0.99190056,0.00009212223,0.000040274022,0.0003800865,0.00065979693,0.0024063918,0.0019715903],"genre_scores_gemma":[0.038292058,0.0007226525,0.9538846,0.00012588633,0.00007554473,0.0006556036,0.0028258741,0.00029753888,0.003120317],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9965263,0.0005785928,0.00030863125,0.0013212981,0.001105947,0.00015917397],"domain_scores_gemma":[0.99821967,0.00024828853,0.00021514544,0.0005627258,0.00066524174,0.000088959256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002163215,0.0014862424,0.000880462,0.0068267705,0.00073020963,0.0026259492,0.0017108978,0.0012890329,0.004048966],"category_scores_gemma":[0.0043959557,0.00059185276,0.0020631577,0.0037529508,0.0010621278,0.0027345042,0.0028337718,0.0011615233,0.0031831795],"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":[0.00015830496,0.00020740047,0.004133647,0.0012161259,0.00024817,0.00047347837,0.0008973566,0.007259863,0.07272127,0.047005482,0.014612067,0.8510669],"study_design_scores_gemma":[0.00009448533,0.00066562695,0.024035715,0.0009363169,0.00041534967,0.004248395,0.002001725,0.4216637,0.10488332,0.12788185,0.3128096,0.0003639213],"about_ca_topic_score_codex":0.0023576084,"about_ca_topic_score_gemma":0.002781269,"teacher_disagreement_score":0.0068267705,"about_ca_system_score_codex":0.0008921484,"about_ca_system_score_gemma":0.0017751415,"threshold_uncertainty_score":0.0135451555},"labels":[],"label_agreement":null},{"id":"W2079949061","doi":"10.1109/cjece.2009.5443855","title":"Player tracking and identification of game systems in basketball using three cameras","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Video Analysis and Summarization","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":"Computer science; Identification (biology); Basketball; Artificial intelligence; Computer vision; Polygon (computer graphics); Bounding overwatch; Process (computing); Heuristic; Minimum bounding box; Tracking (education); Frame (networking); Image (mathematics); Geography","score_opus":0.009480973621556097,"score_gpt":0.18866485932309582,"score_spread":0.17918388570153973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079949061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24039346,0.0006983737,0.75318784,0.000055701217,0.000042812,0.00022866373,0.00031324395,0.0006782828,0.0044015353],"genre_scores_gemma":[0.56803584,0.000778549,0.42655393,0.00004749666,0.000038791015,0.00013800879,0.0005238765,0.000097029115,0.0037863452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951816,0.00006017856,0.000021371206,0.000163719,0.00016633909,0.00007031957],"domain_scores_gemma":[0.9996785,0.00008100299,0.000055612465,0.000040608953,0.00010466712,0.000039681792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036708175,0.0005281279,0.0006425161,0.001886356,0.00039290736,0.0011297151,0.0005466291,0.00048013017,0.0013429819],"category_scores_gemma":[0.0008008918,0.00034333175,0.00044729817,0.0010694223,0.00029820143,0.00075943384,0.0007772453,0.00036357413,0.0004537083],"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":[0.00089456054,0.00019132419,0.027887985,0.00036672186,0.00020612973,0.00048428803,0.00089886115,0.018579463,0.277207,0.00277853,0.0010834191,0.6694218],"study_design_scores_gemma":[0.00010134942,0.00093399786,0.13883276,0.0001316524,0.0004671513,0.0017138873,0.0016591088,0.5377359,0.30123514,0.004271125,0.012718746,0.00019920329],"about_ca_topic_score_codex":0.005036582,"about_ca_topic_score_gemma":0.007840807,"teacher_disagreement_score":0.005036582,"about_ca_system_score_codex":0.00039154803,"about_ca_system_score_gemma":0.00039758437,"threshold_uncertainty_score":0.010014534},"labels":[],"label_agreement":null},{"id":"W208016905","doi":"","title":"Dynamic context extraction in personal communication applications","year":2013,"lang":"en","type":"article","venue":"Conference of the Centre for Advanced Studies on Collaborative Research","topic":"Video Analysis and Summarization","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 Victoria","funders":"","keywords":"Computer science; Context (archaeology); Conversation; Key (lock); Human–computer interaction; Multimedia; Logos Bible Software; World Wide Web; Computer security","score_opus":0.07534952387713918,"score_gpt":0.40459957325982127,"score_spread":0.3292500493826821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W208016905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10438418,0.008766899,0.8669144,0.0007545998,0.00020755791,0.00044231283,0.001747373,0.008173308,0.00860941],"genre_scores_gemma":[0.6410687,0.0028651392,0.34910935,0.00021650863,0.00019716177,0.0003290421,0.002249192,0.00035401667,0.0036109562],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879104,0.00030275885,0.00011223379,0.0003363155,0.00035335115,0.000104297615],"domain_scores_gemma":[0.9983736,0.00083612837,0.00017659375,0.00023504271,0.0002873332,0.00009127735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089593575,0.0008424067,0.00067223044,0.0029661593,0.000760226,0.0016409858,0.00082507235,0.0009447307,0.0013349386],"category_scores_gemma":[0.0047774008,0.0005212496,0.00050511194,0.0022506188,0.000336479,0.0022507205,0.0012824812,0.0008511519,0.0009966342],"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":[0.0004211295,0.00016867049,0.010038204,0.0008825523,0.000109967514,0.0011484993,0.0015610222,0.013251674,0.04581158,0.005732538,0.008976401,0.9118977],"study_design_scores_gemma":[0.000071366354,0.000644191,0.0431,0.0008121074,0.0003154079,0.0035678556,0.0043784645,0.61944664,0.13517636,0.054835048,0.13736638,0.0002862006],"about_ca_topic_score_codex":0.0020487185,"about_ca_topic_score_gemma":0.0026899471,"teacher_disagreement_score":0.0029661593,"about_ca_system_score_codex":0.0004034098,"about_ca_system_score_gemma":0.00049405714,"threshold_uncertainty_score":0.0047382116},"labels":[],"label_agreement":null},{"id":"W2085017358","doi":"10.1109/globalsip.2014.7032281","title":"Arousal content representation of sports videos using dynamic prediction hidden Markov models","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Arousal; Hidden Markov model; Computer science; Artificial intelligence; Residual; Maximization; Pattern recognition (psychology); Statistics; Algorithm; Mathematics; Mathematical optimization; Psychology","score_opus":0.033699574665580795,"score_gpt":0.2534586245311972,"score_spread":0.21975904986561642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085017358","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.124151826,0.00086342636,0.87081736,0.0002318373,0.00007135532,0.00011115701,0.00077879045,0.0009897703,0.0019844975],"genre_scores_gemma":[0.9009333,0.0005713454,0.094901584,0.00007003612,0.00007792414,0.00012381063,0.0014191163,0.00007227763,0.001830533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986076,0.000035873698,0.000008992911,0.00004322599,0.000031360218,0.000019818179],"domain_scores_gemma":[0.9996911,0.00016465511,0.000046297744,0.000019214815,0.00006138329,0.000017394352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003376717,0.00050728756,0.0003772056,0.00091921777,0.0001421922,0.00045490943,0.00044678766,0.00025887648,0.0008351014],"category_scores_gemma":[0.0014410547,0.00021180011,0.0004666421,0.0004837121,0.00012443817,0.0005549676,0.00025077784,0.00059844885,0.00029566887],"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":[0.0007743305,0.00032154465,0.016091978,0.0002596724,0.00020924727,0.00029498155,0.00042363218,0.33207354,0.030842796,0.007267107,0.0047099586,0.6067312],"study_design_scores_gemma":[0.000006123917,0.000041929678,0.0039820164,0.000013864559,0.00002282555,0.000033268705,0.000025630166,0.99057543,0.0021517435,0.0026592892,0.00047620488,0.000011650003],"about_ca_topic_score_codex":0.0047529256,"about_ca_topic_score_gemma":0.0054578045,"teacher_disagreement_score":0.0047529256,"about_ca_system_score_codex":0.0003978748,"about_ca_system_score_gemma":0.0002696966,"threshold_uncertainty_score":0.009450555},"labels":[],"label_agreement":null},{"id":"W2085099937","doi":"10.1109/cvpr.2011.5995562","title":"Identifying players in broadcast sports videos using conditional random fields","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":59,"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":"Conditional random field; Computer science; Artificial intelligence; Computer vision; Identification (biology); Tracking (education); Probabilistic logic; Tracking system; Kalman filter","score_opus":0.05726826947129639,"score_gpt":0.26175516674207183,"score_spread":0.20448689727077546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085099937","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10768794,0.0005777729,0.8834718,0.00030857165,0.00007419015,0.0001429994,0.0007998466,0.004463082,0.0024737224],"genre_scores_gemma":[0.7195261,0.0006003869,0.27100435,0.00020966526,0.0001734199,0.00009974426,0.003610261,0.00016107512,0.004614959],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992944,0.00014197566,0.000025821166,0.0003049543,0.00013018632,0.00010265158],"domain_scores_gemma":[0.9986524,0.00081890286,0.00019743052,0.00013009363,0.00013949079,0.00006178477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013351795,0.0007630897,0.0009641952,0.002217609,0.0004775071,0.00077207305,0.0014602183,0.0010184986,0.0016539547],"category_scores_gemma":[0.002579299,0.00043691794,0.0009082676,0.0011090292,0.0005454744,0.0013615037,0.0004863569,0.0011528763,0.00096186745],"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":[0.0011688496,0.0006280789,0.014885659,0.00025455555,0.0002499201,0.0006107661,0.00035055607,0.17659152,0.048354,0.0062288875,0.008962364,0.74171484],"study_design_scores_gemma":[0.000021401807,0.00009426895,0.007131147,0.00002642483,0.000054261203,0.00017013313,0.000069156806,0.97811216,0.008289476,0.004454951,0.0015377783,0.00003870742],"about_ca_topic_score_codex":0.019270593,"about_ca_topic_score_gemma":0.021255253,"teacher_disagreement_score":0.019270593,"about_ca_system_score_codex":0.0008332017,"about_ca_system_score_gemma":0.00064262305,"threshold_uncertainty_score":0.038316846},"labels":[],"label_agreement":null},{"id":"W2087776597","doi":"10.1145/2487381.2487384","title":"A sketching game for art history instruction","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Carleton University","funders":"Networks of Centres of Excellence of Canada","keywords":"Sketch; Computer science; Painting; Multimedia; Video game; Domain (mathematical analysis); Visual arts; Chose; Human–computer interaction; Art","score_opus":0.01257690645604489,"score_gpt":0.19599163294979774,"score_spread":0.18341472649375284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087776597","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40479633,0.0014733267,0.5337683,0.0008782345,0.00042802628,0.0060945167,0.0017535224,0.013619568,0.037188116],"genre_scores_gemma":[0.5491918,0.000564019,0.42866293,0.00025384364,0.000034631368,0.0024346842,0.0010928132,0.000283383,0.017481973],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999742,0.00007760821,0.000019911222,0.000062302286,0.00006747568,0.00003079458],"domain_scores_gemma":[0.9992224,0.00045152163,0.00003230456,0.00006432136,0.00005842959,0.00017103295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041527342,0.0012890477,0.00050246675,0.0004020814,0.00028938398,0.0008304929,0.0014505017,0.0007553545,0.01153134],"category_scores_gemma":[0.0025117525,0.00030714218,0.0004581493,0.00020718537,0.00030275827,0.0009336215,0.0010550417,0.00072481856,0.0012850349],"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":[0.0054176105,0.0070443046,0.006348372,0.0031298453,0.00027016355,0.0013139882,0.004172747,0.01863981,0.19693297,0.02141295,0.023201374,0.71211594],"study_design_scores_gemma":[0.0049571646,0.015352684,0.047844715,0.0008332567,0.00062321505,0.0047451463,0.0022371784,0.33335984,0.10946628,0.032985277,0.44696724,0.00062799396],"about_ca_topic_score_codex":0.0009847967,"about_ca_topic_score_gemma":0.0012858993,"teacher_disagreement_score":0.01153134,"about_ca_system_score_codex":0.00029078173,"about_ca_system_score_gemma":0.00039919463,"threshold_uncertainty_score":0.038576245},"labels":[],"label_agreement":null},{"id":"W2089298051","doi":"10.1006/jvci.2000.0465","title":"Video Segmentation in the Wavelet Compressed Domain","year":2001,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Analysis and Summarization","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 Alberta","funders":"","keywords":"Wavelet; Computer science; Artificial intelligence; Wavelet transform; Computer vision; Search engine indexing; Data compression; Segmentation; Wavelet packet decomposition; Lifting scheme; Video compression picture types; Motion compensation; Pattern recognition (psychology); Video tracking; Video processing","score_opus":0.023806299682870066,"score_gpt":0.34242041200099177,"score_spread":0.3186141123181217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089298051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043261003,0.00068331865,0.9522851,0.00029397465,0.000106095635,0.000045260476,0.00021276991,0.0005316188,0.002580839],"genre_scores_gemma":[0.4198747,0.0019474082,0.56838256,0.00017593491,0.000411476,0.00008830775,0.0014635031,0.0003369368,0.007319142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972683,0.000058437126,0.000018941784,0.000048487032,0.00011427334,0.000032974243],"domain_scores_gemma":[0.9994399,0.0002362338,0.000054585562,0.0000789171,0.00016085306,0.000029547607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033660926,0.00037265953,0.0005302694,0.0012866858,0.0002325349,0.00093480444,0.00038889179,0.00058564404,0.0023572294],"category_scores_gemma":[0.001749782,0.00022294481,0.00029444773,0.001406116,0.00042824904,0.001190173,0.00044245124,0.0006765008,0.00086194725],"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":[0.0010272012,0.00010642702,0.0005860234,0.0002719887,0.00005602985,0.0002500633,0.00017984505,0.06823305,0.19631946,0.029572522,0.0055424003,0.69785506],"study_design_scores_gemma":[0.000034621447,0.0001143076,0.0012020051,0.000032341904,0.000028543156,0.00024116514,0.00007719746,0.9263308,0.052093077,0.014792244,0.0050338535,0.00001981763],"about_ca_topic_score_codex":0.0017221466,"about_ca_topic_score_gemma":0.0012354696,"teacher_disagreement_score":0.0023572294,"about_ca_system_score_codex":0.00034449683,"about_ca_system_score_gemma":0.0003890451,"threshold_uncertainty_score":0.0078856945},"labels":[],"label_agreement":null},{"id":"W2089344344","doi":"10.1080/10255810305039","title":"An Automatic Video Classification System Based on a Combination of HMM and Video Summarization","year":2003,"lang":"en","type":"article","venue":"International Journal of Smart Engineering System Design","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Automatic summarization; Computer science; Hidden Markov model; Artificial intelligence; Pattern recognition (psychology); Speech recognition","score_opus":0.013207937980244378,"score_gpt":0.22062462413258177,"score_spread":0.20741668615233738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089344344","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03198296,0.00022463656,0.9525129,0.00015439765,0.00010687524,0.00018411291,0.0005056106,0.01283046,0.0014980121],"genre_scores_gemma":[0.32129973,0.00030192776,0.6669676,0.00020468926,0.00017442473,0.00041385117,0.0024829637,0.00026394235,0.007890903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946934,0.000074018826,0.00003985404,0.00021392133,0.00013188527,0.000071103284],"domain_scores_gemma":[0.99884987,0.00030733482,0.00012182954,0.00011185636,0.00053097773,0.00007812326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007919853,0.0006605617,0.0010022211,0.0016493165,0.0004601118,0.00068471994,0.0009616966,0.00081076694,0.0019380852],"category_scores_gemma":[0.0017551199,0.00035647702,0.00052356976,0.0010259256,0.00023110176,0.0010303349,0.00041232223,0.0008237138,0.0017749057],"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":[0.0005511783,0.0002823567,0.003605372,0.00016292177,0.00010753591,0.00013365573,0.00010319795,0.015706252,0.09220061,0.0014166376,0.007973958,0.87775636],"study_design_scores_gemma":[0.00004421051,0.00022387764,0.007008053,0.000026015337,0.00011147471,0.00017560145,0.00007859009,0.935437,0.05103394,0.0017890186,0.004011485,0.000060603357],"about_ca_topic_score_codex":0.0058018775,"about_ca_topic_score_gemma":0.00655936,"teacher_disagreement_score":0.0058018775,"about_ca_system_score_codex":0.00071985944,"about_ca_system_score_gemma":0.00050015206,"threshold_uncertainty_score":0.011536241},"labels":[],"label_agreement":null},{"id":"W2089515781","doi":"10.1109/tpami.2012.242","title":"Learning to Track and Identify Players from Broadcast Sports Videos","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":227,"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":"University of Cambridge","keywords":"Computer science; Artificial intelligence; Conditional random field; Identification (biology); Homography; Computer vision; Machine learning; Task (project management); Supervised learning; Exploit","score_opus":0.012744982261216446,"score_gpt":0.252507597688138,"score_spread":0.23976261542692157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089515781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31481525,0.00028179082,0.6774801,0.0002130699,0.000053913467,0.0002276408,0.00041554973,0.0032467041,0.0032660002],"genre_scores_gemma":[0.7746479,0.00023163483,0.21627676,0.00012979876,0.00008167207,0.00015027296,0.0018173937,0.000109712666,0.0065549454],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995679,0.00007837478,0.00001907632,0.00020989547,0.00006297921,0.00006172316],"domain_scores_gemma":[0.9992329,0.00032773108,0.000121936355,0.000100447134,0.00016334746,0.000053677817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006760113,0.00086839584,0.0005990812,0.0009069942,0.0004138395,0.00066276215,0.0009851068,0.0009079622,0.001128151],"category_scores_gemma":[0.0023905183,0.00041775222,0.00044630814,0.00046323435,0.0003712734,0.0010395393,0.0005934895,0.00096336927,0.0010035483],"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":[0.0005001706,0.00060308527,0.014545034,0.00012696716,0.00013432572,0.00018636408,0.00022525122,0.069125265,0.061948795,0.0012169565,0.0039779744,0.8474098],"study_design_scores_gemma":[0.000022524455,0.0001352549,0.0055935434,0.000009668394,0.000037597638,0.000062992716,0.00011806518,0.9752152,0.01633438,0.0016264468,0.00083137595,0.000012900779],"about_ca_topic_score_codex":0.0069622654,"about_ca_topic_score_gemma":0.011780724,"teacher_disagreement_score":0.0069622654,"about_ca_system_score_codex":0.00041759553,"about_ca_system_score_gemma":0.0005638696,"threshold_uncertainty_score":0.013843477},"labels":[],"label_agreement":null},{"id":"W2091181450","doi":"10.1117/12.476317","title":"Incorporating Audio Cues into Dialog and Action Scene Extraction","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Analysis and Summarization","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":"University of Waterloo","funders":"","keywords":"Dialog box; Computer science; Audio visual; Classifier (UML); Artificial intelligence; Speech recognition; Action recognition; Action (physics); Audio signal processing; Feature extraction; Computer vision; Audio signal; Multimedia; Speech coding; World Wide Web","score_opus":0.013932786053802397,"score_gpt":0.24149062790170528,"score_spread":0.22755784184790287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091181450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052311085,0.0011444878,0.9278353,0.00025665032,0.00018040098,0.0005804735,0.0021630237,0.008837636,0.0066910256],"genre_scores_gemma":[0.22212031,0.00072106207,0.76537055,0.00018890169,0.00020086918,0.00033460147,0.0053516272,0.00048764146,0.005224445],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994967,0.00008083377,0.000033764114,0.00016240255,0.00013437208,0.00009195648],"domain_scores_gemma":[0.9991517,0.0003249983,0.000091298665,0.0000744969,0.00027936802,0.00007814195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044884114,0.001667807,0.00093137735,0.002483402,0.00072432065,0.001300934,0.0008502278,0.0008137112,0.0030424977],"category_scores_gemma":[0.0017067078,0.00043448427,0.0007929655,0.0008109543,0.00036605628,0.001636247,0.00085142726,0.0009480565,0.0019967821],"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":[0.0005316235,0.00025576996,0.0026946287,0.0006709637,0.00008615216,0.0004565449,0.0003590776,0.0076037436,0.28673786,0.002800674,0.006459229,0.6913438],"study_design_scores_gemma":[0.00009232974,0.0009282331,0.027359758,0.00024548112,0.0003549276,0.0012419106,0.0018440878,0.42473233,0.47186667,0.017641272,0.05345482,0.00023821466],"about_ca_topic_score_codex":0.003571666,"about_ca_topic_score_gemma":0.008536227,"teacher_disagreement_score":0.003571666,"about_ca_system_score_codex":0.00039038964,"about_ca_system_score_gemma":0.0007137824,"threshold_uncertainty_score":0.010178149},"labels":[],"label_agreement":null},{"id":"W2091280331","doi":"10.1109/tmm.2014.2306183","title":"Self-Sorting Map: An Efficient Algorithm for Presenting Multimedia Data in Structured Layouts","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Sorting; Cluster analysis; Set (abstract data type); Dimension (graph theory); Data set; Reduction (mathematics); sort; Dimensionality reduction; Data mining; Sorting algorithm; Information retrieval; Algorithm; Theoretical computer science; Artificial intelligence","score_opus":0.021615177247418453,"score_gpt":0.27613678650545276,"score_spread":0.2545216092580343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091280331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057060206,0.00025228274,0.9818071,0.00014923015,0.00011107954,0.00016597749,0.0007621789,0.0090702465,0.001975955],"genre_scores_gemma":[0.03368919,0.00029353536,0.9603444,0.00008950248,0.000048306454,0.0003484539,0.001668875,0.0008208899,0.0026968552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909985,0.00015279352,0.00006849699,0.00018579043,0.0004147275,0.00007843848],"domain_scores_gemma":[0.9986228,0.0003964851,0.000085412394,0.000312095,0.0004774277,0.00010589172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008335627,0.001596508,0.0011618553,0.004708853,0.0010721546,0.00235346,0.002561447,0.0011785591,0.012956005],"category_scores_gemma":[0.0042785783,0.00075299613,0.0011816696,0.0046403483,0.000726038,0.004360237,0.0031951247,0.0010337328,0.0056905104],"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":[0.00044909926,0.00014290678,0.0010286295,0.00045986136,0.000092756134,0.00019598534,0.0006330272,0.022318678,0.01743266,0.01872078,0.035304945,0.9032207],"study_design_scores_gemma":[0.00017793046,0.00037063667,0.0012277026,0.0001371212,0.00007908413,0.00073589274,0.0008964444,0.7137574,0.05715907,0.0896232,0.13563745,0.00019815456],"about_ca_topic_score_codex":0.0024935803,"about_ca_topic_score_gemma":0.0028926039,"teacher_disagreement_score":0.012956005,"about_ca_system_score_codex":0.00091220293,"about_ca_system_score_gemma":0.0011496537,"threshold_uncertainty_score":0.043342113},"labels":[],"label_agreement":null},{"id":"W2092526036","doi":"10.1109/mmsp.2010.5662062","title":"Gaussian mixture vector quantization-based video summarization using independent component analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Codebook; Automatic summarization; Vector quantization; Linde–Buzo–Gray algorithm; Computer science; Artificial intelligence; Pattern recognition (psychology); Histogram; Quantization (signal processing); Gaussian; Feature extraction; Feature vector; Mixture model; Learning vector quantization; Computer vision; Image (mathematics)","score_opus":0.011929209172853068,"score_gpt":0.24689576111216482,"score_spread":0.23496655193931176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092526036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021457602,0.0004946086,0.99603975,0.00005011029,0.000049845574,0.000042130105,0.000056056244,0.000732071,0.00038962084],"genre_scores_gemma":[0.09318708,0.0008877564,0.90229243,0.00010232954,0.00015144484,0.00017136987,0.0008137457,0.00019259697,0.0022012745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991959,0.00015438815,0.00006220712,0.00016397102,0.00037421752,0.000049316248],"domain_scores_gemma":[0.99942386,0.00015814604,0.000053203355,0.00006518295,0.0002764507,0.00002316142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007024532,0.0011467744,0.0013636481,0.0017398503,0.00042269216,0.00088664383,0.0012482841,0.00066631386,0.0015810702],"category_scores_gemma":[0.0020721594,0.00033358714,0.00081271614,0.0019164783,0.00042508106,0.0017711881,0.0006324295,0.0008863317,0.0010413508],"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":[0.0001724824,0.00006556852,0.000436735,0.00026818385,0.00009613246,0.000076911616,0.00012344442,0.06890942,0.04436517,0.008590227,0.005141593,0.8717541],"study_design_scores_gemma":[0.00003301217,0.0001480968,0.0008163884,0.00002415093,0.000080181904,0.00013902539,0.00006024811,0.95613945,0.028070321,0.0065871105,0.0078460295,0.000056114583],"about_ca_topic_score_codex":0.0034837786,"about_ca_topic_score_gemma":0.003312424,"teacher_disagreement_score":0.0034837786,"about_ca_system_score_codex":0.0006240794,"about_ca_system_score_gemma":0.00059373275,"threshold_uncertainty_score":0.0069270134},"labels":[],"label_agreement":null},{"id":"W2093210365","doi":"10.1109/tmm.2012.2225036","title":"A Robust Technique for Motion-Based Video Sequences Temporal Alignment","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","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 Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Trajectory; Computer vision; Dynamic time warping; Motion (physics); Image warping; Motion estimation; Point (geometry); Probabilistic logic; Pattern recognition (psychology); Mathematics","score_opus":0.03577841793015396,"score_gpt":0.2598075192913459,"score_spread":0.22402910136119192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093210365","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014004306,0.00014255763,0.99778736,0.000024774505,0.000033264787,0.000020447926,0.000025788668,0.000387009,0.00017834445],"genre_scores_gemma":[0.04790576,0.00036838333,0.9498178,0.000060129183,0.00012607012,0.000115426614,0.00029093924,0.00018001437,0.0011354291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842864,0.0002034406,0.00010129867,0.0003855488,0.0008002356,0.00008088589],"domain_scores_gemma":[0.9987746,0.00026217764,0.00030882558,0.00030662603,0.00030350636,0.000044207212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008874256,0.0010565172,0.0009313328,0.0016388241,0.00045605478,0.00067639025,0.0014996036,0.0010004906,0.0020169243],"category_scores_gemma":[0.00404956,0.00057212927,0.0011477814,0.0019813192,0.00056225766,0.001829097,0.0011064268,0.0015205512,0.0013989593],"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":[0.0002715677,0.00007877141,0.00043598868,0.0002278153,0.00015137062,0.00024246001,0.00016244157,0.06249516,0.15864061,0.018064627,0.0031798799,0.7560492],"study_design_scores_gemma":[0.00003770273,0.0003502105,0.0015098392,0.000048365997,0.00010109992,0.0010970668,0.000080458274,0.84647644,0.119081974,0.009357498,0.021766433,0.000092831324],"about_ca_topic_score_codex":0.0013334856,"about_ca_topic_score_gemma":0.0012117698,"teacher_disagreement_score":0.0020169243,"about_ca_system_score_codex":0.00046237346,"about_ca_system_score_gemma":0.0007175896,"threshold_uncertainty_score":0.0067473054},"labels":[],"label_agreement":null},{"id":"W2093775807","doi":"10.1145/1940761.1940836","title":"Notes toward a politics of personalization","year":2011,"lang":"en","type":"article","venue":"Proceedings of the 2011 iConference","topic":"Video Analysis and Summarization","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":"University of Toronto","keywords":"Personalization; Recommender system; World Wide Web; Domain (mathematical analysis); Computer science; Resource (disambiguation); Control (management); Knowledge management; Politics; Web 2.0; Internet privacy; The Internet; Political science; Artificial intelligence","score_opus":0.054732911078555296,"score_gpt":0.22428108720301568,"score_spread":0.16954817612446038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2093775807","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010215901,0.015623923,0.037992414,0.4136331,0.0024750722,0.000053495976,0.00019626456,0.00019204548,0.5196177],"genre_scores_gemma":[0.72020996,0.009374291,0.0244679,0.08036859,0.011292762,0.0003592397,0.0001656188,0.00056335604,0.15319832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930322,0.00305408,0.00019337238,0.0019897448,0.0012823764,0.00044808898],"domain_scores_gemma":[0.9923105,0.004310338,0.00038847813,0.0016445075,0.0008354916,0.0005106563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008929532,0.00055395195,0.0005928797,0.001777173,0.0065906118,0.0105155185,0.001371876,0.008451199,0.010173455],"category_scores_gemma":[0.009911821,0.00043692,0.00058247434,0.0018592073,0.042435266,0.018558362,0.005912428,0.016356217,0.002572163],"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":[0.0000072139137,0.000005205823,0.000055830333,0.000007524634,0.0000018698277,0.000010985637,0.00083865586,0.00004972173,0.000024492512,0.9906623,0.0060100653,0.0023261907],"study_design_scores_gemma":[0.0000158758,0.00001011539,0.00022488626,0.00006600996,0.0000043784876,0.00005612995,0.0005495833,0.0003480201,0.00013101197,0.80021054,0.19836882,0.000014635169],"about_ca_topic_score_codex":0.0049226345,"about_ca_topic_score_gemma":0.0032446445,"teacher_disagreement_score":0.0105155185,"about_ca_system_score_codex":0.007067642,"about_ca_system_score_gemma":0.0019594522,"threshold_uncertainty_score":0.051279545},"labels":[],"label_agreement":null},{"id":"W2095625849","doi":"10.1109/iv.2002.1028751","title":"Visualising human dialog","year":2003,"lang":"en","type":"article","venue":"Proceedings Sixth International Conference on Information Visualisation","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Conversation; Dialog box; Computer science; Task (project management); Visualization; Human–computer interaction; Dialog system; Natural language processing; Natural (archaeology); Artificial intelligence; Communication; Psychology; World Wide Web; Engineering","score_opus":0.04652394755698342,"score_gpt":0.31730921767723436,"score_spread":0.27078527012025094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095625849","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0810676,0.005693677,0.81356347,0.0029404454,0.0004493997,0.0003224408,0.002086333,0.007735938,0.0861408],"genre_scores_gemma":[0.62626535,0.0033626873,0.34528127,0.0007229904,0.00028276377,0.000338751,0.0019323727,0.0011717717,0.020641994],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993647,0.00030126405,0.000026646198,0.00011808296,0.00013862792,0.000050610273],"domain_scores_gemma":[0.9987204,0.0008405365,0.00007463224,0.00014288035,0.00014116835,0.000080333804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010801933,0.0007407382,0.00040496586,0.0016615062,0.0008490669,0.0039776275,0.0007254145,0.0014697068,0.017563425],"category_scores_gemma":[0.0041433545,0.00030076483,0.00047086834,0.0007847478,0.0009836658,0.003150572,0.0028677285,0.00096238486,0.002164435],"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":[0.0011243466,0.00013117825,0.0034804288,0.0029915285,0.00013130542,0.0012224226,0.0734562,0.02592447,0.11336477,0.107468806,0.05292564,0.61777896],"study_design_scores_gemma":[0.00016752361,0.000337805,0.011633974,0.0014439436,0.00012170668,0.0031996632,0.023995563,0.10124272,0.035137985,0.1684315,0.653985,0.00030260364],"about_ca_topic_score_codex":0.0017447053,"about_ca_topic_score_gemma":0.001303443,"teacher_disagreement_score":0.017563425,"about_ca_system_score_codex":0.0005278421,"about_ca_system_score_gemma":0.00046354003,"threshold_uncertainty_score":0.058755517},"labels":[],"label_agreement":null},{"id":"W2095704959","doi":"10.1109/tifs.2010.2097593","title":"A Robust and Fast Video Copy Detection System Using Content-Based Fingerprinting","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":155,"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; Fingerprint (computing); Robustness (evolution); Search engine indexing; Artificial intelligence; Computer vision; Database index; Video processing; Feature extraction; Pattern recognition (psychology)","score_opus":0.018542699867186808,"score_gpt":0.20390949713291573,"score_spread":0.1853667972657289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095704959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13879415,0.0019323293,0.84073883,0.00016899922,0.00020996558,0.00040809702,0.00046011843,0.015088616,0.002198863],"genre_scores_gemma":[0.48406258,0.0007707098,0.50925493,0.00019760891,0.00015156144,0.00023205015,0.00079403655,0.00013350489,0.004402995],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992823,0.00006043556,0.000051773593,0.00017020326,0.00036835865,0.00006690946],"domain_scores_gemma":[0.99909794,0.00016105233,0.00014083344,0.0002005386,0.0003475119,0.000052117914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053451274,0.0005250401,0.0012593535,0.0017189574,0.00040779542,0.00081814907,0.0014900764,0.0010025544,0.0013599492],"category_scores_gemma":[0.0017903757,0.00031003478,0.00029983724,0.0010511922,0.00030347583,0.0016500626,0.00065450225,0.0004684319,0.000979074],"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":[0.00066994556,0.000251675,0.0029661085,0.00021349922,0.00008560921,0.00038366727,0.00008465009,0.0048301104,0.3164562,0.0011996037,0.0043725744,0.6684864],"study_design_scores_gemma":[0.00021717056,0.0016456661,0.012103299,0.00007052725,0.00026357968,0.0050196545,0.00009152059,0.45239636,0.51349425,0.001032794,0.0134416465,0.00022348094],"about_ca_topic_score_codex":0.0022759356,"about_ca_topic_score_gemma":0.0012231907,"teacher_disagreement_score":0.0022759356,"about_ca_system_score_codex":0.0005065251,"about_ca_system_score_gemma":0.00058092637,"threshold_uncertainty_score":0.0045494437},"labels":[],"label_agreement":null},{"id":"W2096217132","doi":"10.1145/2556288.2557304","title":"LACES","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Video production; Computer science; Workflow; Video editing; Casual; Multimedia; Video capture; Status quo; Non-linear editing system; Process (computing); CLIPS; Overhead (engineering); Production (economics); Video processing; Computer graphics (images); Smacker video; Artificial intelligence; Database","score_opus":0.013273573665819624,"score_gpt":0.23886020052512913,"score_spread":0.2255866268593095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096217132","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017155362,0.0027642192,0.22513777,0.0023389421,0.0021328637,0.0011121003,0.016608542,0.20289645,0.5298537],"genre_scores_gemma":[0.0923279,0.002416769,0.20442775,0.001675014,0.000892688,0.0013189727,0.044449978,0.028224334,0.62426656],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99800426,0.0002642694,0.00014228544,0.0005266593,0.0008736995,0.00018875248],"domain_scores_gemma":[0.99597484,0.00061936997,0.00018116618,0.0012380733,0.0014704883,0.0005160599],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018179147,0.0011688275,0.000630223,0.0020326434,0.0015331082,0.0052393586,0.0020696584,0.0013605088,0.15668878],"category_scores_gemma":[0.0067589707,0.00051177986,0.0007202197,0.0012619116,0.00080079754,0.0046133804,0.0038875814,0.0016173413,0.10214375],"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":[0.0008598322,0.00021170164,0.0012715673,0.0009164487,0.000047876,0.00056567433,0.0016794072,0.0009587078,0.027264789,0.06174063,0.4623992,0.44208404],"study_design_scores_gemma":[0.000045940345,0.00006160256,0.00059137534,0.00010322304,0.000013773182,0.00033324808,0.00020971143,0.0024477195,0.0077167405,0.005178954,0.98326355,0.00003421787],"about_ca_topic_score_codex":0.002542522,"about_ca_topic_score_gemma":0.0032266746,"teacher_disagreement_score":0.8433112,"about_ca_system_score_codex":0.0010418533,"about_ca_system_score_gemma":0.0016609431,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2097356861","doi":"10.1609/aiide.v8i1.12505","title":"Sports Commentary Recommendation System (SCoReS): Machine Learning for Automated Narrative","year":2012,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Video Analysis and Summarization","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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Acadia University","keywords":"Narrative; Computer science; Psychology; Artificial intelligence; Multimedia; Human–computer interaction; Applied psychology; Literature","score_opus":0.03091632338795975,"score_gpt":0.2819015146953475,"score_spread":0.2509851913073878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097356861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10442121,0.0047100466,0.663371,0.0023446924,0.0011004799,0.002833807,0.048214708,0.15485369,0.01815039],"genre_scores_gemma":[0.1522178,0.0009931658,0.76418835,0.0005425266,0.0005237243,0.0015923207,0.061591577,0.0008786283,0.017471885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990343,0.00027558266,0.00009642498,0.00026149355,0.0002716433,0.00006055693],"domain_scores_gemma":[0.9969511,0.0014375102,0.0002449069,0.00026592202,0.00095322024,0.00014724175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015425532,0.0016267457,0.00090285693,0.0028141995,0.00073880795,0.0008909782,0.001845346,0.0014366289,0.0076758084],"category_scores_gemma":[0.008682734,0.00038580553,0.00066757464,0.0014789278,0.0001735928,0.0013867049,0.00088182796,0.0015040676,0.006488217],"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":[0.00046770604,0.0004281369,0.00455993,0.00073603785,0.00017868007,0.000280279,0.00031770935,0.0074359314,0.011971504,0.0011244917,0.16888916,0.8036105],"study_design_scores_gemma":[0.0003714741,0.0005489804,0.008326854,0.00019933005,0.00022842451,0.00034829284,0.00068985735,0.8617493,0.024494834,0.006434168,0.096453816,0.00015468437],"about_ca_topic_score_codex":0.01253441,"about_ca_topic_score_gemma":0.028331263,"teacher_disagreement_score":0.01253441,"about_ca_system_score_codex":0.0007515076,"about_ca_system_score_gemma":0.0012712998,"threshold_uncertainty_score":0.025678098},"labels":[],"label_agreement":null},{"id":"W2097378710","doi":"10.1109/crv.2006.44","title":"Interpreting Camera Operations in the Context of Content-based Video Indexing and Retrieval","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Sherbrooke","funders":"","keywords":"Search engine indexing; Panning (audio); Computer science; Computer vision; Zoom; Artificial intelligence; Context (archaeology); Image retrieval; Content (measure theory); Motion blur; Focus (optics); Database index; Video tracking; Tracking (education); Motion (physics); Video processing; Image (mathematics); Mathematics","score_opus":0.01618871791042962,"score_gpt":0.23319493045988768,"score_spread":0.21700621254945807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097378710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03488205,0.0023525113,0.95670617,0.00032362106,0.00021307467,0.00025336468,0.00036030653,0.001039255,0.0038696723],"genre_scores_gemma":[0.3235136,0.0025554693,0.66932654,0.00012759447,0.00040988252,0.00014319614,0.0007587428,0.0001810963,0.0029838835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920803,0.00021908495,0.00009497952,0.00013406234,0.00026503793,0.000078872006],"domain_scores_gemma":[0.998434,0.00047607272,0.00022528652,0.00034364397,0.00044531055,0.00007572972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090048823,0.0008387415,0.0009307572,0.0031812026,0.0005014215,0.0031686844,0.0009051019,0.0008378368,0.0019554696],"category_scores_gemma":[0.0048756995,0.00031347864,0.00039843618,0.0029820022,0.0009931816,0.003674296,0.00075392524,0.0007068935,0.0010250347],"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":[0.0007760864,0.00013791988,0.0040487535,0.00089844275,0.00008453787,0.00084167934,0.0009448864,0.023829896,0.25729248,0.04441843,0.004911737,0.6618151],"study_design_scores_gemma":[0.000092789545,0.0008014314,0.01899045,0.00023621808,0.00039209743,0.0029240858,0.0017357677,0.5336995,0.3196351,0.070754714,0.0504399,0.0002978565],"about_ca_topic_score_codex":0.0022826523,"about_ca_topic_score_gemma":0.0018737126,"teacher_disagreement_score":0.0031812026,"about_ca_system_score_codex":0.0007008941,"about_ca_system_score_gemma":0.00046321936,"threshold_uncertainty_score":0.006541729},"labels":[],"label_agreement":null},{"id":"W2100569771","doi":"10.1109/isspa.2007.4555510","title":"Video shot detection via information theoretic classification","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Shot (pellet); Computer science; Artificial intelligence; Class (philosophy); Pattern recognition (psychology); Point (geometry); Precision and recall; Frame (networking); One shot; Transmission (telecommunications); Single shot; Recall; Computer vision; Data mining; Mathematics","score_opus":0.012270156068673857,"score_gpt":0.23171695290647282,"score_spread":0.21944679683779897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100569771","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008098657,0.00039015544,0.98987913,0.0000716514,0.000042120955,0.00006835511,0.00009990737,0.00075001165,0.00059986935],"genre_scores_gemma":[0.22337772,0.00042618238,0.7734876,0.00012608191,0.00017346798,0.00019502171,0.0008275647,0.00008290715,0.0013033651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983339,0.0003485072,0.00011940083,0.00040069534,0.0006697569,0.00012780236],"domain_scores_gemma":[0.9982083,0.0006665178,0.00023564721,0.00020073834,0.0006088412,0.00008009499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012050689,0.00090075174,0.0014185552,0.0056413147,0.000482312,0.0013712295,0.0016736455,0.0010648795,0.0008239705],"category_scores_gemma":[0.004061638,0.0003843835,0.0010264595,0.0022557944,0.0007774612,0.0019123628,0.0008521443,0.0010874262,0.0004916145],"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":[0.00022353871,0.00018759475,0.0024629969,0.00025441765,0.00016942988,0.00015700907,0.00020948473,0.041558,0.03320653,0.013816194,0.006051704,0.9017031],"study_design_scores_gemma":[0.000017133818,0.00013553828,0.002256417,0.000024260664,0.00006641355,0.00017684726,0.00006363867,0.9658224,0.01530614,0.013856961,0.0022232751,0.000051090214],"about_ca_topic_score_codex":0.0035510578,"about_ca_topic_score_gemma":0.0027995396,"teacher_disagreement_score":0.0056413147,"about_ca_system_score_codex":0.0010130367,"about_ca_system_score_gemma":0.00082815054,"threshold_uncertainty_score":0.007350147},"labels":[],"label_agreement":null},{"id":"W2100983561","doi":"10.1109/icassp.2011.5946679","title":"Video thumbnail extraction using video time density function and independent component analysis mixture model","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Thumbnail; Codebook; Artificial intelligence; Vector quantization; Quantization (signal processing); Pattern recognition (psychology); Computer vision; Independent component analysis; Video tracking; Feature extraction; Block-matching algorithm; Feature vector; Video processing; Image (mathematics)","score_opus":0.027503326414139224,"score_gpt":0.22906211211768612,"score_spread":0.2015587857035469,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100983561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031724821,0.00030797056,0.9953779,0.000040281724,0.000037744336,0.000035758392,0.000067931185,0.0006846205,0.00027536807],"genre_scores_gemma":[0.073455915,0.00072380225,0.9231799,0.000063596,0.00007659182,0.00009563384,0.0005743946,0.00014797741,0.0016822288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933016,0.00009325214,0.000051884865,0.0001551635,0.0003262754,0.000043215583],"domain_scores_gemma":[0.9994273,0.00017200671,0.00006289239,0.000081784136,0.0002273509,0.000028638273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005536685,0.00091342314,0.001020046,0.0020828124,0.0003107817,0.0007931303,0.000814537,0.0006251173,0.0016414899],"category_scores_gemma":[0.0021163458,0.00036456174,0.0010011041,0.0017111233,0.0003627199,0.0016234205,0.00055463717,0.000806771,0.00096827804],"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":[0.00017527131,0.000050073515,0.00051800645,0.00023802572,0.000068675276,0.000116808325,0.00008713734,0.029812198,0.0684512,0.004009357,0.00251427,0.89395887],"study_design_scores_gemma":[0.000026251984,0.0001665367,0.0023659144,0.000037671427,0.00008614576,0.00042667397,0.00007838538,0.9170617,0.06526622,0.005616333,0.008789796,0.00007839765],"about_ca_topic_score_codex":0.0022303679,"about_ca_topic_score_gemma":0.00194266,"teacher_disagreement_score":0.0022303679,"about_ca_system_score_codex":0.0004602019,"about_ca_system_score_gemma":0.0004148206,"threshold_uncertainty_score":0.0054913163},"labels":[],"label_agreement":null},{"id":"W2101812544","doi":"10.1007/s00530-004-0137-4","title":"MINDEX: An efficient index structure for salient-object-based queries in video databases","year":2004,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":13,"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; Index (typography); Salient; Object (grammar); Database; Database index; Information retrieval; Data mining; Search engine indexing; Artificial intelligence; World Wide Web","score_opus":0.018172314337810885,"score_gpt":0.26240846474018426,"score_spread":0.24423615040237337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101812544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02704484,0.0022368673,0.9341318,0.00037443332,0.00021818529,0.00041407134,0.006158438,0.027055701,0.0023657298],"genre_scores_gemma":[0.15738603,0.0011942267,0.81734747,0.00037138475,0.00024001417,0.00057078194,0.0161754,0.001974686,0.004739929],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990144,0.00011337233,0.00017937705,0.0001730891,0.0004423915,0.000077332894],"domain_scores_gemma":[0.99746776,0.0008863618,0.0001804765,0.00087372214,0.00042193267,0.00016977805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013628425,0.0011029425,0.0021776843,0.004029438,0.0009990804,0.002885756,0.0021837156,0.0011753909,0.0049559297],"category_scores_gemma":[0.0072873803,0.0008031688,0.00072089396,0.0045650476,0.00055996596,0.0064251823,0.0039877472,0.0011955474,0.0022302575],"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":[0.0020791558,0.00038194674,0.002454815,0.00069434167,0.00016266076,0.0002449835,0.0005986992,0.010681002,0.05296778,0.023129854,0.072731696,0.8338731],"study_design_scores_gemma":[0.0006268514,0.0009804955,0.0035001247,0.00017760874,0.00028685856,0.00092459796,0.00055603526,0.6920107,0.10533831,0.09987322,0.09550202,0.00022312526],"about_ca_topic_score_codex":0.0033445517,"about_ca_topic_score_gemma":0.0045205196,"teacher_disagreement_score":0.0049559297,"about_ca_system_score_codex":0.0009189804,"about_ca_system_score_gemma":0.0010977774,"threshold_uncertainty_score":0.01657921},"labels":[],"label_agreement":null},{"id":"W2102401677","doi":"10.1109/icassp.2005.1415461","title":"Video Shot Boundary Detection Using Independent Component Analysis","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Independent component analysis; Thresholding; Subspace topology; Computer science; Computer vision; Pattern recognition (psychology); Feature vector; Cluster analysis; Shot (pellet); Object detection; Feature (linguistics); Frame (networking); Image (mathematics)","score_opus":0.01688726975257301,"score_gpt":0.23987402531273608,"score_spread":0.22298675556016306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102401677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016438317,0.00043075188,0.98026067,0.00004909215,0.000067427536,0.000100621575,0.00010400701,0.0014247127,0.0011244627],"genre_scores_gemma":[0.20892504,0.00066718773,0.78751296,0.000058902442,0.00009460406,0.00017880625,0.00070643105,0.00020767322,0.0016484387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992428,0.00010670532,0.000041354837,0.0001785564,0.00036345725,0.000067190595],"domain_scores_gemma":[0.9989367,0.00027235135,0.00009888175,0.000095675096,0.0005430654,0.00005339085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006103396,0.00087301945,0.0010283455,0.0039917324,0.00047336743,0.0010072283,0.00095301226,0.0009622551,0.0012949783],"category_scores_gemma":[0.0024977208,0.00029757066,0.00055215333,0.001674664,0.0004568558,0.0014554994,0.0006699731,0.0009521591,0.00076931826],"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":[0.00028730088,0.00010325418,0.0018192105,0.00029543162,0.00012246602,0.00030310356,0.00017984197,0.012057134,0.21269654,0.003798393,0.00432284,0.7640144],"study_design_scores_gemma":[0.00004164909,0.00029135306,0.010773363,0.00007099324,0.0001408088,0.00080018974,0.00022856506,0.7401672,0.22782397,0.008140048,0.011393843,0.00012799505],"about_ca_topic_score_codex":0.0021562418,"about_ca_topic_score_gemma":0.0017930082,"teacher_disagreement_score":0.0039917324,"about_ca_system_score_codex":0.00041585695,"about_ca_system_score_gemma":0.0004671615,"threshold_uncertainty_score":0.004332125},"labels":[],"label_agreement":null},{"id":"W2102905372","doi":"10.5539/ass.v8n11p243","title":"Influences of Image Communication Paradigm of Sports Information on Sports Behavior of University Students","year":2012,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":3,"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":"Consciousness; Habit; Psychology; Sport communication; Mass media; Foundation (evidence); Sport management; Multimedia; Applied psychology; Advertising; Social psychology; Public relations; Communication studies; Sociology; Computer science; Political science","score_opus":0.009451809407562418,"score_gpt":0.2687026667194329,"score_spread":0.2592508573118705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102905372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9979037,0.000051262552,0.000060276598,0.000053698517,0.0000042499423,0.000008008273,0.000020491481,0.0000024661392,0.0018958194],"genre_scores_gemma":[0.99943155,0.00006254669,0.00007264814,0.000015107457,0.0000073982824,0.000006219068,0.00003117822,0.0000017852302,0.00037157306],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99912053,0.00036805304,0.000062441664,0.00008907521,0.00021721075,0.00014268019],"domain_scores_gemma":[0.9947206,0.002394847,0.001117147,0.00015372422,0.0007111676,0.0009026356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050293247,0.00015218633,0.00016236643,0.0007022986,0.00040160812,0.001041074,0.00013083316,0.00029714656,0.002711917],"category_scores_gemma":[0.006619421,0.0000704039,0.00022976243,0.00036489972,0.00024340683,0.0003202253,0.0003600029,0.00049007894,0.0002989655],"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":[0.0006465118,0.0010621086,0.94933856,0.0000638397,0.00008565285,0.00017942372,0.0053932066,0.00016096098,0.00592233,0.00024806277,0.00033665216,0.03656262],"study_design_scores_gemma":[0.000010090255,0.0005160609,0.9902396,0.000017971655,0.000059394733,0.00008148486,0.006582083,0.00051144045,0.0012020771,0.000076450764,0.0006892729,0.000013952227],"about_ca_topic_score_codex":0.002926476,"about_ca_topic_score_gemma":0.0031484927,"teacher_disagreement_score":0.002926476,"about_ca_system_score_codex":0.0003914928,"about_ca_system_score_gemma":0.00047294344,"threshold_uncertainty_score":0.009072304},"labels":[],"label_agreement":null},{"id":"W2104956252","doi":"10.1109/icip.2006.312765","title":"Image Based Temporal Registration of MRI Data for Medical Visualization","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Alberta","funders":"","keywords":"Image registration; Computer science; Visualization; Computer vision; Artificial intelligence; Medical imaging; Data visualization; Image (mathematics); Computer graphics (images)","score_opus":0.022668415283862774,"score_gpt":0.307340852993684,"score_spread":0.28467243770982126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104956252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005962478,0.00074203423,0.987946,0.0003015811,0.00010685541,0.00011753558,0.00036820042,0.002403054,0.0020522766],"genre_scores_gemma":[0.06664914,0.0016364133,0.9276105,0.00009866867,0.00014858355,0.00019239185,0.0009546051,0.0007451771,0.00196452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994374,0.00019396817,0.000052638872,0.00007941868,0.00020614456,0.000030468324],"domain_scores_gemma":[0.99821883,0.0007453428,0.00016713925,0.00046893422,0.0003343032,0.000065426204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008881997,0.0007447763,0.00044914996,0.001551618,0.0003314192,0.001483048,0.00081672566,0.0008008566,0.008926958],"category_scores_gemma":[0.005490889,0.0003701446,0.0005465575,0.0019441443,0.00041277433,0.0012453023,0.0009320621,0.0010592308,0.0023896783],"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":[0.0005128769,0.000106330415,0.000689971,0.0007186705,0.00008436687,0.00033508948,0.00032557687,0.018676015,0.33551487,0.014193164,0.0123382835,0.6165048],"study_design_scores_gemma":[0.00013541432,0.00071846915,0.0067309905,0.000221563,0.00018720533,0.0034582408,0.0003659501,0.4938377,0.35184464,0.02636442,0.11593206,0.00020335485],"about_ca_topic_score_codex":0.00070507155,"about_ca_topic_score_gemma":0.00096259813,"teacher_disagreement_score":0.008926958,"about_ca_system_score_codex":0.0003464157,"about_ca_system_score_gemma":0.0005193717,"threshold_uncertainty_score":0.029863656},"labels":[],"label_agreement":null},{"id":"W2105482673","doi":"10.5430/air.v3n3p49","title":"A unified approach to content-based indexing and retrieval of digital videos from television archives","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Video Analysis and Summarization","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":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Pró-Reitoria de Pesquisa, Universidade Federal do Rio Grande do Sul; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Search engine indexing; Information retrieval; Metadata; Key frame; Key (lock); Precision and recall; Video content analysis; Segmentation; Hash function; Histogram; Image retrieval; Frame (networking); Artificial intelligence; Computer vision; Video tracking; Video processing; Image (mathematics); World Wide Web","score_opus":0.18684833448235447,"score_gpt":0.35224792435034696,"score_spread":0.1653995898679925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105482673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004870862,0.0022958152,0.9872542,0.00017296635,0.00008456277,0.00035821775,0.00059272087,0.00215432,0.002216349],"genre_scores_gemma":[0.052868217,0.0025634356,0.93473834,0.00018492379,0.000249622,0.00062945415,0.003929413,0.00016451697,0.004672049],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966535,0.00043551627,0.0004134584,0.00063194975,0.0016310591,0.00023461157],"domain_scores_gemma":[0.9986933,0.00014084044,0.0001097375,0.00041553107,0.0005604151,0.00008020088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001458522,0.0011770217,0.0019199263,0.010437391,0.0010109956,0.0034839604,0.0022408308,0.0014691413,0.0024082335],"category_scores_gemma":[0.003056801,0.0006166306,0.0014761926,0.0069766873,0.00093240314,0.004419429,0.003019907,0.0013324687,0.0031643978],"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":[0.00018142624,0.00036050772,0.0011866263,0.0007584456,0.00016715044,0.00029550804,0.00048405718,0.00886807,0.083699375,0.019204479,0.015235777,0.8695586],"study_design_scores_gemma":[0.00015241794,0.0008058412,0.008873951,0.0004493149,0.0006157722,0.0027708784,0.0014804213,0.6597675,0.1400048,0.03422662,0.15047449,0.00037797698],"about_ca_topic_score_codex":0.0059536085,"about_ca_topic_score_gemma":0.006127605,"teacher_disagreement_score":0.010437391,"about_ca_system_score_codex":0.0013556276,"about_ca_system_score_gemma":0.0022798087,"threshold_uncertainty_score":0.0118379},"labels":[],"label_agreement":null},{"id":"W2105821374","doi":"10.1145/1026711.1026761","title":"A web-enabled video indexing system","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Search engine indexing; Histogram; Database index; Video browsing; Video tracking; Parsing; Information retrieval; Detector; Artificial intelligence; Video processing; Image (mathematics)","score_opus":0.008034037776007148,"score_gpt":0.20371204799341894,"score_spread":0.1956780102174118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105821374","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029495768,0.0007394651,0.43241274,0.00032159916,0.0002217288,0.0009188835,0.009165048,0.4896249,0.037099794],"genre_scores_gemma":[0.36208978,0.0009789186,0.49899346,0.0010535134,0.00071792264,0.001669358,0.05076272,0.011637879,0.072096504],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953127,0.000039074017,0.000049731767,0.00012064006,0.00021461344,0.000044732456],"domain_scores_gemma":[0.9990753,0.0001381689,0.000053939457,0.00021129212,0.00036226658,0.00015897049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075153157,0.0006495001,0.00087279495,0.0023762216,0.00060669007,0.0014775848,0.0015559492,0.0007021182,0.022410106],"category_scores_gemma":[0.0014408852,0.00034403882,0.0003100581,0.0015788557,0.00020611823,0.0020599116,0.0011704994,0.000676388,0.017164243],"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":[0.00132092,0.00059732265,0.0032561123,0.00041131428,0.00010844721,0.00079447374,0.00023468243,0.0023149012,0.090026654,0.006510445,0.17715086,0.71727383],"study_design_scores_gemma":[0.0008330572,0.0008333409,0.013044089,0.00019898977,0.0003059918,0.0029638277,0.0002673223,0.30676672,0.25991973,0.011472073,0.40300834,0.00038652762],"about_ca_topic_score_codex":0.0018667172,"about_ca_topic_score_gemma":0.0010545503,"teacher_disagreement_score":0.022410106,"about_ca_system_score_codex":0.00060232426,"about_ca_system_score_gemma":0.0006352079,"threshold_uncertainty_score":0.07496923},"labels":[],"label_agreement":null},{"id":"W2106372359","doi":"10.1109/icassp.2005.1415433","title":"Indexing of NFL Video using MPEG-7 Descriptors and MFCC features","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Search engine indexing; Computer science; Mel-frequency cepstrum; Artificial intelligence; Event (particle physics); Pattern recognition (psychology); Feature (linguistics); Feature extraction; Point (geometry); Database index; Support vector machine; Motion (physics); Computer vision; Mathematics","score_opus":0.009072769068421692,"score_gpt":0.20988650282851318,"score_spread":0.20081373376009148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106372359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31630012,0.0022309355,0.65061253,0.00038843488,0.00032382636,0.0008045565,0.0036378582,0.0130277695,0.012674071],"genre_scores_gemma":[0.5925222,0.0008971907,0.38861758,0.00014488418,0.00028082044,0.00041034096,0.009366692,0.0002314322,0.007528739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996885,0.000032835946,0.0000290965,0.000069250214,0.0001349818,0.000045348315],"domain_scores_gemma":[0.99937975,0.00010444133,0.000072970164,0.00008634257,0.00032505512,0.00003151331],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046487967,0.0005683169,0.00064614386,0.004488709,0.00043840797,0.0006585085,0.0005199101,0.00044976713,0.0020349622],"category_scores_gemma":[0.0017598204,0.00011668255,0.000258576,0.0010568394,0.00021004405,0.0010006855,0.0002725545,0.00026058962,0.002018499],"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":[0.00034570877,0.00015630732,0.0036502425,0.0002099311,0.000032264943,0.0001843722,0.00011626435,0.0013806253,0.15838654,0.0005756678,0.0041229534,0.83083904],"study_design_scores_gemma":[0.00012560844,0.0013090386,0.11964886,0.00022032755,0.00024443318,0.0021734717,0.0008978127,0.3461773,0.47112924,0.003623405,0.054218933,0.0002316203],"about_ca_topic_score_codex":0.0051551387,"about_ca_topic_score_gemma":0.0053062243,"teacher_disagreement_score":0.0051551387,"about_ca_system_score_codex":0.0005320249,"about_ca_system_score_gemma":0.0002528076,"threshold_uncertainty_score":0.01025027},"labels":[],"label_agreement":null},{"id":"W2107443454","doi":"10.1109/icip.2000.899609","title":"Video dissolve and wipe detection via spatio-temporal images of chromatic histogram differences","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Histogram; Artificial intelligence; Computer vision; Computer science; Pixel; Chromatic scale; Diagonal; Segmentation; Metric (unit); Frame (networking); Histogram matching; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.011214886681264831,"score_gpt":0.19722093598767376,"score_spread":0.18600604930640893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107443454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10407581,0.0006387116,0.89163136,0.0001658615,0.000052203475,0.00010096868,0.00021263286,0.0011907502,0.0019316983],"genre_scores_gemma":[0.45107177,0.00074647856,0.545727,0.00006737537,0.00007566515,0.00006334964,0.00042708108,0.0001950577,0.0016262212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975365,0.000034736495,0.000012801946,0.000050988394,0.00011180371,0.000036106117],"domain_scores_gemma":[0.99882764,0.00039777794,0.00020760945,0.00017348604,0.00031292805,0.000080573926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039709723,0.00036577877,0.0003723321,0.0024232597,0.00025151056,0.00091591873,0.0005900481,0.00039090335,0.0011230551],"category_scores_gemma":[0.0018201587,0.00024251013,0.00035764882,0.0012381638,0.0005302406,0.0013175085,0.00052200933,0.00064307614,0.0003848576],"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":[0.0005346953,0.00011077242,0.004342186,0.00037307493,0.00009735894,0.00025992934,0.0002855465,0.014605351,0.23616871,0.0071926625,0.0018563153,0.7341734],"study_design_scores_gemma":[0.00007571675,0.00054594106,0.04316413,0.00007889885,0.00014834153,0.0022019602,0.00053457596,0.5511334,0.37010005,0.0134836035,0.01838263,0.00015080723],"about_ca_topic_score_codex":0.0018454232,"about_ca_topic_score_gemma":0.0031640308,"teacher_disagreement_score":0.0024232597,"about_ca_system_score_codex":0.0004288179,"about_ca_system_score_gemma":0.00030550995,"threshold_uncertainty_score":0.0037569404},"labels":[],"label_agreement":null},{"id":"W2108708535","doi":"10.1109/ccece.1999.808072","title":"New architecture for multi-format video browsing and cut detection","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of Ottawa","funders":"","keywords":"Computer science; Architecture; Process (computing); Multimedia; File format; World Wide Web; Database; Operating system","score_opus":0.017898964164087577,"score_gpt":0.24237415743251692,"score_spread":0.22447519326842935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108708535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005640675,0.00020365482,0.97590065,0.000099326535,0.000037889833,0.00016192552,0.00010116046,0.016241325,0.0016133108],"genre_scores_gemma":[0.059806973,0.0002152279,0.93333215,0.00012825204,0.000068678746,0.00021912866,0.0007127271,0.00049108756,0.0050258813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987679,0.00017586368,0.00011644106,0.0003369838,0.0005059117,0.000096882475],"domain_scores_gemma":[0.9973079,0.0006576847,0.00016676709,0.0006917045,0.00096044195,0.0002154653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018506434,0.000792245,0.00095296634,0.0031749408,0.00068378286,0.00267661,0.0031770077,0.0016310364,0.005459244],"category_scores_gemma":[0.0029540327,0.00074817124,0.00063647353,0.0015260711,0.00068184757,0.0034041684,0.0016512044,0.0015657165,0.00255161],"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":[0.0007741418,0.00043831128,0.0033472616,0.00039841598,0.00017816252,0.0004364077,0.0009014946,0.0066306703,0.12799534,0.017690795,0.01233168,0.8288774],"study_design_scores_gemma":[0.0002362359,0.00096534507,0.007743059,0.00019882986,0.00038780182,0.0031139913,0.0005365182,0.65354633,0.19822192,0.035232663,0.09949285,0.00032449805],"about_ca_topic_score_codex":0.0020154773,"about_ca_topic_score_gemma":0.0024117886,"teacher_disagreement_score":0.005459244,"about_ca_system_score_codex":0.000765761,"about_ca_system_score_gemma":0.00097591704,"threshold_uncertainty_score":0.018262982},"labels":[],"label_agreement":null},{"id":"W2109496202","doi":"10.1109/ism.2009.74","title":"Tiny Videos: A Large Dataset for Image and Video Frame Categorization","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Categorization; Computer science; Artificial intelligence; Frame (networking); The Internet; Computer vision; Image (mathematics); Pattern recognition (psychology); World Wide Web","score_opus":0.009083438330803101,"score_gpt":0.2602699501103476,"score_spread":0.2511865117795445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109496202","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12762496,0.0054228967,0.19331557,0.0015213308,0.0019544503,0.0035493036,0.60879415,0.042772487,0.015044916],"genre_scores_gemma":[0.08641615,0.0009861117,0.16660242,0.0002640258,0.00032929293,0.001722975,0.7395514,0.0006425783,0.003485099],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984785,0.00013519102,0.00020895229,0.00037692674,0.000651617,0.00014897302],"domain_scores_gemma":[0.9976374,0.00042348885,0.00027790666,0.0005880384,0.0008318747,0.00024124535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008948799,0.0020870336,0.0012882224,0.0072572,0.0010747418,0.0012466831,0.00236476,0.0017000037,0.004568696],"category_scores_gemma":[0.0054023326,0.00041699762,0.0012844335,0.0049152914,0.00040580274,0.0028966102,0.0016164628,0.0013486249,0.0044657756],"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":[0.0011554515,0.0008580476,0.009643483,0.0019555332,0.00031575622,0.00079008087,0.00026510403,0.00524057,0.045305554,0.0032752282,0.42589328,0.50530183],"study_design_scores_gemma":[0.0005969242,0.0014921619,0.11301863,0.00083565945,0.000538192,0.004537712,0.0020982118,0.25218698,0.10933076,0.014129196,0.5006592,0.0005764648],"about_ca_topic_score_codex":0.017518073,"about_ca_topic_score_gemma":0.022852331,"teacher_disagreement_score":0.017518073,"about_ca_system_score_codex":0.0013200042,"about_ca_system_score_gemma":0.0013617987,"threshold_uncertainty_score":0.03483224},"labels":[],"label_agreement":null},{"id":"W2110121074","doi":"10.1109/icsmc.2007.4414162","title":"MPEG-7 descriptor integration for on-line video surveillance interface","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Automatic summarization; Interface (matter); Video capture; Video tracking; Video processing; Process (computing); Graphical user interface; Video server; Line (geometry); User interface; Domain (mathematical analysis); Real-time computing; Point (geometry); Multimedia; Computer vision; Artificial intelligence; Operating system","score_opus":0.03228464583452429,"score_gpt":0.2958154442892523,"score_spread":0.26353079845472804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110121074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056003984,0.00044190488,0.8982803,0.00019751773,0.0003280972,0.0006386119,0.0006452502,0.021011572,0.02245269],"genre_scores_gemma":[0.5285863,0.0007626857,0.41301066,0.0003955722,0.0004288414,0.000700449,0.006731124,0.0015099997,0.047874365],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949026,0.00007947303,0.000044269826,0.000049205653,0.00027539334,0.0000613186],"domain_scores_gemma":[0.9993857,0.0000638545,0.00003111017,0.000106729545,0.00036825854,0.0000443204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051534356,0.00048001183,0.00034563968,0.00088926026,0.00025480372,0.000797091,0.0010514165,0.00053596415,0.011409645],"category_scores_gemma":[0.001399333,0.0001556687,0.0002556057,0.0004979567,0.00013198332,0.0007480069,0.0005085301,0.0005001911,0.005074704],"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":[0.0012342395,0.0005349329,0.0025562446,0.00022567621,0.000075922886,0.0010111358,0.00026263972,0.0036134752,0.30912012,0.009346293,0.039048374,0.63297087],"study_design_scores_gemma":[0.0002806867,0.001050235,0.0092897285,0.00007911148,0.00023763554,0.0019907113,0.00021049367,0.31975302,0.49090534,0.0039045215,0.17218342,0.000115058996],"about_ca_topic_score_codex":0.0015956333,"about_ca_topic_score_gemma":0.0014369676,"teacher_disagreement_score":0.011409645,"about_ca_system_score_codex":0.0004314137,"about_ca_system_score_gemma":0.00046226414,"threshold_uncertainty_score":0.038169026},"labels":[],"label_agreement":null},{"id":"W2111941304","doi":"10.1109/ccnc08.2007.237","title":"Applications of CSP Solving in Camera Control","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Windsor","funders":"","keywords":"Character (mathematics); Computer vision; Computer science; Smart camera; Artificial intelligence; Constraint (computer-aided design); Camera auto-calibration; Graphics; Computer graphics (images); Pinhole camera model; Camera matrix; Orientation (vector space); Camera resectioning; Mathematics; Geometry","score_opus":0.00803733687226921,"score_gpt":0.21505289390928078,"score_spread":0.20701555703701158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111941304","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002526342,0.0008658112,0.9892181,0.00045659384,0.0000695861,0.00004824232,0.00005716414,0.00033564083,0.006422542],"genre_scores_gemma":[0.31096402,0.0034736847,0.6770945,0.0004289227,0.00043365645,0.00033422493,0.0003705956,0.0003329875,0.006567407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977435,0.00089699065,0.00016515177,0.00040020794,0.00061879633,0.00017530104],"domain_scores_gemma":[0.99519604,0.0035790256,0.00024522812,0.000353774,0.0004982979,0.00012762638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021168806,0.001430658,0.0012230945,0.0008177006,0.00090999703,0.0017912545,0.0018899686,0.0016633589,0.007973109],"category_scores_gemma":[0.007059673,0.00073280535,0.0014694507,0.0018677125,0.0017553746,0.0018637966,0.0021598283,0.0026726222,0.0010932991],"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":[0.0001193843,0.00010245406,0.0006214488,0.0006514368,0.00010986456,0.00031785876,0.00025569877,0.54017115,0.003104155,0.32138565,0.005926469,0.12723446],"study_design_scores_gemma":[0.00005840056,0.00004039387,0.0000971797,0.000044245888,0.000014432563,0.0000970755,0.000039985447,0.802205,0.0016877826,0.18720521,0.008491335,0.000018940442],"about_ca_topic_score_codex":0.0058394847,"about_ca_topic_score_gemma":0.0039473316,"teacher_disagreement_score":0.007973109,"about_ca_system_score_codex":0.0012558331,"about_ca_system_score_gemma":0.001655129,"threshold_uncertainty_score":0.02667272},"labels":[],"label_agreement":null},{"id":"W2112687835","doi":"10.1109/iv.2009.19","title":"BrowseLine: 2D Timeline Visualization of Web Browsing Histories","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Memorial University of Newfoundland","funders":"","keywords":"Timeline; Computer science; World Wide Web; Web navigation; Information retrieval; Visualization; Task (project management); Web page; Recall; Information visualization; Human–computer interaction; Representation (politics); Artificial intelligence; Cognitive psychology","score_opus":0.009932523916813919,"score_gpt":0.25179489438994074,"score_spread":0.2418623704731268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112687835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065309264,0.0015080749,0.8358112,0.0006298674,0.00019761517,0.00035439827,0.008553727,0.07856263,0.009073249],"genre_scores_gemma":[0.3010685,0.0019100778,0.6743381,0.00026735675,0.00011664835,0.0005106485,0.0072580897,0.005232889,0.009297712],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999826,0.00004598433,0.000012632372,0.000034047673,0.00006213503,0.000019248553],"domain_scores_gemma":[0.9988048,0.0006367847,0.00013270289,0.00012383777,0.00018903328,0.00011276234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000526711,0.00092222414,0.00044469372,0.002077992,0.000363509,0.0013970867,0.00074978935,0.0007674174,0.014839817],"category_scores_gemma":[0.0028606988,0.00033286883,0.00041172814,0.0009949825,0.0002224047,0.0019724006,0.0014674852,0.0006760742,0.001782681],"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":[0.002315101,0.00026177,0.0064682756,0.0024427844,0.00016370263,0.001445938,0.0101607675,0.010032388,0.11818263,0.010159489,0.11921173,0.71915543],"study_design_scores_gemma":[0.00052575965,0.00078664854,0.024544865,0.0011908867,0.00021754701,0.0035951894,0.0039290492,0.35324508,0.112741336,0.021835215,0.4768261,0.00056233176],"about_ca_topic_score_codex":0.0032934865,"about_ca_topic_score_gemma":0.0059673926,"teacher_disagreement_score":0.014839817,"about_ca_system_score_codex":0.00022843327,"about_ca_system_score_gemma":0.00043672536,"threshold_uncertainty_score":0.049644172},"labels":[],"label_agreement":null},{"id":"W2112767951","doi":"10.3758/bf03192797","title":"The use of digital video recorders (DVRs) for capturing digital video files for use in both The Observer and Ethovision","year":2006,"lang":"en","type":"article","venue":"Behavior Research Methods","topic":"Video Analysis and Summarization","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":"Agriculture Food and Rural Development","funders":"","keywords":"Computer science; Observer (physics); Digital recording; Digital video; Software; Uncompressed video; Data compression; Multimedia; Video capture; Video processing; Real-time computing; Computer vision; Video tracking; Computer graphics (images); Computer hardware; Telecommunications","score_opus":0.34555193713925075,"score_gpt":0.47788746533170984,"score_spread":0.13233552819245908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112767951","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081068285,0.0017973435,0.87992615,0.00059099327,0.0008114488,0.0072489954,0.0015988403,0.003290057,0.023667885],"genre_scores_gemma":[0.13572817,0.003018931,0.8261808,0.00053262553,0.00029983767,0.008315376,0.0010383159,0.00072383514,0.024162028],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99364144,0.0019733412,0.0008889652,0.0014277832,0.001857621,0.00021075444],"domain_scores_gemma":[0.97700864,0.008383093,0.0019640047,0.0060279607,0.0059547764,0.0006614563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063355253,0.001088005,0.00056236243,0.0020794373,0.0009987234,0.0010635204,0.0016027107,0.0009236271,0.015138959],"category_scores_gemma":[0.018192546,0.00086678215,0.0006883682,0.0009430247,0.0010539979,0.0014040398,0.0018297387,0.0016806257,0.003569771],"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":[0.0013143485,0.00095727964,0.0073316977,0.0017825066,0.00013640108,0.00040414,0.0013961651,0.0005434713,0.5093813,0.0049353032,0.008115627,0.4637018],"study_design_scores_gemma":[0.00074461475,0.017341925,0.12121403,0.0014521722,0.00089868973,0.009083609,0.0018388206,0.014194603,0.6389979,0.006629841,0.18707688,0.0005269103],"about_ca_topic_score_codex":0.0012601118,"about_ca_topic_score_gemma":0.00447645,"teacher_disagreement_score":0.015138959,"about_ca_system_score_codex":0.0003266783,"about_ca_system_score_gemma":0.001659398,"threshold_uncertainty_score":0.050644875},"labels":[],"label_agreement":null},{"id":"W2112801673","doi":"10.1109/icassp.2004.1327291","title":"Practical MPEG-7 image indexing &amp; retrieval for undergraduates","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Search engine indexing; Computer science; Multimedia; ASCII; Information retrieval; Set (abstract data type); Parsing; Index (typography); Image retrieval; Image (mathematics); Artificial intelligence; World Wide Web; Programming language","score_opus":0.04405913042278311,"score_gpt":0.3276901220365744,"score_spread":0.2836309916137913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112801673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2428069,0.0013830817,0.5187455,0.006590251,0.0020331417,0.0055184043,0.0045944625,0.022041654,0.19628654],"genre_scores_gemma":[0.3843655,0.0013149861,0.36264014,0.0013382646,0.0007281558,0.0022063558,0.0075607407,0.0016265807,0.23821928],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992964,0.00010908714,0.000039867133,0.00013723757,0.00030934633,0.00010792198],"domain_scores_gemma":[0.9981692,0.00030177156,0.000051106683,0.00036226987,0.0005987392,0.0005168552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018322296,0.00101588,0.00067681324,0.00066157983,0.00078992057,0.0011544939,0.0011039664,0.0009798066,0.08342167],"category_scores_gemma":[0.0038191862,0.00036908826,0.00036449466,0.00073290453,0.00031724162,0.001283833,0.0013977941,0.00097155257,0.03135047],"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":[0.0015442948,0.005912222,0.0024085727,0.0004045973,0.000019276751,0.0005456758,0.0008893988,0.0026105768,0.11491726,0.007357437,0.20945486,0.65393585],"study_design_scores_gemma":[0.0009496993,0.014501685,0.016034205,0.00020644879,0.00005021009,0.0038716132,0.0020058223,0.05261851,0.19258331,0.030280417,0.6867097,0.00018845715],"about_ca_topic_score_codex":0.00048958964,"about_ca_topic_score_gemma":0.0009798664,"teacher_disagreement_score":0.08342167,"about_ca_system_score_codex":0.0005318712,"about_ca_system_score_gemma":0.000566469,"threshold_uncertainty_score":0.2790733},"labels":[],"label_agreement":null},{"id":"W2113131653","doi":"10.1109/tmm.2012.2236306","title":"VideoPuzzle: Descriptive One-Shot Video Composition","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":12,"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 Key Research and Development Program of China; Ministry of Education, India; Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Shot (pellet); Computer science; Video tracking; Computer vision; Consistency (knowledge bases); Matching (statistics); Frame (networking); Video compression picture types; Artificial intelligence; Video processing; CLIPS; Task (project management); Video capture; Multimedia; Video browsing; Computer graphics (images)","score_opus":0.05150986222623572,"score_gpt":0.26949804401782856,"score_spread":0.21798818179159285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113131653","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0359024,0.00038225122,0.9384509,0.000064846565,0.00011123853,0.0005781751,0.00046961752,0.018733738,0.0053069186],"genre_scores_gemma":[0.21076885,0.0005508594,0.7707465,0.0001621177,0.00009983734,0.00031230622,0.0034307572,0.0012037395,0.012725075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996049,0.000043990764,0.000019733625,0.00014775284,0.00014469137,0.000038977745],"domain_scores_gemma":[0.99972636,0.00004826938,0.000029447194,0.00007204687,0.00006433079,0.000059516402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004196435,0.0010207108,0.0006137365,0.0011309804,0.0004540648,0.00077557616,0.0012319661,0.00058989465,0.00674308],"category_scores_gemma":[0.0009113151,0.0003375859,0.0006661602,0.0005992519,0.00028417175,0.0012788263,0.0011621547,0.00044498377,0.0018207672],"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":[0.0009575501,0.00034221486,0.0011786203,0.0005491045,0.00012962059,0.00067979645,0.00050605024,0.010535266,0.25085282,0.0040018284,0.012007832,0.7182593],"study_design_scores_gemma":[0.00022946562,0.0010664103,0.006143206,0.00009821403,0.00016620303,0.003078722,0.00073725526,0.58840513,0.3084471,0.00775506,0.083711185,0.00016207308],"about_ca_topic_score_codex":0.0021244981,"about_ca_topic_score_gemma":0.0021474285,"teacher_disagreement_score":0.00674308,"about_ca_system_score_codex":0.0002580325,"about_ca_system_score_gemma":0.00031181035,"threshold_uncertainty_score":0.022557855},"labels":[],"label_agreement":null},{"id":"W2115341683","doi":"10.1109/ccece.1995.528206","title":"A multimedia query user interface","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Query language; Multimedia; Interface (matter); User interface; Information retrieval; Query expansion; World Wide Web; Human–computer interaction","score_opus":0.016415169210186594,"score_gpt":0.22413332514528608,"score_spread":0.20771815593509949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115341683","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004986631,0.00074501033,0.9048447,0.0008376089,0.00019900476,0.000727174,0.0026650932,0.06772406,0.017270735],"genre_scores_gemma":[0.12648015,0.0014038266,0.79258716,0.0045198575,0.00038979587,0.0020445473,0.009419972,0.0072234245,0.055931292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989747,0.00026899794,0.000113063346,0.0001631432,0.0003929564,0.00008716418],"domain_scores_gemma":[0.9985286,0.0006833911,0.000058052658,0.00012889081,0.0004625491,0.00013845779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015847688,0.0013297215,0.00092965196,0.0011555568,0.00045739763,0.0023110446,0.002151845,0.001977824,0.035652924],"category_scores_gemma":[0.004747069,0.0004326383,0.00069136027,0.0007766237,0.0004967329,0.0024165094,0.0018076814,0.0011327917,0.011837035],"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":[0.002603511,0.00041125366,0.0015627564,0.0019290822,0.00012974518,0.001741875,0.0019825858,0.004282502,0.15806481,0.06225079,0.29023266,0.47480845],"study_design_scores_gemma":[0.0005363748,0.0008395821,0.0014287678,0.00045399438,0.00015869953,0.0041480935,0.00048002007,0.122143544,0.07646858,0.020312034,0.7727417,0.00028860747],"about_ca_topic_score_codex":0.0017182931,"about_ca_topic_score_gemma":0.00094206247,"teacher_disagreement_score":0.035652924,"about_ca_system_score_codex":0.00047629495,"about_ca_system_score_gemma":0.0005191309,"threshold_uncertainty_score":0.11927098},"labels":[],"label_agreement":null},{"id":"W2117300534","doi":"10.1109/mmcs.1995.484908","title":"A multimedia news delivery system over an ATM network","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":true,"ca_institutions":"University of Ottawa","funders":"University of Ottawa","keywords":"Computer science; Multimedia; World Wide Web; Videoconferencing; Multimedia database","score_opus":0.016227626479809302,"score_gpt":0.2033565818052231,"score_spread":0.1871289553254138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117300534","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43776953,0.0017093262,0.4106737,0.0012683212,0.00050294946,0.0013178476,0.0051711537,0.06558398,0.0760032],"genre_scores_gemma":[0.78070426,0.0010287006,0.16334003,0.00031016482,0.00026645933,0.0002908293,0.0041600796,0.00070514565,0.04919432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997502,0.000041068957,0.00001959996,0.000053323456,0.000092165865,0.000043722684],"domain_scores_gemma":[0.9995227,0.000088054745,0.000033135355,0.00006987594,0.00015446884,0.0001318796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041944935,0.00035089024,0.00031583683,0.0005944799,0.00096498785,0.0011499423,0.00063718576,0.00050144596,0.010335808],"category_scores_gemma":[0.0007772997,0.00018298443,0.0001637352,0.0010272498,0.00022011723,0.0011230573,0.00043647084,0.0003714042,0.0029004316],"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":[0.0038742942,0.000550966,0.007448029,0.00054760405,0.00012300903,0.002790032,0.0016215397,0.015113338,0.2517121,0.013353632,0.081430495,0.621435],"study_design_scores_gemma":[0.00084802957,0.002548015,0.017094264,0.000112882866,0.00050365494,0.0045832,0.0014284028,0.27734214,0.2070488,0.0058701923,0.48236865,0.00025178277],"about_ca_topic_score_codex":0.007146571,"about_ca_topic_score_gemma":0.0045693615,"teacher_disagreement_score":0.010335808,"about_ca_system_score_codex":0.0007854507,"about_ca_system_score_gemma":0.0005556229,"threshold_uncertainty_score":0.034576714},"labels":[],"label_agreement":null},{"id":"W2117849405","doi":"10.1145/2207676.2208622","title":"#EpicPlay","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CLIPS; Computer science; Event (particle physics); Multimedia; Social media; World Wide Web; Human–computer interaction; Artificial intelligence","score_opus":0.010695073956738884,"score_gpt":0.22168582370705775,"score_spread":0.21099074975031887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117849405","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023449302,0.0011545923,0.07876948,0.0011141706,0.0019229061,0.0011942049,0.080853716,0.12628472,0.6852569],"genre_scores_gemma":[0.08800404,0.0011412041,0.057482306,0.0012291651,0.00044340995,0.0009799062,0.1335921,0.028692001,0.6884358],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994097,0.0000713271,0.000051574185,0.00016515217,0.00022161684,0.000080656944],"domain_scores_gemma":[0.9993807,0.00012006207,0.000038000057,0.00016163453,0.00021226003,0.00008721508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045633852,0.0014553784,0.0004326086,0.0013775168,0.0008504571,0.0025968468,0.0010668564,0.0006892245,0.26406977],"category_scores_gemma":[0.0019117875,0.0004731008,0.00047174783,0.00079775025,0.00034868554,0.002748921,0.0025759218,0.0007517905,0.1388643],"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":[0.00090720283,0.00012989751,0.0034496444,0.0007372528,0.000026475005,0.0003845218,0.0009182525,0.00042561485,0.0099386955,0.017289734,0.6287518,0.33704096],"study_design_scores_gemma":[0.00002816893,0.000056442215,0.0024492878,0.000060128794,0.000010245644,0.00021745797,0.00015326016,0.0009632763,0.0031756,0.0018981616,0.9909614,0.00002657731],"about_ca_topic_score_codex":0.0020703962,"about_ca_topic_score_gemma":0.0048118117,"teacher_disagreement_score":0.26406977,"about_ca_system_score_codex":0.00046226502,"about_ca_system_score_gemma":0.0004685129,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2118941095","doi":"10.1109/icme.2011.6011961","title":"SmartAd: A smart system for effective advertising in online videos","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Relevance (law); Revenue; Online advertising; Online video; Advertising; Multimedia; World Wide Web; The Internet; Business","score_opus":0.016886677976231078,"score_gpt":0.2343794307493694,"score_spread":0.21749275277313831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118941095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104413345,0.0038910354,0.5446522,0.00092389004,0.00081531954,0.0022980284,0.0047738138,0.30060062,0.037631754],"genre_scores_gemma":[0.59094155,0.0014224288,0.35687786,0.0014934288,0.00080073794,0.001060332,0.00661283,0.002646097,0.038144734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949336,0.000080251506,0.000045487846,0.0001496507,0.00017768156,0.00005356553],"domain_scores_gemma":[0.99888366,0.0003766195,0.000103851795,0.00025506897,0.0002369046,0.00014386802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008058575,0.00084998034,0.001039773,0.0024268804,0.00053130224,0.0014229572,0.0015159906,0.001047398,0.011849329],"category_scores_gemma":[0.002213895,0.00045930545,0.00035956965,0.0010269759,0.00035641313,0.0032220138,0.0013047229,0.0007760408,0.0051009823],"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":[0.0019580864,0.0011097012,0.0054855924,0.00074927515,0.0001868801,0.0005813024,0.00047492818,0.0027964476,0.050424457,0.005081864,0.09799864,0.8331528],"study_design_scores_gemma":[0.0009744196,0.001775376,0.01809925,0.00017590895,0.0004336976,0.0024230937,0.00051142974,0.5551849,0.12216366,0.013686537,0.2840877,0.0004840083],"about_ca_topic_score_codex":0.0024970463,"about_ca_topic_score_gemma":0.0029612193,"teacher_disagreement_score":0.011849329,"about_ca_system_score_codex":0.0005406873,"about_ca_system_score_gemma":0.0004902016,"threshold_uncertainty_score":0.03963989},"labels":[],"label_agreement":null},{"id":"W2119306803","doi":"10.1109/vecims.2009.5068881","title":"An algorithm for measurement and detection of path cheating in virtual environments","year":2009,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems./Proceedings of the ... IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems","topic":"Video Analysis and Summarization","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":"Cheating; Path (computing); Computer science; Controller (irrigation); Process (computing); Algorithm; Computer network; Psychology; Social psychology; Operating system","score_opus":0.06836971882521779,"score_gpt":0.2723950895419653,"score_spread":0.20402537071674748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119306803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00749049,0.0000591242,0.99021786,0.00004579962,0.000029289848,0.00015901252,0.000040967145,0.0015204333,0.00043707833],"genre_scores_gemma":[0.11636018,0.0000477861,0.88203806,0.00003953123,0.000018255145,0.0002659039,0.00014500592,0.00009618726,0.0009891351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971757,0.000717862,0.00022813778,0.0006002147,0.0010754879,0.00020262993],"domain_scores_gemma":[0.9941453,0.0024840238,0.0007734631,0.0008967389,0.0014733083,0.00022716614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017956943,0.0011106441,0.0013038886,0.0021157053,0.0011703243,0.0016650099,0.0025261538,0.0019146011,0.0024751988],"category_scores_gemma":[0.010953759,0.0005689478,0.000456345,0.0010568897,0.0008961726,0.0020976888,0.0019473315,0.0015957064,0.0012125272],"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":[0.0006468586,0.0004520819,0.0067293257,0.00020257349,0.00015452922,0.00022533412,0.00038118992,0.092527226,0.035352204,0.020226888,0.004189816,0.83891195],"study_design_scores_gemma":[0.000063016196,0.00018897928,0.0018108201,0.000021877902,0.00002867848,0.00036465187,0.000094926116,0.9698439,0.016280474,0.008583777,0.0026622347,0.000056632718],"about_ca_topic_score_codex":0.0021876437,"about_ca_topic_score_gemma":0.0021609077,"teacher_disagreement_score":0.0025261538,"about_ca_system_score_codex":0.00088039273,"about_ca_system_score_gemma":0.001374974,"threshold_uncertainty_score":0.009496629},"labels":[],"label_agreement":null},{"id":"W2122960198","doi":"10.1109/hicss.2003.1174247","title":"Developing video services for mobile users","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; National Research Council Canada","funders":"","keywords":"Computer science; Video processing; Search engine indexing; Video tracking; Multimedia; Software; Image processing; Uncompressed video; World Wide Web; Artificial intelligence; Operating system","score_opus":0.016944207495237958,"score_gpt":0.2565924874767904,"score_spread":0.23964827998155247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122960198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06653489,0.0012382477,0.87444013,0.0013738399,0.00018233221,0.0016599692,0.0006551764,0.021275952,0.032639466],"genre_scores_gemma":[0.25036094,0.0026811438,0.70382077,0.00055846246,0.00022929594,0.0006291874,0.0029953856,0.0022972748,0.03642763],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968994,0.00006125533,0.000020468246,0.0000470615,0.00011734902,0.000063884414],"domain_scores_gemma":[0.9994568,0.00007649285,0.00003172986,0.00007548548,0.00023443783,0.00012498487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064416276,0.0005332723,0.00029175475,0.00076095987,0.0005037364,0.0011340653,0.0010062088,0.0010356604,0.0059457463],"category_scores_gemma":[0.0017163001,0.00027372586,0.00034904588,0.0007632548,0.0002586791,0.0024123208,0.0010384074,0.0006014675,0.005294251],"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":[0.00041964278,0.00030077074,0.0030210633,0.0005130065,0.00003820991,0.0007927519,0.0010359045,0.0032708237,0.10533635,0.026019018,0.03390239,0.82535],"study_design_scores_gemma":[0.0002780316,0.00096532406,0.008257654,0.00035369018,0.00015380392,0.0022284142,0.003207382,0.15392253,0.14284189,0.024567246,0.66310877,0.000115250725],"about_ca_topic_score_codex":0.0032965185,"about_ca_topic_score_gemma":0.0022170257,"teacher_disagreement_score":0.0059457463,"about_ca_system_score_codex":0.0004987709,"about_ca_system_score_gemma":0.00082745403,"threshold_uncertainty_score":0.019890487},"labels":[],"label_agreement":null},{"id":"W2123234527","doi":"10.1109/ism.2008.53","title":"Tiny Videos: Non-parametric Content-Based Video Retrieval and Recognition","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Parametric statistics; Sampling (signal processing); Video retrieval; Similarity (geometry); Dimension (graph theory); Artificial intelligence; Scope (computer science); Computer vision; Range (aeronautics); Pattern recognition (psychology); Information retrieval; Image (mathematics); Mathematics","score_opus":0.05421736019771699,"score_gpt":0.22618578369044323,"score_spread":0.17196842349272623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123234527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03697661,0.0018267019,0.9432463,0.00020201167,0.00019590625,0.0005914249,0.0026374466,0.011448681,0.0028748955],"genre_scores_gemma":[0.21455455,0.0013769404,0.76615524,0.00019827344,0.00034208287,0.00076166383,0.009997512,0.0006245553,0.005989145],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990734,0.00011366353,0.000078777266,0.00022358136,0.00043831,0.00007229222],"domain_scores_gemma":[0.9986136,0.00043751224,0.00017729693,0.0004339852,0.00026608416,0.00007151587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007360917,0.00092428294,0.0013887958,0.0035452906,0.0004123014,0.0010027732,0.002250498,0.00072520546,0.003568096],"category_scores_gemma":[0.0037684168,0.0003854375,0.00064864336,0.0028507146,0.00045132847,0.0025834267,0.0013422129,0.0006348317,0.002570126],"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":[0.0005928876,0.00028476494,0.0015176568,0.0004224892,0.00011054196,0.00019516586,0.00013076614,0.010158281,0.07983934,0.0037386282,0.01594459,0.8870649],"study_design_scores_gemma":[0.00018241374,0.00068852963,0.007662249,0.00007038641,0.00014558627,0.0015339756,0.00032192568,0.7963461,0.14193042,0.011995626,0.03897093,0.00015186278],"about_ca_topic_score_codex":0.0036163619,"about_ca_topic_score_gemma":0.0047633285,"teacher_disagreement_score":0.0036163619,"about_ca_system_score_codex":0.0005660472,"about_ca_system_score_gemma":0.00048292155,"threshold_uncertainty_score":0.011936426},"labels":[],"label_agreement":null},{"id":"W2123493506","doi":"10.1109/cvprw.2010.5543733","title":"A new player-enabled rapid video navigation method using temporal quantization and repeated weighted boosting search","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Codebook; Quantization (signal processing); Video tracking; Boosting (machine learning); Video processing","score_opus":0.025755516114312007,"score_gpt":0.30064169841372973,"score_spread":0.27488618229941775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123493506","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042634276,0.00014611837,0.9947395,0.00003022071,0.000024898809,0.000029502917,0.000016344182,0.00034673023,0.00040328226],"genre_scores_gemma":[0.13670565,0.00022618983,0.8596932,0.000108794,0.00006802703,0.00015089536,0.0001933603,0.0001602162,0.002693637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994141,0.00013294438,0.000033104214,0.00012307828,0.00026028103,0.000036442427],"domain_scores_gemma":[0.9994936,0.00016359965,0.000050125138,0.00006059032,0.00019512571,0.000036846566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008078672,0.00063734764,0.0009547367,0.000912216,0.000385051,0.0005546366,0.0014819573,0.0006828998,0.0016472059],"category_scores_gemma":[0.002040173,0.00034728504,0.00049659534,0.00094367366,0.00036714302,0.0015787616,0.0007178437,0.0006397896,0.0005508633],"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":[0.00028347468,0.00014336672,0.0009502537,0.0001748695,0.000097436525,0.0001120457,0.00020648865,0.11726287,0.047649488,0.017958097,0.004842877,0.8103189],"study_design_scores_gemma":[0.000036362,0.000113787784,0.0003008655,0.000007660095,0.00002325229,0.00013383143,0.000026100202,0.98413277,0.00844171,0.0035241346,0.0032359099,0.0000235755],"about_ca_topic_score_codex":0.0028242723,"about_ca_topic_score_gemma":0.003124339,"teacher_disagreement_score":0.0028242723,"about_ca_system_score_codex":0.00045492392,"about_ca_system_score_gemma":0.0007979426,"threshold_uncertainty_score":0.005615592},"labels":[],"label_agreement":null},{"id":"W2123629344","doi":"10.1109/icip.2002.1040056","title":"Rule-based scene extraction from video","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Video Analysis and Summarization","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":"University of Waterloo","funders":"","keywords":"Dialog box; Computer science; Artificial intelligence; Computer vision; Set (abstract data type); Feature extraction; Action (physics); Cluster analysis; Image (mathematics); Pattern recognition (psychology)","score_opus":0.03290965250960337,"score_gpt":0.29817692881652674,"score_spread":0.26526727630692337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123629344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04515619,0.00038488157,0.93971485,0.00010913966,0.00005547971,0.00043040692,0.0015310007,0.00934353,0.003274451],"genre_scores_gemma":[0.21971653,0.00048683042,0.7711444,0.00010351676,0.00004692184,0.0003425058,0.0055161305,0.00027865727,0.0023645514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995709,0.000037039215,0.00003741854,0.00019920037,0.00010375588,0.00005167256],"domain_scores_gemma":[0.9995546,0.00011857285,0.000065079716,0.000062809944,0.00016648734,0.000032450655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021243295,0.0011167071,0.0010740827,0.002421438,0.0004124852,0.0010105344,0.0009946594,0.0006277239,0.0015702097],"category_scores_gemma":[0.0011177699,0.00040294477,0.0010107221,0.0009492881,0.00033302212,0.00086673844,0.00042851857,0.00058032124,0.0014559923],"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":[0.00033254537,0.00023544741,0.004715642,0.00044190366,0.00012681319,0.00072946795,0.0002663911,0.020325523,0.20385756,0.0028254483,0.006848873,0.75929433],"study_design_scores_gemma":[0.00004041538,0.00028401174,0.017682128,0.000095571966,0.00022924248,0.00083554786,0.0004416738,0.73144156,0.22708546,0.0065258285,0.015228983,0.000109592715],"about_ca_topic_score_codex":0.0046052113,"about_ca_topic_score_gemma":0.0055845175,"teacher_disagreement_score":0.0046052113,"about_ca_system_score_codex":0.0003793388,"about_ca_system_score_gemma":0.0005969799,"threshold_uncertainty_score":0.009156823},"labels":[],"label_agreement":null},{"id":"W2124250523","doi":"10.1109/cvprw.2010.5543575","title":"A computer-vision-assisted system for Videodescription scripting","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer Research Institute of Montréal","funders":"","keywords":"Automatic summarization; Computer science; Scripting language; Usability; Process (computing); Post-production; Multimedia; Software; Search engine indexing; Computer graphics (images); Human–computer interaction; Artificial intelligence; Operating system","score_opus":0.01506745463973723,"score_gpt":0.24592975211770368,"score_spread":0.23086229747796644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124250523","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031237258,0.000380678,0.9010563,0.0001216612,0.00013587401,0.0005282197,0.0008088859,0.06134192,0.0043891673],"genre_scores_gemma":[0.17903335,0.0003598793,0.8021644,0.00023547525,0.00011714274,0.0005436931,0.0027742684,0.0016311744,0.0131405825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996587,0.000040492367,0.000026907343,0.00012336779,0.00012197413,0.000028519624],"domain_scores_gemma":[0.99949086,0.00015131995,0.000041543477,0.000094779985,0.00015188887,0.00006957542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006324596,0.00078171573,0.00059574965,0.00094962196,0.0004235931,0.0007022614,0.0014410163,0.0007423162,0.013047259],"category_scores_gemma":[0.0011453971,0.00035061705,0.000392687,0.0004615143,0.0002963925,0.00076202914,0.0005770524,0.00063692534,0.0050847945],"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":[0.0006647073,0.00029747374,0.0011377763,0.0002950994,0.000061507606,0.00035923906,0.0002567487,0.0018116697,0.36515012,0.0016013315,0.013249528,0.61511487],"study_design_scores_gemma":[0.00023930098,0.0013846211,0.006100264,0.000072157934,0.00016736102,0.0024494068,0.00012735532,0.22858022,0.66874397,0.0022910137,0.089665085,0.00017931091],"about_ca_topic_score_codex":0.0014756598,"about_ca_topic_score_gemma":0.0016956006,"teacher_disagreement_score":0.013047259,"about_ca_system_score_codex":0.00034290258,"about_ca_system_score_gemma":0.00037799214,"threshold_uncertainty_score":0.04364741},"labels":[],"label_agreement":null},{"id":"W2124333148","doi":"10.1109/6046.845016","title":"Automatic key video object plane selection using the shape information in the MPEG-4 compressed domain","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","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 British Columbia","funders":"","keywords":"Computer science; Video tracking; Computer vision; Multiview Video Coding; Artificial intelligence; Video compression picture types; Hausdorff distance; MPEG-4; Block-matching algorithm; Decoding methods; Motion compensation; Object (grammar); Video processing; Coding (social sciences); Data compression; Algorithm; Mathematics","score_opus":0.012883741067359095,"score_gpt":0.23491841348836823,"score_spread":0.22203467242100913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124333148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03205172,0.0003529559,0.96594894,0.00004703332,0.00002891359,0.00007266034,0.00005241855,0.0007109162,0.0007344581],"genre_scores_gemma":[0.25448033,0.0007509969,0.7426608,0.00006539211,0.000057069527,0.00009679454,0.00045370648,0.00017121798,0.0012636584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996151,0.00006480903,0.000024953077,0.00005413484,0.00020416523,0.00003692897],"domain_scores_gemma":[0.9994855,0.0001445184,0.00007143169,0.000097933684,0.00017111159,0.00002941675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044810583,0.0005559831,0.0007955374,0.0012214199,0.0003310147,0.00086436723,0.00054834335,0.00038256642,0.00086427585],"category_scores_gemma":[0.0016023841,0.00020923653,0.0003488395,0.00094752066,0.00040490372,0.0012569543,0.0006882928,0.0004568421,0.0006723189],"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":[0.0007611692,0.000052897427,0.0011818488,0.00011889329,0.000030756844,0.00020964399,0.00017043755,0.016842512,0.31661022,0.0072326935,0.0022345865,0.6545543],"study_design_scores_gemma":[0.00008768041,0.00020082653,0.0026639504,0.000022833638,0.000057949124,0.0009459601,0.00019331662,0.55588263,0.42286846,0.0063089062,0.010704004,0.000063420965],"about_ca_topic_score_codex":0.001195819,"about_ca_topic_score_gemma":0.0009793567,"teacher_disagreement_score":0.0012214199,"about_ca_system_score_codex":0.00034508176,"about_ca_system_score_gemma":0.00042844302,"threshold_uncertainty_score":0.0028912425},"labels":[],"label_agreement":null},{"id":"W2124817266","doi":"10.1109/tpami.2010.118","title":"Tiny Videos: A Large Data Set for Nonparametric Video Retrieval and Frame Classification","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":44,"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; Cluster analysis; Set (abstract data type); Data set; Frame (networking); Computer vision; Pattern recognition (psychology); Precision and recall; Affinity propagation; Video compression picture types; Video tracking; Video processing; Fuzzy clustering","score_opus":0.041838974855430075,"score_gpt":0.3089191624046754,"score_spread":0.2670801875492453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124817266","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47547162,0.0059926915,0.24580614,0.0019755447,0.001713794,0.0025061527,0.2404094,0.0166619,0.0094626825],"genre_scores_gemma":[0.4393242,0.0013046291,0.23858833,0.00036194088,0.0004456226,0.002097907,0.31415263,0.00046181248,0.0032629555],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99867177,0.00017629947,0.00016065159,0.00029666148,0.00057542603,0.00011930281],"domain_scores_gemma":[0.9976622,0.0006213412,0.00022255424,0.00063902116,0.0006728932,0.00018198957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010893652,0.0012501945,0.0010766874,0.004058996,0.00082133425,0.0009181253,0.0015938157,0.0013240731,0.0024335647],"category_scores_gemma":[0.0058888355,0.00028005822,0.00082461577,0.0033843638,0.0005118735,0.0016818333,0.0012064156,0.000921338,0.001921836],"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":[0.002357695,0.0014081785,0.03174032,0.0017976745,0.00039024386,0.0018399532,0.00041481032,0.027660655,0.050403196,0.004418257,0.17952903,0.69804],"study_design_scores_gemma":[0.00054786593,0.0018238728,0.12561484,0.00047660037,0.00039256414,0.0062529636,0.0022852384,0.5579146,0.10772545,0.013818094,0.18267371,0.00047415588],"about_ca_topic_score_codex":0.012903117,"about_ca_topic_score_gemma":0.015308684,"teacher_disagreement_score":0.012903117,"about_ca_system_score_codex":0.000986106,"about_ca_system_score_gemma":0.00087793864,"threshold_uncertainty_score":0.025656044},"labels":[],"label_agreement":null},{"id":"W2127297595","doi":"10.1109/iai.1998.666852","title":"Scene change detection in MPEG domain","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Search engine indexing; Computer vision; Decoding methods; Bitstream; Artificial intelligence; Change detection; Segmentation; Video retrieval; Video compression picture types; Image segmentation; Multiview Video Coding; Shot (pellet); Video processing; Database index; Video tracking; Transform coding; Image (mathematics); Discrete cosine transform; Algorithm","score_opus":0.032811077709644865,"score_gpt":0.214579418413131,"score_spread":0.18176834070348613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127297595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38973755,0.0022830141,0.5920362,0.00023175457,0.00015714562,0.00026213366,0.0011018829,0.0041145,0.010075825],"genre_scores_gemma":[0.7413791,0.0014553758,0.24787392,0.00011964946,0.00013881474,0.00009960943,0.0025888553,0.00021411816,0.006130512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997663,0.000022212307,0.00001027724,0.000038123704,0.00013122165,0.000031906664],"domain_scores_gemma":[0.99978596,0.00004357061,0.000034656816,0.000034784327,0.0000861331,0.0000148468325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014286127,0.00031389046,0.00028687186,0.0013238229,0.00023539648,0.0004615128,0.00035404935,0.00033627023,0.001140349],"category_scores_gemma":[0.0006990413,0.00011721225,0.0002036686,0.00084672467,0.00018401287,0.00051610963,0.00026460693,0.00028105217,0.0006589423],"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":[0.00047004718,0.00007717176,0.003681197,0.00014961662,0.00003851557,0.00036042652,0.00012597986,0.008131348,0.43325454,0.0023123056,0.0039075064,0.5474914],"study_design_scores_gemma":[0.000035208886,0.00052335917,0.04142179,0.000033262986,0.00008374141,0.0017065377,0.00022888073,0.33179864,0.59755707,0.0029063837,0.023647701,0.000057342873],"about_ca_topic_score_codex":0.0018425339,"about_ca_topic_score_gemma":0.0023415089,"teacher_disagreement_score":0.0018425339,"about_ca_system_score_codex":0.00022977122,"about_ca_system_score_gemma":0.0002136996,"threshold_uncertainty_score":0.003814876},"labels":[],"label_agreement":null},{"id":"W2127428839","doi":"10.1109/icme.2011.6011985","title":"A content-based video fast-forward playback method using video time density function and rate distortion theory","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Video processing; Quantization (signal processing); Video tracking; Distortion (music); Video post-processing; Computer vision; Artificial intelligence; Real-time computing; Video compression picture types; Bandwidth (computing); Telecommunications","score_opus":0.04206044840533571,"score_gpt":0.23997830400545747,"score_spread":0.19791785560012176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127428839","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018191141,0.00034301402,0.9970763,0.000035434252,0.00005371973,0.000044924684,0.000036371508,0.00031641533,0.00027478219],"genre_scores_gemma":[0.058909733,0.0009398134,0.93619734,0.000062790634,0.00018642952,0.00011813824,0.00028962028,0.00014550249,0.0031506177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910873,0.00014629427,0.000069719885,0.0001632725,0.00047749974,0.00003454345],"domain_scores_gemma":[0.99894255,0.00028856934,0.00009468933,0.00011381873,0.00051704113,0.00004330144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010288307,0.0011103536,0.0010056348,0.0024092314,0.00040191377,0.0012388162,0.0014984773,0.0007735935,0.0024560413],"category_scores_gemma":[0.0025233943,0.0004174451,0.0007458232,0.0012598075,0.00040585746,0.0026864687,0.00058981363,0.0008815667,0.0010837095],"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":[0.00030365554,0.00010329448,0.0006193816,0.0004111967,0.00011028154,0.00014521167,0.00019007987,0.019579928,0.058713146,0.012925239,0.0028907764,0.90400773],"study_design_scores_gemma":[0.00009596625,0.00045867282,0.0015371522,0.000043597625,0.00019338906,0.0010934363,0.00016972271,0.8879493,0.08467691,0.0052684285,0.01838407,0.00012938608],"about_ca_topic_score_codex":0.0022720518,"about_ca_topic_score_gemma":0.0017337713,"teacher_disagreement_score":0.0024560413,"about_ca_system_score_codex":0.0007151953,"about_ca_system_score_gemma":0.0005374892,"threshold_uncertainty_score":0.008216262},"labels":[],"label_agreement":null},{"id":"W2127877789","doi":"10.1109/icme.2011.6012188","title":"Gesture recognition on a mobile device for remote event generation","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Gesture; Computer science; Android (operating system); Mobile phone; Gesture recognition; Event (particle physics); Phone; Presentation (obstetrics); Orientation (vector space); Mobile device; Mobile computing; Human–computer interaction; Artificial intelligence; Operating system","score_opus":0.08614552880779552,"score_gpt":0.2716749140991406,"score_spread":0.18552938529134505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127877789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04868338,0.0012380611,0.8958413,0.00034643282,0.000517845,0.00073796953,0.0008107438,0.03189282,0.019931374],"genre_scores_gemma":[0.39377686,0.0010358405,0.57015806,0.00046082464,0.00023959742,0.0007631132,0.0013231353,0.001412509,0.030830026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995542,0.00005412767,0.000035478504,0.0001254201,0.00018685902,0.000043857966],"domain_scores_gemma":[0.99962187,0.00013082949,0.000037538546,0.00006178704,0.00010706328,0.00004098349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027537625,0.00079620094,0.00066612527,0.0006181711,0.0003428421,0.0006871838,0.0009630288,0.0007265564,0.013500517],"category_scores_gemma":[0.001033208,0.00026754505,0.00045928272,0.0003583333,0.00020164627,0.0006259522,0.00056188134,0.0004594581,0.004893705],"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":[0.00067801744,0.00011735627,0.0011100483,0.0004882573,0.000051902894,0.0010688411,0.00035351436,0.0018119277,0.42213747,0.0024219188,0.015359791,0.55440104],"study_design_scores_gemma":[0.00030998964,0.001040592,0.011776158,0.0003220165,0.00029054756,0.004282974,0.000327843,0.13704017,0.6490838,0.0033546928,0.19185269,0.00031851482],"about_ca_topic_score_codex":0.0010321377,"about_ca_topic_score_gemma":0.001972055,"teacher_disagreement_score":0.013500517,"about_ca_system_score_codex":0.00022688755,"about_ca_system_score_gemma":0.00021226733,"threshold_uncertainty_score":0.04516369},"labels":[],"label_agreement":null},{"id":"W2127967898","doi":"10.1109/vecims.2009.5068927","title":"Authoring edutainment content through video annotations and 3D model augmentation","year":2009,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems./Proceedings of the ... IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurement Systems","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Leverage (statistics); Visualization; Metaphor; Augmented reality; Annotation; Multimedia; Gesture; Human–computer interaction; Artificial intelligence","score_opus":0.13234115455495968,"score_gpt":0.2893134049606264,"score_spread":0.15697225040566673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127967898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039545942,0.000367366,0.8927805,0.00027401582,0.0005819527,0.00041989578,0.0011743817,0.035836242,0.029019702],"genre_scores_gemma":[0.3483354,0.0009922083,0.59577036,0.00015459117,0.00026424162,0.00051614916,0.0035367059,0.003407553,0.04702283],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996401,0.00009563132,0.000023966202,0.00006252938,0.00015134155,0.000026464086],"domain_scores_gemma":[0.9982158,0.00084283866,0.00011463124,0.00036981754,0.00036511832,0.00009194678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044026683,0.0010897903,0.0004218871,0.0011993068,0.0003262379,0.0013678467,0.0011177772,0.0007654878,0.014994396],"category_scores_gemma":[0.0028886166,0.0003119525,0.00049315894,0.00065348693,0.000360584,0.0015757243,0.0013401927,0.00068311975,0.0054678405],"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":[0.00072795316,0.00030493614,0.0014348275,0.001611822,0.000049326125,0.0012696153,0.0017831753,0.02407235,0.14322941,0.010228952,0.035143334,0.78014433],"study_design_scores_gemma":[0.00009839229,0.00065352616,0.0030624843,0.00042873216,0.00012842794,0.0011383675,0.0012008871,0.35629815,0.2692087,0.0088062035,0.3587501,0.00022605527],"about_ca_topic_score_codex":0.0007474725,"about_ca_topic_score_gemma":0.0017315364,"teacher_disagreement_score":0.014994396,"about_ca_system_score_codex":0.00027912948,"about_ca_system_score_gemma":0.00035669765,"threshold_uncertainty_score":0.050161183},"labels":[],"label_agreement":null},{"id":"W2128237130","doi":"10.1142/s0219467803000919","title":"VIEWS OR POINTS OF VIEW ON IMAGES","year":2003,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Video Analysis and Summarization","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":"Computer science; Object (grammar); GRASP; Representation (politics); Class (philosophy); Interpretation (philosophy); Image (mathematics); Exploit; Logical data model; Semantics (computer science); Point (geometry); Data model (GIS); Object model; Information retrieval; Artificial intelligence; Data modeling; Database; Programming language","score_opus":0.02259649952180506,"score_gpt":0.29857524039866734,"score_spread":0.2759787408768623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128237130","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015779028,0.009977717,0.83790284,0.0023270117,0.00154406,0.0003501134,0.0021091918,0.0031763946,0.12683372],"genre_scores_gemma":[0.2989752,0.021122672,0.6036691,0.0022491796,0.0024453294,0.00055502733,0.003760375,0.0015246007,0.065698415],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983658,0.00032706658,0.00011845148,0.000360803,0.00068999856,0.00013784503],"domain_scores_gemma":[0.9976948,0.00067651016,0.00019920722,0.00078276166,0.0005124969,0.00013415722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013847366,0.0010753936,0.00076862023,0.0035091785,0.00072349596,0.0065305135,0.0012285033,0.0020923116,0.013456218],"category_scores_gemma":[0.004921042,0.0005189807,0.0010071832,0.0028702235,0.002820851,0.0094935745,0.0031356288,0.001808046,0.0046848585],"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":[0.0002595518,0.000046404806,0.0012842057,0.0008417618,0.00009735518,0.0012200314,0.0022008389,0.0018730328,0.020349974,0.75420487,0.026920674,0.19070132],"study_design_scores_gemma":[0.00005321452,0.00013254222,0.0023900564,0.00065415754,0.000121162295,0.0023877246,0.001966032,0.010241922,0.017365776,0.40610695,0.5584584,0.00012202993],"about_ca_topic_score_codex":0.0010716971,"about_ca_topic_score_gemma":0.0009650573,"teacher_disagreement_score":0.013456218,"about_ca_system_score_codex":0.0005894798,"about_ca_system_score_gemma":0.00039560065,"threshold_uncertainty_score":0.045015574},"labels":[],"label_agreement":null},{"id":"W2129134598","doi":"10.1109/icip.2002.1039977","title":"Scene change detection using DC coefficients","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Video Analysis and Summarization","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 Metropolitan University","funders":"","keywords":"Discrete cosine transform; Histogram; Frame (networking); Algorithm; Computer science; Computational complexity theory; Energy (signal processing); Computer vision; Artificial intelligence; Mathematics; Image (mathematics); Telecommunications; Statistics","score_opus":0.07809778120919272,"score_gpt":0.31508673401925547,"score_spread":0.23698895281006274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129134598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044300463,0.0006583558,0.95030004,0.00013069091,0.00012437157,0.000118707416,0.0002259207,0.0012321912,0.0029093085],"genre_scores_gemma":[0.24030164,0.00076507556,0.75475526,0.00009933623,0.0001367895,0.00009553491,0.00077539263,0.00012430831,0.0029466841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996766,0.000020224035,0.00001527872,0.00007453767,0.00017231579,0.000041140964],"domain_scores_gemma":[0.99954706,0.00007526578,0.000042196483,0.000054756863,0.00024605702,0.000034673983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023581319,0.0005675369,0.00048878085,0.0036945727,0.00040570102,0.0008006487,0.00071784435,0.0005479141,0.0015553375],"category_scores_gemma":[0.00087689486,0.00029311198,0.00036213011,0.0018157192,0.00034212234,0.00093929574,0.00049578614,0.000604436,0.000886883],"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":[0.00018629196,0.00011262202,0.003324688,0.00014819157,0.00005180482,0.00017659161,0.00007601719,0.008645233,0.16020404,0.0032681806,0.0033257324,0.8204806],"study_design_scores_gemma":[0.000059190552,0.00036318586,0.0156935,0.000041132476,0.0001468462,0.0013871446,0.00016652109,0.6003416,0.34426266,0.0039773257,0.0334356,0.00012526041],"about_ca_topic_score_codex":0.00245831,"about_ca_topic_score_gemma":0.0034547956,"teacher_disagreement_score":0.0036945727,"about_ca_system_score_codex":0.0003020479,"about_ca_system_score_gemma":0.0004113531,"threshold_uncertainty_score":0.0052030683},"labels":[],"label_agreement":null},{"id":"W2132652252","doi":"10.1109/icme.2011.6011934","title":"Hybrid low-delay compression of motion capture data","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Motion capture; Data compression; Compression (physics); Visualization; Graphics; Motion (physics); Data visualization; Scheme (mathematics); Multimedia; Computer graphics (images); Artificial intelligence","score_opus":0.04734844635517932,"score_gpt":0.24116756944775067,"score_spread":0.19381912309257135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132652252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15178515,0.0020379312,0.8386829,0.0003483342,0.0003020826,0.00030170774,0.00070927467,0.001684745,0.0041479557],"genre_scores_gemma":[0.5773431,0.0017814107,0.40898556,0.00027363966,0.00031332843,0.0003494718,0.0020905193,0.00016457388,0.008698427],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997242,0.000032506032,0.000019064642,0.00003706002,0.00016014655,0.00002700196],"domain_scores_gemma":[0.9993825,0.0002401698,0.000052588013,0.00009545535,0.00020343349,0.000025847145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030526947,0.00055441045,0.00033871538,0.0013034714,0.00027997277,0.00046731537,0.00048805992,0.00043701314,0.0019391903],"category_scores_gemma":[0.0014802727,0.00013004881,0.00025939805,0.0011356501,0.00022974687,0.0006392261,0.000502426,0.00038068465,0.0005396439],"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":[0.00095970166,0.00016124414,0.0014759185,0.0003365328,0.00007311138,0.00033517025,0.0001589291,0.019751582,0.27023402,0.002939363,0.0038829315,0.6996914],"study_design_scores_gemma":[0.0001473509,0.0008809418,0.009654856,0.00012159552,0.00012726754,0.0018085338,0.00017883291,0.51893234,0.43928626,0.0030318466,0.025706228,0.00012387287],"about_ca_topic_score_codex":0.0012285325,"about_ca_topic_score_gemma":0.0018342277,"teacher_disagreement_score":0.0019391903,"about_ca_system_score_codex":0.00023669867,"about_ca_system_score_gemma":0.00025102327,"threshold_uncertainty_score":0.0064871907},"labels":[],"label_agreement":null},{"id":"W2132728086","doi":"","title":"York_University at TRECVID 2010.","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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","score_opus":0.006566516142653424,"score_gpt":0.17967092542653598,"score_spread":0.17310440928388257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132728086","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062531354,0.026901817,0.02567019,0.0050375117,0.0093699265,0.0011170383,0.7485805,0.06874775,0.108322114],"genre_scores_gemma":[0.007982109,0.0039155274,0.019762838,0.0006560443,0.000822812,0.0008161122,0.8795197,0.0032869587,0.08323801],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.997083,0.00040426094,0.00016930487,0.00074732094,0.0012264639,0.00036972848],"domain_scores_gemma":[0.99624324,0.00043719134,0.00015496505,0.00075569755,0.001818078,0.0005907546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003393976,0.003866277,0.0031764854,0.009579972,0.0021448692,0.0040605403,0.0031583793,0.0022961076,0.11867599],"category_scores_gemma":[0.0068424135,0.0009471393,0.0010163796,0.006900867,0.00079767575,0.0040550525,0.0027064686,0.0026924952,0.12123045],"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":[0.00005085809,0.000024874698,0.00011115178,0.0002345251,0.000018424236,0.000029481711,0.000012615012,0.00020250304,0.0005735075,0.0004583431,0.9721469,0.026136814],"study_design_scores_gemma":[0.00017179287,0.00008561179,0.0038207425,0.00023228105,0.00004427356,0.000243295,0.00010600276,0.007660445,0.0034900208,0.0034953787,0.98054874,0.000101283],"about_ca_topic_score_codex":0.11966459,"about_ca_topic_score_gemma":0.14214936,"teacher_disagreement_score":0.11966459,"about_ca_system_score_codex":0.0037069325,"about_ca_system_score_gemma":0.003979799,"threshold_uncertainty_score":0.39701074},"labels":[],"label_agreement":null},{"id":"W2133018938","doi":"10.1145/569005.569006","title":"Planning animation cinematography and shot structure to communicate theme and mood","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Western University","funders":"","keywords":"Cinematography; Animation; Narrative; Computer science; Framing (construction); Multimedia; Shot (pellet); Theme (computing); Plot (graphics); Pluralistic walkthrough; Human–computer interaction; Computer graphics (images); Visual arts; Art; World Wide Web; Engineering; Usability","score_opus":0.030104280690465637,"score_gpt":0.2482481791509398,"score_spread":0.21814389846047416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133018938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029010916,0.00026655535,0.950196,0.0001721935,0.000075333024,0.00057881064,0.00026046977,0.0059117363,0.013527848],"genre_scores_gemma":[0.22987211,0.0004606657,0.75814575,0.00006360658,0.000052700037,0.00046355763,0.0006211883,0.0007482111,0.009572258],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997311,0.00009646343,0.000017264669,0.00006954484,0.000065516884,0.000020015796],"domain_scores_gemma":[0.9993861,0.00033063488,0.00004639277,0.00009684818,0.000097199154,0.000042826745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061070465,0.00076921965,0.00029512562,0.00064576743,0.0004397377,0.00087972236,0.00053446234,0.00045003518,0.008001889],"category_scores_gemma":[0.002355296,0.00039702645,0.00032068815,0.00037461967,0.00052060635,0.0009424494,0.00065423944,0.00053484784,0.0013990572],"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":[0.00059604394,0.00024333614,0.0020025126,0.0010065429,0.00006511426,0.00060601573,0.0062207105,0.028739309,0.30584744,0.03814294,0.015989253,0.60054076],"study_design_scores_gemma":[0.00024447578,0.00084555644,0.007997101,0.00035706154,0.00023456507,0.0017610241,0.0030151196,0.41393372,0.30402556,0.03162593,0.23575,0.0002098604],"about_ca_topic_score_codex":0.0008738555,"about_ca_topic_score_gemma":0.0014577723,"teacher_disagreement_score":0.008001889,"about_ca_system_score_codex":0.00027931077,"about_ca_system_score_gemma":0.00036037542,"threshold_uncertainty_score":0.026768982},"labels":[],"label_agreement":null},{"id":"W2134694373","doi":"10.1109/crv.2009.20","title":"Video Pause Detection Using Wavelets","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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","keywords":"Computer science; Histogram; Artificial intelligence; Wavelet; Computer vision; Search engine indexing; Wavelet transform; Entropy (arrow of time); Digital video; Shot (pellet); Video processing; Video denoising; Pixel; Video tracking; Pattern recognition (psychology); Multiview Video Coding; Image (mathematics); Frame (networking)","score_opus":0.015633809142387583,"score_gpt":0.2399904037840556,"score_spread":0.22435659464166802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134694373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14141396,0.0009931665,0.85458505,0.00009573946,0.00009811833,0.000059813116,0.0002520278,0.00090943027,0.0015926398],"genre_scores_gemma":[0.6441361,0.0011813034,0.35155767,0.000046811816,0.00014202196,0.00006314616,0.0007930559,0.00012781045,0.0019520984],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964786,0.00005334139,0.000026934213,0.00007733996,0.00015409458,0.00004048772],"domain_scores_gemma":[0.99931526,0.00023776107,0.000101738864,0.000079441386,0.00021995931,0.0000459183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055504305,0.00041004308,0.00048967387,0.0020083685,0.00018037148,0.0006657052,0.00043001506,0.00046204388,0.0007678084],"category_scores_gemma":[0.002009616,0.0002083512,0.0004129658,0.001251439,0.00023581386,0.0009798225,0.00048251337,0.0004997591,0.0004545288],"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":[0.00087504555,0.00009181919,0.0058598127,0.00031823403,0.00010351202,0.000409372,0.00025716604,0.015409765,0.22830287,0.004236275,0.0026292496,0.7415069],"study_design_scores_gemma":[0.0000878197,0.00067394064,0.032267015,0.000082996376,0.00018315211,0.0016741682,0.00040314434,0.74227256,0.20219986,0.006950252,0.013098446,0.00010661855],"about_ca_topic_score_codex":0.00053659163,"about_ca_topic_score_gemma":0.00042362924,"teacher_disagreement_score":0.0020083685,"about_ca_system_score_codex":0.00018787426,"about_ca_system_score_gemma":0.0001756737,"threshold_uncertainty_score":0.00293535},"labels":[],"label_agreement":null},{"id":"W2135598964","doi":"10.1145/2502081.2502277","title":"Jiku director","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"National Research Foundation Singapore","keywords":"Mashup; Shot (pellet); Upload; Computer science; Event (particle physics); Pixel; Computer vision; Computer graphics (images); Artificial intelligence; Quality (philosophy); Multimedia; World Wide Web; The Internet; Web 2.0","score_opus":0.004564445234850589,"score_gpt":0.1762628633056463,"score_spread":0.17169841807079572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135598964","genre_codex":"software","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073829144,0.0019655977,0.2889716,0.003027192,0.0018694871,0.0009627032,0.0076360884,0.42047167,0.20126647],"genre_scores_gemma":[0.33793056,0.0014533021,0.4121988,0.0027985508,0.00093616516,0.00089251244,0.022199078,0.019640345,0.2019507],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994253,0.00008377565,0.000029611267,0.00015645176,0.00019185347,0.000113067756],"domain_scores_gemma":[0.9987324,0.00023283932,0.00005725083,0.0004064676,0.000234949,0.0003361444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008346229,0.0006736992,0.0004485052,0.00067650655,0.000931407,0.00140226,0.0009974854,0.00061040674,0.03849051],"category_scores_gemma":[0.0018891754,0.00042033195,0.00035908152,0.00051038753,0.00032039543,0.0022804346,0.0020882017,0.001011432,0.0164027],"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":[0.0017732838,0.0006975438,0.006462421,0.0007865673,0.00008026743,0.0014075207,0.0018551361,0.0012398574,0.07408102,0.011455915,0.5426725,0.35748792],"study_design_scores_gemma":[0.00017689152,0.00036126655,0.009626165,0.00011683511,0.00006273188,0.0019052648,0.0006218932,0.027572092,0.041172918,0.0031871935,0.9150516,0.00014510208],"about_ca_topic_score_codex":0.0016608719,"about_ca_topic_score_gemma":0.0030875143,"teacher_disagreement_score":0.03849051,"about_ca_system_score_codex":0.0003148243,"about_ca_system_score_gemma":0.0005957312,"threshold_uncertainty_score":0.12876356},"labels":[],"label_agreement":null},{"id":"W2136771689","doi":"10.1109/tvcg.2006.194","title":"Visual Signatures in Video Visualization","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":54,"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; Royal Society","keywords":"Visualization; Computer science; Data visualization; Visual analytics; Pipeline (software); Information visualization; Set (abstract data type); Interactive visual analysis; Process (computing); Computer vision; Artificial intelligence; Human–computer interaction","score_opus":0.009063503889180799,"score_gpt":0.2561166880877572,"score_spread":0.24705318419857641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136771689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012962222,0.014358049,0.9484112,0.0031082346,0.00069332763,0.00024095661,0.00025802475,0.0015470667,0.018420925],"genre_scores_gemma":[0.2997334,0.015470319,0.67383933,0.00091221445,0.0011675415,0.00045733142,0.0004246158,0.00056444516,0.0074307784],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9970914,0.0014542151,0.00014508088,0.00036520668,0.0008132374,0.0001307038],"domain_scores_gemma":[0.99319047,0.0043189274,0.00050046615,0.00077921274,0.0009400902,0.00027080174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022783515,0.0009300108,0.00073198386,0.002479065,0.00080457603,0.00457548,0.0013078102,0.0019526865,0.005340586],"category_scores_gemma":[0.016681293,0.0006457179,0.0005457745,0.0025393916,0.0029207033,0.0048384797,0.0025645737,0.0020690183,0.0012756087],"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":[0.00041047015,0.00008529084,0.0018356197,0.0019335208,0.00007257708,0.00067577895,0.0023867998,0.02368612,0.031102156,0.2780534,0.019341562,0.6404167],"study_design_scores_gemma":[0.00013692222,0.0005620164,0.0037540884,0.0011852316,0.000117252464,0.0041509774,0.0018523872,0.18724822,0.04962465,0.5241211,0.22691926,0.0003277875],"about_ca_topic_score_codex":0.0016786141,"about_ca_topic_score_gemma":0.0008452266,"teacher_disagreement_score":0.005340586,"about_ca_system_score_codex":0.00091324036,"about_ca_system_score_gemma":0.00068909384,"threshold_uncertainty_score":0.017866015},"labels":[],"label_agreement":null},{"id":"W2138476053","doi":"10.1109/icmla.2009.32","title":"Video Copy Detection Using Temporally Informative Representative Images","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":37,"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; Hash function; Artificial intelligence; Computer vision; Robustness (evolution); Dynamic perfect hashing; Video tracking; Block-matching algorithm; Pattern recognition (psychology); Hash table; Video processing; Double hashing","score_opus":0.016050068142297483,"score_gpt":0.2809996741277888,"score_spread":0.26494960598549133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138476053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23518056,0.0011870407,0.7588832,0.00012415457,0.000107175925,0.0001890396,0.00026031994,0.0018868532,0.002181643],"genre_scores_gemma":[0.67090535,0.00072055333,0.3256389,0.00006539103,0.00009833767,0.00007338683,0.00043921798,0.0000757566,0.0019830617],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994747,0.00007460916,0.000027241891,0.00010910503,0.00027011635,0.000044223398],"domain_scores_gemma":[0.9987796,0.00025912098,0.00029900967,0.00027007968,0.00033025467,0.00006199398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004931208,0.000490947,0.0006263524,0.0021386081,0.0002485465,0.00056814007,0.0006822951,0.00052481116,0.00076320284],"category_scores_gemma":[0.002782261,0.0002783458,0.0003867727,0.0010417849,0.00038379838,0.0013064675,0.00074830925,0.0003498451,0.0005015879],"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":[0.0008934278,0.00010454732,0.008051333,0.00037547314,0.00013475948,0.00071294163,0.00030732984,0.013159142,0.26357085,0.0037984913,0.0023667691,0.70652485],"study_design_scores_gemma":[0.00007546318,0.0011309566,0.020411272,0.00007336833,0.00019176846,0.006785147,0.00039366816,0.38688827,0.5684246,0.0034951419,0.011981843,0.00014848524],"about_ca_topic_score_codex":0.00048750473,"about_ca_topic_score_gemma":0.0005687382,"teacher_disagreement_score":0.0021386081,"about_ca_system_score_codex":0.00034141872,"about_ca_system_score_gemma":0.00037843743,"threshold_uncertainty_score":0.0026079416},"labels":[],"label_agreement":null},{"id":"W2139152022","doi":"10.1145/1866029.1866054","title":"Chronicle","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Workflow; Computer science; World Wide Web; Data science; Multimedia; Information retrieval; Human–computer interaction; Database","score_opus":0.00485582728920764,"score_gpt":0.2115549172030283,"score_spread":0.20669908991382066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139152022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02566909,0.005752609,0.5272582,0.004475834,0.0016303386,0.0024678414,0.024391681,0.17561848,0.23273598],"genre_scores_gemma":[0.10476948,0.0048854495,0.5921656,0.0041107587,0.0011779965,0.0035536995,0.050508533,0.0174759,0.22135262],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876356,0.00027084965,0.00012012016,0.00029797896,0.00043429632,0.00011320084],"domain_scores_gemma":[0.99426466,0.0018591903,0.0003392712,0.0015840123,0.001237148,0.00071573356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019184931,0.0008071024,0.00050348265,0.00241821,0.0013789074,0.0032905592,0.0019637137,0.0011821481,0.08343197],"category_scores_gemma":[0.007920848,0.0004966631,0.0005435777,0.0014679184,0.00055762305,0.007136752,0.0043968637,0.0013077378,0.029748539],"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":[0.00040419318,0.00016126358,0.0021959252,0.0014882986,0.000039832634,0.00045537937,0.002901019,0.00039840673,0.008378549,0.010484726,0.4043279,0.56876445],"study_design_scores_gemma":[0.00004256208,0.00011340236,0.0014907093,0.00028747946,0.000020890166,0.00061492756,0.00052819145,0.0018097027,0.0032936085,0.003308701,0.9884305,0.00005927712],"about_ca_topic_score_codex":0.0026868775,"about_ca_topic_score_gemma":0.008060698,"teacher_disagreement_score":0.08343197,"about_ca_system_score_codex":0.0005918358,"about_ca_system_score_gemma":0.0016312219,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2140278677","doi":"10.1109/crv.2011.12","title":"Using Line and Ellipse Features for Rectification of Broadcast Hockey Video","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of British Columbia","funders":"McGill University","keywords":"Computer vision; Robustness (evolution); Computer science; Artificial intelligence; Ellipse; Homography; Bundle adjustment; Image (mathematics); Mathematics","score_opus":0.1009780922878621,"score_gpt":0.2856016001602165,"score_spread":0.18462350787235443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140278677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16944072,0.0006399629,0.8166465,0.00016638149,0.00010611898,0.00015403218,0.0005177171,0.0072760386,0.005052417],"genre_scores_gemma":[0.57838464,0.00055234093,0.4160707,0.00005985232,0.00008379942,0.00006631249,0.0012126924,0.0004763148,0.0030933195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996948,0.000029121225,0.000015555643,0.00010633912,0.00010990371,0.00004426601],"domain_scores_gemma":[0.99958664,0.00007752293,0.000073836585,0.00009271274,0.00015097811,0.000018233219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003861451,0.00056746573,0.00061445055,0.0022415537,0.00030852482,0.0006098851,0.00060465175,0.0005106844,0.0015170916],"category_scores_gemma":[0.0014251833,0.00037348483,0.00033800473,0.0014067884,0.00022818359,0.001077266,0.00047615106,0.00060502096,0.00095117575],"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":[0.00023097651,0.00006311247,0.0019116949,0.000117579926,0.0000586576,0.00021765135,0.00022375378,0.015486036,0.12919115,0.0009861479,0.0037874205,0.84772575],"study_design_scores_gemma":[0.00006340542,0.0002166321,0.030403487,0.000052885745,0.00010637973,0.00095277495,0.0005459263,0.74530107,0.20036013,0.002293089,0.019593574,0.000110606765],"about_ca_topic_score_codex":0.0037740578,"about_ca_topic_score_gemma":0.0054980987,"teacher_disagreement_score":0.0037740578,"about_ca_system_score_codex":0.00030941528,"about_ca_system_score_gemma":0.00029378373,"threshold_uncertainty_score":0.007504165},"labels":[],"label_agreement":null},{"id":"W2140582509","doi":"10.1109/icassp.2000.859231","title":"Video object summarization in the MPEG-4 compressed domain","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Video tracking; Computer science; Video compression picture types; Multiview Video Coding; Block-matching algorithm; Computer vision; Artificial intelligence; Video post-processing; Video denoising; Hausdorff distance; Smacker video; Motion compensation; Object (grammar); Video processing; Uncompressed video","score_opus":0.016901621426964122,"score_gpt":0.21231963351834587,"score_spread":0.19541801209138177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140582509","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028683715,0.0014185222,0.96501523,0.00015897075,0.00011479679,0.00014942225,0.0002674964,0.0014767835,0.0027151278],"genre_scores_gemma":[0.20057976,0.002435875,0.7878076,0.00013655153,0.00034199585,0.0001656856,0.002171259,0.00024748704,0.0061137634],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960893,0.000053348474,0.000032925956,0.000056706678,0.00021475009,0.00003341335],"domain_scores_gemma":[0.9995554,0.000096750584,0.000063893305,0.00007613511,0.00018721617,0.00002064903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036190095,0.0007232281,0.0008016778,0.0017246914,0.00034264033,0.0009328221,0.00061301305,0.0006149433,0.0015594274],"category_scores_gemma":[0.0012293666,0.00016473462,0.00042568732,0.0016239523,0.00034238302,0.0013426406,0.0005042463,0.0004206243,0.0013062118],"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":[0.00042378492,0.00007195723,0.00045563886,0.00029484287,0.00005771992,0.00035538143,0.00017755857,0.027243244,0.23527443,0.0117475195,0.0056760767,0.7182218],"study_design_scores_gemma":[0.00007246286,0.0006038526,0.0034521604,0.000053263688,0.00017651342,0.0011683191,0.00030253042,0.5641327,0.36983547,0.016657649,0.04346517,0.00007986568],"about_ca_topic_score_codex":0.0010401569,"about_ca_topic_score_gemma":0.0009831272,"teacher_disagreement_score":0.0017246914,"about_ca_system_score_codex":0.00039144946,"about_ca_system_score_gemma":0.00029898714,"threshold_uncertainty_score":0.005216837},"labels":[],"label_agreement":null},{"id":"W2141453545","doi":"10.1109/ccece.2006.277603","title":"A Modular Distributed Video Surveillance System Over IP","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Modular design; Listing (finance); Event (particle physics); Real-time computing; The Internet; Video processing; Video tracking; Embedded system; Computer hardware; Operating system","score_opus":0.003944995849422991,"score_gpt":0.1850275485546459,"score_spread":0.18108255270522292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141453545","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13716578,0.0003659803,0.78029394,0.0002966734,0.00014755555,0.0006662858,0.000550767,0.0628887,0.017624209],"genre_scores_gemma":[0.71359533,0.00021561186,0.26884338,0.00027585053,0.00016801832,0.00051194924,0.001448146,0.00059556874,0.014346046],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996308,0.000048236743,0.000021326805,0.0001177746,0.00013031128,0.000051453186],"domain_scores_gemma":[0.9995863,0.00006570843,0.00003819486,0.00010293158,0.00012986035,0.000076982134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004286575,0.0005372352,0.0005178896,0.0006216538,0.00031065344,0.0007271597,0.0011368208,0.0004923773,0.008974454],"category_scores_gemma":[0.00066471397,0.00023046804,0.00021163076,0.0004286616,0.00019928628,0.001094508,0.0007603133,0.0004364542,0.003231956],"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":[0.0020400295,0.00075695163,0.005815173,0.00040154762,0.00012467132,0.0007947618,0.00038094746,0.022164859,0.28896835,0.006587689,0.028484264,0.6434807],"study_design_scores_gemma":[0.0009814217,0.003104186,0.01175722,0.00008567455,0.00030152578,0.002360994,0.00015039949,0.7018382,0.18775338,0.0057324925,0.085774146,0.00016040355],"about_ca_topic_score_codex":0.0007750054,"about_ca_topic_score_gemma":0.0003329812,"teacher_disagreement_score":0.008974454,"about_ca_system_score_codex":0.0002922637,"about_ca_system_score_gemma":0.00031895202,"threshold_uncertainty_score":0.030022562},"labels":[],"label_agreement":null},{"id":"W2141914391","doi":"10.1109/icegic.2010.5716878","title":"A heuristic pathfinding approach based on precomputing and post-adjusting strategies for online game environment","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pathfinding; Heuristic; Computer science; Point (geometry); Human–computer interaction; Artificial intelligence; Theoretical computer science; Mathematics; Shortest path problem","score_opus":0.01467015102461579,"score_gpt":0.2274813644831933,"score_spread":0.2128112134585775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141914391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01048049,0.000040977364,0.987444,0.00003228809,0.000009336513,0.00005034201,0.000022684428,0.0007256882,0.0011942397],"genre_scores_gemma":[0.20681879,0.00008137073,0.7908988,0.000031317493,0.0000114658515,0.00013510237,0.00010452305,0.00009374632,0.0018249104],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996239,0.000091939946,0.000019999261,0.00011359999,0.00010168297,0.00004888783],"domain_scores_gemma":[0.99955267,0.00016077825,0.000040933388,0.000124374,0.00008938074,0.00003182361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035275795,0.0006969426,0.0006475701,0.0009486122,0.0007250603,0.0008520968,0.0016538942,0.0005587297,0.002360597],"category_scores_gemma":[0.0012050712,0.00031337913,0.00046918934,0.0008903828,0.0006847519,0.0011848926,0.0008335603,0.0005322146,0.00038296782],"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":[0.00019005928,0.0003266018,0.0016640156,0.00019383318,0.00008864881,0.00018976422,0.0004920974,0.25029296,0.023850149,0.032028947,0.0030528372,0.6876301],"study_design_scores_gemma":[0.000048045888,0.00020908412,0.00068831455,0.000011447259,0.000051748,0.00016645191,0.00021701267,0.970989,0.008819417,0.012166251,0.0065861964,0.000046974637],"about_ca_topic_score_codex":0.009799252,"about_ca_topic_score_gemma":0.011727914,"teacher_disagreement_score":0.009799252,"about_ca_system_score_codex":0.0006109154,"about_ca_system_score_gemma":0.0014520129,"threshold_uncertainty_score":0.0194844},"labels":[],"label_agreement":null},{"id":"W2142181671","doi":"10.1007/s10899-013-9391-8","title":"The Impact of Sound in Modern Multiline Video Slot Machine Play","year":2013,"lang":"en","type":"article","venue":"Journal of Gambling Studies","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":131,"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":"Ontario Problem Gambling Research Centre","keywords":"Sound (geography); Psychology; Acoustics; Physics","score_opus":0.04510698318119403,"score_gpt":0.3583052978181934,"score_spread":0.3131983146369994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142181671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9976707,0.0001252423,0.0009041345,0.000023301973,0.000014123768,0.000018121757,0.000020431014,0.000010403383,0.0012134113],"genre_scores_gemma":[0.99845684,0.00008397743,0.0010917212,0.000019875617,0.000015815365,0.000011284991,0.000023487912,0.0000051591173,0.0002917282],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993455,0.0002763207,0.000028706198,0.00007247671,0.00021419449,0.00006277656],"domain_scores_gemma":[0.9984921,0.00089174934,0.00024867625,0.000056011922,0.00014188915,0.0001696242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005980996,0.00027704,0.00018514064,0.0005141341,0.0002190808,0.00079695164,0.0002285753,0.00029968433,0.0022867215],"category_scores_gemma":[0.003987007,0.0001271526,0.00015445186,0.00019132164,0.0005000567,0.00028997418,0.0005144265,0.00024321013,0.00019684125],"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":[0.010174189,0.0015233611,0.35544974,0.0007726025,0.0002853864,0.002969804,0.007725911,0.0018026186,0.33432403,0.0010670072,0.0006350729,0.28327027],"study_design_scores_gemma":[0.00006661487,0.00510399,0.9695497,0.00011145556,0.00012995602,0.002218249,0.0042819777,0.0031627857,0.012213701,0.00063928234,0.0024719315,0.000050448587],"about_ca_topic_score_codex":0.00076970906,"about_ca_topic_score_gemma":0.0018077666,"teacher_disagreement_score":0.0022867215,"about_ca_system_score_codex":0.00021186071,"about_ca_system_score_gemma":0.00016083034,"threshold_uncertainty_score":0.0076498985},"labels":[],"label_agreement":null},{"id":"W2144576373","doi":"10.1145/2534303.2534311","title":"Geo-clouds","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Dalhousie University","funders":"","keywords":"Visualization; Computer science; Tag cloud; Computer graphics (images); Data visualization; Information retrieval; Artificial intelligence","score_opus":0.0065026280089368485,"score_gpt":0.19274918953245074,"score_spread":0.18624656152351388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144576373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008930348,0.0008179726,0.8232863,0.0014170631,0.0007244031,0.0002495311,0.022776617,0.114673786,0.027124],"genre_scores_gemma":[0.16640863,0.0016559915,0.76125157,0.0005632971,0.0003497221,0.0003417971,0.039684966,0.0075207804,0.0222233],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929845,0.00007789003,0.00005329926,0.00016211985,0.00033030272,0.00007796828],"domain_scores_gemma":[0.99838006,0.00022168833,0.00011320702,0.00060711184,0.00052220543,0.00015565417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005949127,0.0010599918,0.0008246628,0.0025602595,0.0013553997,0.0032847389,0.002058674,0.0009057981,0.015438947],"category_scores_gemma":[0.0026228975,0.0006131519,0.0008674237,0.0040250793,0.00040452235,0.0036286418,0.002668449,0.0012489978,0.010022064],"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":[0.00068191666,0.0001387676,0.008109967,0.00088924286,0.0002520384,0.000585949,0.0019653195,0.017855283,0.025841527,0.08074269,0.39236307,0.4705742],"study_design_scores_gemma":[0.0000856998,0.00006131404,0.0029522758,0.00015467682,0.000102120735,0.0004491115,0.0006753481,0.15900835,0.033809997,0.031448282,0.7711053,0.00014750614],"about_ca_topic_score_codex":0.02004098,"about_ca_topic_score_gemma":0.032848224,"teacher_disagreement_score":0.02004098,"about_ca_system_score_codex":0.0008207897,"about_ca_system_score_gemma":0.0015147893,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2144654514","doi":"","title":"Personalization based on domain ontology","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Algoma University; Université de Montréal","funders":"","keywords":"Personalization; Automatic summarization; Computer science; Ontology; Information retrieval; Semantics (computer science); Domain (mathematical analysis); Multimedia; Video browsing; World Wide Web; Search engine indexing; Video tracking; Object (grammar); Artificial intelligence","score_opus":0.005105318827811573,"score_gpt":0.1988839057699995,"score_spread":0.19377858694218794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144654514","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035538346,0.00055319397,0.9467257,0.00071338494,0.000046624154,0.00042699886,0.00075127644,0.0033842926,0.011860141],"genre_scores_gemma":[0.31542763,0.0012839086,0.67217696,0.0002966492,0.00006124969,0.0004359361,0.003812649,0.00032946025,0.0061755017],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997736,0.000602907,0.00025778107,0.0004985241,0.0007711805,0.00013359306],"domain_scores_gemma":[0.996908,0.0010431113,0.00023646154,0.0011632136,0.00052074227,0.00012845822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021739036,0.00050301926,0.0006054216,0.0021865675,0.00092633895,0.0022105111,0.0010188741,0.000639065,0.0021810264],"category_scores_gemma":[0.006871281,0.0004051553,0.0011740722,0.0023983538,0.000514114,0.00453366,0.0019559322,0.0010741737,0.0006994358],"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":[0.00037963237,0.0005084778,0.008004668,0.00047369584,0.00020885485,0.00060229615,0.0023839409,0.028607538,0.024846217,0.084420055,0.016518647,0.83304596],"study_design_scores_gemma":[0.00010829126,0.00017358993,0.0068007293,0.0002137293,0.00037256605,0.0018929039,0.0019701414,0.5793966,0.042836864,0.14979775,0.2162754,0.00016141229],"about_ca_topic_score_codex":0.0067995596,"about_ca_topic_score_gemma":0.0059561147,"teacher_disagreement_score":0.0067995596,"about_ca_system_score_codex":0.0012337604,"about_ca_system_score_gemma":0.0015116696,"threshold_uncertainty_score":0.013519943},"labels":[],"label_agreement":null},{"id":"W2146475095","doi":"10.5555/777092.777210","title":"Detection and classification of motion boundaries","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Toronto; University of Waterloo","funders":"","keywords":"Motion (physics); Artificial intelligence; Computer vision; Boundary (topology); Segmentation; Trajectory; Computer science; Movement (music); Motion analysis; Object (grammar); Contextual image classification; Set (abstract data type); Pattern recognition (psychology); Mathematics; Image (mathematics); Physics; Mathematical analysis; Acoustics","score_opus":0.02270329312048459,"score_gpt":0.21511527163373192,"score_spread":0.19241197851324734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146475095","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36163473,0.00068827457,0.6318964,0.00020986375,0.0000777497,0.00020535034,0.00037275962,0.0022283886,0.002686394],"genre_scores_gemma":[0.7671991,0.00023653102,0.2296483,0.000055253782,0.00004932958,0.00008972561,0.001012395,0.00012443773,0.0015849109],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995579,0.00005038435,0.000024509283,0.00017228152,0.000118552554,0.00007633618],"domain_scores_gemma":[0.9990337,0.00024933607,0.0001957483,0.0001510446,0.0002789562,0.00009123079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004248664,0.00052516296,0.0006287888,0.0015689536,0.0005275523,0.00062106125,0.0006844214,0.0009491751,0.00094889296],"category_scores_gemma":[0.0026017334,0.00031622883,0.0003345767,0.0006155015,0.00044998495,0.0009060329,0.00060029776,0.0006227838,0.000474671],"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":[0.0009771966,0.00022936917,0.024908356,0.00030280513,0.000101250545,0.00053624506,0.0006360105,0.041132808,0.33685374,0.006601886,0.0033020293,0.58441836],"study_design_scores_gemma":[0.0000532494,0.00047177915,0.046148654,0.000057634345,0.00008297684,0.00057693,0.00036223288,0.81148595,0.12444334,0.008817454,0.0074318023,0.0000680543],"about_ca_topic_score_codex":0.0021387679,"about_ca_topic_score_gemma":0.0020156777,"teacher_disagreement_score":0.0021387679,"about_ca_system_score_codex":0.0003402165,"about_ca_system_score_gemma":0.00037151843,"threshold_uncertainty_score":0.004252672},"labels":[],"label_agreement":null},{"id":"W2146967572","doi":"10.1109/icme.2011.6011973","title":"An efficient technique for motion-based view-variant video sequences synchronization","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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","funders":"","keywords":"Computer science; Computer vision; Trajectory; Artificial intelligence; Dynamic time warping; Image warping; Synchronization (alternating current); Motion (physics); Point (geometry); Motion estimation; Object (grammar); Probabilistic logic; Mathematics","score_opus":0.02042799472227046,"score_gpt":0.24501095643723883,"score_spread":0.22458296171496836,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146967572","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016134214,0.00012048129,0.99751055,0.000022678041,0.000044275355,0.000030201147,0.000025727233,0.0003197386,0.00031293475],"genre_scores_gemma":[0.08061157,0.00044035673,0.9162384,0.000062717234,0.00017610028,0.00017405163,0.00038475447,0.00013558878,0.0017763622],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993129,0.000113023874,0.000047042315,0.0001642519,0.0003215433,0.000041299478],"domain_scores_gemma":[0.9994373,0.00014854917,0.00009665495,0.00017227288,0.00012201983,0.000023227094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052075146,0.000727516,0.00070642243,0.0012045652,0.0004327701,0.00045806568,0.0011763121,0.00053785875,0.0025823407],"category_scores_gemma":[0.0020147946,0.0004069853,0.00053822383,0.0015977474,0.00036015047,0.0013414187,0.0010224866,0.0009086784,0.0011345007],"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":[0.00020487135,0.00006298015,0.0003371848,0.00018236393,0.00008345583,0.00020905973,0.00019235698,0.017272163,0.17833664,0.021021573,0.0042046816,0.7778926],"study_design_scores_gemma":[0.0001122443,0.0004631824,0.0022905075,0.00006216224,0.00013194807,0.0017280855,0.00014483482,0.7208046,0.20080504,0.0142905135,0.05906383,0.00010320164],"about_ca_topic_score_codex":0.0007559904,"about_ca_topic_score_gemma":0.0010432558,"teacher_disagreement_score":0.0025823407,"about_ca_system_score_codex":0.00030877962,"about_ca_system_score_gemma":0.0005327584,"threshold_uncertainty_score":0.008638799},"labels":[],"label_agreement":null},{"id":"W2151606584","doi":"","title":"TREC video Retrieval evaluation TRECVID 2011 (slides)","year":2009,"lang":"en","type":"article","venue":"TNO Repository","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":3,"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":"Nanjing University; Peking University; Istanbul Teknik Üniversitesi; Zhejiang University; Universidad Carlos III de Madrid; Centre National de la Recherche Scientifique; Magyar Tudományos Akadémia; Università degli Studi di Brescia; University of Surrey; Fudan University; Aalto-Yliopisto; Sun Yat-sen University; Academia Sinica; Philipps-Universität Marburg; University of Glasgow; Universiteit van Amsterdam; Shandong University; Simon Fraser University; University of Ottawa; University of Central Florida","keywords":"Computer science; Information retrieval; Artificial intelligence","score_opus":0.013466068678585168,"score_gpt":0.2467027094875221,"score_spread":0.23323664080893694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151606584","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06912363,0.039092146,0.043368515,0.009866154,0.02349953,0.007918654,0.62818205,0.07981127,0.099138014],"genre_scores_gemma":[0.025882263,0.004492543,0.0262478,0.001374916,0.0011071973,0.0011981926,0.8674417,0.0020996132,0.07015572],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.994859,0.0010108711,0.00039892917,0.0010260446,0.002021163,0.00068406394],"domain_scores_gemma":[0.9936373,0.00076309254,0.00016448386,0.000942778,0.0039314386,0.0005608977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005221436,0.006005932,0.004359583,0.007133847,0.0037871087,0.003768175,0.0035855048,0.004295008,0.03519201],"category_scores_gemma":[0.00859035,0.001240643,0.002379134,0.0067815958,0.0010365646,0.00517485,0.0022492658,0.0031790924,0.035091173],"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":[0.00044675556,0.00026409468,0.0002457027,0.0007500115,0.00010189042,0.000072844196,0.000018742116,0.0008016309,0.0043542497,0.00029286696,0.9401603,0.0524911],"study_design_scores_gemma":[0.0024199274,0.0022667,0.038822636,0.0014638418,0.0018981326,0.002405358,0.0008591833,0.08721011,0.0902895,0.004990073,0.7668616,0.0005129141],"about_ca_topic_score_codex":0.15519093,"about_ca_topic_score_gemma":0.20386156,"teacher_disagreement_score":0.15519093,"about_ca_system_score_codex":0.0050390004,"about_ca_system_score_gemma":0.005378075,"threshold_uncertainty_score":0.30857527},"labels":[],"label_agreement":null},{"id":"W2152246040","doi":"10.1109/crv.2012.68","title":"In Situ Motion Capture of Speed Skating: Escaping the Treadmill","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Motion capture; Speed skating; Computer science; Motion (physics); Computer vision; Artificial intelligence; Treadmill; Computer graphics (images); Simulation","score_opus":0.01761768294439564,"score_gpt":0.241340475516179,"score_spread":0.22372279257178335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152246040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4215734,0.0010732841,0.5508162,0.00039483124,0.00025206644,0.0005301748,0.003499364,0.0042503756,0.01761022],"genre_scores_gemma":[0.81707424,0.0008580942,0.16973446,0.00024326675,0.0000899404,0.00025648822,0.0024459823,0.00033518363,0.0089623],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99980956,0.000021012938,0.000009846152,0.00006161174,0.000068170215,0.000029721097],"domain_scores_gemma":[0.9998454,0.000031014668,0.000022521534,0.00002488607,0.00005229811,0.0000238881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017306772,0.00047678535,0.0004738297,0.0007241909,0.00031686257,0.0005979204,0.000533418,0.0005446442,0.0028573303],"category_scores_gemma":[0.0005779694,0.00027445858,0.00023784541,0.0004782681,0.00018982182,0.00040194078,0.0006366941,0.0003249748,0.00082231313],"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":[0.0006677051,0.00024175746,0.011765529,0.00075678073,0.00012394637,0.0007755967,0.0012382887,0.011825123,0.63342947,0.0013610807,0.008241453,0.3295732],"study_design_scores_gemma":[0.00020322004,0.0016210593,0.21470891,0.00045150498,0.0003278534,0.00520887,0.0023289886,0.27843568,0.42634842,0.0036621168,0.06641016,0.00029313404],"about_ca_topic_score_codex":0.0024370376,"about_ca_topic_score_gemma":0.0058035306,"teacher_disagreement_score":0.0028573303,"about_ca_system_score_codex":0.00018491941,"about_ca_system_score_gemma":0.00031936247,"threshold_uncertainty_score":0.009558737},"labels":[],"label_agreement":null},{"id":"W2157256679","doi":"10.1109/icme.2002.1035553","title":"Modeling of video objects in a video databases","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Video tracking; Video compression picture types; Computer vision; Artificial intelligence; Video post-processing; Smacker video; Video processing; Motion compensation; Set (abstract data type); Multiview Video Coding; Block-matching algorithm; Computer graphics (images)","score_opus":0.029553327671372052,"score_gpt":0.2569305755051498,"score_spread":0.22737724783377775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157256679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024303397,0.001497721,0.9674982,0.0005157185,0.00008304717,0.0002235358,0.0028948947,0.00081017544,0.0021732543],"genre_scores_gemma":[0.4279431,0.004583209,0.5482544,0.00022312802,0.00023189907,0.00065297796,0.011557896,0.00018994833,0.006363531],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979532,0.0004328214,0.0002998372,0.00057717494,0.0006344697,0.00010261759],"domain_scores_gemma":[0.99814796,0.00064050005,0.00027772083,0.00043876347,0.0004303476,0.00006473415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015100979,0.0008843624,0.0009769738,0.002121185,0.00053649495,0.0038547427,0.002993764,0.0012607162,0.0010696572],"category_scores_gemma":[0.005114622,0.00049545587,0.0010227658,0.0038964918,0.00046091992,0.004902239,0.000781617,0.00091685343,0.00090752024],"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":[0.00045459316,0.00026163223,0.010352539,0.00066954346,0.00023887597,0.00093014806,0.0007061339,0.6295442,0.008996749,0.08280171,0.008762003,0.2562819],"study_design_scores_gemma":[0.000015075495,0.000057650734,0.0010042441,0.00003581128,0.000056003606,0.0001760803,0.00012405869,0.9720825,0.0025498061,0.01393848,0.009940538,0.000019842613],"about_ca_topic_score_codex":0.015490458,"about_ca_topic_score_gemma":0.010034984,"teacher_disagreement_score":0.015490458,"about_ca_system_score_codex":0.0012311109,"about_ca_system_score_gemma":0.0008401601,"threshold_uncertainty_score":0.03080064},"labels":[],"label_agreement":null},{"id":"W2158307947","doi":"10.1109/icce.1996.517350","title":"A QUERY INTERFACE FOR MULTIMEDIA DATABASE","year":2005,"lang":"en","type":"article","venue":"International Conference on Consumer Electronics","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Multimedia database; View; Query language; Database; Interface (matter); User interface; Bridge (graph theory); Database schema; Information retrieval; Semantics (computer science); Multimedia; Graphics; Graphical user interface; World Wide Web; Database design; Programming language","score_opus":0.034147182705458155,"score_gpt":0.3173444360713629,"score_spread":0.2831972533659048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158307947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040753605,0.0006173629,0.8788353,0.0007431809,0.00018854253,0.0006962791,0.0035045522,0.10055581,0.010783647],"genre_scores_gemma":[0.15217453,0.001450042,0.76111525,0.0044263466,0.00049442303,0.0028877982,0.016399816,0.012942813,0.048109047],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99844354,0.00040850238,0.00023681897,0.0002509378,0.00051591353,0.00014420565],"domain_scores_gemma":[0.99823415,0.00074268563,0.000074035015,0.00024278619,0.0005460607,0.00016026206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017844856,0.0013882966,0.0010372808,0.0016410196,0.0006046918,0.002496166,0.0028279163,0.0021390396,0.036508776],"category_scores_gemma":[0.0050293324,0.0005780653,0.00075510156,0.0012218253,0.00060339266,0.0034118823,0.0022707118,0.0015771668,0.01428472],"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":[0.00264453,0.00041466192,0.002006562,0.0020549065,0.00014037862,0.002789693,0.0024397685,0.0042552203,0.13413195,0.08903266,0.3996379,0.3604517],"study_design_scores_gemma":[0.00048583094,0.000528528,0.001291541,0.0003816452,0.0001368869,0.0046648197,0.00038793113,0.13385522,0.060019936,0.021517675,0.7764405,0.00028950258],"about_ca_topic_score_codex":0.002356615,"about_ca_topic_score_gemma":0.0013793114,"teacher_disagreement_score":0.036508776,"about_ca_system_score_codex":0.0007237532,"about_ca_system_score_gemma":0.00066164666,"threshold_uncertainty_score":0.12213397},"labels":[],"label_agreement":null},{"id":"W2158490568","doi":"10.1109/isspa.2012.6310685","title":"Content-based video copy detection using nearest-neighbor mapping","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Frame (networking); Artificial intelligence; k-nearest neighbors algorithm; Matching (statistics); Computer vision; Pattern recognition (psychology); Feature (linguistics); False alarm; Sliding window protocol; Feature extraction; Window (computing); Mathematics","score_opus":0.0783191359986431,"score_gpt":0.25522500374728374,"score_spread":0.17690586774864064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158490568","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5570398,0.0031187942,0.41582483,0.00018876165,0.000248764,0.0007114309,0.0013082372,0.013727785,0.0078315865],"genre_scores_gemma":[0.71923906,0.0005301505,0.27429193,0.000056575187,0.00006278515,0.000113898,0.0022501976,0.00024300742,0.003212405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975108,0.00037579297,0.00016491041,0.00047550097,0.0013033561,0.00016955237],"domain_scores_gemma":[0.99777836,0.0007691054,0.00015888757,0.0005144433,0.0007014882,0.00007768125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012132389,0.00091971667,0.0011399243,0.0034250878,0.00043762632,0.0010229041,0.001315379,0.0008334507,0.0018014524],"category_scores_gemma":[0.006241531,0.00020822669,0.00060551567,0.001854832,0.00034001822,0.0017675926,0.0008863701,0.00037049362,0.0010646397],"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":[0.0009027322,0.0004002696,0.0068575623,0.00037402942,0.00027404755,0.0002963921,0.0001299158,0.033059835,0.077098325,0.00084560196,0.0040679458,0.8756933],"study_design_scores_gemma":[0.00008968996,0.00077082025,0.015439155,0.000025636487,0.00015556933,0.0016607883,0.00017220169,0.7909529,0.18338335,0.0018731834,0.0053771376,0.00009956597],"about_ca_topic_score_codex":0.00959543,"about_ca_topic_score_gemma":0.007488572,"teacher_disagreement_score":0.00959543,"about_ca_system_score_codex":0.0006773724,"about_ca_system_score_gemma":0.0005730658,"threshold_uncertainty_score":0.019079149},"labels":[],"label_agreement":null},{"id":"W2159225997","doi":"10.1109/cvprw.2010.5543266","title":"Automatic segmentation of video to aid the study of faucet usability for older adults","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Usability; Computer science; Computer graphics (images); Computer vision; Multimedia; Artificial intelligence; Human–computer interaction","score_opus":0.010198424390190274,"score_gpt":0.2733516005357883,"score_spread":0.263153176145598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159225997","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6634447,0.0014420206,0.32344002,0.00022744722,0.00014531316,0.00073626626,0.002403424,0.0024129408,0.005747965],"genre_scores_gemma":[0.71226203,0.0013456222,0.28052726,0.00014027469,0.00010610364,0.0005623094,0.0018978866,0.00018847061,0.0029698801],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998275,0.00003518746,0.0000133964095,0.00005113987,0.00005042212,0.000022332451],"domain_scores_gemma":[0.9989833,0.00044897708,0.00013268448,0.000060147388,0.00033614933,0.000038660695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041495508,0.00037918624,0.0003527593,0.0019634762,0.0002352172,0.0005043905,0.00033710524,0.0005843176,0.0017853846],"category_scores_gemma":[0.002534212,0.00016288373,0.00020285365,0.00073941867,0.00017346638,0.0004951761,0.0002552852,0.00026862035,0.00059794256],"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":[0.0011441701,0.00018973048,0.017779455,0.00067248393,0.00004494215,0.00039934332,0.0011989553,0.0019700676,0.42607635,0.0006795612,0.003242294,0.54660267],"study_design_scores_gemma":[0.00015772559,0.0018680877,0.5713539,0.0004009973,0.00020747275,0.0020873898,0.003067192,0.1265158,0.26838335,0.0030025104,0.022791272,0.00016430566],"about_ca_topic_score_codex":0.0030744665,"about_ca_topic_score_gemma":0.00756129,"teacher_disagreement_score":0.0030744665,"about_ca_system_score_codex":0.00023733324,"about_ca_system_score_gemma":0.00027706168,"threshold_uncertainty_score":0.0061131716},"labels":[],"label_agreement":null},{"id":"W2159329831","doi":"10.1109/tmm.2008.2004907","title":"Confidence Evolution in Multimedia Systems","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Multimedia","score_opus":0.019927659524028897,"score_gpt":0.23449570577118192,"score_spread":0.21456804624715303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159329831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.117466,0.0020858152,0.87290585,0.0009486129,0.00007567392,0.000101647674,0.00035019236,0.0010953948,0.0049708406],"genre_scores_gemma":[0.9156139,0.00043483955,0.0811255,0.00014858363,0.00013188082,0.000090747264,0.00038586534,0.00010208108,0.0019666653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99554104,0.0013197071,0.00028391602,0.00091614,0.0016070624,0.00033220695],"domain_scores_gemma":[0.97089696,0.020834718,0.00295712,0.0017812153,0.0029314084,0.00059849594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042359196,0.00067829224,0.00089765317,0.002675766,0.00080868986,0.0027622078,0.0014887106,0.0013181544,0.002590292],"category_scores_gemma":[0.044597093,0.0006359266,0.0005498216,0.0020413625,0.0011752733,0.0037932862,0.0019295022,0.0013578727,0.00046894452],"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":[0.00063229667,0.0001780618,0.025384722,0.00042004877,0.00024498493,0.00074404065,0.0012299947,0.4730311,0.009279311,0.11330326,0.0062747193,0.36927748],"study_design_scores_gemma":[0.000012984306,0.000059476453,0.0024719448,0.00001689754,0.000020827449,0.00017044556,0.00007038454,0.95789105,0.0022636137,0.03548435,0.0015103709,0.000027789629],"about_ca_topic_score_codex":0.0038895316,"about_ca_topic_score_gemma":0.0020017095,"teacher_disagreement_score":0.0042359196,"about_ca_system_score_codex":0.0018863936,"about_ca_system_score_gemma":0.00059187785,"threshold_uncertainty_score":0.022401989},"labels":[],"label_agreement":null},{"id":"W2161239058","doi":"10.1109/iecon.1999.822187","title":"Presenter tracking in a classroom environment","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"McGill University","funders":"McGill University","keywords":"Computer science; Tracking (education); Multimedia; Psychology; Pedagogy","score_opus":0.014463312987632898,"score_gpt":0.22538012907549984,"score_spread":0.21091681608786694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161239058","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3133405,0.00059027574,0.65522563,0.0004208043,0.00017912571,0.00010507454,0.0006163909,0.01211608,0.01740612],"genre_scores_gemma":[0.7294623,0.00040461362,0.24526674,0.00009849304,0.00011784981,0.0000520226,0.001099774,0.00050622254,0.022992056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957234,0.00005937243,0.000014477492,0.00016808037,0.00012278285,0.00006308842],"domain_scores_gemma":[0.9991265,0.00020646914,0.000093921815,0.00015727685,0.00029731044,0.00011848839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042154256,0.0004081828,0.0006447469,0.0013098514,0.0006223242,0.0012088652,0.00077503046,0.0011192369,0.005156315],"category_scores_gemma":[0.0019358312,0.00031330923,0.00025521877,0.0009927517,0.00026936512,0.00084081164,0.00086845714,0.0006124688,0.003514837],"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":[0.00080046925,0.0003563401,0.009768815,0.0001404768,0.00007168218,0.00080203376,0.0011400742,0.028231926,0.16325861,0.0048258384,0.015219358,0.77538437],"study_design_scores_gemma":[0.00019092867,0.0007131327,0.03062529,0.000041794683,0.00014860579,0.0019129573,0.00095193234,0.7348458,0.18235123,0.0049994052,0.043101,0.00011790199],"about_ca_topic_score_codex":0.0042338623,"about_ca_topic_score_gemma":0.007945947,"teacher_disagreement_score":0.005156315,"about_ca_system_score_codex":0.00053496176,"about_ca_system_score_gemma":0.00040565827,"threshold_uncertainty_score":0.017249584},"labels":[],"label_agreement":null},{"id":"W2161337144","doi":"10.1145/2470654.2466149","title":"Swifter","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Autodesk (Canada)","funders":"","keywords":"Timeline; Computer science; Cache; Task (project management); Operating system; Engineering","score_opus":0.004126199594658827,"score_gpt":0.17305714133480635,"score_spread":0.16893094174014753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161337144","genre_codex":"software","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056961253,0.0022199552,0.44001576,0.0008739688,0.0013774324,0.0013175057,0.01325489,0.45535153,0.028627655],"genre_scores_gemma":[0.31814742,0.0016222029,0.54702336,0.0009531734,0.00045031527,0.0011977206,0.039500255,0.021423167,0.06968245],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990476,0.00006612173,0.000091996444,0.00027756928,0.00041217095,0.00010453664],"domain_scores_gemma":[0.99831057,0.000490909,0.00016074948,0.00043673697,0.00048331844,0.000117674696],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00093506364,0.0015189484,0.000887457,0.001551595,0.0005424033,0.001284442,0.0022356727,0.0009851514,0.021009214],"category_scores_gemma":[0.0045223227,0.00059783884,0.0005159299,0.0011307558,0.00031506212,0.002095479,0.0010985358,0.0009941933,0.010140894],"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":[0.0019809206,0.0003471407,0.0019844791,0.001000502,0.00022721288,0.0004591354,0.00040363226,0.01125934,0.07275574,0.0049578864,0.31952637,0.58509773],"study_design_scores_gemma":[0.00081714237,0.000890529,0.005354682,0.00011675629,0.00014216213,0.0012668815,0.00027518076,0.4603786,0.19934861,0.007751951,0.32342264,0.00023480791],"about_ca_topic_score_codex":0.0047111777,"about_ca_topic_score_gemma":0.005000329,"teacher_disagreement_score":0.9789908,"about_ca_system_score_codex":0.00060139556,"about_ca_system_score_gemma":0.0009994707,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2162680787","doi":"10.1353/nin.0.0061","title":"Realizing a Kid's Dream of Computerized Baseball","year":2009,"lang":"en","type":"article","venue":"Nine","topic":"Video Analysis and Summarization","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":"Dream; Aeronautics; History; Psychology; Engineering; Neuroscience","score_opus":0.006815191111707687,"score_gpt":0.22260083309570944,"score_spread":0.21578564198400174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162680787","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09399329,0.011814773,0.0110635655,0.2729224,0.011640679,0.00012207503,0.001116276,0.0024618488,0.59486514],"genre_scores_gemma":[0.37556186,0.010681187,0.02383778,0.08731232,0.0017545671,0.00021737383,0.0013610945,0.0006452291,0.49862853],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876606,0.00015590039,0.00003480911,0.00024312324,0.00039144378,0.00040869496],"domain_scores_gemma":[0.99779874,0.000074846634,0.000054877954,0.00005758009,0.00025990148,0.0017541043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013527779,0.00088582304,0.00034405704,0.00055044424,0.0041370196,0.0063378317,0.0011736875,0.0031479374,0.037035804],"category_scores_gemma":[0.0019299146,0.00042509346,0.0005809745,0.00025410575,0.0022885108,0.005578716,0.005956329,0.00631616,0.010988036],"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":[0.00009807237,0.00029938747,0.008376571,0.00018053877,0.000021997901,0.0014081342,0.010754166,0.00014681774,0.0018947904,0.032392725,0.859806,0.08462075],"study_design_scores_gemma":[0.00002499402,0.00018013037,0.0049103303,0.00032819164,0.0000196505,0.0022168048,0.01983686,0.0002154454,0.00058552064,0.005459092,0.96617335,0.000049636717],"about_ca_topic_score_codex":0.0087307915,"about_ca_topic_score_gemma":0.020649001,"teacher_disagreement_score":0.037035804,"about_ca_system_score_codex":0.0018906866,"about_ca_system_score_gemma":0.0019695144,"threshold_uncertainty_score":0.123897076},"labels":[],"label_agreement":null},{"id":"W2163220995","doi":"10.1109/icassp.2011.5946660","title":"A novel vector quantization-based video summarization method using independent component analysis mixture model","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"","keywords":"Codebook; Automatic summarization; Linde–Buzo–Gray algorithm; Vector quantization; Computer science; Independent component analysis; Pattern recognition (psychology); Artificial intelligence; Subspace topology; Feature vector; Learning vector quantization; k-nearest neighbors algorithm; Quantization (signal processing); Component (thermodynamics); Feature (linguistics); Feature extraction; Algorithm","score_opus":0.060278912210075725,"score_gpt":0.2838433819446588,"score_spread":0.22356446973458308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163220995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012256737,0.00039125863,0.99716836,0.00005781291,0.00005830236,0.000038792215,0.000058528545,0.0006485786,0.0003527305],"genre_scores_gemma":[0.06765977,0.0007870069,0.9263548,0.00013282002,0.00021040416,0.00018868639,0.0008006884,0.00023429633,0.00363147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990897,0.00016690229,0.00007098104,0.00022663019,0.0003932596,0.00005246532],"domain_scores_gemma":[0.99938977,0.00013623116,0.000057762285,0.000069050235,0.000315321,0.000031775788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007598505,0.0010865171,0.0013141755,0.0015788646,0.00051628996,0.00094198796,0.0015225345,0.0007887077,0.0024673152],"category_scores_gemma":[0.0018977636,0.00041496713,0.0009407547,0.0016008766,0.00037788533,0.0021786657,0.00072283123,0.0012000953,0.0016170801],"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":[0.00015097723,0.00006609585,0.00034776694,0.00026851584,0.00009941996,0.00006517677,0.00012720082,0.04547951,0.041919407,0.009102397,0.008009563,0.89436394],"study_design_scores_gemma":[0.00003396407,0.00014894009,0.00059018,0.000022388798,0.0000811019,0.00015860153,0.000050848685,0.96012497,0.020931425,0.005635843,0.012165483,0.000056242938],"about_ca_topic_score_codex":0.0033901606,"about_ca_topic_score_gemma":0.0031282178,"teacher_disagreement_score":0.0033901606,"about_ca_system_score_codex":0.00065486075,"about_ca_system_score_gemma":0.0007089336,"threshold_uncertainty_score":0.008253932},"labels":[],"label_agreement":null},{"id":"W2168735438","doi":"10.1109/crv.2006.53","title":"Object Extraction and Reconstruction in Active Video","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 vision; Artificial intelligence; Computer science; Object (grammar); Gestalt psychology; Video tracking; 3D single-object recognition; Cognitive neuroscience of visual object recognition; Motion (physics); Block-matching algorithm; Method; Object-oriented programming; Psychology","score_opus":0.005615951392029579,"score_gpt":0.21768370050242936,"score_spread":0.21206774911039977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168735438","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026448693,0.00010508656,0.9967765,0.00002431264,0.000010499505,0.000014255948,0.000013514662,0.00012734697,0.00028351948],"genre_scores_gemma":[0.10989129,0.00050860445,0.88661736,0.00007805602,0.00006747582,0.00009425092,0.00020838993,0.0000895768,0.0024449162],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941957,0.00010510046,0.000032814965,0.00016380998,0.00022367355,0.000055161763],"domain_scores_gemma":[0.99936515,0.00026711563,0.00009333602,0.00011735004,0.00013029217,0.000026839172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072227314,0.0006093433,0.0007778914,0.0013734486,0.00030432272,0.0010472763,0.0011186614,0.0010508827,0.0011090159],"category_scores_gemma":[0.0018747707,0.00057789317,0.00072799803,0.0010927671,0.00087731227,0.0018788582,0.0010932841,0.0007716663,0.00067794556],"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":[0.00026182857,0.00007575752,0.00101194,0.00031066147,0.00007959675,0.0003711513,0.0005005526,0.10823865,0.13254695,0.067355685,0.0021988489,0.6870484],"study_design_scores_gemma":[0.000023444425,0.000103147315,0.00094296824,0.000031152384,0.000026667041,0.00045194733,0.00008631343,0.89015114,0.06842465,0.029813316,0.009905807,0.000039428964],"about_ca_topic_score_codex":0.0009291003,"about_ca_topic_score_gemma":0.0007828794,"teacher_disagreement_score":0.0013734486,"about_ca_system_score_codex":0.00038788453,"about_ca_system_score_gemma":0.0003317334,"threshold_uncertainty_score":0.0038198233},"labels":[],"label_agreement":null},{"id":"W2171438637","doi":"10.1109/ccece.1995.526423","title":"A scene change detection algorithm for MPEG compressed video sequences","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of Ottawa","funders":"","keywords":"Computer science; Change detection; Search engine indexing; Artificial intelligence; Computer vision; Scene statistics; Domain (mathematical analysis); Video retrieval; Data compression; Video processing; Pixel; Algorithm","score_opus":0.055000118788022745,"score_gpt":0.24700974844544174,"score_spread":0.192009629657419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171438637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01281207,0.0007336189,0.9792797,0.00017587049,0.000170409,0.000324071,0.00024768105,0.003699426,0.0025572018],"genre_scores_gemma":[0.058020793,0.0005597407,0.9358866,0.00012063018,0.00009848891,0.00018929366,0.0008431557,0.00017885646,0.0041025546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950004,0.00003598951,0.000029582488,0.0001017995,0.0002914718,0.00004117868],"domain_scores_gemma":[0.99958986,0.00007285838,0.000050171915,0.000039373102,0.00022388945,0.00002385013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004074577,0.00089829793,0.0006386677,0.0029605676,0.00068389036,0.00082559214,0.0008208272,0.0008600893,0.0028296274],"category_scores_gemma":[0.0012097752,0.00035202524,0.0005521975,0.0018157933,0.00040572113,0.0010168982,0.0005078137,0.000855343,0.002138536],"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":[0.00027392132,0.000095283925,0.0009073849,0.0001372421,0.00005972801,0.00018516106,0.00008657551,0.0074945586,0.1293225,0.004074328,0.009067192,0.84829617],"study_design_scores_gemma":[0.000120296056,0.0005335741,0.0076529495,0.000052205727,0.000128564,0.0017766866,0.00012896518,0.66111535,0.26327518,0.004121698,0.060976684,0.000117969204],"about_ca_topic_score_codex":0.0033687362,"about_ca_topic_score_gemma":0.0046991305,"teacher_disagreement_score":0.0033687362,"about_ca_system_score_codex":0.000584302,"about_ca_system_score_gemma":0.0005808422,"threshold_uncertainty_score":0.009466112},"labels":[],"label_agreement":null},{"id":"W2171693751","doi":"10.1109/icme.2004.1394181","title":"iARM - an interactive video retrieval system","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Metropolitan University","funders":"","keywords":"Computer science; Relevance feedback; Search engine indexing; Relevance (law); Video retrieval; Interactive video; Multimedia; Information retrieval; Image retrieval; Video processing; Automatic indexing; Artificial intelligence; Image (mathematics)","score_opus":0.00941196344771201,"score_gpt":0.24413617340009383,"score_spread":0.23472420995238183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171693751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038241804,0.0014901722,0.62979895,0.0004347411,0.0002037297,0.0011102922,0.0051068813,0.29389164,0.029721703],"genre_scores_gemma":[0.33772117,0.0009881638,0.59490055,0.00081748946,0.00052087056,0.0011163498,0.024419202,0.004005683,0.035510547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992799,0.00012876802,0.000066146305,0.00018512964,0.00026635642,0.00007372838],"domain_scores_gemma":[0.9988826,0.0002486046,0.00007056827,0.0003198366,0.00036137004,0.000116999894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073386,0.0007011506,0.0009991465,0.0016203298,0.00050700695,0.001135637,0.0024581475,0.0010333478,0.014656946],"category_scores_gemma":[0.0028497253,0.0002965276,0.00047940738,0.0007905207,0.00023388903,0.0019267531,0.001448745,0.00069310324,0.010837187],"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":[0.0025543242,0.00068759464,0.0020484661,0.0007085659,0.00020164221,0.0007183041,0.0003467008,0.008643987,0.14514941,0.007988101,0.14773928,0.68321365],"study_design_scores_gemma":[0.000760368,0.0013352946,0.005192908,0.0001281266,0.0003386663,0.002313843,0.00023161646,0.5233088,0.16297351,0.0069677047,0.29611725,0.0003319642],"about_ca_topic_score_codex":0.0020824734,"about_ca_topic_score_gemma":0.0017663428,"teacher_disagreement_score":0.014656946,"about_ca_system_score_codex":0.0005496248,"about_ca_system_score_gemma":0.0005338834,"threshold_uncertainty_score":0.04903233},"labels":[],"label_agreement":null},{"id":"W2174938631","doi":"10.1642/0004-8038(2000)117[0831:sfsasm]2.0.co;2","title":"Suggestions for Slides at Scientific Meetings","year":2000,"lang":"en","type":"article","venue":"The Auk","topic":"Video Analysis and Summarization","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":"University of Toronto; University of Cambridge; American Ornithologists' Union","keywords":"Computer science; Simple (philosophy); Order (exchange); Multimedia; Epistemology","score_opus":0.012289874305953708,"score_gpt":0.2272367638857935,"score_spread":0.2149468895798398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2174938631","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":"reporting","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":"reporting","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00438501,0.009571197,0.047476467,0.17078699,0.23353538,0.011683991,0.025760077,0.024721025,0.4720798],"genre_scores_gemma":[0.011019741,0.0051363567,0.058285728,0.021814078,0.037584834,0.0050413692,0.011926436,0.0046082465,0.8445831],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99567056,0.0014182181,0.00046753106,0.00038186111,0.0015939502,0.00046795086],"domain_scores_gemma":[0.97798616,0.0037610412,0.0016036523,0.0018039616,0.009109166,0.0057360297],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006485034,0.001823722,0.00085901644,0.0042197797,0.0023542878,0.004533086,0.002765249,0.0033831804,0.6772307],"category_scores_gemma":[0.03425236,0.0007981258,0.0014957523,0.0020651803,0.00062439137,0.004361937,0.003742084,0.003603228,0.4902111],"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":[0.000057423404,0.000029784502,0.00008270981,0.00027771917,0.0000033703675,0.0000847579,0.00007583555,0.000033671888,0.00046712643,0.0005332943,0.9480278,0.05032654],"study_design_scores_gemma":[0.000034052307,0.000035036246,0.0003964692,0.0001622399,0.000007761704,0.00008794522,0.00026905953,0.000059563376,0.00027600594,0.001215014,0.99743915,0.000017688897],"about_ca_topic_score_codex":0.00081789773,"about_ca_topic_score_gemma":0.002941649,"teacher_disagreement_score":0.99351496,"about_ca_system_score_codex":0.0016381067,"about_ca_system_score_gemma":0.0023648445,"threshold_uncertainty_score":0.46039116},"labels":[],"label_agreement":null},{"id":"W2180208870","doi":"10.1162/1054746053890297","title":"Wolverine: A Distributed Scene-Graph Library","year":2005,"lang":"en","type":"article","venue":"PRESENCE Virtual and Augmented Reality","topic":"Video Analysis and Summarization","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":"Polytechnique Montréal","funders":"","keywords":"Serialization; Computer science; Graph; Scene graph; Multimedia; World Wide Web; Information retrieval; Computer graphics (images); Theoretical computer science; Programming language","score_opus":0.01279222873532625,"score_gpt":0.23418813276524747,"score_spread":0.2213959040299212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2180208870","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034852081,0.00022928142,0.683908,0.00011537473,0.00005598473,0.00018351682,0.0034704143,0.29656866,0.011983622],"genre_scores_gemma":[0.11753773,0.0015005977,0.68105197,0.0006976651,0.000081394865,0.0012898095,0.043160997,0.11034168,0.0443381],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99944764,0.00007677601,0.00003867641,0.00011743912,0.0002477873,0.00007160318],"domain_scores_gemma":[0.9990814,0.00024944262,0.000050095485,0.00039124824,0.00011616389,0.000111681125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007674361,0.0014740294,0.0010187844,0.0016795883,0.00094035495,0.0022054173,0.004492475,0.0010936018,0.028788725],"category_scores_gemma":[0.0020391867,0.0011367368,0.0013273945,0.0018464281,0.00067835336,0.0049980143,0.0040878854,0.0018337196,0.010568977],"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":[0.0012928878,0.0004850154,0.002055484,0.0017579525,0.00022932616,0.0007550063,0.001214587,0.023665102,0.026103111,0.0878443,0.29330978,0.56128746],"study_design_scores_gemma":[0.0002945109,0.00018928025,0.0013966971,0.00019304661,0.00009718831,0.0006458631,0.00028019506,0.14164056,0.043630317,0.051869053,0.75953704,0.00022627281],"about_ca_topic_score_codex":0.0050819027,"about_ca_topic_score_gemma":0.010460386,"teacher_disagreement_score":0.028788725,"about_ca_system_score_codex":0.00075812836,"about_ca_system_score_gemma":0.001128267,"threshold_uncertainty_score":0.096307874},"labels":[],"label_agreement":null},{"id":"W2219103962","doi":"10.1007/978-3-319-11782-9_7","title":"Indexing, Object Segmentation, and Event Detection in News and Sports Videos","year":2014,"lang":"en","type":"book-chapter","venue":"","topic":"Video Analysis and Summarization","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 science; Computer vision; Segmentation; Pattern recognition (psychology); Histogram; Search engine indexing; Video tracking; Object (grammar)","score_opus":0.00692647123167606,"score_gpt":0.2120457000194151,"score_spread":0.20511922878773906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219103962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03922233,0.04981948,0.8676861,0.000994662,0.0014728738,0.00045811018,0.0058988375,0.011694947,0.022752622],"genre_scores_gemma":[0.16124707,0.036624998,0.71034265,0.00045069022,0.0024206568,0.00039890574,0.03181853,0.0015666303,0.055129874],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994405,0.000048231697,0.000046039866,0.00021798369,0.00017498502,0.00007223897],"domain_scores_gemma":[0.99940145,0.00025865377,0.000051603725,0.000088918176,0.00015732252,0.000041977382],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005960069,0.0014120834,0.0015853884,0.0037379852,0.0006107131,0.0023238633,0.0017256719,0.0010948422,0.0059020002],"category_scores_gemma":[0.0016115522,0.00056465185,0.00095027493,0.0055659064,0.0004686116,0.0024380893,0.00073293207,0.00085323956,0.0049694194],"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":[0.0001618737,0.000088631416,0.00060700363,0.00054649264,0.000046071043,0.0000802758,0.0001135015,0.0023733587,0.028630413,0.0031034746,0.026059788,0.93818915],"study_design_scores_gemma":[0.000125278,0.0010681085,0.0336211,0.0006078956,0.0007023691,0.0027872925,0.0015775025,0.409837,0.2254146,0.092185125,0.23177248,0.00030117788],"about_ca_topic_score_codex":0.0049345093,"about_ca_topic_score_gemma":0.00675626,"teacher_disagreement_score":0.0059020002,"about_ca_system_score_codex":0.0006837793,"about_ca_system_score_gemma":0.000610366,"threshold_uncertainty_score":0.019744158},"labels":[],"label_agreement":null},{"id":"W2251216540","doi":"","title":"Getting More from Segmentation Evaluation","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of Ottawa","funders":"","keywords":"Computer science; Segmentation; Measure (data warehouse); Artificial intelligence; Precision and recall; Window (computing); Sensitivity (control systems); Image segmentation; Pattern recognition (psychology); Computer vision; Data mining; Engineering","score_opus":0.029829933053343305,"score_gpt":0.3007590947675822,"score_spread":0.2709291617142389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2251216540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031978253,0.008073327,0.91957766,0.004706185,0.0016137018,0.00030212748,0.0018615916,0.016639976,0.015247145],"genre_scores_gemma":[0.26297095,0.0025719528,0.6974648,0.004223424,0.001967797,0.0004049515,0.008855442,0.011670616,0.009870079],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.97390217,0.008912791,0.0019928797,0.0052337465,0.009064979,0.00089349947],"domain_scores_gemma":[0.9522344,0.020930566,0.002466632,0.0072296294,0.0155148795,0.001623858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013538395,0.0037172153,0.003264084,0.005960182,0.0014104822,0.0067592906,0.0028989068,0.0044306214,0.008306169],"category_scores_gemma":[0.072513826,0.0012840738,0.0014007017,0.0033243643,0.0016794405,0.016669018,0.004717592,0.0043772673,0.004897456],"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":[0.0007379279,0.00024128186,0.005484042,0.0011552027,0.00032759173,0.00028170933,0.0007868941,0.018336974,0.035448715,0.017914023,0.07292814,0.8463574],"study_design_scores_gemma":[0.00027700313,0.001616282,0.015410761,0.0009824702,0.0006855641,0.001572933,0.001365564,0.5275024,0.10548629,0.17155547,0.1729003,0.00064492243],"about_ca_topic_score_codex":0.0031628653,"about_ca_topic_score_gemma":0.0055044615,"teacher_disagreement_score":0.013538395,"about_ca_system_score_codex":0.0018858219,"about_ca_system_score_gemma":0.001408389,"threshold_uncertainty_score":0.07159871},"labels":[],"label_agreement":null},{"id":"W2252874070","doi":"10.5539/jel.v5n1p154","title":"Effects of Infographics on Students Achievement and Attitude towards Geography Lessons","year":2016,"lang":"en","type":"article","venue":"Journal of Education and Learning","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":79,"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":"Infographic; Mathematics education; Class (philosophy); Scope (computer science); Teaching method; Test (biology); Natural (archaeology); Psychology; Geography; Pedagogy; Computer science; Mathematics; Statistics; Ecology","score_opus":0.008441423722953529,"score_gpt":0.29466498979216754,"score_spread":0.286223566069214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2252874070","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9984176,0.0000332264,0.000030258836,0.00005167337,0.00000951248,0.000009257584,0.000031970798,0.00001032957,0.0014060533],"genre_scores_gemma":[0.9972717,0.000070731505,0.00013350479,0.000025129342,0.0000089603145,0.000023994988,0.000083331906,0.000003867066,0.002378753],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99913603,0.00026482253,0.0000691775,0.000121217774,0.00023227459,0.00017650845],"domain_scores_gemma":[0.992234,0.0029845869,0.0016724301,0.0003402967,0.00046319934,0.0023055645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008473392,0.0003989871,0.00046684762,0.000520524,0.00050304807,0.0011625007,0.00036788298,0.00049472065,0.008164469],"category_scores_gemma":[0.004772164,0.00014312415,0.0006123745,0.00038200265,0.0003889552,0.00039457073,0.0006692755,0.0011244114,0.00071654987],"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":[0.010993284,0.08454053,0.63955164,0.00096990407,0.0006314101,0.0008156704,0.01678479,0.0016341609,0.021623833,0.0011060301,0.003995652,0.21735302],"study_design_scores_gemma":[0.00012426093,0.013168262,0.97423005,0.0000848476,0.00021645465,0.00004740511,0.004571481,0.00080484385,0.0037207974,0.00019953708,0.002791295,0.00004076704],"about_ca_topic_score_codex":0.002161495,"about_ca_topic_score_gemma":0.0025151083,"teacher_disagreement_score":0.008164469,"about_ca_system_score_codex":0.0003424005,"about_ca_system_score_gemma":0.00041805656,"threshold_uncertainty_score":0.027312875},"labels":[],"label_agreement":null},{"id":"W2257224367","doi":"10.4018/ijismd.2016010103","title":"Interpretive Strategies for Screen-Based Creative Technologies","year":2016,"lang":"en","type":"article","venue":"International Journal of Information System Modeling and Design","topic":"Video Analysis and Summarization","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 Regina","funders":"","keywords":"Vagueness; Computer science; Relation (database); Variety (cybernetics); Multimedia; Data science; Human–computer interaction; Artificial intelligence; Data mining","score_opus":0.024925340355901653,"score_gpt":0.2603105003836761,"score_spread":0.23538516002777443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257224367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029459884,0.0007068484,0.9016703,0.0023553849,0.00017414523,0.0003257934,0.00010968945,0.00066498393,0.06453307],"genre_scores_gemma":[0.5347479,0.00057937094,0.44136605,0.00047409927,0.000102583435,0.00079779146,0.00029513094,0.00046030115,0.021176863],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9887024,0.007235922,0.0006558808,0.00096870924,0.0020852718,0.00035188554],"domain_scores_gemma":[0.9825813,0.012680029,0.0007730125,0.0021232346,0.0015588225,0.0002836652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0077265687,0.0016038899,0.00060741964,0.0046517705,0.0022174467,0.012736791,0.001945228,0.0025323299,0.010294717],"category_scores_gemma":[0.032770474,0.0006858063,0.0013718284,0.0017817824,0.010644996,0.012679954,0.006014758,0.0023865395,0.0017596545],"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":[0.00010240481,0.000051541752,0.00061204925,0.0003074488,0.000035408353,0.00045855762,0.04957356,0.0023354087,0.00516965,0.8696947,0.0013934746,0.0702658],"study_design_scores_gemma":[0.00007641631,0.00012504008,0.00055324734,0.0004673198,0.00007324871,0.0006016774,0.0266882,0.03554758,0.011016749,0.8368866,0.08788348,0.00008048534],"about_ca_topic_score_codex":0.00095846393,"about_ca_topic_score_gemma":0.0007155239,"teacher_disagreement_score":0.012736791,"about_ca_system_score_codex":0.0023001211,"about_ca_system_score_gemma":0.0010263842,"threshold_uncertainty_score":0.04086244},"labels":[],"label_agreement":null},{"id":"W2259527008","doi":"","title":"Microtagging Internet Videos","year":2009,"lang":"en","type":"article","venue":"E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education","topic":"Video Analysis and Summarization","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":"Athabasca University","funders":"","keywords":"The Internet; Computer science; Internet privacy; World Wide Web","score_opus":0.04960830216745232,"score_gpt":0.2804242906005506,"score_spread":0.23081598843309825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2259527008","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76700586,0.0048268824,0.12625735,0.0016548114,0.002166819,0.00066233624,0.04409913,0.007532717,0.04579411],"genre_scores_gemma":[0.81200457,0.0023359002,0.07315499,0.00035872855,0.0014401464,0.00028680664,0.0592884,0.00050579855,0.050624624],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99960905,0.00004533308,0.000021190102,0.00011092807,0.00014054112,0.00007305264],"domain_scores_gemma":[0.9992392,0.00018810999,0.00008426634,0.00012187725,0.00030727932,0.000059273796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026555342,0.00048268822,0.00033953818,0.0045422507,0.00035734073,0.0009475296,0.00030088474,0.00042232647,0.0041624554],"category_scores_gemma":[0.0012987402,0.00016741872,0.00031887967,0.0031748249,0.0001290081,0.0011049565,0.0006328386,0.00048397295,0.003993788],"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":[0.0008313381,0.0002668565,0.017646672,0.0005569273,0.00013967228,0.00035873908,0.0006168063,0.00242665,0.13726251,0.0031828426,0.0597675,0.7769436],"study_design_scores_gemma":[0.0001237733,0.0013423541,0.23699078,0.00024313269,0.00051809795,0.0025089995,0.005113363,0.30496868,0.18901478,0.010707898,0.2482529,0.00021522281],"about_ca_topic_score_codex":0.0048158644,"about_ca_topic_score_gemma":0.01403295,"teacher_disagreement_score":0.0048158644,"about_ca_system_score_codex":0.00031765806,"about_ca_system_score_gemma":0.0002926733,"threshold_uncertainty_score":0.013924837},"labels":[],"label_agreement":null},{"id":"W2266089641","doi":"","title":"Authoring Multimedia, Designing Animations for Physics Education","year":2000,"lang":"en","type":"article","venue":"EdMedia: World Conference on Educational Media and Technology","topic":"Video Analysis and Summarization","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 Winnipeg","funders":"","keywords":"Multimedia; Computer science; Animation; World Wide Web; Human–computer interaction; Computer graphics (images)","score_opus":0.03313833960607905,"score_gpt":0.28033982097866705,"score_spread":0.24720148137258802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266089641","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022781573,0.0010125882,0.92695785,0.00061372697,0.00039914498,0.00018816085,0.00048270117,0.006068942,0.04149537],"genre_scores_gemma":[0.22738077,0.002226061,0.7025727,0.00017379918,0.00023871764,0.0002490494,0.0011175707,0.0020741979,0.06396705],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998062,0.000054713004,0.000013356796,0.000038976083,0.00006966949,0.000017086842],"domain_scores_gemma":[0.9994406,0.000337608,0.000028853801,0.0000663439,0.00008089085,0.000045654408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045224512,0.00058901287,0.00025267072,0.00080048013,0.00064203166,0.0017910192,0.0005405193,0.00093048764,0.016571514],"category_scores_gemma":[0.0027617405,0.0004250355,0.0003200724,0.000612123,0.00041296185,0.0018165584,0.00073803106,0.0005326663,0.0030983267],"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":[0.00015064771,0.00016331006,0.00088686566,0.00072727865,0.000031605614,0.00044007853,0.0017010891,0.032300033,0.11491283,0.049609322,0.037624396,0.76145256],"study_design_scores_gemma":[0.00009699052,0.00030017263,0.0020468251,0.00021119944,0.00012543083,0.0010294477,0.0012744077,0.2536464,0.19090715,0.06534722,0.4849187,0.000096043834],"about_ca_topic_score_codex":0.00086021965,"about_ca_topic_score_gemma":0.0019351146,"teacher_disagreement_score":0.016571514,"about_ca_system_score_codex":0.00039988212,"about_ca_system_score_gemma":0.0003092737,"threshold_uncertainty_score":0.055437267},"labels":[],"label_agreement":null},{"id":"W2266561132","doi":"10.1016/j.cviu.2016.01.008","title":"Special Issue on Individual and Group Activities in Video Event Analysis","year":2016,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Exploit; Relation (database); Benchmark (surveying); Artificial intelligence; Semantics (computer science); Object (grammar); Event (particle physics); Graph; Spatial relation; Theoretical computer science; Data mining; Programming language","score_opus":0.022377402147310755,"score_gpt":0.2640054781327596,"score_spread":0.24162807598544883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2266561132","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017762661,0.047131848,0.026728613,0.016704978,0.8706122,0.00031416892,0.00072878733,0.0010055333,0.03499764],"genre_scores_gemma":[0.005179452,0.0332615,0.005095355,0.004418983,0.8351109,0.00021868231,0.0017965301,0.0008068418,0.11411175],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982804,0.00027584174,0.00025930314,0.00039478947,0.0006183112,0.00017130116],"domain_scores_gemma":[0.9916688,0.002151278,0.00043212395,0.0007549173,0.003566674,0.0014261336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029831012,0.0026151338,0.0030224088,0.005421366,0.001597701,0.004983556,0.0025280013,0.0036847026,0.0750882],"category_scores_gemma":[0.0047844136,0.000790503,0.0019737396,0.0034687484,0.0010357788,0.0047076554,0.002945804,0.0030866575,0.031446762],"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":[0.000097857475,0.00011503629,0.00036156803,0.00072885945,0.00006434994,0.00027415456,0.0000352981,0.00025203032,0.0011703415,0.001548562,0.8936475,0.10170448],"study_design_scores_gemma":[0.00002107297,0.0001707719,0.001585382,0.00040746434,0.000073920084,0.00088061835,0.00006895118,0.0014620756,0.0006699931,0.0033844828,0.9912435,0.000031697906],"about_ca_topic_score_codex":0.00094034785,"about_ca_topic_score_gemma":0.0025359928,"teacher_disagreement_score":0.0750882,"about_ca_system_score_codex":0.0010896896,"about_ca_system_score_gemma":0.001555621,"threshold_uncertainty_score":0.25119507},"labels":[],"label_agreement":null},{"id":"W2281758774","doi":"","title":"Proceedings of the 1st ACM international workshop on Events in multimedia","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"","keywords":"Computer science; Event (particle physics); Context (archaeology); Domain (mathematical analysis); Multimedia; Variety (cybernetics); World Wide Web; Beijing; Data science; China; Artificial intelligence","score_opus":0.01553089275841601,"score_gpt":0.25734829247265456,"score_spread":0.24181739971423855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2281758774","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012414621,0.15573967,0.3333763,0.047214247,0.12064825,0.0012002117,0.006176387,0.010997318,0.312233],"genre_scores_gemma":[0.049770053,0.078855634,0.134087,0.009083083,0.029483512,0.0006827954,0.017366178,0.0034525194,0.67721915],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99848384,0.00038980978,0.0001329636,0.00027305004,0.00054836506,0.0001720329],"domain_scores_gemma":[0.9972222,0.0007437655,0.000086822445,0.00042165178,0.00084302976,0.0006825361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002605271,0.0014632745,0.0011808921,0.0015867323,0.0010780186,0.005859252,0.002282588,0.0020502089,0.088422276],"category_scores_gemma":[0.004333501,0.0005210752,0.0010099593,0.0014927144,0.0010916985,0.0074643833,0.0033934696,0.004234084,0.030652126],"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":[0.00020776255,0.000118851014,0.00033102464,0.00044551646,0.000047103935,0.00022381773,0.00043819,0.00042304775,0.0037078324,0.013836341,0.7413615,0.23885906],"study_design_scores_gemma":[0.000010928117,0.000038453694,0.000357966,0.0001727792,0.000021506605,0.00020140705,0.00019511141,0.0010662759,0.0007393954,0.0046640774,0.9925132,0.000019016235],"about_ca_topic_score_codex":0.0024694498,"about_ca_topic_score_gemma":0.004685377,"teacher_disagreement_score":0.088422276,"about_ca_system_score_codex":0.0010695929,"about_ca_system_score_gemma":0.0012691934,"threshold_uncertainty_score":0.29580194},"labels":[],"label_agreement":null},{"id":"W2284728253","doi":"10.1080/19346182.2012.708974","title":"Reliable jump detection for snow sports with low-cost MEMS inertial sensors","year":2011,"lang":"en","type":"article","venue":"Sports Technology","topic":"Video Analysis and Summarization","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, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inertial measurement unit; Step detection; Jump; Microelectromechanical systems; Reliability (semiconductor); Acceleration; Units of measurement; Simulation; Computer science; Algorithm; Power (physics); Engineering; Artificial intelligence; Global Positioning System; Telecommunications; Materials science; Physics","score_opus":0.007392389804096767,"score_gpt":0.19724879997221698,"score_spread":0.18985641016812022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284728253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27352035,0.00072500575,0.72085446,0.00012531296,0.00010724854,0.000079770456,0.00021245702,0.002276543,0.0020988942],"genre_scores_gemma":[0.74331576,0.00035105532,0.25355282,0.000047898695,0.00008683711,0.000083645304,0.0003383592,0.000065610824,0.0021580143],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998369,0.00002262571,0.000008521604,0.000028989662,0.000088887886,0.000014101927],"domain_scores_gemma":[0.99976045,0.00009015274,0.000034619523,0.000018725805,0.00008399659,0.00001206847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019703917,0.00034937332,0.00032734952,0.00055129954,0.00020016666,0.00026400163,0.00034707173,0.00034185694,0.0009496473],"category_scores_gemma":[0.00078955933,0.00014760732,0.00014093173,0.00034078248,0.00012313799,0.0003573755,0.00020989851,0.00020170935,0.0003463107],"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":[0.00062531326,0.00009319913,0.009171573,0.00028318388,0.000059082708,0.00016829847,0.00011840417,0.010234302,0.40700746,0.0008751396,0.0033568444,0.5680072],"study_design_scores_gemma":[0.000084198866,0.00059226644,0.04210763,0.000033366578,0.00009981304,0.0005019233,0.00014759699,0.6505939,0.29745308,0.0009878636,0.007343056,0.000055199274],"about_ca_topic_score_codex":0.0008589513,"about_ca_topic_score_gemma":0.0021054116,"teacher_disagreement_score":0.0009496473,"about_ca_system_score_codex":0.00013522829,"about_ca_system_score_gemma":0.00019654175,"threshold_uncertainty_score":0.0031769276},"labels":[],"label_agreement":null},{"id":"W229520775","doi":"10.1016/j.cag.2015.05.002","title":"Perceptually motivated LSPIHT for motion capture data compression","year":2015,"lang":"en","type":"article","venue":"Computers & Graphics","topic":"Video Analysis and Summarization","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":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Motion capture; Data compression; Scalability; Process (computing); Computer vision; Set (abstract data type); Motion (physics); Artificial intelligence; Algorithm","score_opus":0.09796671706833099,"score_gpt":0.2909898399248436,"score_spread":0.19302312285651263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W229520775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026730893,0.00047421572,0.9688826,0.00019321099,0.00011802835,0.00005469489,0.00017117235,0.00055935053,0.0028157902],"genre_scores_gemma":[0.38464758,0.0009844127,0.6041386,0.00030729087,0.00024090157,0.00012362505,0.00072715944,0.00021009035,0.008620278],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998386,0.000034842637,0.000008711399,0.000018453076,0.00008456782,0.000014778872],"domain_scores_gemma":[0.99972826,0.00010492118,0.00002151672,0.00004932239,0.00008236709,0.000013604477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021114582,0.00042046318,0.0002654618,0.00037740005,0.00020287279,0.0004266121,0.00032272958,0.00041457824,0.0035208561],"category_scores_gemma":[0.001055365,0.00015258389,0.00022902362,0.00054902636,0.00022500643,0.0005200789,0.00043976828,0.0006152654,0.00072793144],"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":[0.0006993995,0.00018331976,0.0005316772,0.00032202504,0.000042296648,0.0002296187,0.00008225688,0.07204143,0.3254071,0.019874746,0.005676301,0.5749098],"study_design_scores_gemma":[0.000018283794,0.00014449548,0.00070522085,0.000026963107,0.000016438937,0.00019094843,0.000015778971,0.9306005,0.059926126,0.004254275,0.0040838234,0.000017060584],"about_ca_topic_score_codex":0.00083182694,"about_ca_topic_score_gemma":0.0018325713,"teacher_disagreement_score":0.0035208561,"about_ca_system_score_codex":0.00024709784,"about_ca_system_score_gemma":0.00038466096,"threshold_uncertainty_score":0.011778414},"labels":[],"label_agreement":null},{"id":"W2333027183","doi":"10.1142/9789812791993_0019","title":"MODEL-BASED VIDEO SUMMARIZATION FOR MOBILE USERS","year":2000,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Multimedia; Information retrieval","score_opus":0.01430849339394425,"score_gpt":0.24214659143489273,"score_spread":0.22783809804094848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333027183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046207827,0.00074557937,0.94534683,0.0003521441,0.00010150662,0.00018171569,0.0007301866,0.0045860256,0.0017481202],"genre_scores_gemma":[0.56583196,0.0008172839,0.423747,0.00012760973,0.0001794282,0.00023253748,0.0034616608,0.00041478395,0.005187705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954385,0.00012736509,0.000033159704,0.00010741634,0.00013523812,0.00005296945],"domain_scores_gemma":[0.99889576,0.00038220402,0.000091710055,0.000129451,0.00045589745,0.000044901757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060636865,0.00097055745,0.0010273942,0.001239175,0.0005157964,0.0011408948,0.0007199167,0.0008566803,0.0024077422],"category_scores_gemma":[0.002822519,0.00033878445,0.0007054879,0.00087615126,0.00016634622,0.0014484988,0.0005307442,0.00063015125,0.001399835],"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":[0.0015441529,0.00037786856,0.0024553835,0.00038682483,0.00019085953,0.00028093893,0.00031322206,0.104644455,0.07983964,0.0039861784,0.01656687,0.7894137],"study_design_scores_gemma":[0.000028313447,0.00017493746,0.000888307,0.000011930955,0.00007076388,0.00009105797,0.000102436854,0.9747525,0.019344155,0.002262703,0.0022528071,0.000019974837],"about_ca_topic_score_codex":0.006321741,"about_ca_topic_score_gemma":0.0070988513,"teacher_disagreement_score":0.006321741,"about_ca_system_score_codex":0.0005819982,"about_ca_system_score_gemma":0.00047027093,"threshold_uncertainty_score":0.012569845},"labels":[],"label_agreement":null},{"id":"W2351703639","doi":"","title":"The Three-dimensional Analysis on Blocking Technique for Elite Male Volleyball Players at Home and abroad","year":2003,"lang":"en","type":"article","venue":"Sport Science and Technology","topic":"Video Analysis and Summarization","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":"Elite; Blocking (statistics); Kinematics; China; Advertising; Psychology; Simulation; Physical medicine and rehabilitation; Computer science; Medicine; Political science; Business; Physics; Law","score_opus":0.0064109540054189595,"score_gpt":0.2352350141551317,"score_spread":0.22882406014971274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2351703639","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7861391,0.0027582443,0.16878854,0.0006366945,0.000290504,0.00038701057,0.000854683,0.00049660046,0.03964861],"genre_scores_gemma":[0.85878634,0.0034088718,0.11577566,0.000075400625,0.00009323147,0.00019697839,0.0007942167,0.00012005902,0.020749219],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9998647,0.00003206486,0.000008046394,0.000022651946,0.000048839716,0.000023553264],"domain_scores_gemma":[0.99979645,0.000038111153,0.000022853621,0.000017542945,0.00009882161,0.000026254034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003275034,0.00033234022,0.00017207763,0.00168915,0.0005991488,0.00047825073,0.00024948097,0.00022090695,0.0030704646],"category_scores_gemma":[0.0005423842,0.00016728706,0.00028803578,0.0010000952,0.00024441356,0.0002337165,0.00024800655,0.00028525645,0.0005101013],"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":[0.0008151553,0.00017390234,0.047130745,0.0006225512,0.00010989241,0.0010971006,0.0044578607,0.00135823,0.33955455,0.0029841035,0.00844634,0.59324944],"study_design_scores_gemma":[0.00008504881,0.001306296,0.7707233,0.0002700054,0.0006255465,0.0068167383,0.009152389,0.019226847,0.12675984,0.0016120945,0.06321771,0.00020426328],"about_ca_topic_score_codex":0.013168195,"about_ca_topic_score_gemma":0.02498055,"teacher_disagreement_score":0.013168195,"about_ca_system_score_codex":0.00026292127,"about_ca_system_score_gemma":0.00038028616,"threshold_uncertainty_score":0.026183069},"labels":[],"label_agreement":null},{"id":"W2353662033","doi":"","title":"A Fast Inter Prediction Algorithm Based on H.264","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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":"Computer science; Algorithm","score_opus":0.005563122903155692,"score_gpt":0.2240744415595151,"score_spread":0.2185113186563594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2353662033","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014582583,0.0012751243,0.97519255,0.00018523292,0.000331435,0.00014479211,0.00032569806,0.0039186776,0.0040439144],"genre_scores_gemma":[0.09595089,0.0010890338,0.8877491,0.00016779298,0.0002677282,0.00013297681,0.001361711,0.0002768001,0.013004123],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970067,0.00002980238,0.000014900913,0.00004852897,0.00017056655,0.000035607565],"domain_scores_gemma":[0.9996346,0.00006818761,0.000026401374,0.00004394335,0.00020711741,0.000019705907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050270866,0.0008191332,0.0006304555,0.0011235409,0.00050321803,0.0006020003,0.00084486697,0.0005347385,0.0036480192],"category_scores_gemma":[0.00083730934,0.00027482962,0.00031614382,0.0008108558,0.00018914629,0.00078407506,0.000394576,0.0007872341,0.0020693974],"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":[0.00037765809,0.00010947878,0.00042378833,0.00011762073,0.00004253056,0.00007944019,0.00004829779,0.012224883,0.07549505,0.003208739,0.013866208,0.89400625],"study_design_scores_gemma":[0.0001752742,0.00057238684,0.0031876548,0.00005490042,0.00015077798,0.00040913143,0.000066196095,0.830271,0.124981195,0.00409103,0.035950508,0.00009001106],"about_ca_topic_score_codex":0.006871056,"about_ca_topic_score_gemma":0.011759032,"teacher_disagreement_score":0.006871056,"about_ca_system_score_codex":0.00031220898,"about_ca_system_score_gemma":0.0009157957,"threshold_uncertainty_score":0.0136621},"labels":[],"label_agreement":null},{"id":"W2354937835","doi":"","title":"The Realization of the Detecting System of Communication Information Real-time Video","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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; Software; Real-time computing; Video processing; Embedded system; Computer hardware; Realization (probability); Digital signal processing; Video tracking; Video capture; Operating system","score_opus":0.003185721202672268,"score_gpt":0.1898926505687594,"score_spread":0.18670692936608713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2354937835","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02371467,0.000517716,0.960306,0.00034274935,0.00033688423,0.00017048234,0.00008816868,0.0014499024,0.013073485],"genre_scores_gemma":[0.49637216,0.000815348,0.48423356,0.0003275384,0.000341571,0.0003264563,0.00038944068,0.00012863142,0.017065318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994435,0.00006421545,0.000029867857,0.000136797,0.00025650905,0.00006910079],"domain_scores_gemma":[0.9996018,0.0000498089,0.000025826745,0.00004597139,0.00024218603,0.000034441164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005093956,0.00039275235,0.0003595347,0.0005757594,0.00052469486,0.0009634693,0.0010021639,0.0006742766,0.002617265],"category_scores_gemma":[0.00084694254,0.00030273895,0.00029058082,0.00031010338,0.000361019,0.0012762164,0.00042120795,0.00079274643,0.00082019734],"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":[0.00036856375,0.000117232186,0.0020747744,0.00068813516,0.00006765165,0.0006056496,0.0010522916,0.006876539,0.398241,0.09088513,0.008168044,0.49085495],"study_design_scores_gemma":[0.00025251418,0.0011575604,0.004837363,0.00016078232,0.00025125162,0.0024616802,0.0004459838,0.20171884,0.6026958,0.014770631,0.17107414,0.00017336948],"about_ca_topic_score_codex":0.00149286,"about_ca_topic_score_gemma":0.0011168704,"teacher_disagreement_score":0.002617265,"about_ca_system_score_codex":0.0005234505,"about_ca_system_score_gemma":0.0011231829,"threshold_uncertainty_score":0.008755624},"labels":[],"label_agreement":null},{"id":"W2357278511","doi":"","title":"Audio Highlight Scene Extraction Using Support Vector Machines","year":2004,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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; Support vector machine; Artificial intelligence; Sample (material); Machine learning; Pattern recognition (psychology); Data mining; Speech recognition","score_opus":0.012278715934308324,"score_gpt":0.2653679547677476,"score_spread":0.25308923883343926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2357278511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13062935,0.00059423695,0.85575724,0.0002025744,0.000118373995,0.00014531826,0.0008983485,0.010314577,0.0013400317],"genre_scores_gemma":[0.51470864,0.00037794805,0.47750342,0.00008045006,0.00014951821,0.00012329847,0.0028928816,0.0003220093,0.0038418756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995035,0.000074100186,0.00004173776,0.00012944874,0.00015781572,0.000093404145],"domain_scores_gemma":[0.9990909,0.00034892085,0.00012260403,0.00008791487,0.00029171506,0.000058070287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045097477,0.0012854176,0.0011263555,0.0034868426,0.00037573223,0.0007487436,0.0007236014,0.0007840377,0.001745501],"category_scores_gemma":[0.0017993579,0.00040641986,0.00075277546,0.0015851536,0.00017581563,0.0010891535,0.00043826218,0.0007962866,0.0020105736],"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":[0.00031313018,0.00012462142,0.0031084234,0.00013934275,0.00006884474,0.00019180782,0.00003703126,0.013003182,0.09964677,0.00028209362,0.003621967,0.8794628],"study_design_scores_gemma":[0.000060474114,0.00035783648,0.015511788,0.00003183682,0.000121265446,0.00034546465,0.00024632356,0.8355392,0.13960476,0.0020988078,0.0060284594,0.00005382189],"about_ca_topic_score_codex":0.0019190265,"about_ca_topic_score_gemma":0.0027282927,"teacher_disagreement_score":0.0034868426,"about_ca_system_score_codex":0.00024195494,"about_ca_system_score_gemma":0.0003559213,"threshold_uncertainty_score":0.0058392286},"labels":[],"label_agreement":null},{"id":"W2359296044","doi":"","title":"Statistical Analysis on the 2010～2011 Season WCBA Finals","year":2011,"lang":"en","type":"article","venue":"Journal of Yibin University","topic":"Video Analysis and Summarization","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":"Victory; Quarter (Canadian coin); GRASP; Collocation (remote sensing); Computer science; Statistical analysis; Statistics; Psychology; Operations research; Mathematics; History; Machine learning; Political science; Archaeology","score_opus":0.037730792747181136,"score_gpt":0.20760515498850382,"score_spread":0.16987436224132269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2359296044","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9203306,0.002973665,0.010118944,0.0004457191,0.00023435746,0.003717496,0.04845005,0.0002837998,0.013445421],"genre_scores_gemma":[0.93337977,0.001738392,0.011295687,0.00011114006,0.00017986438,0.008565814,0.03637744,0.00008183255,0.008269948],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9956577,0.0007907963,0.0009630734,0.00065055856,0.0015595423,0.00037838516],"domain_scores_gemma":[0.9711811,0.008536866,0.004391373,0.0006488029,0.014655831,0.00058597774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063709375,0.00040776085,0.0008426215,0.019754047,0.00075334887,0.0008542606,0.00046878168,0.00021016406,0.004582563],"category_scores_gemma":[0.019155852,0.0001369555,0.0010072725,0.013528445,0.00046302885,0.00058731734,0.00064491126,0.0003547761,0.00056077354],"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":[0.0026609732,0.00047260823,0.587143,0.008136826,0.0013827822,0.0012222967,0.014386024,0.001995356,0.009148692,0.0030067365,0.037231714,0.333213],"study_design_scores_gemma":[0.000034468445,0.0010128447,0.9435907,0.0003599638,0.0005768948,0.00016461423,0.019546319,0.0017185372,0.0028084184,0.00027386443,0.029855967,0.00005745297],"about_ca_topic_score_codex":0.011096021,"about_ca_topic_score_gemma":0.017836813,"teacher_disagreement_score":0.019754047,"about_ca_system_score_codex":0.001493847,"about_ca_system_score_gemma":0.0024075222,"threshold_uncertainty_score":0.033693135},"labels":[],"label_agreement":null},{"id":"W2360215652","doi":"","title":"A Shot Boundary Detection Algorithm Based on Improving The Twin-threshold Method","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":3,"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":"Shot (pellet); Computer science; Algorithm; Boundary (topology); Single shot; Change detection; Artificial intelligence; Mathematics; Optics; Materials science","score_opus":0.008843545900444592,"score_gpt":0.2518645032985876,"score_spread":0.24302095739814303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2360215652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010599538,0.0005009198,0.98662424,0.00006162136,0.00011353851,0.00005939069,0.000038172493,0.0012751783,0.00072732416],"genre_scores_gemma":[0.11938399,0.00049873395,0.8775752,0.00008152501,0.00011943343,0.00008581679,0.00025447927,0.00028893203,0.0017118356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99740964,0.0002734496,0.00021605748,0.00056728034,0.0013624048,0.00017127435],"domain_scores_gemma":[0.9964289,0.0007535374,0.00021679938,0.00036992595,0.0020575398,0.00017327964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016296019,0.0009984148,0.0016848024,0.0042866245,0.0008703836,0.0020050262,0.0020301002,0.0015794511,0.0026713717],"category_scores_gemma":[0.006312208,0.00068953895,0.0009779764,0.0022983777,0.00076305703,0.004417055,0.0015127714,0.0016659795,0.001535309],"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":[0.00038295254,0.00012924185,0.0022629015,0.00033880328,0.00012917211,0.00023438636,0.0002854927,0.014423163,0.14907707,0.0066602887,0.0037923818,0.8222842],"study_design_scores_gemma":[0.00009042727,0.00040585827,0.0037934978,0.000056907404,0.00021516072,0.0018555241,0.0001702511,0.8082939,0.16398281,0.006438208,0.014508808,0.00018863726],"about_ca_topic_score_codex":0.0028282902,"about_ca_topic_score_gemma":0.0019513615,"teacher_disagreement_score":0.0042866245,"about_ca_system_score_codex":0.0005970152,"about_ca_system_score_gemma":0.0008653204,"threshold_uncertainty_score":0.008936584},"labels":[],"label_agreement":null},{"id":"W2364665652","doi":"","title":"Research and Application of Video Analysis Technologies","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"CAE (Canada)","funders":"","keywords":"Computer science; Coding (social sciences); Key (lock); Multimedia; Video tracking; Video processing; Artificial intelligence; Computer security","score_opus":0.034784828553744616,"score_gpt":0.3081208070391624,"score_spread":0.2733359784854178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364665652","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028646916,0.0866327,0.76043326,0.0038671251,0.0012015649,0.00038184048,0.00032689932,0.0013799678,0.11712967],"genre_scores_gemma":[0.3744697,0.1055734,0.47599688,0.0012618735,0.001921024,0.00044775865,0.00065554783,0.000311435,0.03936239],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99842095,0.0003934212,0.0000927616,0.00026939576,0.0007319026,0.00009160992],"domain_scores_gemma":[0.99699783,0.0013909397,0.00014017933,0.00023612361,0.001128839,0.0001061991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017107935,0.0006735952,0.00052036846,0.004781608,0.00063363824,0.0033411693,0.0011602527,0.0012512703,0.0037271297],"category_scores_gemma":[0.0046979347,0.00034178962,0.0005360748,0.0030086352,0.0009574485,0.0032324153,0.0008863743,0.0010665571,0.0018770508],"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":[0.00014358763,0.00011713146,0.0028310928,0.0011453041,0.000054179043,0.00044589487,0.0010611551,0.0038046069,0.044989176,0.13742378,0.0069969185,0.8009872],"study_design_scores_gemma":[0.00006813981,0.00072188955,0.007171634,0.0018798016,0.0001702075,0.0034462176,0.0024200468,0.10702581,0.17958738,0.17267999,0.5245985,0.00023032105],"about_ca_topic_score_codex":0.0014421641,"about_ca_topic_score_gemma":0.00056099676,"teacher_disagreement_score":0.004781608,"about_ca_system_score_codex":0.0012272841,"about_ca_system_score_gemma":0.0010315797,"threshold_uncertainty_score":0.012468517},"labels":[],"label_agreement":null},{"id":"W2365698872","doi":"","title":"Analysis of Several Motion Estimation Search Algorithms Based on H.264 Video Coding","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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; Motion estimation; Coding (social sciences); Encoder; Block-matching algorithm; Computer vision; Quarter-pixel motion; Matching (statistics); Motion compensation; Algorithm; Artificial intelligence; Fast motion; Video tracking; Video processing; Statistics; Mathematics","score_opus":0.00955357828873896,"score_gpt":0.2476437584566573,"score_spread":0.23809018016791833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2365698872","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09487328,0.011459682,0.88645756,0.00016378364,0.00013293223,0.00017470228,0.00023018864,0.0009798597,0.0055280565],"genre_scores_gemma":[0.5327182,0.011516454,0.44808236,0.00013457134,0.0001955443,0.00021296614,0.0014511531,0.0002583783,0.005430458],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989083,0.0001541563,0.00008358774,0.00012287634,0.00066665263,0.00006447578],"domain_scores_gemma":[0.99809307,0.00083727884,0.00012619,0.00009584917,0.0008219611,0.00002558552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009455339,0.00060176104,0.0005785244,0.0020651899,0.00036940048,0.0004698515,0.00057770975,0.0004915881,0.0011803792],"category_scores_gemma":[0.0033243326,0.00023375887,0.0005976463,0.0017807098,0.00023065404,0.0009643363,0.00016297042,0.00038466245,0.00029769013],"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":[0.0005862215,0.000109010056,0.0047489204,0.00057733344,0.0001924297,0.00011128218,0.00008680222,0.08004891,0.041045405,0.009253722,0.00225501,0.86098486],"study_design_scores_gemma":[0.00009813226,0.00081861985,0.016218796,0.00010249629,0.00037657714,0.0007440926,0.00008719063,0.8713761,0.094524905,0.0040712627,0.01147695,0.000104945735],"about_ca_topic_score_codex":0.004466836,"about_ca_topic_score_gemma":0.0033918505,"teacher_disagreement_score":0.004466836,"about_ca_system_score_codex":0.0005261365,"about_ca_system_score_gemma":0.0007404143,"threshold_uncertainty_score":0.008881688},"labels":[],"label_agreement":null},{"id":"W2366128575","doi":"","title":"Comparative Analysis on Li Na and Her Major Rival's Run Characteristics in 2011 Australian Open","year":2012,"lang":"en","type":"article","venue":"Journal of Guizhou Normal University","topic":"Video Analysis and Summarization","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":"Quarter (Canadian coin); Object (grammar); Research Object; Computer science; Statistics; Psychology; Econometrics; Mathematics; Sociology; History; Artificial intelligence; Regional science; Archaeology","score_opus":0.030681830539433803,"score_gpt":0.26339893459310115,"score_spread":0.23271710405366736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2366128575","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9866567,0.00009110499,0.00025665615,0.00016881707,0.000010445312,0.000013173384,0.00010449343,0.000006550469,0.012691874],"genre_scores_gemma":[0.9929047,0.00006471856,0.00015782088,0.000022142824,0.000005360347,0.000011160423,0.00015392184,0.0000058783426,0.006674258],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9986701,0.0002829485,0.00008008208,0.00016704616,0.0005862694,0.00021352166],"domain_scores_gemma":[0.99571157,0.0009388192,0.0010164492,0.0001420982,0.0016156358,0.0005755252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012738645,0.000100588164,0.00023450759,0.00234847,0.002021727,0.0018351183,0.00042981876,0.00030602855,0.0032315273],"category_scores_gemma":[0.006988766,0.00009026085,0.00014640966,0.0026994685,0.0008611193,0.0012323145,0.001317035,0.00057280314,0.00033725347],"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":[0.00039731705,0.00017086111,0.58691025,0.00018473521,0.00006743386,0.0018570366,0.27639902,0.00045076828,0.0039832224,0.01614357,0.006578836,0.106857054],"study_design_scores_gemma":[0.0000014254093,0.00010278781,0.84511787,0.000038406946,0.000015329008,0.00015462893,0.13658252,0.0011509422,0.0003994245,0.00042082474,0.01597783,0.00003805809],"about_ca_topic_score_codex":0.0715502,"about_ca_topic_score_gemma":0.18799993,"teacher_disagreement_score":0.0715502,"about_ca_system_score_codex":0.003163324,"about_ca_system_score_gemma":0.001145713,"threshold_uncertainty_score":0.14226747},"labels":[],"label_agreement":null},{"id":"W2371497650","doi":"","title":"Improve the Experience of Users of Video Conference System with Multi-modal Machine Attention","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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; Modal; Focus (optics); Synchronizing; Bionics; Human–computer interaction; Multimedia; Mode (computer interface); TRACE (psycholinguistics); Object (grammar); Artificial intelligence; Telecommunications","score_opus":0.007121744558912971,"score_gpt":0.2146790083270632,"score_spread":0.2075572637681502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2371497650","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93674934,0.0014578691,0.0354924,0.0008817144,0.00017794744,0.00020386856,0.00035771893,0.002763808,0.021915196],"genre_scores_gemma":[0.97323936,0.0005311403,0.011777504,0.00038829306,0.00013192688,0.00010665186,0.00043219715,0.00022271633,0.013170124],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995036,0.00022821286,0.000023831566,0.00006296194,0.00009147848,0.00008983764],"domain_scores_gemma":[0.9983986,0.00067935354,0.00010164311,0.00014869824,0.00037513263,0.00029659364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078658736,0.00060092745,0.00038999552,0.00044557123,0.0004278425,0.0009923446,0.00050548476,0.0008538565,0.013043601],"category_scores_gemma":[0.0049171695,0.00012170715,0.0004233281,0.0001760656,0.00018004894,0.0013586947,0.0010521329,0.00058272586,0.0023838913],"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":[0.004075734,0.0039658053,0.055421807,0.001979024,0.00028151475,0.002678554,0.045576345,0.0028859347,0.18062662,0.0012833484,0.04718022,0.6540451],"study_design_scores_gemma":[0.0007363625,0.026591161,0.36841416,0.00058918545,0.0013676005,0.014105275,0.0674079,0.0440708,0.18086676,0.0031716195,0.2915655,0.0011135828],"about_ca_topic_score_codex":0.0004920665,"about_ca_topic_score_gemma":0.00050897285,"teacher_disagreement_score":0.013043601,"about_ca_system_score_codex":0.00017572027,"about_ca_system_score_gemma":0.00012983457,"threshold_uncertainty_score":0.04363525},"labels":[],"label_agreement":null},{"id":"W2372057727","doi":"","title":"A Camera Motion Detection System Based on the Global Motion Movement Information","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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; Computer vision; Artificial intelligence; Motion (physics); Camera auto-calibration; Movement (music); Motion estimation; Smart camera; Camera resectioning; Motion field; Computer graphics (images)","score_opus":0.0070219247881559725,"score_gpt":0.18898621406580896,"score_spread":0.18196428927765299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2372057727","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03480974,0.0015366984,0.95142245,0.0001339473,0.00031961745,0.00030826553,0.0003882384,0.0067874254,0.0042936387],"genre_scores_gemma":[0.26036516,0.0013212563,0.7262564,0.0002469343,0.00029231445,0.00033053797,0.0011484108,0.00027973155,0.009759299],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995371,0.000055081877,0.000023405308,0.0001418358,0.00021087089,0.00003175357],"domain_scores_gemma":[0.9994541,0.00010334305,0.00007070966,0.000059054953,0.00026435163,0.000048524125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041197907,0.0007218922,0.0007467278,0.0014703185,0.0003942756,0.00067198544,0.0007506,0.0007261667,0.0029264635],"category_scores_gemma":[0.0008335575,0.000404483,0.00029496386,0.0008702244,0.0002463761,0.00097117637,0.00036933162,0.00067430845,0.0013706713],"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":[0.00035588286,0.00010676415,0.0027145797,0.00046972063,0.000116555864,0.00019326758,0.00017274998,0.0023562578,0.39066502,0.0017625488,0.007186006,0.5939007],"study_design_scores_gemma":[0.00032350552,0.0021113937,0.032327663,0.000159532,0.00059645594,0.0033683009,0.0001900421,0.22741127,0.63429266,0.0015494974,0.09727747,0.00039223282],"about_ca_topic_score_codex":0.0017859652,"about_ca_topic_score_gemma":0.0025850995,"teacher_disagreement_score":0.0029264635,"about_ca_system_score_codex":0.0003019788,"about_ca_system_score_gemma":0.00043275463,"threshold_uncertainty_score":0.009790003},"labels":[],"label_agreement":null},{"id":"W2373723928","doi":"","title":"Simple Analysis on Digital Video Monitoring System","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Analysis and Summarization","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; Digital video; Process (computing); Coding (social sciences); Video processing; Video capture; Simple (philosophy); Computer hardware; Multimedia; Frame (networking); Telecommunications; Operating system","score_opus":0.007758098893526795,"score_gpt":0.24272096120771933,"score_spread":0.23496286231419253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2373723928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19011524,0.0018537794,0.7454933,0.0006472076,0.0004245236,0.00066055445,0.0005083883,0.0046454957,0.055651534],"genre_scores_gemma":[0.85644466,0.0014660648,0.10143764,0.00027205318,0.00025182654,0.00024893347,0.00081017666,0.00013745355,0.03893116],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99917394,0.00008556743,0.000033352087,0.00013638654,0.0005161287,0.000054566615],"domain_scores_gemma":[0.9995048,0.000104663304,0.00003345642,0.000043181848,0.00029069895,0.000023166962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027151784,0.0004877504,0.0003262774,0.0014415379,0.0004257884,0.00078030647,0.00047727185,0.0004846384,0.0073304046],"category_scores_gemma":[0.0010310855,0.00020220866,0.00031715483,0.0007269075,0.00022892887,0.0011050792,0.00044701618,0.00032795712,0.0011793706],"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":[0.0006830411,0.00013261868,0.009260109,0.00080727437,0.000060827315,0.0006985804,0.0005103204,0.012683929,0.3446454,0.0209662,0.012092114,0.59745955],"study_design_scores_gemma":[0.0001961892,0.0012761983,0.04256732,0.0002088224,0.0002884515,0.002582018,0.00056896807,0.4210305,0.40640137,0.017212393,0.10745214,0.00021569678],"about_ca_topic_score_codex":0.0015783574,"about_ca_topic_score_gemma":0.000992251,"teacher_disagreement_score":0.0073304046,"about_ca_system_score_codex":0.000492363,"about_ca_system_score_gemma":0.0004078922,"threshold_uncertainty_score":0.024522662},"labels":[],"label_agreement":null},{"id":"W2373873503","doi":"","title":"Research on Television Sports Broadcasting Viewer's Motivations","year":2011,"lang":"en","type":"article","venue":"Beijing Tiyu Daxue xuebao","topic":"Video Analysis and Summarization","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":"Broadcasting (networking); Reading (process); Context (archaeology); Advertising; Logical analysis; Sociology; Psychology; Media studies; Political science; Computer science; History; Business","score_opus":0.14797178561663227,"score_gpt":0.34189599377197555,"score_spread":0.19392420815534328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2373873503","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8946685,0.0015902781,0.0025938451,0.0008531642,0.000040212686,0.000053156986,0.00023379174,0.000011183616,0.09995576],"genre_scores_gemma":[0.99413776,0.0010057118,0.00070047745,0.00007960195,0.000030745647,0.00003348334,0.00017218849,0.000009299947,0.003830752],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9992712,0.0003368072,0.000030823703,0.00008283268,0.0002068495,0.00007153506],"domain_scores_gemma":[0.99251455,0.005059415,0.0011841647,0.00014106701,0.0008552639,0.0002454698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016686107,0.00019561352,0.00014565646,0.0027377242,0.0009285609,0.0022343558,0.00039040227,0.0003871558,0.003990318],"category_scores_gemma":[0.006962559,0.00014611328,0.00018865385,0.0034903253,0.00081965514,0.0014393822,0.00045768855,0.0005923166,0.00024952737],"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":[0.00031053423,0.0002336044,0.5020617,0.0012188329,0.0001467997,0.00071921264,0.16445957,0.00041477126,0.0069519933,0.12556888,0.004120303,0.1937938],"study_design_scores_gemma":[0.000016561444,0.00015179782,0.69691396,0.0005210599,0.00008852423,0.0005603244,0.23873332,0.0011951262,0.0016938205,0.010188897,0.049889296,0.00004739052],"about_ca_topic_score_codex":0.005093058,"about_ca_topic_score_gemma":0.008401839,"teacher_disagreement_score":0.005093058,"about_ca_system_score_codex":0.0012642533,"about_ca_system_score_gemma":0.0007182895,"threshold_uncertainty_score":0.013348937},"labels":[],"label_agreement":null},{"id":"W2397656541","doi":"10.1109/mmdbms.1995.520424","title":"A multimedia query specification language","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Multimedia database; Multimedia; Query language; Graphics; Information retrieval; Multimedia information retrieval; Database; World Wide Web","score_opus":0.017274378329574593,"score_gpt":0.21421428407042697,"score_spread":0.19693990574085238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397656541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010661453,0.0004219526,0.97860944,0.0011155554,0.00014021285,0.0005566023,0.002456818,0.00953724,0.0060959477],"genre_scores_gemma":[0.038517006,0.001129337,0.9349976,0.0020066479,0.00037247338,0.0014610437,0.0082121,0.0021641809,0.011139675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9931612,0.0015844535,0.0016523649,0.0009119753,0.0022931623,0.0003969033],"domain_scores_gemma":[0.9929409,0.0027242457,0.0005954622,0.0012116653,0.0021459856,0.00038161696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065619177,0.0017654208,0.0012479257,0.0026147084,0.0015087187,0.0058768215,0.003884761,0.0025786282,0.0124531835],"category_scores_gemma":[0.008338861,0.001261819,0.0020811725,0.0028001452,0.0021585657,0.008306064,0.0028807372,0.004504658,0.009236302],"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":[0.0003953658,0.00020407757,0.0006774896,0.0012924813,0.000092701644,0.0007216263,0.0014600158,0.009449262,0.025686843,0.70624936,0.09880882,0.15496199],"study_design_scores_gemma":[0.00019211415,0.00030830936,0.0002807502,0.00036300998,0.000115931885,0.0019826705,0.00049096724,0.095997445,0.024477828,0.14974724,0.72583354,0.00021023366],"about_ca_topic_score_codex":0.005353789,"about_ca_topic_score_gemma":0.0035878976,"teacher_disagreement_score":0.0124531835,"about_ca_system_score_codex":0.001875841,"about_ca_system_score_gemma":0.004184409,"threshold_uncertainty_score":0.04166001},"labels":[],"label_agreement":null},{"id":"W2399428441","doi":"10.1109/wacv.2016.7477704","title":"Video summarization for remote invigilation of online exams","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; Multimedia; Hidden Markov model; Online video; World Wide Web; Artificial intelligence; Information retrieval","score_opus":0.020676548110916303,"score_gpt":0.2547932254603872,"score_spread":0.2341166773494709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399428441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22228085,0.0014958073,0.7613105,0.00035148038,0.00024545792,0.00044995558,0.0018278228,0.008921139,0.0031169953],"genre_scores_gemma":[0.68222564,0.0010554631,0.30807972,0.00011092733,0.00027116554,0.0001744528,0.003937213,0.00032152148,0.0038238757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970067,0.000055510052,0.00002382054,0.00008425534,0.000107619,0.000028132978],"domain_scores_gemma":[0.99901676,0.0002527863,0.00020520981,0.00012458814,0.00033208993,0.000068489404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037617967,0.0006960585,0.00039209652,0.0016626567,0.00020629144,0.0005308016,0.00047024825,0.0004076001,0.0014406445],"category_scores_gemma":[0.0021010113,0.00014069218,0.0003147217,0.0005830419,0.00012642358,0.0004991538,0.00042019793,0.00035559197,0.0008698956],"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":[0.00068093673,0.00011640858,0.0033018498,0.00033004125,0.000053402524,0.0004534643,0.00039903546,0.009102816,0.1963241,0.00074562157,0.005726709,0.7827656],"study_design_scores_gemma":[0.000074749405,0.0014946624,0.045846153,0.000111688794,0.00023760003,0.0014600395,0.0010642951,0.62592566,0.2878843,0.0026623409,0.033129282,0.00010922459],"about_ca_topic_score_codex":0.0012389891,"about_ca_topic_score_gemma":0.001924093,"teacher_disagreement_score":0.0016626567,"about_ca_system_score_codex":0.00022438515,"about_ca_system_score_gemma":0.00022171868,"threshold_uncertainty_score":0.0048194528},"labels":[],"label_agreement":null},{"id":"W2400464007","doi":"10.29173/cais452","title":"The MPEG-7 Initiative for Multimedia Content Description","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Video Analysis and Summarization","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":"Multimedia; Computer science; Context (archaeology); MPEG-4; World Wide Web; Content (measure theory); Sociology; Geography","score_opus":0.04983688453221168,"score_gpt":0.24151826678128688,"score_spread":0.1916813822490752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400464007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050002215,0.01973666,0.7459659,0.007766302,0.0047380533,0.0053163623,0.024525424,0.014065059,0.17288595],"genre_scores_gemma":[0.033787206,0.039851934,0.68026876,0.0038240817,0.0035293505,0.005270488,0.11679824,0.004490724,0.11217917],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99644285,0.0006450889,0.0003477031,0.00017658419,0.0021865012,0.00020135877],"domain_scores_gemma":[0.9964265,0.0006040598,0.0002776761,0.00050713145,0.0019160052,0.0002686485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003856307,0.002357485,0.0009888422,0.0059807496,0.0013623443,0.0040790136,0.0024045282,0.0024911738,0.012964027],"category_scores_gemma":[0.006659453,0.0005321667,0.0008141227,0.0046725413,0.001424606,0.0038328674,0.0019647316,0.0031610108,0.0155196525],"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":[0.00034417628,0.00015793134,0.00062086346,0.0010676956,0.00008215497,0.0003872349,0.00037968913,0.0032987224,0.028760696,0.09417933,0.34023842,0.53048307],"study_design_scores_gemma":[0.000064199725,0.000119622055,0.001007643,0.00047632516,0.000056599896,0.0003727849,0.00017026889,0.0061021126,0.014521459,0.018042887,0.95896643,0.000099683144],"about_ca_topic_score_codex":0.011129038,"about_ca_topic_score_gemma":0.0055899494,"teacher_disagreement_score":0.012964027,"about_ca_system_score_codex":0.0021294188,"about_ca_system_score_gemma":0.0038560908,"threshold_uncertainty_score":0.043369055},"labels":[],"label_agreement":null},{"id":"W2401881932","doi":"","title":"Ein erweiterbares Tool zur Annotation von Videos.","year":2011,"lang":"de","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Analysis and Summarization","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":"Computer science; Annotation; Preprocessor; Segmentation; Artificial intelligence; Computer vision; Computer graphics (images); Automatic image annotation; Image processing; Field (mathematics); Information retrieval; Image (mathematics)","score_opus":0.020022770693413534,"score_gpt":0.2183032662958781,"score_spread":0.19828049560246455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401881932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013591693,0.00041773092,0.96703357,0.00015649866,0.00015096445,0.00017588705,0.0014463157,0.025508827,0.0037510816],"genre_scores_gemma":[0.02719052,0.00069846876,0.9470299,0.00037221482,0.00013180949,0.0005863283,0.00809583,0.004390081,0.01150493],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99748105,0.0005044277,0.0002882189,0.00065253396,0.00091763027,0.0001561739],"domain_scores_gemma":[0.99650574,0.0011829355,0.00020577679,0.00078262837,0.0010990584,0.00022388875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021014062,0.0018242395,0.00074048975,0.0041830563,0.00097407115,0.00326016,0.0018279413,0.0021647422,0.0147643555],"category_scores_gemma":[0.0076317005,0.0009897443,0.0009788703,0.0015437411,0.00081806927,0.0033801717,0.0034495855,0.0018010864,0.0140219815],"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":[0.00037400023,0.0001394401,0.0011289377,0.0010036958,0.0000889408,0.001438035,0.0011887228,0.002119837,0.07665024,0.01975586,0.07368457,0.8224277],"study_design_scores_gemma":[0.00012390567,0.00030479877,0.0035489686,0.0010620838,0.00015952466,0.006902634,0.0008931356,0.053993884,0.14202587,0.045531772,0.74516153,0.0002919462],"about_ca_topic_score_codex":0.0015812215,"about_ca_topic_score_gemma":0.0015379414,"teacher_disagreement_score":0.0147643555,"about_ca_system_score_codex":0.0004027101,"about_ca_system_score_gemma":0.0009163212,"threshold_uncertainty_score":0.049391747},"labels":[],"label_agreement":null},{"id":"W2406989974","doi":"10.1201/9781003059325-16","title":"Fast Forward with your VCR: Visualizing Single-Video Viewing Statistics for Navigation and Sharing","year":2020,"lang":"en","type":"book-chapter","venue":"","topic":"Video Analysis and Summarization","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 British Columbia","funders":"","keywords":"Computer science; Computer graphics (images); Statistics; Mathematics","score_opus":0.04775504918142347,"score_gpt":0.2687501339930632,"score_spread":0.2209950848116397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406989974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041771814,0.005314039,0.87667406,0.0009754794,0.00070284866,0.00044926876,0.0073324754,0.033038624,0.033741467],"genre_scores_gemma":[0.16941772,0.0035687436,0.7936119,0.00035570315,0.0004295397,0.0005649252,0.006616333,0.004536227,0.020898843],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997726,0.00006057545,0.000011854606,0.00004034237,0.00009469799,0.000019955576],"domain_scores_gemma":[0.998796,0.00062152103,0.000091420356,0.00016044694,0.00022686864,0.000103755665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005333778,0.0011184472,0.0004156802,0.001826155,0.00030964546,0.0017401245,0.0009001069,0.0005634731,0.013100484],"category_scores_gemma":[0.0024686065,0.00024337646,0.00047963063,0.0019399077,0.0002508747,0.0019825406,0.0008586956,0.0007927514,0.00375416],"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":[0.0005391447,0.00015491669,0.0033804679,0.0018907909,0.00011328188,0.00051575847,0.00237908,0.010561782,0.057833318,0.019101808,0.1662574,0.73727226],"study_design_scores_gemma":[0.00020879695,0.00071526563,0.025426501,0.0011991422,0.00020479056,0.0024571377,0.0022660145,0.27131057,0.058526367,0.041044824,0.59613866,0.0005019698],"about_ca_topic_score_codex":0.0018731118,"about_ca_topic_score_gemma":0.0035704414,"teacher_disagreement_score":0.013100484,"about_ca_system_score_codex":0.0003705911,"about_ca_system_score_gemma":0.00042594224,"threshold_uncertainty_score":0.043825448},"labels":[],"label_agreement":null},{"id":"W2517270245","doi":"10.1109/bigmm.2016.39","title":"An Empirical Study of the Textual Content of Online Videos","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Information retrieval; Cluster analysis; Multimedia; Video retrieval; Annotation; Content (measure theory); Empirical research; World Wide Web; Artificial intelligence","score_opus":0.056255279805803846,"score_gpt":0.3124358080669544,"score_spread":0.25618052826115056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2517270245","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99507314,0.00020356366,0.0010083127,0.000162026,0.000012215823,0.00009177915,0.0012063561,0.0000142936115,0.0022282712],"genre_scores_gemma":[0.99437153,0.00029877276,0.0019260322,0.000082227896,0.000049776754,0.00010448607,0.0024506715,0.0000135097525,0.0007029217],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99806625,0.00090398465,0.0002041705,0.00023604484,0.0004747351,0.00011480135],"domain_scores_gemma":[0.9192552,0.05823607,0.011357685,0.001842845,0.0077496865,0.0015585014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026196125,0.00017557877,0.00017770768,0.002475883,0.0004852127,0.000986215,0.0004497611,0.0005441889,0.0020832643],"category_scores_gemma":[0.050587315,0.00010953971,0.00018757803,0.0023712679,0.0006018652,0.002265016,0.00050119887,0.00059097,0.00055074075],"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":[0.00083522254,0.0019524537,0.8859578,0.0010908018,0.0001421921,0.0007997573,0.015210062,0.0008167567,0.0067344075,0.0014963116,0.004043854,0.080920376],"study_design_scores_gemma":[0.000029163082,0.0009021095,0.9565857,0.00024782852,0.0000858065,0.0008605551,0.023976622,0.0065382416,0.0031555705,0.0005987675,0.0069788615,0.000040795512],"about_ca_topic_score_codex":0.0034650762,"about_ca_topic_score_gemma":0.0039133863,"teacher_disagreement_score":0.0034650762,"about_ca_system_score_codex":0.00050661684,"about_ca_system_score_gemma":0.00038514734,"threshold_uncertainty_score":0.013853967},"labels":[],"label_agreement":null},{"id":"W2527334262","doi":"","title":"CRIM's content-based copy detection system for TRECVID","year":2009,"lang":"en","type":"article","venue":"TRECVID","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":23,"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; Artificial intelligence; Probabilistic logic; Pattern recognition (psychology); Key (lock); Matching (statistics); Nearest neighbor search; Feature (linguistics); k-nearest neighbors algorithm; Task (project management); Feature vector; Shot (pellet); Mathematics; Statistics","score_opus":0.03680703612985442,"score_gpt":0.24531971422114324,"score_spread":0.20851267809128882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527334262","genre_codex":"software","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03490179,0.0027959526,0.16111697,0.00077461154,0.0024492084,0.004198511,0.119888626,0.6445775,0.02929676],"genre_scores_gemma":[0.12937015,0.0008216339,0.4452552,0.0008622786,0.00074088905,0.005285342,0.35034016,0.039575495,0.027748784],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9919391,0.0011895568,0.00064644706,0.0023503434,0.003113861,0.00076063373],"domain_scores_gemma":[0.9899012,0.0013794081,0.000530604,0.0041258074,0.0035741334,0.00048891664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006899371,0.005764243,0.0025939348,0.008395488,0.0018101557,0.0028336816,0.006717795,0.0037300435,0.032703742],"category_scores_gemma":[0.017604712,0.0016742838,0.0024853188,0.0048428695,0.000771241,0.0043403185,0.0036950272,0.0027652674,0.03382302],"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":[0.00122115,0.0005186075,0.0017790628,0.0013445641,0.000556546,0.00030678284,0.00015238441,0.00430829,0.025280494,0.0012504021,0.7305025,0.23277915],"study_design_scores_gemma":[0.0012936993,0.0016584091,0.023392158,0.00038373304,0.0007186204,0.0030526062,0.00053444295,0.34109583,0.2145414,0.0070135156,0.40548214,0.0008333694],"about_ca_topic_score_codex":0.028196843,"about_ca_topic_score_gemma":0.030811518,"teacher_disagreement_score":0.032703742,"about_ca_system_score_codex":0.0033809126,"about_ca_system_score_gemma":0.0027258187,"threshold_uncertainty_score":0.10940486},"labels":[],"label_agreement":null},{"id":"W2535212372","doi":"10.1109/tnnls.2016.2614653","title":"Hair Segmentation Using Heuristically-Trained Neural Networks","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Video Analysis and Summarization","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 Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Classifier (UML); Pattern recognition (psychology); Artificial neural network; False positive paradox; Heuristic; Binary classification; Confidence interval; Training set; Binary number; Machine learning; Mathematics; Statistics; Support vector machine","score_opus":0.015898411309077296,"score_gpt":0.2359997003453438,"score_spread":0.2201012890362665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2535212372","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038321063,0.0004700255,0.9542595,0.00014627798,0.00011684874,0.00011058311,0.00009773492,0.0030359416,0.0034420663],"genre_scores_gemma":[0.38309756,0.0002685592,0.6106047,0.0003082534,0.00015086286,0.00015303443,0.0005231975,0.00034616273,0.004547731],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891436,0.00016899066,0.000050719056,0.00038064984,0.00035288458,0.000132423],"domain_scores_gemma":[0.9986878,0.00045993592,0.00016883685,0.00022997377,0.00039742803,0.00005612825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011288433,0.00094061444,0.0010489114,0.0016910173,0.0005861156,0.0012768809,0.0015444579,0.001182745,0.0027769383],"category_scores_gemma":[0.0033529194,0.0006553618,0.0007286519,0.0010134522,0.0008415378,0.0013871487,0.00080514094,0.0009792416,0.0013883295],"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":[0.0003716712,0.00015997473,0.0025383318,0.00015127366,0.00011164266,0.00014671206,0.00014079602,0.20958728,0.053589888,0.003006844,0.0044940985,0.72570145],"study_design_scores_gemma":[0.000010610133,0.000059260463,0.00071511354,0.00001176503,0.000013574322,0.0000584875,0.000021481392,0.9866414,0.009324181,0.0021083774,0.0010222099,0.000013506862],"about_ca_topic_score_codex":0.0036254176,"about_ca_topic_score_gemma":0.006943167,"teacher_disagreement_score":0.0036254176,"about_ca_system_score_codex":0.000911504,"about_ca_system_score_gemma":0.00062694063,"threshold_uncertainty_score":0.009289801},"labels":[],"label_agreement":null},{"id":"W2540032788","doi":"10.1109/itict.2005.1609640","title":"Imm_Analyser: A Tool for Retrieving Data from Multimedia Documents","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Université du Québec à Montréal","funders":"","keywords":"Analyser; Computer science; Bar (unit); Software; Spectrum analyzer; Multimedia; Operating system; Telecommunications","score_opus":0.02520729112265323,"score_gpt":0.27802264836758106,"score_spread":0.2528153572449278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2540032788","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003968663,0.0010607705,0.4614275,0.00023703113,0.00019296883,0.0006020212,0.07096276,0.4510888,0.010459485],"genre_scores_gemma":[0.032827362,0.0012588277,0.75767034,0.00046381773,0.00028723793,0.0014663383,0.15703273,0.028512662,0.020480737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99765754,0.00029995773,0.00042125332,0.00053286087,0.00095509907,0.00013329304],"domain_scores_gemma":[0.996166,0.001722193,0.00040099298,0.0006010565,0.0008984716,0.0002112873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027542268,0.0027055487,0.0018011549,0.008876451,0.00091279374,0.003972134,0.0022223133,0.00155851,0.042877425],"category_scores_gemma":[0.008625802,0.0012395586,0.0017317126,0.0060227397,0.0005857043,0.003767815,0.0025550507,0.0015126718,0.042891447],"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":[0.0012880479,0.0001981952,0.0032345578,0.0030564044,0.00038042752,0.0011683524,0.0006986535,0.0021071017,0.036652282,0.006185785,0.34247452,0.6025557],"study_design_scores_gemma":[0.00033633635,0.0002618411,0.009441228,0.00038558943,0.000245582,0.002496813,0.0004846002,0.041955773,0.1035946,0.010966104,0.8294796,0.00035205082],"about_ca_topic_score_codex":0.0020875132,"about_ca_topic_score_gemma":0.0021011052,"teacher_disagreement_score":0.042877425,"about_ca_system_score_codex":0.00059365184,"about_ca_system_score_gemma":0.0013308339,"threshold_uncertainty_score":0.1434393},"labels":[],"label_agreement":null},{"id":"W2544232357","doi":"10.1109/icece.2006.355629","title":"Ontology-Based Unification of MPEG-7 Semantic Descriptions","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Ontology; Information retrieval; Semantics (computer science); Unification; Knowledge representation and reasoning; Abstraction; Domain (mathematical analysis); Semantic technology; Semantic computing; Representation (politics); Set (abstract data type); Description logic; Natural language processing; Semantic Web; Artificial intelligence; Programming language","score_opus":0.011899868096859325,"score_gpt":0.21312372697562298,"score_spread":0.20122385887876365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544232357","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008324673,0.000820651,0.95729285,0.0010830419,0.00036169906,0.00091998087,0.0013415151,0.0017429315,0.02811279],"genre_scores_gemma":[0.12150949,0.002418324,0.85629505,0.00047994935,0.0001614466,0.00091111055,0.0068058153,0.00040559156,0.011013178],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99478996,0.0012954117,0.00096016785,0.0005367412,0.0020655314,0.00035215897],"domain_scores_gemma":[0.9970299,0.00068661856,0.0002787155,0.0008019335,0.0010303003,0.00017250342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005416558,0.0008014082,0.0008818969,0.0047140224,0.0019252076,0.0047416454,0.002301993,0.0012176532,0.0022424369],"category_scores_gemma":[0.0074765743,0.0007601898,0.0025072242,0.0034806165,0.002113173,0.007214715,0.003967249,0.0028990102,0.00091371126],"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":[0.000090066555,0.0001796614,0.00077764085,0.00033903448,0.0001156204,0.00077689224,0.0023673696,0.01289832,0.004734405,0.82896656,0.010519274,0.13823518],"study_design_scores_gemma":[0.00008603038,0.000072085226,0.0013215236,0.0008950361,0.00036051107,0.0007675266,0.0023183809,0.15370424,0.013924025,0.36867133,0.4576881,0.00019119958],"about_ca_topic_score_codex":0.023417939,"about_ca_topic_score_gemma":0.022092825,"teacher_disagreement_score":0.023417939,"about_ca_system_score_codex":0.0035371517,"about_ca_system_score_gemma":0.0068321005,"threshold_uncertainty_score":0.046563268},"labels":[],"label_agreement":null},{"id":"W2546473017","doi":"10.1109/itict.2007.4475654","title":"Predicting interactive properties by mining educational multimedia presentations","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 du Québec à Montréal","funders":"","keywords":"Interactivity; Computer science; Multimedia; Interactive media","score_opus":0.01801736240105902,"score_gpt":0.27707220146356076,"score_spread":0.25905483906250176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546473017","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71315604,0.0020293777,0.27156633,0.00041564088,0.000073489,0.0005449881,0.0046637603,0.004298119,0.0032522406],"genre_scores_gemma":[0.88420916,0.0007591812,0.10638702,0.000029310842,0.00012169927,0.00025215978,0.0070693595,0.00011383771,0.0010582823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986613,0.0003276211,0.00015518896,0.00026935374,0.00047754016,0.00010897415],"domain_scores_gemma":[0.9852754,0.010286645,0.0017646254,0.0006115534,0.0017027106,0.00035910562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012773953,0.0015232394,0.0009801358,0.008175744,0.00042679315,0.0014614724,0.0010070844,0.000982951,0.001509062],"category_scores_gemma":[0.013024915,0.00030238414,0.0009728367,0.0031208522,0.00031237153,0.0020847337,0.0007544981,0.0008046531,0.0007788144],"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":[0.0012022373,0.0013315971,0.097959995,0.001325676,0.00036193145,0.0008120219,0.0006712211,0.07322398,0.04754297,0.0013732247,0.003116605,0.7710785],"study_design_scores_gemma":[0.000101284684,0.0016177754,0.09753895,0.00014658776,0.0005371016,0.0009826426,0.0014369828,0.81436646,0.06845376,0.008972351,0.005705931,0.0001402351],"about_ca_topic_score_codex":0.0016181082,"about_ca_topic_score_gemma":0.0024662954,"teacher_disagreement_score":0.008175744,"about_ca_system_score_codex":0.00047899832,"about_ca_system_score_gemma":0.00048915047,"threshold_uncertainty_score":0.00675565},"labels":[],"label_agreement":null},{"id":"W2550082179","doi":"10.1142/s1793351x16400122","title":"An Empirical Study of the Textual Content of Online Videos","year":2016,"lang":"en","type":"article","venue":"International Journal of Semantic Computing","topic":"Video Analysis and Summarization","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; Information retrieval; Cluster analysis; Multimedia; Video retrieval; Annotation; Empirical research; Content (measure theory); World Wide Web; Artificial intelligence","score_opus":0.04197526496538793,"score_gpt":0.3309049403531081,"score_spread":0.28892967538772013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550082179","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9948053,0.00021212714,0.0010827602,0.00016974023,0.000012962082,0.00010158877,0.0011757562,0.000014466648,0.0024252979],"genre_scores_gemma":[0.99468416,0.00029306853,0.0019867383,0.00008589952,0.000047050562,0.00011709627,0.002149161,0.000014447792,0.0006224066],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9975425,0.001178136,0.0002672027,0.00027922468,0.0006057862,0.0001272084],"domain_scores_gemma":[0.9056381,0.068721846,0.013307452,0.0020728104,0.008548211,0.0017115893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029996636,0.00017430967,0.00018343916,0.0026937118,0.0005285401,0.0011276116,0.00046957366,0.00058027613,0.0021894772],"category_scores_gemma":[0.058734667,0.000117736956,0.00018718567,0.0026208176,0.0007116491,0.002550118,0.0005926159,0.0006184714,0.0005352627],"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":[0.0009223216,0.0019498379,0.8807224,0.0012968867,0.00014410279,0.00089627935,0.022055207,0.00075860793,0.007045439,0.0017813982,0.0037692154,0.07865829],"study_design_scores_gemma":[0.000034306948,0.0009592821,0.94403666,0.0003165549,0.00009645177,0.0009880958,0.034621377,0.0063822544,0.0034711796,0.000755056,0.00828905,0.00004973812],"about_ca_topic_score_codex":0.0033088666,"about_ca_topic_score_gemma":0.0035397473,"teacher_disagreement_score":0.0033088666,"about_ca_system_score_codex":0.00053981575,"about_ca_system_score_gemma":0.000422017,"threshold_uncertainty_score":0.015863955},"labels":[],"label_agreement":null},{"id":"W2553840079","doi":"10.1109/jsen.2017.2671420","title":"Development of a Self-Calibrated Motion Capture System by Nonlinear Trilateration of Multiple Kinects v2","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Video Analysis and Summarization","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":"University of Ottawa","funders":"","keywords":"Trilateration; Computer science; Benchmark (surveying); Calibration; Computer vision; Nonlinear system; Motion capture; Position (finance); Protocol (science); Synchronization (alternating current); Motion (physics); Artificial intelligence; Real-time computing; Mathematics","score_opus":0.01416740741701949,"score_gpt":0.22801914288858713,"score_spread":0.21385173547156763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553840079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021182256,0.00035511816,0.971798,0.00011631784,0.00013300443,0.00036949627,0.00027983545,0.003811783,0.001954145],"genre_scores_gemma":[0.22036716,0.00060279854,0.77102315,0.00028348112,0.00007399717,0.0009112626,0.0011776509,0.00021399847,0.005346474],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99928755,0.000068728164,0.00007137499,0.00017288084,0.00035741276,0.000042114658],"domain_scores_gemma":[0.9996006,0.000046510744,0.000057586232,0.00006808275,0.00018848765,0.000038750677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007748071,0.0005648903,0.0006598856,0.0008495613,0.00029160184,0.0005474558,0.0013701316,0.00064885477,0.0025821638],"category_scores_gemma":[0.0008160496,0.00038696488,0.00032747656,0.0005196668,0.00024859555,0.0010353812,0.0010465198,0.0006384112,0.0011206458],"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":[0.00040638357,0.00024823518,0.006637947,0.00075566105,0.00010160926,0.000392078,0.00044138439,0.010631456,0.47612286,0.0051010773,0.007449506,0.4917118],"study_design_scores_gemma":[0.00022545962,0.0012063433,0.029192982,0.00017174406,0.00017755036,0.0023345621,0.00027213912,0.4918327,0.4048963,0.0017771662,0.06759582,0.00031732107],"about_ca_topic_score_codex":0.0018049137,"about_ca_topic_score_gemma":0.0022912887,"teacher_disagreement_score":0.0025821638,"about_ca_system_score_codex":0.00031168136,"about_ca_system_score_gemma":0.001065291,"threshold_uncertainty_score":0.008638203},"labels":[],"label_agreement":null},{"id":"W2557828239","doi":"10.1145/2992138.2992139","title":"ISSv3","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Concordia University","funders":"Concordia University","keywords":"Augmented reality; Computer science; Immersion (mathematics); Virtual reality; Multimedia; Android (operating system); Computer-mediated reality; Human–computer interaction; Virtual world; Focus (optics); Mixed reality; Metaverse; Mobile apps; Computer graphics (images); World Wide Web","score_opus":0.0065078660610504695,"score_gpt":0.1949516749718733,"score_spread":0.18844380891082282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557828239","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010611851,0.001144297,0.0522053,0.000926353,0.0015381202,0.00053966633,0.01578452,0.043791965,0.87345797],"genre_scores_gemma":[0.08907117,0.0010600017,0.031291977,0.0012349535,0.00040403113,0.00048877235,0.078030124,0.009088495,0.7893305],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993253,0.000069063295,0.00002981805,0.00009432545,0.0004046163,0.00007700726],"domain_scores_gemma":[0.99934644,0.000056162055,0.000017441718,0.00016398226,0.00031546192,0.00010051643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076342147,0.00066111505,0.00047278343,0.0010439383,0.001148907,0.0023129135,0.0011570189,0.0010496156,0.26079905],"category_scores_gemma":[0.00123673,0.00030051492,0.00049113773,0.0009789696,0.00031555543,0.0012569483,0.0019807064,0.0008217244,0.16248722],"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":[0.00037703235,0.00013451172,0.001802332,0.00047629548,0.00003273748,0.00021910826,0.0003490769,0.0016384969,0.018331004,0.021217925,0.5770962,0.3783253],"study_design_scores_gemma":[0.000023514174,0.000059984763,0.0006094185,0.000026738953,0.0000066159573,0.000078373334,0.000051695948,0.0011995157,0.003146315,0.0014537295,0.9933327,0.000011363762],"about_ca_topic_score_codex":0.005004986,"about_ca_topic_score_gemma":0.006148209,"teacher_disagreement_score":0.26079905,"about_ca_system_score_codex":0.00070075155,"about_ca_system_score_gemma":0.0011918049,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2563833050","doi":"10.5445/ir/1000056619","title":"Story Understanding through Semantic Analysis and Automatic Alignment of Text and Video","year":2016,"lang":"en","type":"article","venue":"Repository KITopen (Karlsruhe Institute of Technology)","topic":"Video Analysis and Summarization","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":"Karlsruhe House of Young Scientists; University of Toronto","keywords":"Computer science; Natural language processing; Artificial intelligence; Semantics (computer science); Information retrieval","score_opus":0.015586409963890355,"score_gpt":0.23513841965517518,"score_spread":0.21955200969128483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563833050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08174225,0.0014454338,0.88727,0.0010252661,0.0002757265,0.0006552914,0.0072183837,0.012002889,0.008364628],"genre_scores_gemma":[0.28255492,0.001083107,0.68667275,0.00016637909,0.0003140914,0.00046457277,0.024012199,0.00097388803,0.003758112],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998207,0.00048605516,0.00016788904,0.00066085253,0.00034557527,0.000132659],"domain_scores_gemma":[0.99657816,0.0012537107,0.0005169091,0.00040832482,0.0011093847,0.0001335558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013405879,0.001496585,0.00064467994,0.008645878,0.0009585352,0.0030802872,0.0011661462,0.0014194113,0.003991922],"category_scores_gemma":[0.007122035,0.00036080633,0.0010633072,0.0036465086,0.0007502478,0.0059692767,0.0017273148,0.0013771633,0.0032093525],"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":[0.00056077313,0.00031609365,0.0047609066,0.00093500305,0.00019348934,0.00048214677,0.00273917,0.005030647,0.06286905,0.01065091,0.024624031,0.8868377],"study_design_scores_gemma":[0.0001302373,0.00066026207,0.027357193,0.00043645356,0.00050241465,0.0011943699,0.013386919,0.6451668,0.12931155,0.083917566,0.09769972,0.0002365389],"about_ca_topic_score_codex":0.0033849606,"about_ca_topic_score_gemma":0.0040961364,"teacher_disagreement_score":0.008645878,"about_ca_system_score_codex":0.0006775764,"about_ca_system_score_gemma":0.0008852809,"threshold_uncertainty_score":0.013354361},"labels":[],"label_agreement":null},{"id":"W2564353236","doi":"10.1145/3001773.3001782","title":"DJ-MVP","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Artificial intelligence; Segmentation; Computer vision; Speech recognition; Beat (acoustics); Audio analyzer; Audio signal processing; Audio signal; Speech coding","score_opus":0.0065705646313648286,"score_gpt":0.19249330378930857,"score_spread":0.18592273915794374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564353236","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03221132,0.002305157,0.61137384,0.002404466,0.002991405,0.0010094076,0.034672268,0.09960441,0.21342777],"genre_scores_gemma":[0.3063225,0.00156767,0.3994019,0.0015049201,0.0011564969,0.0006738473,0.10864828,0.0113299,0.16939443],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993339,0.0000851849,0.000046338882,0.00016765595,0.00027465736,0.00009214014],"domain_scores_gemma":[0.99901223,0.00013609581,0.000038135448,0.00035613042,0.0003335739,0.00012383088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006482404,0.00072324113,0.00050323317,0.0010534704,0.00087069056,0.0015032028,0.0015565598,0.00091281027,0.049644865],"category_scores_gemma":[0.0033321562,0.0002784254,0.00042854718,0.001164698,0.00032042197,0.0016797916,0.002296421,0.0011407975,0.025902849],"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":[0.0010542254,0.00028929237,0.0012388602,0.00071979495,0.00008344008,0.0006068904,0.00023084531,0.0057387054,0.034340907,0.023175867,0.43531522,0.49720594],"study_design_scores_gemma":[0.00027075573,0.00029921118,0.0021247533,0.000073777,0.000052360294,0.001059084,0.00023994215,0.08511082,0.046650685,0.023124455,0.84091514,0.000079000114],"about_ca_topic_score_codex":0.0019587164,"about_ca_topic_score_gemma":0.0027274718,"teacher_disagreement_score":0.049644865,"about_ca_system_score_codex":0.0004946596,"about_ca_system_score_gemma":0.000739239,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2568294962","doi":"","title":"Outline of unilateral production by the Japan Consortium of Vancouver winter Olympic Games, and the operations in NHK's on-site studio","year":2010,"lang":"en","type":"article","venue":"映画テレビ技術","topic":"Video Analysis and Summarization","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":"Studio; Production (economics); Advertising; Visual arts; Geography; History; Art; Business; Economics","score_opus":0.005295247758922462,"score_gpt":0.21608869484955404,"score_spread":0.21079344709063158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2568294962","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026349213,0.0035851095,0.037396394,0.022611951,0.013021789,0.017829772,0.019724164,0.0022900214,0.8571917],"genre_scores_gemma":[0.040752485,0.0018062256,0.018521745,0.0016107935,0.0013181779,0.0033238085,0.0067408523,0.0004267004,0.9254992],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99898225,0.00012049438,0.000052744967,0.0001545139,0.00037789275,0.0003120865],"domain_scores_gemma":[0.9954776,0.0002278929,0.0001382566,0.00023999713,0.0020173083,0.0018990433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039111082,0.0007899608,0.00030422208,0.0025876593,0.0047712177,0.0061515137,0.0016635418,0.0016720011,0.12677342],"category_scores_gemma":[0.002506529,0.0006916631,0.00046150156,0.0015491445,0.0008838957,0.0021428312,0.0030269786,0.0019070702,0.04133598],"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":[0.0009991957,0.0009937572,0.007921686,0.00066967064,0.000017295666,0.0010987134,0.0012017606,0.0028323492,0.016290005,0.05212371,0.6797805,0.23607141],"study_design_scores_gemma":[0.000047265592,0.00025703435,0.017508766,0.00007294574,0.0000045436004,0.00009909869,0.0011028684,0.00040813957,0.0010726743,0.0017282402,0.97767514,0.000023266193],"about_ca_topic_score_codex":0.07819063,"about_ca_topic_score_gemma":0.1466378,"teacher_disagreement_score":0.9218094,"about_ca_system_score_codex":0.0059716622,"about_ca_system_score_gemma":0.01904836,"threshold_uncertainty_score":0.42409933},"labels":[],"label_agreement":null},{"id":"W2576618580","doi":"","title":"Graphical View of Blog Content Using B2G.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Video Analysis and Summarization","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":"Concordia University","funders":"","keywords":"Computer science; Microblogging; Content (measure theory); Social media; Multimedia; World Wide Web; Mathematics","score_opus":0.4270729361813214,"score_gpt":0.3803942316333476,"score_spread":0.04667870454797379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576618580","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.068610996,0.0036577606,0.1355856,0.005039624,0.00568087,0.0012146409,0.500964,0.10315819,0.1760883],"genre_scores_gemma":[0.48655736,0.0050114654,0.21897763,0.0029941355,0.0015926434,0.0011696272,0.1757473,0.010711802,0.097237945],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99992776,0.000009516979,0.0000043691743,0.00001685337,0.000021602564,0.000019846857],"domain_scores_gemma":[0.999423,0.00022668159,0.000044942168,0.000059181664,0.0001813757,0.00006488017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012994654,0.00084413105,0.00031071773,0.0026267085,0.00033855232,0.0010985296,0.00032374714,0.0006175982,0.07417414],"category_scores_gemma":[0.0007670201,0.00019679716,0.00030601106,0.002271667,0.00016764858,0.0005906907,0.00053621887,0.00071102503,0.01068525],"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":[0.0015738874,0.00019667225,0.0036371066,0.0026311155,0.00010877652,0.0014618856,0.00104317,0.0052359975,0.0538693,0.005899976,0.6853649,0.23897715],"study_design_scores_gemma":[0.00038111984,0.0004239033,0.06347671,0.0014078228,0.00023691094,0.0016037436,0.0025820194,0.08326394,0.02426476,0.013736096,0.8084433,0.00017962798],"about_ca_topic_score_codex":0.00970606,"about_ca_topic_score_gemma":0.0159277,"teacher_disagreement_score":0.07417414,"about_ca_system_score_codex":0.00022892299,"about_ca_system_score_gemma":0.00041580127,"threshold_uncertainty_score":0.24813718},"labels":[],"label_agreement":null},{"id":"W2577358469","doi":"10.1109/tmm.2017.2655423","title":"Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Codec; Animation; Motion capture; Artificial intelligence; Coding (social sciences); Rendering (computer graphics); Computer animation; Computer vision; Automotive industry; Motion (physics); Computer graphics (images); Computer hardware","score_opus":0.03524122005034014,"score_gpt":0.2720617423097473,"score_spread":0.23682052225940714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577358469","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034272913,0.00066621735,0.9550413,0.00025010825,0.00023282216,0.00025723845,0.00024055684,0.0034440353,0.005594776],"genre_scores_gemma":[0.3610185,0.0011266063,0.61568993,0.00069106626,0.000282143,0.00062593084,0.0011372907,0.0006190018,0.018809516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995735,0.00005768102,0.000026911954,0.000056954515,0.00023216254,0.00005274972],"domain_scores_gemma":[0.9987664,0.00027194576,0.00008629706,0.00024498085,0.0005889375,0.000041483683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042643462,0.0007845093,0.00041813907,0.0010683261,0.00036618003,0.0007716439,0.00085461064,0.0006121225,0.0043329294],"category_scores_gemma":[0.002338233,0.00017867121,0.00033567686,0.00075851067,0.00046541356,0.0010990032,0.00082002074,0.0009654339,0.0015033818],"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":[0.00090158737,0.00020814333,0.0010499349,0.00030086588,0.00005268201,0.0002950027,0.00023216213,0.013075372,0.20490432,0.0074285395,0.007825668,0.7637257],"study_design_scores_gemma":[0.000097921584,0.00045619986,0.0034955882,0.00019251081,0.00008919927,0.0011152907,0.00015286641,0.4122075,0.53221387,0.0046781315,0.045156825,0.000144095],"about_ca_topic_score_codex":0.0022137738,"about_ca_topic_score_gemma":0.0037553068,"teacher_disagreement_score":0.0043329294,"about_ca_system_score_codex":0.00044751953,"about_ca_system_score_gemma":0.0006514002,"threshold_uncertainty_score":0.014495075},"labels":[],"label_agreement":null},{"id":"W2586561638","doi":"10.29173/cais27","title":"Text as a Tool for Organizing Moving Image Collections","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Video Analysis and Summarization","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":"Université de Montréal","funders":"","keywords":"Search engine indexing; Variety (cybernetics); Computer science; Information retrieval; Shot (pellet); Image (mathematics); Order (exchange); Quality (philosophy); World Wide Web; Data science; Multimedia; Artificial intelligence; Business","score_opus":0.013318204007796475,"score_gpt":0.23046041231856385,"score_spread":0.21714220831076736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586561638","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059628036,0.0030427324,0.8175814,0.0012214644,0.0005885086,0.003196627,0.02524339,0.06098939,0.028508402],"genre_scores_gemma":[0.111428045,0.0010074795,0.84531164,0.00018912161,0.00040163592,0.0016449821,0.025255114,0.0027860692,0.011975914],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979893,0.00058701076,0.000292308,0.00045276267,0.0005757656,0.000102781436],"domain_scores_gemma":[0.9909871,0.004711526,0.0008408276,0.0013253392,0.0016766861,0.00045845675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002755499,0.0012483882,0.0010555363,0.016523603,0.0020386435,0.0041764034,0.002061361,0.0008757455,0.01627204],"category_scores_gemma":[0.011581423,0.0006173157,0.00056072016,0.011057828,0.0010508893,0.008657669,0.0028478033,0.00086780987,0.007874937],"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":[0.0009358335,0.00024457672,0.0026116637,0.0024962581,0.000115413895,0.00089628634,0.007132838,0.0032383995,0.040960487,0.014788522,0.061770216,0.8648095],"study_design_scores_gemma":[0.0005021615,0.0014358843,0.022773098,0.0010306014,0.0006191531,0.0030168078,0.018016525,0.09720476,0.104183756,0.05766698,0.6929226,0.0006277636],"about_ca_topic_score_codex":0.0037285644,"about_ca_topic_score_gemma":0.0041313227,"teacher_disagreement_score":0.016523603,"about_ca_system_score_codex":0.0009345591,"about_ca_system_score_gemma":0.0010272138,"threshold_uncertainty_score":0.054435372},"labels":[],"label_agreement":null},{"id":"W2586596788","doi":"10.1109/smc.2016.7844429","title":"Optimized per-joint compression of hand motion data","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 science; Data compression; Lossy compression; Redundancy (engineering); Motion capture; Computer vision; Artificial intelligence; Joint (building); Motion (physics); Quarter-pixel motion; Compression (physics); Focus (optics); Motion compensation; Engineering","score_opus":0.05422195276107825,"score_gpt":0.26334471740869403,"score_spread":0.20912276464761578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586596788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14281842,0.0017540896,0.8491034,0.00029140714,0.00021167792,0.00014337154,0.00039308477,0.0014994239,0.0037851748],"genre_scores_gemma":[0.53215635,0.001245459,0.45725167,0.00014199888,0.00020854399,0.00013990277,0.0010865004,0.00025131708,0.0075182244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968195,0.000033755943,0.000023600556,0.000042814227,0.00018945224,0.000028378821],"domain_scores_gemma":[0.99944013,0.00016172417,0.00007218667,0.00013828727,0.00016809949,0.000019528066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028978626,0.0005787823,0.0003694716,0.000861785,0.00020556887,0.00040024784,0.00044936576,0.00041982374,0.0015591445],"category_scores_gemma":[0.0013819049,0.00011211739,0.00023873897,0.00078546983,0.00027849418,0.00054875616,0.0003612772,0.00037019563,0.0005069777],"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":[0.0007083457,0.00012875399,0.0010741024,0.0002769976,0.000050402377,0.00037263634,0.00013832773,0.0348964,0.36810112,0.0033455112,0.0029571766,0.5879501],"study_design_scores_gemma":[0.000061864186,0.00048032263,0.0052376473,0.00006385524,0.00006752141,0.0014840765,0.000094718154,0.54389066,0.43470943,0.0020181225,0.011840071,0.000051744304],"about_ca_topic_score_codex":0.0008905521,"about_ca_topic_score_gemma":0.0012468405,"teacher_disagreement_score":0.0015591445,"about_ca_system_score_codex":0.00022513518,"about_ca_system_score_gemma":0.0003476163,"threshold_uncertainty_score":0.0052158237},"labels":[],"label_agreement":null},{"id":"W2588749939","doi":"10.29173/cais123","title":"Open System for Indexing and Retrieving Multimedia Information","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":3,"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":"Search engine indexing; Computer science; Adapter (computing); Multimedia; World Wide Web; Information retrieval","score_opus":0.027696611772629713,"score_gpt":0.24557983033038966,"score_spread":0.21788321855775994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588749939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063384846,0.0020988015,0.6450711,0.0008353664,0.0007009351,0.002399134,0.018085353,0.2743253,0.050145485],"genre_scores_gemma":[0.07182772,0.002978078,0.6160532,0.0012794418,0.001092177,0.00413928,0.111528665,0.016476031,0.1746254],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967188,0.0003742455,0.00055228994,0.0006787868,0.0014120847,0.00026383792],"domain_scores_gemma":[0.9930823,0.0011371896,0.00043619898,0.0027026262,0.0020135446,0.00062817836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004390273,0.0016431106,0.0020217493,0.008214113,0.0020507767,0.00667589,0.0044559794,0.0029666557,0.051826693],"category_scores_gemma":[0.011023636,0.0009760281,0.0011430347,0.005792777,0.0012390581,0.0080045955,0.0071466086,0.0024844194,0.06334769],"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":[0.0014134223,0.00053218496,0.0015361652,0.0013162062,0.0001886673,0.00073641376,0.0010276488,0.0011574073,0.0530584,0.041654088,0.2228711,0.6745084],"study_design_scores_gemma":[0.00030748092,0.0003355705,0.0031525737,0.00028458578,0.0001505794,0.0010903485,0.0002992425,0.026733171,0.04215573,0.026118759,0.8990943,0.00027768707],"about_ca_topic_score_codex":0.0040132636,"about_ca_topic_score_gemma":0.0032382065,"teacher_disagreement_score":0.051826693,"about_ca_system_score_codex":0.0016118838,"about_ca_system_score_gemma":0.003263507,"threshold_uncertainty_score":0.17337751},"labels":[],"label_agreement":null},{"id":"W2589931047","doi":"","title":"Autonomous tennis ball retriever","year":2008,"lang":"en","type":"dissertation","venue":"DR-NTU (Nanyang Technological University)","topic":"Video Analysis and Summarization","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":"Labrador Retriever; Ball (mathematics); Tennis ball; Physical medicine and rehabilitation; Computer science; Medicine; Engineering; Mechanical engineering; Mathematics; Surgery; sports equipment","score_opus":0.010960551447814966,"score_gpt":0.19093227540747443,"score_spread":0.17997172395965946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2589931047","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25543523,0.0047271554,0.20418683,0.0015892572,0.0011936299,0.0016293847,0.0035000574,0.006187334,0.52155113],"genre_scores_gemma":[0.3671443,0.0023114786,0.063747704,0.0002696934,0.00024503533,0.00050017395,0.0040418534,0.00038799294,0.5613518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999647,0.000027799933,0.000010139427,0.000096038326,0.00018903468,0.000030065205],"domain_scores_gemma":[0.9997528,0.000029223658,0.000011106582,0.000023240988,0.00012961138,0.00005390751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034236003,0.00033974994,0.0002838287,0.0005390718,0.00045074723,0.00071956316,0.00038657954,0.00040246948,0.02009368],"category_scores_gemma":[0.00067599904,0.00014995318,0.00017497226,0.00043222215,0.00018729476,0.0005230239,0.00053631165,0.00034615674,0.009204103],"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":[0.00075508,0.0005691471,0.00087779097,0.0006368877,0.000027340171,0.00040347685,0.0013755517,0.0032934996,0.22914822,0.0114455875,0.07533011,0.67613727],"study_design_scores_gemma":[0.00027633688,0.0024325836,0.018929292,0.00015248996,0.000050082926,0.0010246694,0.0012457,0.031399496,0.16457663,0.0060467115,0.77376986,0.000096116164],"about_ca_topic_score_codex":0.001394798,"about_ca_topic_score_gemma":0.0031514647,"teacher_disagreement_score":0.02009368,"about_ca_system_score_codex":0.00031852658,"about_ca_system_score_gemma":0.0005626646,"threshold_uncertainty_score":0.06722003},"labels":[],"label_agreement":null},{"id":"W2611646823","doi":"10.24124/2011/bpgub751","title":"Heuristic Path Finding Method for Online Game Environment.","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Video Analysis and Summarization","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":"Library and Archives Canada","funders":"University of Northern British Columbia","keywords":"Pathfinding; Computer science; Path (computing); Bottleneck; Heuristic; Human–computer interaction; Shortest path problem; Artificial intelligence; Theoretical computer science; Embedded system; Operating system","score_opus":0.02865099821462448,"score_gpt":0.296164553459914,"score_spread":0.2675135552452895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611646823","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025010032,0.00015176824,0.99326295,0.000068234964,0.000034280245,0.00013107492,0.00013216067,0.0013644587,0.0023540615],"genre_scores_gemma":[0.028387863,0.00016445662,0.967609,0.00005030815,0.0000149342795,0.00023150315,0.00039150953,0.00020034029,0.0029500665],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995198,0.00012293996,0.000034369335,0.0001354229,0.0001488374,0.000038738453],"domain_scores_gemma":[0.99923766,0.00040831586,0.000052189866,0.00007692989,0.00019787374,0.00002702972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005830517,0.0011475306,0.0005957883,0.002030273,0.0008635601,0.0008744494,0.0013437791,0.0009373338,0.011750778],"category_scores_gemma":[0.002507187,0.00037512393,0.0008536767,0.0016774063,0.00057793525,0.0014000742,0.0007795551,0.0007763115,0.0026770963],"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":[0.00012343895,0.00019704204,0.0010805619,0.00053792243,0.00007900454,0.00019299294,0.0003721527,0.09762514,0.01259858,0.025808843,0.015483415,0.84590095],"study_design_scores_gemma":[0.00008236567,0.00013507242,0.0008138641,0.000063928484,0.000065199936,0.00044519667,0.00031923028,0.92001396,0.011402699,0.030520722,0.036088835,0.000048938695],"about_ca_topic_score_codex":0.0067403396,"about_ca_topic_score_gemma":0.008435028,"teacher_disagreement_score":0.011750778,"about_ca_system_score_codex":0.0008627327,"about_ca_system_score_gemma":0.0015526296,"threshold_uncertainty_score":0.039310277},"labels":[],"label_agreement":null},{"id":"W2621216577","doi":"","title":"Modélisation multidimensionnelle vidéo dans le sport le cas Vert et Or Football","year":2011,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Video Analysis and Summarization","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":"Football; Political science","score_opus":0.03041727779662551,"score_gpt":0.21149560310302817,"score_spread":0.18107832530640267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621216577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08814161,0.0015344706,0.8969352,0.0009495598,0.00018975623,0.00035050215,0.0016422512,0.0014538631,0.0088028535],"genre_scores_gemma":[0.49404597,0.0030170726,0.48266572,0.00026154774,0.00006380887,0.0007939396,0.00425092,0.00041779946,0.014483312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986682,0.00033331788,0.00010240681,0.00033303857,0.00048049615,0.00008259958],"domain_scores_gemma":[0.99850756,0.0007635439,0.000087254804,0.00014151025,0.00044203823,0.00005801502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667645,0.0007962866,0.0005753776,0.0012369816,0.0006588811,0.0040125395,0.0014197864,0.0016212441,0.0031851425],"category_scores_gemma":[0.0043003415,0.00061304844,0.0016668187,0.0011079784,0.00089419214,0.0022873601,0.0014174855,0.0012590392,0.00067449064],"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":[0.00041910575,0.00023105972,0.011373124,0.0011580939,0.00022702667,0.0007668906,0.0024347384,0.7515963,0.03606608,0.0413694,0.00434043,0.15001786],"study_design_scores_gemma":[0.000024672678,0.00012540052,0.003132527,0.0001355073,0.000055168683,0.00016897611,0.00050786155,0.9637874,0.007671289,0.0059721987,0.018366685,0.00005236563],"about_ca_topic_score_codex":0.048977785,"about_ca_topic_score_gemma":0.041736316,"teacher_disagreement_score":0.048977785,"about_ca_system_score_codex":0.0019439,"about_ca_system_score_gemma":0.002331546,"threshold_uncertainty_score":0.09738541},"labels":[],"label_agreement":null},{"id":"W2625214388","doi":"10.1145/3055635.3056606","title":"Audience Activity Recommendation Using Stacked-LSTM Based Sequence Learning","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Recommender system; Computer science; Recurrent neural network; Task (project management); Context (archaeology); Similarity (geometry); Metric (unit); Artificial neural network; Sequence (biology); Artificial intelligence; Machine learning; Image (mathematics); Engineering","score_opus":0.11105485566094855,"score_gpt":0.3483385694440543,"score_spread":0.23728371378310573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625214388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.098010726,0.0015005814,0.8882262,0.00054040743,0.00030259605,0.00010586916,0.0006668783,0.0061318497,0.0045149606],"genre_scores_gemma":[0.8232448,0.0006347413,0.1667164,0.00027087252,0.0001959276,0.00010411782,0.0012833962,0.00012714113,0.0074226647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997662,0.000041373813,0.000015837886,0.00009212709,0.000045249966,0.000039248745],"domain_scores_gemma":[0.9995395,0.00018655625,0.000047849597,0.000044840584,0.00014875291,0.000032522228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000479374,0.00085789664,0.00068806525,0.0006059298,0.0002452053,0.00058712455,0.00092083856,0.0008698294,0.0024859183],"category_scores_gemma":[0.0016388273,0.00043023258,0.0006054672,0.000823083,0.00018209606,0.0012646641,0.0004003593,0.0011486062,0.0013293815],"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":[0.00050221034,0.00044746752,0.0025003296,0.00021139247,0.00029026813,0.00020955768,0.00018910033,0.21635729,0.032372452,0.0024229207,0.0066565834,0.7378405],"study_design_scores_gemma":[0.000008778912,0.000059031892,0.00039409526,0.000006553509,0.000023092343,0.000019330953,0.000009990659,0.9956786,0.0023899383,0.0009565215,0.00044627255,0.000007755412],"about_ca_topic_score_codex":0.01325222,"about_ca_topic_score_gemma":0.023576139,"teacher_disagreement_score":0.01325222,"about_ca_system_score_codex":0.00058011996,"about_ca_system_score_gemma":0.0005469744,"threshold_uncertainty_score":0.0263502},"labels":[],"label_agreement":null},{"id":"W2631558258","doi":"","title":"High Definition visual attention based video summarization","year":2015,"lang":"en","type":"article","venue":"Computer Vision Theory and Applications (VISAPP), 2014 International Conference on","topic":"Video Analysis and Summarization","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":"Automatic summarization; Artificial intelligence; Computer science; Frame (networking); Key frame; Feature (linguistics); Shot (pellet); Histogram; Computer vision; Construct (python library); Video tracking; Visualization; Pattern recognition (psychology); Reference frame; Histogram of oriented gradients; Block-matching algorithm; Key (lock); Video processing; Image (mathematics)","score_opus":0.02894511363994841,"score_gpt":0.2952617320729818,"score_spread":0.2663166184330334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2631558258","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028685376,0.0015671708,0.9614403,0.00015712998,0.00018800187,0.00034546107,0.0006629456,0.004478228,0.002475465],"genre_scores_gemma":[0.29680002,0.0010865672,0.68967825,0.00015928758,0.0003419986,0.00035536697,0.004164517,0.00043957817,0.0069745556],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903154,0.00013168713,0.00007776814,0.00030719434,0.00035228403,0.0000995463],"domain_scores_gemma":[0.99828726,0.0003093459,0.00018546697,0.00015684274,0.0009823045,0.00007885902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008674836,0.0013004427,0.0010677716,0.0033235112,0.00051162863,0.0012531728,0.0011455368,0.0006042747,0.0028103404],"category_scores_gemma":[0.0031892871,0.00026657825,0.0007470901,0.0018181144,0.0002971851,0.0016386143,0.0010954619,0.0007140675,0.0013189367],"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":[0.00042974943,0.00011516082,0.0009414813,0.00041237968,0.00009261732,0.00016574432,0.00026491526,0.009691875,0.07744767,0.0023965451,0.008457908,0.899584],"study_design_scores_gemma":[0.00012907035,0.0013764655,0.015974734,0.00011701127,0.00043842377,0.0010374086,0.0006496911,0.72407097,0.2070919,0.010540712,0.038421866,0.00015188084],"about_ca_topic_score_codex":0.0029747202,"about_ca_topic_score_gemma":0.003269736,"teacher_disagreement_score":0.0033235112,"about_ca_system_score_codex":0.00062985404,"about_ca_system_score_gemma":0.0005754777,"threshold_uncertainty_score":0.0094015},"labels":[],"label_agreement":null},{"id":"W2656567088","doi":"10.29173/cais149","title":"Structured Display and Browsing of Documentary Information: VIBE in Action","year":2013,"lang":"en","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Video Analysis and Summarization","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; Information retrieval; Action (physics); Interface (matter); World Wide Web; User interface; Graphical user interface; Human–computer interaction; Programming language","score_opus":0.011624245517906622,"score_gpt":0.22549520694757125,"score_spread":0.21387096142966464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2656567088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04193921,0.0015203187,0.8503191,0.0022979237,0.00045908746,0.00071983546,0.0013599647,0.057680923,0.0437037],"genre_scores_gemma":[0.20311032,0.0018141037,0.7453287,0.0019234599,0.00023118319,0.00084628805,0.002330992,0.0040811356,0.04033375],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993813,0.0002704878,0.000037314276,0.00009374207,0.00015341953,0.000063683925],"domain_scores_gemma":[0.9979165,0.0014595822,0.000077073,0.00021288333,0.0001404405,0.00019339965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015618184,0.0009214674,0.0006974685,0.0012687988,0.0004502063,0.0020396973,0.0010749968,0.0017877247,0.01926192],"category_scores_gemma":[0.004867297,0.00031181393,0.00043065683,0.00067965663,0.0008005149,0.002794475,0.0023061025,0.0009970621,0.0046084016],"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":[0.0028713539,0.00052697596,0.002361033,0.0017264457,0.00010636809,0.0009846438,0.007545333,0.0019211967,0.24794869,0.03398681,0.098399624,0.60162157],"study_design_scores_gemma":[0.0007725626,0.001784569,0.012661611,0.0013623872,0.00021358128,0.002266632,0.0026992573,0.062339373,0.0912102,0.038417373,0.78584766,0.00042480408],"about_ca_topic_score_codex":0.0009611237,"about_ca_topic_score_gemma":0.00141961,"teacher_disagreement_score":0.01926192,"about_ca_system_score_codex":0.00015272974,"about_ca_system_score_gemma":0.00030107985,"threshold_uncertainty_score":0.06443757},"labels":[],"label_agreement":null},{"id":"W2724181509","doi":"10.29173/cais180","title":"PériCulture2 : utilisation du péritexte pour l’indexation automatique des objets multimédias","year":2013,"lang":"fr","type":"article","venue":"Proceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI","topic":"Video Analysis and Summarization","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":"Université de Montréal","funders":"","keywords":"Indexation; Humanities; Search engine indexing; Computer science; Art; Information retrieval","score_opus":0.027906489194766168,"score_gpt":0.24091207219275765,"score_spread":0.2130055829979915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2724181509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22786902,0.0026492751,0.7307075,0.000284875,0.00031750608,0.0006531135,0.002886785,0.023617717,0.011014193],"genre_scores_gemma":[0.2577904,0.00064869015,0.7251347,0.00009270774,0.000104230036,0.00047289106,0.0034751035,0.00216709,0.010114188],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711835,0.0006455284,0.0002647433,0.00050207466,0.0012845978,0.00018472054],"domain_scores_gemma":[0.99186337,0.00388542,0.00032310857,0.0018049894,0.0018533687,0.000269623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023200435,0.0011213057,0.0013841123,0.004533712,0.000880647,0.003446058,0.0013743737,0.0010066939,0.0057963626],"category_scores_gemma":[0.009673706,0.00044678638,0.0009126994,0.0035923868,0.0008755412,0.0034666555,0.0020365189,0.000877648,0.0030233774],"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":[0.0013543385,0.00027320193,0.0031566226,0.0010542098,0.000109746725,0.00030800814,0.0020704726,0.0051459656,0.15181005,0.002705063,0.0049265744,0.82708573],"study_design_scores_gemma":[0.0003875641,0.0024720791,0.0303666,0.00017396483,0.00022283927,0.002780606,0.0023178416,0.27037755,0.5866268,0.0061354865,0.09776082,0.0003777632],"about_ca_topic_score_codex":0.004677936,"about_ca_topic_score_gemma":0.004243912,"teacher_disagreement_score":0.0057963626,"about_ca_system_score_codex":0.0005919148,"about_ca_system_score_gemma":0.00083436765,"threshold_uncertainty_score":0.019390821},"labels":[],"label_agreement":null},{"id":"W2741433478","doi":"10.1145/3105831.3105854","title":"Social Media Mining","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":44,"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","keywords":"Box office; Social media; Revenue; Computer science; Sentiment analysis; Public opinion; Data science; World Wide Web; Advertising; Business; Artificial intelligence; Political science","score_opus":0.042518595457110904,"score_gpt":0.2852896384434811,"score_spread":0.2427710429863702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2741433478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17035963,0.016545197,0.36598596,0.008507109,0.0031097273,0.011690116,0.24668713,0.014074632,0.16304055],"genre_scores_gemma":[0.46643895,0.01279356,0.277416,0.001588691,0.0022468464,0.004000682,0.18382657,0.00055569876,0.05113294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966137,0.00059585104,0.00040493935,0.0008679939,0.0012988519,0.00021865746],"domain_scores_gemma":[0.995643,0.0017856631,0.0007392321,0.00054511556,0.0011274227,0.00015953416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018737493,0.0018250297,0.0012073893,0.015055741,0.001771199,0.002666976,0.0017571795,0.0012413462,0.0072293472],"category_scores_gemma":[0.007747857,0.00043276514,0.0017020191,0.010262575,0.00042991218,0.0031198855,0.0015387719,0.0010162295,0.007684138],"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":[0.00028915552,0.0006830396,0.041371327,0.0026496497,0.0005926529,0.0016148146,0.00086546066,0.0066325725,0.0074253245,0.01371726,0.12644781,0.79771096],"study_design_scores_gemma":[0.00014148167,0.0004870871,0.08724138,0.0013993693,0.0007937632,0.0037905956,0.004619415,0.21397264,0.027963378,0.045682516,0.61364865,0.0002597203],"about_ca_topic_score_codex":0.0052861557,"about_ca_topic_score_gemma":0.00786211,"teacher_disagreement_score":0.015055741,"about_ca_system_score_codex":0.00092392205,"about_ca_system_score_gemma":0.0015038685,"threshold_uncertainty_score":0.024184585},"labels":[],"label_agreement":null},{"id":"W2746441252","doi":"10.1109/cvpr.2017.427","title":"Sports Field Localization via Deep Structured Models","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":132,"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; Markov random field; Field (mathematics); Artificial intelligence; Segmentation; Image segmentation; Inference; Computer vision; Mathematics","score_opus":0.01026594853725991,"score_gpt":0.23188009055335576,"score_spread":0.22161414201609586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2746441252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010069822,0.00030575844,0.9854387,0.00021863909,0.000035994435,0.00003748487,0.00034942923,0.0023069317,0.0012372246],"genre_scores_gemma":[0.5288678,0.0007105208,0.45187077,0.0006313377,0.00028597718,0.00024176511,0.0057399916,0.00086808146,0.01078369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968934,0.00004809441,0.000011244047,0.00014148696,0.00005995764,0.000049915885],"domain_scores_gemma":[0.9995171,0.00019842539,0.00008120839,0.00007812983,0.0000827729,0.00004223858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041384582,0.0014326586,0.0010782244,0.001325232,0.0003744681,0.00100251,0.0020824613,0.0013408023,0.0026484178],"category_scores_gemma":[0.0014261268,0.0007826023,0.00089246506,0.0011007178,0.000603366,0.0019664238,0.0011287745,0.001828472,0.0018764518],"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":[0.00034963564,0.00018706993,0.0012508734,0.00019906198,0.0001479938,0.00019317886,0.00013957503,0.6237018,0.0188878,0.014821792,0.012897875,0.32722327],"study_design_scores_gemma":[0.000007918908,0.000020299543,0.00014608761,0.000007837173,0.000008482964,0.000013589379,0.000014119861,0.99006057,0.0012392366,0.007653294,0.00082336785,0.000005257737],"about_ca_topic_score_codex":0.01061018,"about_ca_topic_score_gemma":0.021313988,"teacher_disagreement_score":0.01061018,"about_ca_system_score_codex":0.0009799349,"about_ca_system_score_gemma":0.0009819925,"threshold_uncertainty_score":0.021096885},"labels":[],"label_agreement":null},{"id":"W2762919278","doi":"10.1093/llc/fqx035","title":"Knowledge creation through recommender systems","year":2017,"lang":"en","type":"article","venue":"Digital Scholarship in the Humanities","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":8,"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":"Metadata; Digital humanities; Interpretation (philosophy); World Wide Web; Recommender system; Computer science; Process (computing); Library science; Art","score_opus":0.12565703745388654,"score_gpt":0.3246402205344504,"score_spread":0.19898318308056387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2762919278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.085292265,0.0093116425,0.82630646,0.008924946,0.000585216,0.0011268485,0.0011293014,0.0022135521,0.06510978],"genre_scores_gemma":[0.5861012,0.0040760976,0.39041385,0.0012100325,0.00042587193,0.0006420143,0.0014978481,0.00011858415,0.015514538],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944722,0.0028092524,0.0003460356,0.0011713381,0.000965309,0.00023580031],"domain_scores_gemma":[0.98212105,0.011518366,0.0007944478,0.0026791985,0.0024235863,0.00046325257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069813896,0.0010221812,0.0015964105,0.0034703393,0.0018921251,0.004735677,0.0026372457,0.0026333968,0.008315051],"category_scores_gemma":[0.024628386,0.00093684805,0.0014313639,0.0034796442,0.0010844924,0.0069295666,0.0029670731,0.0021856388,0.0034365472],"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":[0.00042470405,0.0010085292,0.018565828,0.0011977277,0.0011997209,0.00046002198,0.00320716,0.085249685,0.003743476,0.10184679,0.039016034,0.7440803],"study_design_scores_gemma":[0.00032184945,0.0005015466,0.0048234626,0.00043720397,0.0007175066,0.0005331118,0.0017980833,0.72271675,0.0035327724,0.16274053,0.10166916,0.0002080113],"about_ca_topic_score_codex":0.011958822,"about_ca_topic_score_gemma":0.021518169,"teacher_disagreement_score":0.011958822,"about_ca_system_score_codex":0.0014273035,"about_ca_system_score_gemma":0.0018389339,"threshold_uncertainty_score":0.03692156},"labels":[],"label_agreement":null},{"id":"W2766819565","doi":"10.1109/access.2017.2769140","title":"Soccer Video Structure Analysis by Parallel Feature Fusion Network and Hidden-to-Observable Transferring Markov Model","year":2017,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Hidden Markov model; Computer science; Feature (linguistics); Artificial intelligence; Markov model; Markov chain; Observable; Markov process; Pattern recognition (psychology); Machine learning; Mathematics; Statistics; Physics","score_opus":0.02020785058892053,"score_gpt":0.2780454585506624,"score_spread":0.2578376079617419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2766819565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1037641,0.00033610672,0.8915044,0.00032059662,0.000053913765,0.000070599286,0.00031375847,0.0020961254,0.0015403966],"genre_scores_gemma":[0.8743176,0.00028131757,0.12075976,0.00014972658,0.000059114776,0.00010736927,0.0011507701,0.00008353097,0.0030908629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996165,0.00005866888,0.00001601897,0.00013514956,0.00010099141,0.00007262695],"domain_scores_gemma":[0.9995153,0.00018687882,0.00008597004,0.000043013744,0.00013792611,0.000030841522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007472183,0.000916943,0.00079021376,0.0009334939,0.00043922893,0.00054803584,0.0011502227,0.00074346154,0.0010438896],"category_scores_gemma":[0.0017647066,0.00046959182,0.0009961411,0.00069324,0.00037565827,0.0013747612,0.0007686086,0.0011310914,0.0002808893],"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":[0.0003485742,0.00020098207,0.003207251,0.00005037531,0.00011845872,0.00013554277,0.00006756233,0.69964284,0.0073537403,0.002767279,0.0023670266,0.28374034],"study_design_scores_gemma":[0.000001647335,0.000009093246,0.00021130005,8.0427475e-7,0.0000043802056,0.00000452074,0.000002836506,0.9985784,0.0004770654,0.0006574373,0.00005024074,0.0000022556485],"about_ca_topic_score_codex":0.02309659,"about_ca_topic_score_gemma":0.015335576,"teacher_disagreement_score":0.02309659,"about_ca_system_score_codex":0.0013438251,"about_ca_system_score_gemma":0.0010542882,"threshold_uncertainty_score":0.045924306},"labels":[],"label_agreement":null},{"id":"W2769466079","doi":"10.1007/978-3-319-70353-4_37","title":"A Domain Independent Approach to Video Summarization","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"University of Victoria","funders":"","keywords":"Automatic summarization; Computer science; Domain (mathematical analysis); Shot (pellet); Slicing; Video tracking; Artificial intelligence; Video processing; Similarity (geometry); Perspective (graphical); Video compression picture types; Computer vision; Similarity measure; Information retrieval; Image (mathematics); Computer graphics (images)","score_opus":0.01640434308089216,"score_gpt":0.23864851211440652,"score_spread":0.22224416903351435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769466079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011698396,0.00067482376,0.99414814,0.00012667065,0.00009938777,0.00008770146,0.00018250615,0.0010120446,0.0024988514],"genre_scores_gemma":[0.042203065,0.0019704371,0.936481,0.00025893486,0.00055894733,0.000331941,0.002693362,0.00057837344,0.014923974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985568,0.00039963523,0.0001388383,0.00036410062,0.00044975925,0.00009096627],"domain_scores_gemma":[0.99731916,0.000816678,0.00015968666,0.0005154276,0.0011026314,0.00008651497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015548822,0.0014609805,0.0012456942,0.0028787558,0.0008345032,0.0022336151,0.00234987,0.0013264006,0.005521334],"category_scores_gemma":[0.0036788688,0.0005940268,0.0016902431,0.0027261388,0.000651624,0.0028695331,0.0021349743,0.0025328482,0.0048873555],"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":[0.00023147662,0.00024457456,0.0003593769,0.00066818757,0.00019118407,0.00023834713,0.0003268521,0.018732157,0.060180664,0.032785676,0.021355128,0.86468637],"study_design_scores_gemma":[0.000062332154,0.0003926457,0.0017052329,0.00019471491,0.00039304022,0.0007341495,0.00048147936,0.72316307,0.089648925,0.09837218,0.084722236,0.00012994917],"about_ca_topic_score_codex":0.0011085368,"about_ca_topic_score_gemma":0.0017634832,"teacher_disagreement_score":0.005521334,"about_ca_system_score_codex":0.000567443,"about_ca_system_score_gemma":0.00075282605,"threshold_uncertainty_score":0.018470705},"labels":[],"label_agreement":null},{"id":"W2777630948","doi":"10.5539/ijel.v8n2p244","title":"Pragmatics of Sports News Reports","year":2017,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Video Analysis and Summarization","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":"Pragmatics; Relevance (law); Computer science; Dialectic; Football; Bridging (networking); Logical analysis; Statistical analysis; Psychology; Linguistics; Political science; Mathematical statistics; Epistemology; Mathematics; Statistics; Computer security","score_opus":0.013348996555668666,"score_gpt":0.2842008411884781,"score_spread":0.2708518446328094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2777630948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3252721,0.005254724,0.35675332,0.013555449,0.0006862229,0.0010243958,0.0016595157,0.0011206581,0.29467365],"genre_scores_gemma":[0.9588393,0.0005909724,0.03563945,0.00021116833,0.00019887171,0.00019580734,0.00048590446,0.00015414275,0.0036844306],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9724988,0.018407771,0.002031001,0.0016522222,0.004788347,0.00062192173],"domain_scores_gemma":[0.9559442,0.028691499,0.005535899,0.0020083783,0.007095834,0.0007242086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011709922,0.00060195033,0.0004895418,0.0066894987,0.0029490062,0.010422886,0.0010859702,0.0015863283,0.003744186],"category_scores_gemma":[0.05584697,0.0009202132,0.0005809347,0.0032390328,0.006190833,0.010937153,0.0029898908,0.0015670842,0.0008219641],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028009905,0.00010911511,0.0054502613,0.00088735466,0.00008542445,0.0012013972,0.11542656,0.001364153,0.007677434,0.7984777,0.0047348514,0.06430566],"study_design_scores_gemma":[0.00019288494,0.0003422889,0.03233739,0.0012009897,0.00030754646,0.0018556687,0.14670509,0.020461896,0.00907256,0.517485,0.26968807,0.00035064103],"about_ca_topic_score_codex":0.0034088127,"about_ca_topic_score_gemma":0.0022913476,"teacher_disagreement_score":0.011709922,"about_ca_system_score_codex":0.0027019714,"about_ca_system_score_gemma":0.0019586764,"threshold_uncertainty_score":0.06192875},"labels":[],"label_agreement":null},{"id":"W2786524829","doi":"10.1145/3047010","title":"ATR-Vis","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; International Development Research Centre","keywords":"Computer science; Variety (cybernetics); Terminology; Task (project management); Information retrieval; Set (abstract data type); Visualization; Data science; World Wide Web; Data mining; Artificial intelligence","score_opus":0.048978138937205005,"score_gpt":0.29976558278340987,"score_spread":0.2507874438462049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786524829","genre_codex":"software","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024106322,0.0031187835,0.18371814,0.002259042,0.0013055295,0.0015112882,0.21235305,0.40921232,0.16241547],"genre_scores_gemma":[0.12667927,0.0020236326,0.28467333,0.0017997978,0.00058439764,0.0024725539,0.41769165,0.038615685,0.1254597],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99860483,0.000285358,0.00009289219,0.00036113488,0.0005100364,0.00014583697],"domain_scores_gemma":[0.9975546,0.0005458175,0.00016951728,0.00087985815,0.00063395506,0.00021637946],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015939303,0.001548579,0.0006746308,0.002560374,0.000855409,0.0034870198,0.0015539624,0.0015911721,0.06988068],"category_scores_gemma":[0.0064744703,0.0006004754,0.0012048119,0.002037151,0.00041067527,0.003505655,0.0030861294,0.0013797127,0.07513471],"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":[0.0010653834,0.00021359915,0.003542906,0.001479408,0.000114877876,0.00026240668,0.0013158866,0.0028801067,0.018335607,0.012075163,0.6799309,0.27878377],"study_design_scores_gemma":[0.00016604009,0.00021747581,0.0041589793,0.00021748742,0.000043646494,0.00047581564,0.0006853417,0.025430506,0.012758746,0.015012151,0.9407105,0.00012330734],"about_ca_topic_score_codex":0.00560389,"about_ca_topic_score_gemma":0.009078515,"teacher_disagreement_score":0.93011934,"about_ca_system_score_codex":0.00071202783,"about_ca_system_score_gemma":0.0010743681,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2789454435","doi":"10.1109/ipta.2017.8310120","title":"Effective keyframe extraction from RGB and RGB-D video sequences","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Concordia University","funders":"","keywords":"Automatic summarization; Computer science; Artificial intelligence; RGB color model; Computer vision; Histogram; Feature extraction; Filter (signal processing); Digital video; Pattern recognition (psychology); Image (mathematics); Frame (networking)","score_opus":0.010755063123832847,"score_gpt":0.2719958333924439,"score_spread":0.26124077026861103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789454435","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02022722,0.0010082085,0.9752627,0.00009202532,0.0001169563,0.00017562612,0.0002623308,0.0016362016,0.0012187492],"genre_scores_gemma":[0.120211825,0.0019217996,0.87310314,0.0000862926,0.00016739701,0.00016104586,0.0011715471,0.00030356986,0.0028734358],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996351,0.00003252695,0.000032263088,0.00009077213,0.00016438842,0.000044983997],"domain_scores_gemma":[0.99951375,0.00011688797,0.00007439281,0.000059371163,0.00021025151,0.000025314548],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028301482,0.0011036904,0.00074768264,0.002743358,0.00040325467,0.0008135705,0.0005701279,0.00048994186,0.0023066676],"category_scores_gemma":[0.0011688097,0.0003397219,0.00055066857,0.0014072881,0.0002480283,0.0013424903,0.00054336217,0.0004511603,0.00148318],"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":[0.00017358035,0.00003538725,0.00049689127,0.00034783196,0.000033651082,0.00015311255,0.0001104386,0.002947422,0.42058823,0.0012956857,0.0019151596,0.57190263],"study_design_scores_gemma":[0.000043957618,0.00048008154,0.011810342,0.000109272085,0.0001614139,0.0015265545,0.0004069797,0.138655,0.8046684,0.004735831,0.03729947,0.00010261306],"about_ca_topic_score_codex":0.0018074387,"about_ca_topic_score_gemma":0.0024081625,"teacher_disagreement_score":0.002743358,"about_ca_system_score_codex":0.0003617983,"about_ca_system_score_gemma":0.0004698119,"threshold_uncertainty_score":0.007716596},"labels":[],"label_agreement":null},{"id":"W2793977096","doi":"10.18178/ijiet.2018.8.7.1086","title":"Local Sequence Alignment for Scan Path Similarity Assessment","year":2018,"lang":"en","type":"article","venue":"International Journal of Information and Education Technology","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":8,"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":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity (geometry); Path (computing); Sequence (biology); Computer science; Artificial intelligence; Pattern recognition (psychology); Biology; Genetics; Computer network","score_opus":0.010630439013550671,"score_gpt":0.31340883726471086,"score_spread":0.3027783982511602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793977096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077926226,0.00067988556,0.9104199,0.00009450468,0.00008057445,0.0007377217,0.0018408593,0.005698864,0.0025214711],"genre_scores_gemma":[0.23985407,0.00024755282,0.75496477,0.000034199333,0.000026571597,0.0008868809,0.0025147027,0.00044080673,0.0010304288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806195,0.00048525885,0.00026443595,0.00056658406,0.00051784713,0.00010385824],"domain_scores_gemma":[0.995352,0.001987563,0.00087865506,0.00037965932,0.0011664481,0.00023559539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017319995,0.0011444967,0.0010711235,0.0052844235,0.000869802,0.0013153276,0.0009240853,0.00097307784,0.010832245],"category_scores_gemma":[0.013079941,0.0003382379,0.00070928707,0.0043597175,0.00044808895,0.001655222,0.001075285,0.00091932726,0.0030227867],"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":[0.0016478914,0.00034590613,0.010702544,0.0008630607,0.00023842997,0.00038005886,0.0006520182,0.014944969,0.08269988,0.004244647,0.0054643936,0.87781626],"study_design_scores_gemma":[0.00018138037,0.0015842322,0.052363165,0.00022036057,0.0002590121,0.0015331338,0.0015355716,0.8247462,0.07879048,0.014671865,0.023889992,0.00022466178],"about_ca_topic_score_codex":0.0041848207,"about_ca_topic_score_gemma":0.0058965,"teacher_disagreement_score":0.010832245,"about_ca_system_score_codex":0.00054274907,"about_ca_system_score_gemma":0.0017734473,"threshold_uncertainty_score":0.03623748},"labels":[],"label_agreement":null},{"id":"W2799306936","doi":"10.1109/wacv.2018.00053","title":"Camera Selection for Broadcasting Soccer Games","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Broadcasting (networking); Event (particle physics); Artificial intelligence; Selection (genetic algorithm); Computer vision; Cover (algebra); Operator (biology)","score_opus":0.017873095936130994,"score_gpt":0.2670403606917449,"score_spread":0.24916726475561393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799306936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19331634,0.0015600977,0.79252195,0.00031060752,0.00012806368,0.00022184596,0.00072174525,0.0051856544,0.0060336743],"genre_scores_gemma":[0.85529494,0.00041882717,0.13923217,0.00007347257,0.00005865201,0.00004497151,0.00092380383,0.000178793,0.0037744252],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970514,0.000050129678,0.000008781146,0.00010300009,0.0000799741,0.000052925585],"domain_scores_gemma":[0.9996793,0.000095320574,0.000050449904,0.000032946275,0.00009577791,0.000046207217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003774808,0.00080615276,0.00043896772,0.0007694751,0.0003142345,0.00037628133,0.00063549436,0.0003936985,0.0022324764],"category_scores_gemma":[0.001227536,0.00021328454,0.0002958042,0.00036798874,0.00016263511,0.00041757722,0.0002857821,0.0006097407,0.0007172665],"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":[0.00089783286,0.00024917323,0.012548159,0.00019106432,0.00007856549,0.00032431257,0.00015506719,0.07124185,0.09896998,0.0018740625,0.010931291,0.8025387],"study_design_scores_gemma":[0.00005418868,0.00014264988,0.0110962745,0.00002797645,0.00004746164,0.000296465,0.000108799824,0.93581903,0.045666177,0.001649399,0.005065729,0.000025929245],"about_ca_topic_score_codex":0.014134561,"about_ca_topic_score_gemma":0.0245911,"teacher_disagreement_score":0.014134561,"about_ca_system_score_codex":0.00043300385,"about_ca_system_score_gemma":0.0005929597,"threshold_uncertainty_score":0.028104544},"labels":[],"label_agreement":null},{"id":"W2804194431","doi":"10.1007/978-3-030-01258-8_22","title":"Video Summarization Using Fully Convolutional Sequence Networks","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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 Manitoba","funders":"","keywords":"Automatic summarization; Computer science; Benchmark (surveying); Segmentation; Semantics (computer science); Video tracking; Artificial intelligence; Information retrieval; Multi-document summarization; Convolutional neural network; Video processing","score_opus":0.02967537556820681,"score_gpt":0.27827924699985407,"score_spread":0.24860387143164725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804194431","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022304395,0.0019450189,0.964007,0.00041299383,0.00040139686,0.00017531718,0.0013795025,0.006586701,0.0027876685],"genre_scores_gemma":[0.26457384,0.002431355,0.69072425,0.0004840085,0.00082673656,0.00025345918,0.013203442,0.000840899,0.026661934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994961,0.00006516112,0.00003625884,0.00017459218,0.00014904863,0.00007887293],"domain_scores_gemma":[0.9992111,0.00020295719,0.0000904813,0.0001267263,0.00030692856,0.00006169089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063222036,0.0020802736,0.0012813568,0.0022074957,0.000507135,0.0012326926,0.001019044,0.0012070984,0.00513312],"category_scores_gemma":[0.0017108717,0.0005582652,0.0011559024,0.0015013231,0.0003121393,0.0017206601,0.0010004058,0.0016008826,0.0038819774],"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":[0.000703864,0.00016170453,0.0005519517,0.00028346005,0.00015274236,0.00023272888,0.00007495659,0.034402475,0.09430904,0.0024672511,0.013915503,0.85274434],"study_design_scores_gemma":[0.000030266088,0.0002637783,0.0015097995,0.000051072395,0.00013046445,0.00016070099,0.00006997632,0.918265,0.06332172,0.005875715,0.010287179,0.0000343013],"about_ca_topic_score_codex":0.0060720146,"about_ca_topic_score_gemma":0.010066362,"teacher_disagreement_score":0.0060720146,"about_ca_system_score_codex":0.0006658387,"about_ca_system_score_gemma":0.00081637973,"threshold_uncertainty_score":0.017171979},"labels":[],"label_agreement":null},{"id":"W2806283251","doi":"10.1145/3197026.3203865","title":"ViDeX","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Microsoft","keywords":"Computer science; Multimedia; Video tracking; Video processing; Artificial intelligence","score_opus":0.00794060494159353,"score_gpt":0.22573532085179263,"score_spread":0.2177947159101991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806283251","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027914734,0.0020388353,0.03727568,0.0015863768,0.0023891407,0.00047835152,0.020473827,0.05139399,0.88157237],"genre_scores_gemma":[0.01324747,0.0020291614,0.01698421,0.0013847897,0.00069403,0.00031399744,0.027171068,0.010872361,0.92730296],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992834,0.00008592469,0.000037720372,0.00013956214,0.00038625218,0.00006714771],"domain_scores_gemma":[0.9985258,0.00026223253,0.00007345133,0.00032148958,0.00044432044,0.00037271297],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009345186,0.0009876239,0.00078080856,0.0015475345,0.0009856711,0.0035679177,0.0016885012,0.0017749963,0.7254162],"category_scores_gemma":[0.002809011,0.0005219046,0.0005082059,0.00093363883,0.00040235426,0.0026270146,0.0029582388,0.0016014613,0.5662316],"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":[0.00045567923,0.00013270864,0.00041821058,0.0006817559,0.00002162883,0.00021130912,0.00014721707,0.00030461396,0.0102744885,0.013247417,0.66369617,0.31040883],"study_design_scores_gemma":[0.000038860864,0.000042947464,0.00040134514,0.000076062,0.0000064502433,0.00017061528,0.000027533888,0.00041142365,0.0019810395,0.0017536372,0.9950783,0.000011866835],"about_ca_topic_score_codex":0.00068885094,"about_ca_topic_score_gemma":0.00089765445,"teacher_disagreement_score":0.27458382,"about_ca_system_score_codex":0.00050433015,"about_ca_system_score_gemma":0.0007850115,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2807062241","doi":"10.1109/icst.2018.00028","title":"Web Canvas Testing Through Visual Inference","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 British Columbia","funders":"","keywords":"Computer science; Visualization; Web application; Inference; Graphics; Object (grammar); Computer graphics (images); Information retrieval; Artificial intelligence; World Wide Web","score_opus":0.04427851998219271,"score_gpt":0.3103740959698748,"score_spread":0.2660955759876821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807062241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15592393,0.00041387614,0.7416275,0.0003858376,0.000079173675,0.0005037932,0.0010758222,0.09478199,0.005208035],"genre_scores_gemma":[0.69196093,0.00019902003,0.30168492,0.00028246688,0.000035530065,0.00022987644,0.0016716986,0.0020409701,0.0018945754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99401253,0.0015482958,0.00039530615,0.0011522649,0.0024489248,0.00044262354],"domain_scores_gemma":[0.9765645,0.014328573,0.0024315242,0.0038164114,0.0025394503,0.00031943267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023161012,0.0017854866,0.00068155973,0.0029126548,0.00050953036,0.0019960771,0.0032872811,0.0013871863,0.002647461],"category_scores_gemma":[0.023663778,0.00077508664,0.0013817501,0.00078457996,0.0016220277,0.0028854925,0.0020850266,0.0010955809,0.0008516825],"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":[0.0015646218,0.00092997885,0.052273966,0.0018323396,0.00043834432,0.0022239317,0.0013851703,0.19049107,0.12881646,0.017117634,0.011507406,0.5914191],"study_design_scores_gemma":[0.000071455564,0.00021123144,0.005164252,0.00014381795,0.00009699725,0.0004446293,0.00017888704,0.8831483,0.09190304,0.013682713,0.0048791403,0.00007561159],"about_ca_topic_score_codex":0.005858,"about_ca_topic_score_gemma":0.0065814303,"teacher_disagreement_score":0.005858,"about_ca_system_score_codex":0.0010476565,"about_ca_system_score_gemma":0.0014402247,"threshold_uncertainty_score":0.012248874},"labels":[],"label_agreement":null},{"id":"W2839086991","doi":"10.4000/books.pum.12402","title":"Radioscopie de l'information télévisée au Canada","year":2000,"lang":"fr","type":"book","venue":"Presses de l’Université de Montréal eBooks","topic":"Video Analysis and Summarization","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":"Political science; Humanities; Philosophy","score_opus":0.005122433607113025,"score_gpt":0.1555179859193157,"score_spread":0.15039555231220267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2839086991","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09174836,0.014760567,0.009381923,0.009642684,0.0013923966,0.00013018027,0.003878475,0.0009492301,0.86811614],"genre_scores_gemma":[0.261843,0.00827706,0.0038998132,0.0005067962,0.00025644447,0.000029717003,0.0010688984,0.00019073309,0.7239275],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99959964,0.000019350417,0.00000689792,0.000038721086,0.00026464945,0.00007071476],"domain_scores_gemma":[0.9995167,0.000108498556,0.000024691308,0.00002784768,0.00027654867,0.00004566912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003018504,0.00029807125,0.00014851581,0.002273757,0.003096635,0.0024142715,0.00038694163,0.00045250778,0.028053276],"category_scores_gemma":[0.00094527315,0.00016722758,0.00013075896,0.0035718614,0.0012937709,0.000575359,0.0005045783,0.0005432415,0.0019866943],"study_design_candidate":"qualitative","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":[0.0002723457,0.000028936343,0.006376304,0.00058486615,0.000026472242,0.000649326,0.012681131,0.0028679306,0.014398157,0.08729349,0.32781357,0.5470075],"study_design_scores_gemma":[0.000008020882,0.000020631327,0.018545117,0.00010626657,0.000012890233,0.0001490206,0.003342417,0.00086332136,0.0021869845,0.0009534326,0.9737891,0.000022751994],"about_ca_topic_score_codex":0.9513804,"about_ca_topic_score_gemma":0.969544,"teacher_disagreement_score":0.048619628,"about_ca_system_score_codex":0.019715117,"about_ca_system_score_gemma":0.016839046,"threshold_uncertainty_score":0.14304382},"labels":[],"label_agreement":null},{"id":"W2891614047","doi":"10.1109/ssci.2018.8628877","title":"A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Cricket; Computer science; Support vector machine; Artificial intelligence; Automatic summarization; Convolutional neural network; Feature extraction; Classifier (UML); Pattern recognition (psychology); Machine learning","score_opus":0.03330449312302853,"score_gpt":0.29593100182672755,"score_spread":0.262626508703699,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891614047","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35525978,0.004008091,0.05099931,0.001155637,0.0020342781,0.003873534,0.52301896,0.031060161,0.028590238],"genre_scores_gemma":[0.11419095,0.0006109426,0.06781267,0.00023259399,0.00016554068,0.0011385102,0.8072836,0.00034641795,0.008218856],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99875605,0.00016961661,0.00012235681,0.00029654393,0.00045275156,0.00020261631],"domain_scores_gemma":[0.998782,0.00015416607,0.000101912425,0.00028696266,0.000507016,0.00016794859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094305706,0.0025220981,0.0010030722,0.0023269798,0.0009098978,0.00083689584,0.0017539456,0.0016457002,0.0054149167],"category_scores_gemma":[0.0019737198,0.0002738094,0.0011234162,0.0019311099,0.0003916509,0.0007668714,0.0008892158,0.0012254006,0.0057139774],"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":[0.002885652,0.003197963,0.016102444,0.0021711932,0.00041003939,0.0013598097,0.00023177214,0.013827854,0.07207116,0.0013197236,0.40395212,0.48247024],"study_design_scores_gemma":[0.001292389,0.0045850724,0.1801942,0.0006324374,0.0004851652,0.0035812256,0.0015816117,0.24735214,0.13956717,0.0032639725,0.4170821,0.00038254203],"about_ca_topic_score_codex":0.017955419,"about_ca_topic_score_gemma":0.042260017,"teacher_disagreement_score":0.017955419,"about_ca_system_score_codex":0.0009546498,"about_ca_system_score_gemma":0.00093688286,"threshold_uncertainty_score":0.03570181},"labels":[],"label_agreement":null},{"id":"W2895470062","doi":"10.1145/3243394.3243689","title":"Real-time Video Summarization on Commodity Hardware","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Ontario Tech University","funders":"","keywords":"Automatic summarization; Rank (graph theory); Duration (music); Video tracking; Video processing; Feature extraction; Multi-document summarization","score_opus":0.014370573556701249,"score_gpt":0.2434239628522393,"score_spread":0.22905338929553806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895470062","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027271245,0.0009525474,0.93988323,0.0002726979,0.00023370974,0.00031861078,0.0018776322,0.025829785,0.003360621],"genre_scores_gemma":[0.21190892,0.0005848784,0.772205,0.00017299595,0.00021282861,0.00027739734,0.0066700336,0.0009627302,0.0070051514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948704,0.000064332016,0.00004701409,0.00014646444,0.00021055348,0.000044557404],"domain_scores_gemma":[0.99916494,0.00017375113,0.00011732195,0.0001804028,0.00030612512,0.00005753106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044004698,0.0013558742,0.0005846151,0.0014580763,0.00038145916,0.00083233084,0.0009842962,0.00049230596,0.0052648135],"category_scores_gemma":[0.0022916866,0.00029341172,0.0005770755,0.0010538368,0.00018945475,0.0013830016,0.0008209841,0.0007241174,0.0023487604],"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":[0.00042895964,0.000105939194,0.0006308336,0.00034158985,0.00009721898,0.00028276327,0.0001698052,0.0166678,0.100884035,0.0016130707,0.026009776,0.85276824],"study_design_scores_gemma":[0.00014224857,0.0006654733,0.0035888054,0.00007243988,0.00014727972,0.0006515618,0.00039703457,0.7263768,0.19568025,0.007623794,0.06457045,0.00008388801],"about_ca_topic_score_codex":0.0025668899,"about_ca_topic_score_gemma":0.005005197,"teacher_disagreement_score":0.0052648135,"about_ca_system_score_codex":0.0004602741,"about_ca_system_score_gemma":0.00046393368,"threshold_uncertainty_score":0.017612576},"labels":[],"label_agreement":null},{"id":"W2900419615","doi":"10.1109/icmcs.2018.8525880","title":"NLP-Enriched Automatic Video Segmentation","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Lakehead University","funders":"","keywords":"Computer science; Exploit; Artificial intelligence; Cosine similarity; Segmentation; Matching (statistics); Natural language processing; Video browsing; Key (lock); Process (computing); Feature (linguistics); Feature extraction; Information retrieval; Video processing; Pattern recognition (psychology); Video tracking","score_opus":0.011045624518278603,"score_gpt":0.25139774069697984,"score_spread":0.24035211617870125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900419615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023670008,0.0006471975,0.9637585,0.00022346975,0.00013815238,0.00026052885,0.0015999925,0.006965897,0.0027362702],"genre_scores_gemma":[0.16131179,0.000666097,0.81968546,0.0001388938,0.0002577618,0.00034883068,0.012066975,0.0005990073,0.004925169],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928844,0.00011593749,0.00006436881,0.00028339084,0.00015114884,0.00009670555],"domain_scores_gemma":[0.99905056,0.0003296104,0.00010021383,0.00011503145,0.0003635572,0.00004106982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047985403,0.001133467,0.0007985929,0.0031757695,0.0006588475,0.0010873802,0.000922464,0.00095496775,0.0033332363],"category_scores_gemma":[0.002194926,0.0002659446,0.00075212127,0.002319515,0.00049836957,0.0014070275,0.0008857395,0.000991401,0.002815854],"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":[0.00048041274,0.00017835876,0.00094528036,0.00043052458,0.00005595169,0.00033425086,0.00020764301,0.0125786485,0.15924595,0.004033346,0.011685977,0.8098235],"study_design_scores_gemma":[0.00007653714,0.0002127569,0.0046710004,0.000070427464,0.00009921725,0.0004886556,0.0005419858,0.8551195,0.1061514,0.011825008,0.02069773,0.000045707555],"about_ca_topic_score_codex":0.006626018,"about_ca_topic_score_gemma":0.007579626,"teacher_disagreement_score":0.006626018,"about_ca_system_score_codex":0.0006883417,"about_ca_system_score_gemma":0.0010270757,"threshold_uncertainty_score":0.0131748915},"labels":[],"label_agreement":null},{"id":"W2912979690","doi":"10.1145/3257574","title":"Session details: Image warping and interpolation","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Image warping; Session (web analytics); Computer science; Interpolation (computer graphics); Computer graphics (images); Artificial intelligence; Computer vision; Image (mathematics); Image scaling; Image processing; World Wide Web","score_opus":0.009098256827720554,"score_gpt":0.24560930739377268,"score_spread":0.23651105056605212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912979690","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018344402,0.014552417,0.35921025,0.011384971,0.032756716,0.003176334,0.025797274,0.02951552,0.505262],"genre_scores_gemma":[0.087583885,0.009669597,0.076838344,0.0012852161,0.006261353,0.0010367001,0.0130863385,0.003537435,0.80070114],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997489,0.000043268712,0.000013743851,0.00007335736,0.00007352643,0.000047195157],"domain_scores_gemma":[0.9991142,0.0002687383,0.00002186656,0.00020850448,0.00020865467,0.0001779957],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000679498,0.0014966056,0.0016345286,0.0007243811,0.000848204,0.0018942162,0.0008592969,0.0022168695,0.5273495],"category_scores_gemma":[0.0022239857,0.0002651724,0.0010579666,0.000915087,0.00026435655,0.0010168629,0.0011768983,0.0016274499,0.2736432],"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":[0.0016036775,0.00031971306,0.0003101645,0.0006526825,0.00007065878,0.00019218426,0.00006117965,0.00074564415,0.03783832,0.0028794545,0.58415467,0.3711716],"study_design_scores_gemma":[0.00039321653,0.0010244132,0.0042533856,0.00016987245,0.000115087336,0.0009116869,0.00010595246,0.0136857545,0.043518506,0.008680681,0.92704165,0.000099774334],"about_ca_topic_score_codex":0.0007061219,"about_ca_topic_score_gemma":0.0013951868,"teacher_disagreement_score":0.5273495,"about_ca_system_score_codex":0.0002499226,"about_ca_system_score_gemma":0.00043244532,"threshold_uncertainty_score":0.6741786},"labels":[],"label_agreement":null},{"id":"W2914223569","doi":"","title":"Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Personalization; Computer science; Multimedia; Adaptation (eye); World Wide Web; Context (archaeology); Scalability","score_opus":0.04383836217813286,"score_gpt":0.30312054909850633,"score_spread":0.25928218692037347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914223569","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03322415,0.071849324,0.3912812,0.06785892,0.05043676,0.0019639793,0.0046128677,0.008269526,0.37050334],"genre_scores_gemma":[0.113681145,0.041799214,0.14827348,0.0057620476,0.010843948,0.0013495571,0.01287569,0.0019430236,0.6634719],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984125,0.00053581345,0.00011380488,0.00025976787,0.00046206816,0.0002160463],"domain_scores_gemma":[0.9976463,0.00065134774,0.00006826089,0.00037266035,0.000691105,0.0005704053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027853863,0.00122094,0.0013529925,0.0011016215,0.0015144099,0.0058636786,0.0021265936,0.0022268042,0.05830904],"category_scores_gemma":[0.0045454553,0.0006135842,0.0008081637,0.0012084731,0.0011154228,0.007748938,0.0036060468,0.0031701352,0.018354189],"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":[0.00031436855,0.0002561601,0.00084114424,0.00041127158,0.000061018873,0.00032468658,0.0013168305,0.0006738051,0.0047366465,0.017696459,0.7142122,0.2591554],"study_design_scores_gemma":[0.000024397905,0.00006903203,0.0011700507,0.00021982314,0.000039513692,0.00032888146,0.0008288214,0.0060697347,0.0010737971,0.007930512,0.98220485,0.00004046559],"about_ca_topic_score_codex":0.005594496,"about_ca_topic_score_gemma":0.012206481,"teacher_disagreement_score":0.05830904,"about_ca_system_score_codex":0.0011655011,"about_ca_system_score_gemma":0.0019176387,"threshold_uncertainty_score":0.19506317},"labels":[],"label_agreement":null},{"id":"W2920379571","doi":"10.1108/lht-05-2018-0071","title":"Visualising and revitalising traditional Chinese martial arts","year":2019,"lang":"en","type":"article","venue":"Library Hi Tech","topic":"Video Analysis and Summarization","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":"University of British Columbia","funders":"","keywords":"Exhibition; Context (archaeology); Visual arts; Martial arts; Questionnaire; Cultural heritage; The arts; Wonder; Sociology; Psychology; History; Art; Social science","score_opus":0.011105890645208552,"score_gpt":0.21596702459204278,"score_spread":0.20486113394683422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920379571","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90069115,0.0014140288,0.005804256,0.0008815874,0.00023744396,0.00012260381,0.00019910454,0.00029229905,0.090357624],"genre_scores_gemma":[0.96817553,0.00087168405,0.0059925397,0.00014714978,0.00006448694,0.00004472306,0.0000920271,0.000072764786,0.02453904],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99986684,0.000031286923,0.0000034003615,0.000015896561,0.00004001475,0.000042508032],"domain_scores_gemma":[0.99986696,0.000039780447,0.000013160617,0.000020379828,0.000017680384,0.000042153228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037028245,0.00044760507,0.00013499818,0.00076289667,0.0012930866,0.0021098403,0.00043251106,0.00031661315,0.0078078685],"category_scores_gemma":[0.00042835218,0.00010948081,0.00033406448,0.0005783078,0.0012852583,0.0009446051,0.0019477273,0.0004961331,0.00041012792],"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":[0.0005085525,0.0002150045,0.03402665,0.0030807857,0.00010768957,0.005688347,0.3088939,0.004185352,0.12783192,0.028385049,0.029714974,0.4573618],"study_design_scores_gemma":[0.000049440532,0.0005902713,0.18208896,0.00094626105,0.00016627151,0.0031933328,0.25737336,0.0050855023,0.01992457,0.0038384292,0.52658856,0.00015509727],"about_ca_topic_score_codex":0.00574089,"about_ca_topic_score_gemma":0.017924419,"teacher_disagreement_score":0.0078078685,"about_ca_system_score_codex":0.0006249984,"about_ca_system_score_gemma":0.0007850411,"threshold_uncertainty_score":0.026119947},"labels":[],"label_agreement":null},{"id":"W2921848958","doi":"10.1109/wacv.2019.00179","title":"Keep Your Eye on the Puck: Automatic Hockey Videography","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"York University","funders":"","keywords":"Videography; Computer science; Computer vision; Artificial intelligence; Amateur; Ice hockey; Gaze; Process (computing); Geography","score_opus":0.010647623715664883,"score_gpt":0.22383265007420752,"score_spread":0.21318502635854264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921848958","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24476713,0.0011762507,0.7211219,0.00044747404,0.0004039431,0.00031603276,0.0015811784,0.014335364,0.015850727],"genre_scores_gemma":[0.74368143,0.00071736146,0.23789078,0.00021771132,0.00019180203,0.00011083746,0.0022408357,0.00071922166,0.014230174],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998292,0.00002303182,0.0000043818623,0.00006772062,0.000038804734,0.000036766713],"domain_scores_gemma":[0.9998105,0.000045159413,0.000019323812,0.000028485163,0.00006661634,0.000029949719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022844807,0.000673713,0.0003586973,0.00083999283,0.00029121316,0.00067795266,0.00056555297,0.0004598145,0.0026202768],"category_scores_gemma":[0.0008586715,0.00021406302,0.00017197136,0.00038397717,0.00028879195,0.0006378888,0.0006923259,0.000537505,0.0015177918],"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":[0.00069450896,0.00013258007,0.0051773246,0.000208855,0.000094214935,0.00029662662,0.00040813608,0.015266673,0.13594237,0.0020180824,0.021418307,0.8183422],"study_design_scores_gemma":[0.00007966513,0.00030131167,0.026478328,0.000119611024,0.00008909767,0.0005944268,0.0006639172,0.7933027,0.1410741,0.004439726,0.032807354,0.000049742746],"about_ca_topic_score_codex":0.0057382258,"about_ca_topic_score_gemma":0.013501103,"teacher_disagreement_score":0.0057382258,"about_ca_system_score_codex":0.00032399507,"about_ca_system_score_gemma":0.0003902806,"threshold_uncertainty_score":0.01140964},"labels":[],"label_agreement":null},{"id":"W2938584417","doi":"10.1109/cvpr.2019.00809","title":"Video Summarization by Learning From Unpaired Data","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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 Manitoba","funders":"","keywords":"Automatic summarization; Computer science; Benchmark (surveying); Storyboard; Key (lock); Set (abstract data type); Artificial intelligence; Ground truth; Video tracking; Video editing; Constraint (computer-aided design); Training set; Image (mathematics); Raw data; Computer vision; Information retrieval; Video processing; Multimedia; Mathematics","score_opus":0.039476314068724844,"score_gpt":0.2520814649683417,"score_spread":0.21260515089961687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938584417","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024873156,0.00065981696,0.9717065,0.0002970587,0.000054704687,0.00012530724,0.00041801357,0.0012089604,0.00065646094],"genre_scores_gemma":[0.5203972,0.00093763176,0.46637523,0.00044694083,0.00052251184,0.0005226569,0.0057335086,0.00034117737,0.0047231447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988292,0.00030992148,0.00007112419,0.0005129297,0.0002021305,0.000074665855],"domain_scores_gemma":[0.9969909,0.001468925,0.00053667946,0.00049993076,0.000367684,0.00013569227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018579988,0.0015231922,0.0013802141,0.0012979314,0.00037327732,0.00095221034,0.0017454446,0.00136951,0.0015973717],"category_scores_gemma":[0.0074165403,0.0005265501,0.0008452938,0.0011009937,0.00092554797,0.0024808084,0.0012788525,0.0017681506,0.00080254226],"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":[0.0005905818,0.00031089896,0.0026671547,0.0005530636,0.00021968801,0.00036232176,0.00030788084,0.50831,0.029664015,0.010303213,0.008076727,0.43863443],"study_design_scores_gemma":[0.000036379224,0.00033971216,0.00065793557,0.000027198206,0.000043176027,0.00013265331,0.000055060904,0.9707394,0.010011638,0.015376577,0.002557234,0.000022978864],"about_ca_topic_score_codex":0.0010560538,"about_ca_topic_score_gemma":0.0013859293,"teacher_disagreement_score":0.0018579988,"about_ca_system_score_codex":0.0007186103,"about_ca_system_score_gemma":0.000526598,"threshold_uncertainty_score":0.009826124},"labels":[],"label_agreement":null},{"id":"W2951095449","doi":"10.48550/arxiv.1809.06217","title":"A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Cricket; Support vector machine; Artificial intelligence; Computer science; Automatic summarization; Classifier (UML); Convolutional neural network; Pattern recognition (psychology); Machine learning; Feature extraction","score_opus":0.0874194716542897,"score_gpt":0.2241855343980195,"score_spread":0.1367660627437298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951095449","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32084045,0.0043625575,0.054194864,0.0012383314,0.0022006785,0.0036308754,0.5480214,0.03582788,0.029683027],"genre_scores_gemma":[0.10589425,0.00062442734,0.0670993,0.00022831596,0.0001859335,0.0010848411,0.81674457,0.00039585773,0.0077425577],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873525,0.00017643126,0.00013041473,0.00031552013,0.0004372946,0.00020514887],"domain_scores_gemma":[0.99875116,0.0001662704,0.00010482163,0.00030021323,0.00050689373,0.00017069261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009435977,0.0025237424,0.0009570422,0.0025737835,0.0008714354,0.000888016,0.0016840765,0.0016160181,0.0056057447],"category_scores_gemma":[0.0021526027,0.00026535397,0.0011021753,0.0020735946,0.00040185457,0.000794233,0.0009028053,0.0012820398,0.006099166],"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":[0.002751679,0.0028368966,0.015050274,0.0022750983,0.0003681955,0.0012062481,0.00026638474,0.012715022,0.064583205,0.0014473568,0.43696114,0.45953855],"study_design_scores_gemma":[0.0011258032,0.0040232684,0.16520807,0.00064581906,0.0004355161,0.0033014747,0.0016329665,0.23621029,0.12491635,0.003814821,0.45832902,0.00035655955],"about_ca_topic_score_codex":0.016578812,"about_ca_topic_score_gemma":0.03812112,"teacher_disagreement_score":0.016578812,"about_ca_system_score_codex":0.00097579014,"about_ca_system_score_gemma":0.00089759036,"threshold_uncertainty_score":0.032964647},"labels":[],"label_agreement":null},{"id":"W2965022010","doi":"10.1109/tvcg.2019.2934654","title":"Semantic Concept Spaces: Guided Topic Model Refinement using Word-Embedding Projections","year":2019,"lang":"en","type":"preprint","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Analysis and Summarization","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":"Ontario Tech University","funders":"","keywords":"Computer science; Word embedding; Process (computing); Embedding; Semantics (computer science); Space (punctuation); Domain (mathematical analysis); Word (group theory); Point (geometry); Artificial intelligence; Human–computer interaction; Natural language processing; Information retrieval; Programming language","score_opus":0.056400385748752856,"score_gpt":0.3296264073778721,"score_spread":0.27322602162911924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965022010","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051955497,0.0001329623,0.9875316,0.00014564625,0.000024638068,0.00013642652,0.00019870682,0.0059584994,0.0006760053],"genre_scores_gemma":[0.086070426,0.00019427328,0.91026896,0.000090700705,0.00004874051,0.00040719658,0.00084900897,0.0009103452,0.0011604284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959902,0.0017681102,0.0002653052,0.0010044671,0.00084819406,0.00012365342],"domain_scores_gemma":[0.9916659,0.0051804157,0.00061850564,0.0012701919,0.0010110139,0.00025396084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004513924,0.0021803488,0.0011400572,0.0029945523,0.00088264333,0.0038241802,0.0023648895,0.001471446,0.0058471607],"category_scores_gemma":[0.01841805,0.000848383,0.0021874676,0.0024165716,0.0014702156,0.00754923,0.004612564,0.0024017845,0.002185856],"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":[0.0010233406,0.0005470505,0.0030375037,0.0014191293,0.00034562376,0.00047827052,0.015368211,0.05561621,0.04571112,0.078311265,0.017714776,0.78042746],"study_design_scores_gemma":[0.00021111689,0.0002386077,0.0010832864,0.00018562471,0.00015233003,0.0003335223,0.0023672094,0.7728875,0.03322464,0.13966288,0.049483594,0.00016968217],"about_ca_topic_score_codex":0.002824185,"about_ca_topic_score_gemma":0.003889992,"teacher_disagreement_score":0.0058471607,"about_ca_system_score_codex":0.00083256443,"about_ca_system_score_gemma":0.0014608505,"threshold_uncertainty_score":0.023872197},"labels":[],"label_agreement":null},{"id":"W2965362349","doi":"","title":"A Methodology for Student Video Interaction Patterns Analysis and Classification.","year":2019,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Analysis and Summarization","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":"Jewish General Hospital; Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.03022807831287726,"score_gpt":0.30012102196600354,"score_spread":0.26989294365312627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965362349","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002328372,0.00024494145,0.9894739,0.000094786854,0.000051148505,0.00046904912,0.0015235705,0.0050708037,0.00074344303],"genre_scores_gemma":[0.014322487,0.000119366734,0.97831833,0.00004228217,0.00003986235,0.0007913751,0.004083018,0.00017425942,0.002109037],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961033,0.000853804,0.0005136799,0.0009289896,0.0013970452,0.00020317169],"domain_scores_gemma":[0.99494946,0.0015770566,0.0004867591,0.00081159093,0.001908975,0.0002661825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002920413,0.0011131952,0.00093631184,0.006833877,0.0011688921,0.002275299,0.00197736,0.001242192,0.00562766],"category_scores_gemma":[0.00967123,0.0006250514,0.0013924118,0.0043636416,0.0006126878,0.0015370395,0.0017602912,0.0015235526,0.0047713057],"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":[0.00014962941,0.00022559478,0.002821343,0.0004169629,0.000134063,0.000095141266,0.00040652434,0.0020206512,0.017698532,0.0056564007,0.020371094,0.9500039],"study_design_scores_gemma":[0.00022636633,0.00084946223,0.036073156,0.0004651784,0.00044284383,0.0022179093,0.0021665436,0.59196055,0.086350255,0.06905224,0.2099628,0.00023266586],"about_ca_topic_score_codex":0.005576907,"about_ca_topic_score_gemma":0.011328538,"teacher_disagreement_score":0.006833877,"about_ca_system_score_codex":0.00095075404,"about_ca_system_score_gemma":0.0020425888,"threshold_uncertainty_score":0.018826425},"labels":[],"label_agreement":null},{"id":"W2967775316","doi":"10.1109/cvprw.2019.00305","title":"Sports Camera Calibration via Synthetic Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":101,"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; Artificial intelligence; Robustness (evolution); Computer vision; Camera auto-calibration; Camera resectioning; Pose; Smart camera; Generative adversarial network; Feature (linguistics); Feature extraction; Synthetic data; Image (mathematics)","score_opus":0.012177861993891544,"score_gpt":0.22542014054525333,"score_spread":0.2132422785513618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967775316","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36894724,0.0011599294,0.60269827,0.00058973156,0.00055167696,0.0006510817,0.007573744,0.0073968386,0.010431453],"genre_scores_gemma":[0.80949885,0.00029749633,0.17156197,0.00018723337,0.000067438414,0.0002527929,0.015556025,0.00043233624,0.0021458378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824655,0.00045544695,0.000078548524,0.0005309213,0.0005588124,0.00012978502],"domain_scores_gemma":[0.9972173,0.0005998683,0.0003699405,0.0008076662,0.0008851025,0.00012019813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014373946,0.0011725471,0.00057916826,0.0011011757,0.00037725724,0.0007941278,0.0013066952,0.0011105308,0.0020052332],"category_scores_gemma":[0.005381389,0.00038672864,0.0005971993,0.0010454287,0.0008100962,0.0009638871,0.0011635238,0.0011046265,0.0010362308],"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":[0.0006680766,0.000533965,0.016200125,0.0005592835,0.00025846792,0.00049424474,0.00024400548,0.6104561,0.041700386,0.005529762,0.023389874,0.2999657],"study_design_scores_gemma":[0.00008505697,0.0003024229,0.010407211,0.00008216345,0.00003669331,0.0005324019,0.00025471323,0.9209615,0.044445194,0.005556529,0.017261079,0.00007502244],"about_ca_topic_score_codex":0.0044126767,"about_ca_topic_score_gemma":0.008174236,"teacher_disagreement_score":0.0044126767,"about_ca_system_score_codex":0.00079674245,"about_ca_system_score_gemma":0.0005727166,"threshold_uncertainty_score":0.0087739825},"labels":[],"label_agreement":null},{"id":"W2972899537","doi":"10.1145/3351253","title":"FMT","year":2019,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Video Analysis and Summarization","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":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Fiducial marker; Computer science; Usability; CLIPS; Object (grammar); Computer vision; Artificial intelligence; Field (mathematics); Human–computer interaction","score_opus":0.006120203254591511,"score_gpt":0.22401128259356018,"score_spread":0.21789107933896867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972899537","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023901602,0.0034732735,0.14028971,0.003924294,0.0040139533,0.0012988828,0.03827168,0.07551369,0.70931286],"genre_scores_gemma":[0.14220598,0.0023698532,0.08409073,0.004208446,0.0012619753,0.0016086817,0.05301364,0.00698872,0.70425206],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999435,0.00008339909,0.000040808838,0.0001410767,0.00022274081,0.00007687431],"domain_scores_gemma":[0.9985879,0.00031967883,0.00008353783,0.00031061642,0.00055196317,0.00014619948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010540014,0.00069669867,0.00037535667,0.0011806424,0.00073773117,0.0016733148,0.0011746292,0.0013705394,0.30381173],"category_scores_gemma":[0.004060043,0.00027658345,0.00042993858,0.0008559663,0.0002507092,0.001958267,0.0017166432,0.00059194444,0.16544378],"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":[0.00045791108,0.000087537,0.0016111878,0.00045693523,0.00001972134,0.0002806026,0.00051616825,0.0002441255,0.010053957,0.0051617613,0.44757998,0.53353006],"study_design_scores_gemma":[0.00005768828,0.00014634461,0.0034115626,0.00013983223,0.000017982995,0.00061959674,0.00017417545,0.0015070366,0.004299534,0.0020092807,0.9875852,0.000031833468],"about_ca_topic_score_codex":0.002554822,"about_ca_topic_score_gemma":0.0035403061,"teacher_disagreement_score":0.30381173,"about_ca_system_score_codex":0.00055664754,"about_ca_system_score_gemma":0.0006533521,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2976260611","doi":"10.48550/arxiv.1905.09618","title":"Automatic Generation of Level Maps with the Do What's Possible\\n Representation","year":2019,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Video Analysis and Summarization","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 Guelph","funders":"","keywords":"Computer science; Representation (politics); Politics","score_opus":0.07892838773623302,"score_gpt":0.18456403420144474,"score_spread":0.10563564646521172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976260611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013968677,0.00009638992,0.9710852,0.00010816955,0.000105991516,0.00016541917,0.00038836105,0.008435005,0.0056467596],"genre_scores_gemma":[0.17496742,0.00015890897,0.81369704,0.00009102133,0.000045442983,0.00027498306,0.0013753383,0.0023200004,0.007069771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996599,0.00006025072,0.000018728566,0.000080684986,0.00013930231,0.000041205614],"domain_scores_gemma":[0.9988979,0.00042687668,0.00006421395,0.00027030648,0.0002833479,0.00005728255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000475789,0.0008847922,0.00048716355,0.0013597013,0.00060286757,0.0015439953,0.0011552155,0.00074598455,0.01218109],"category_scores_gemma":[0.0034646776,0.0003715393,0.00067086564,0.0007632425,0.0006015489,0.0014073135,0.0018831106,0.0009315924,0.003893378],"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":[0.0004924648,0.00015309616,0.0012012777,0.0005297043,0.0000750588,0.0006299863,0.00110085,0.037753694,0.104718596,0.046689197,0.025794165,0.780862],"study_design_scores_gemma":[0.00009716462,0.00022214983,0.0012505276,0.00008046969,0.00007306474,0.00064200436,0.0006115953,0.66781306,0.19980644,0.05422103,0.075084805,0.000097668824],"about_ca_topic_score_codex":0.00078943255,"about_ca_topic_score_gemma":0.0011896867,"teacher_disagreement_score":0.01218109,"about_ca_system_score_codex":0.0003574433,"about_ca_system_score_gemma":0.0005268684,"threshold_uncertainty_score":0.040749848},"labels":[],"label_agreement":null},{"id":"W2978500877","doi":"10.1007/s11432-018-9908-4","title":"Recursive narrative alignment for movie narrating","year":2019,"lang":"en","type":"article","venue":"Science China Information Sciences","topic":"Video Analysis and Summarization","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":"Western University; St. Lawrence College","funders":"","keywords":"Narrative; Psychology; Computer science; Aesthetics; Cognitive science; Art; Literature","score_opus":0.010090356382087865,"score_gpt":0.27014929460692405,"score_spread":0.26005893822483617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2978500877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04021117,0.0006719513,0.9456042,0.00032513155,0.00010675665,0.00033625308,0.0015419024,0.0065743774,0.0046282513],"genre_scores_gemma":[0.26213405,0.0003283606,0.7252941,0.000070860675,0.00011276141,0.0003219303,0.0065784417,0.00064697844,0.00451244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866796,0.0004117272,0.00011745476,0.00045609087,0.00021425044,0.00013245524],"domain_scores_gemma":[0.99776244,0.0009522753,0.00020805186,0.00032218357,0.0006359955,0.000119064316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011275096,0.0008142704,0.00080448715,0.0023035968,0.0013257154,0.0016152797,0.0012858846,0.00083590904,0.008643851],"category_scores_gemma":[0.005785798,0.00049858313,0.00084771396,0.0018449823,0.0004668543,0.0025134378,0.0018687254,0.0012328041,0.003925159],"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":[0.0007483201,0.00026980095,0.0025791093,0.00051176525,0.000104387706,0.00035934628,0.0023913996,0.0134587,0.035699513,0.03081065,0.018493593,0.8945734],"study_design_scores_gemma":[0.000071657916,0.0003525821,0.0049690004,0.00015397782,0.00019135691,0.0003562154,0.0024702656,0.848023,0.041965477,0.055621564,0.045738626,0.00008622928],"about_ca_topic_score_codex":0.003961809,"about_ca_topic_score_gemma":0.005103214,"teacher_disagreement_score":0.008643851,"about_ca_system_score_codex":0.000709523,"about_ca_system_score_gemma":0.0011541354,"threshold_uncertainty_score":0.028916538},"labels":[],"label_agreement":null},{"id":"W298976848","doi":"10.1007/978-3-319-10599-4_34","title":"Discovering Video Clusters from Visual Features and Noisy Tags","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Cluster analysis; Consistency (knowledge bases); Margin (machine learning); Artificial intelligence; Visualization; Pattern recognition (psychology); Information retrieval; Machine learning","score_opus":0.0072578812683532085,"score_gpt":0.22725329163267932,"score_spread":0.21999541036432613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W298976848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.162362,0.004445533,0.81447643,0.0005514545,0.00043822388,0.00073988695,0.0064047393,0.0050817,0.005499981],"genre_scores_gemma":[0.44038892,0.0029107253,0.51950747,0.00020445847,0.0006965853,0.0004197777,0.023274785,0.000769856,0.0118273785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905187,0.00007778234,0.000058728136,0.00040073736,0.0002615201,0.00014939898],"domain_scores_gemma":[0.99852127,0.00045480413,0.00018312322,0.0001854174,0.00053442875,0.000121015364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060221954,0.0023921984,0.0017524234,0.0071400236,0.0010875666,0.0025467481,0.0017604001,0.001365777,0.0019077375],"category_scores_gemma":[0.002946321,0.0006353117,0.0016080842,0.0077945744,0.00060969533,0.0019976955,0.0013777097,0.0011193788,0.0032746303],"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":[0.0013296965,0.0004529709,0.011275694,0.00075393775,0.00034700407,0.0007222185,0.0005673945,0.016100673,0.0657836,0.0033481016,0.023148807,0.87616986],"study_design_scores_gemma":[0.00013710104,0.0006615124,0.021937087,0.00022796357,0.0009291188,0.0014187143,0.0025646319,0.85060793,0.063231155,0.03333719,0.02477428,0.00017332335],"about_ca_topic_score_codex":0.011587724,"about_ca_topic_score_gemma":0.015260318,"teacher_disagreement_score":0.011587724,"about_ca_system_score_codex":0.0010266164,"about_ca_system_score_gemma":0.0011025608,"threshold_uncertainty_score":0.023040593},"labels":[],"label_agreement":null},{"id":"W2990414876","doi":"","title":"Étude comparative de méthodes d'extraction de données Twitter : le cas des matches de l'équipe nationale masculine du Canada de hockey sur glace aux JO d'hiver de Sotchi 2014","year":2016,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Video Analysis and Summarization","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":"Laurentian University; Wilfrid Laurier University; University of Ottawa","funders":"","keywords":"Humanities; Art","score_opus":0.03810269038134119,"score_gpt":0.2620753981752964,"score_spread":0.22397270779395523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990414876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34198168,0.012328745,0.5876891,0.0025762,0.0010692778,0.0021150722,0.027170721,0.011694053,0.013375143],"genre_scores_gemma":[0.33563647,0.0038452053,0.6065608,0.00035686902,0.0002507066,0.0017547844,0.028748862,0.0014394334,0.021406824],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.988296,0.0034733098,0.0015024752,0.0020529185,0.0040896875,0.00058558636],"domain_scores_gemma":[0.9581629,0.030262262,0.0011605679,0.0023363272,0.0076297834,0.00044808848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009647597,0.0017750595,0.0013197862,0.011137931,0.0015784866,0.004538783,0.0015315594,0.001783426,0.0046388838],"category_scores_gemma":[0.034409057,0.0007427726,0.0015723035,0.0060099554,0.00080362515,0.0029755884,0.0017608989,0.0012805472,0.0039202347],"study_design_candidate":"observational","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":[0.0020180356,0.00045595103,0.054842744,0.0035970588,0.0019023959,0.00048149115,0.00658434,0.00694609,0.07109185,0.004301207,0.019391738,0.82838714],"study_design_scores_gemma":[0.0007296432,0.0013042246,0.25594565,0.0013182877,0.0022830826,0.0029170662,0.022548767,0.34632453,0.1593719,0.007901433,0.19858,0.0007753576],"about_ca_topic_score_codex":0.06215151,"about_ca_topic_score_gemma":0.090469465,"teacher_disagreement_score":0.9378485,"about_ca_system_score_codex":0.0016369513,"about_ca_system_score_gemma":0.0036321504,"threshold_uncertainty_score":0.1235795},"labels":[],"label_agreement":null},{"id":"W2990420886","doi":"10.20380/gi2019.15","title":"VideoWhiz: Non-Linear Interactive Overviews for Recipe Videos","year":2019,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Recipe; Automatic summarization; Computer science; Workflow; Multimedia; Presentation (obstetrics); Key (lock); Non-linear editing system; World Wide Web; Information retrieval; Human–computer interaction; Artificial intelligence; Video processing; Video tracking; Smacker video","score_opus":0.02648182364426034,"score_gpt":0.28591646170295265,"score_spread":0.2594346380586923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2990420886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.065541156,0.0014984331,0.7915094,0.0004829403,0.00021989639,0.0015854766,0.009450549,0.1160686,0.013643623],"genre_scores_gemma":[0.2159747,0.0012666757,0.74156576,0.00034741702,0.00017305999,0.001422008,0.014449572,0.0057332753,0.019067604],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977535,0.000057584217,0.000016422615,0.00005757576,0.00006334626,0.000029676208],"domain_scores_gemma":[0.9981877,0.0011347167,0.00013085314,0.00015876746,0.0002477302,0.00014015444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006972403,0.001522718,0.00043487045,0.0017271339,0.00029976608,0.0009965303,0.0010833095,0.00062778726,0.024510907],"category_scores_gemma":[0.0037283713,0.0003511782,0.0005518802,0.00064452447,0.00020952299,0.0017440361,0.0012637513,0.0006833177,0.0033735826],"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":[0.0023788086,0.0004137429,0.002930511,0.0032663825,0.00016480235,0.0010881796,0.0035770303,0.0063615674,0.11936992,0.0047623604,0.097366884,0.7583198],"study_design_scores_gemma":[0.00092795806,0.0027335307,0.026829576,0.0011540004,0.0004478142,0.0020575675,0.0038359088,0.21825467,0.17315465,0.01712849,0.5530156,0.00046024122],"about_ca_topic_score_codex":0.0013069509,"about_ca_topic_score_gemma":0.003728614,"teacher_disagreement_score":0.024510907,"about_ca_system_score_codex":0.00027673246,"about_ca_system_score_gemma":0.0003244037,"threshold_uncertainty_score":0.081997216},"labels":[],"label_agreement":null},{"id":"W2992034981","doi":"","title":"Ten Things TLs Should Know about Video Description.","year":2004,"lang":"en","type":"article","venue":"Teacher librarian","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"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":"Computer science; Need to know; Multimedia; Internet privacy; World Wide Web; Computer security","score_opus":0.028426010864831094,"score_gpt":0.23606588724639663,"score_spread":0.20763987638156553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992034981","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0151353385,0.03784728,0.21733269,0.43413588,0.013102717,0.00090788654,0.0059487727,0.009207249,0.26638213],"genre_scores_gemma":[0.26727772,0.063319236,0.2989242,0.06370152,0.00989896,0.00094681705,0.011692694,0.0025565499,0.28168225],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99736005,0.0010721934,0.00030543056,0.00021740259,0.0008585718,0.00018632798],"domain_scores_gemma":[0.9849711,0.004745258,0.0007365924,0.0010554324,0.0071315,0.0013601797],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054944362,0.0008059872,0.00033598777,0.0025731653,0.002158082,0.0053144353,0.0016193537,0.0031111017,0.02945088],"category_scores_gemma":[0.032932352,0.00051488396,0.00035180867,0.0014432535,0.002643763,0.01432391,0.0016030181,0.0033660561,0.015128313],"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":[0.00022455423,0.000093889845,0.004379424,0.0011209405,0.00002830431,0.0005228626,0.0028415252,0.00025196772,0.0034542354,0.026724061,0.34508485,0.6152734],"study_design_scores_gemma":[0.000030046102,0.00015374122,0.00885772,0.003041176,0.00006976208,0.0019580063,0.016894192,0.0018435481,0.0066797105,0.068404526,0.8918879,0.00017973775],"about_ca_topic_score_codex":0.012016014,"about_ca_topic_score_gemma":0.020830745,"teacher_disagreement_score":0.02945088,"about_ca_system_score_codex":0.0018995907,"about_ca_system_score_gemma":0.002497463,"threshold_uncertainty_score":0.09852308},"labels":[],"label_agreement":null},{"id":"W2997637071","doi":"10.3138/jelis.61.1.2018-0003","title":"Multimedia Approaches to Learning the Foundations of Library and Information Science","year":2020,"lang":"en","type":"article","venue":"Journal of Education for Library and Information Science","topic":"Video Analysis and Summarization","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":"Deliverable; Computer science; Class (philosophy); Multimedia; Field (mathematics); World Wide Web; Engineering","score_opus":0.03931639097218285,"score_gpt":0.24725203790801234,"score_spread":0.2079356469358295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997637071","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24657919,0.006635002,0.29118088,0.0136117125,0.00037962315,0.0006320777,0.000246069,0.0008919838,0.43984345],"genre_scores_gemma":[0.84624064,0.004780106,0.10041981,0.0009703134,0.00029304827,0.0004610468,0.00016528576,0.00015952675,0.04651031],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985129,0.0010086901,0.000042327698,0.00012095435,0.00021557038,0.00009950753],"domain_scores_gemma":[0.99609286,0.0030054734,0.0002202835,0.0002004356,0.00025088328,0.00023006806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015361301,0.00050973316,0.00017433593,0.0026414946,0.0021609191,0.0055031665,0.0011302953,0.0012067144,0.009238886],"category_scores_gemma":[0.004946791,0.00018161644,0.00022267883,0.001898319,0.0033017178,0.004748322,0.0028665576,0.0011457703,0.0008903493],"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":[0.00011291969,0.00036144868,0.0033800332,0.0011916264,0.000025891302,0.00089191104,0.1352516,0.0027356488,0.007913511,0.3398038,0.009974198,0.49835747],"study_design_scores_gemma":[0.00005730083,0.00043940588,0.00585526,0.0013442287,0.000069518595,0.0011476254,0.12976693,0.0071665123,0.01389595,0.14501302,0.6951782,0.000066019995],"about_ca_topic_score_codex":0.0019275784,"about_ca_topic_score_gemma":0.0057571423,"teacher_disagreement_score":0.009238886,"about_ca_system_score_codex":0.0028891636,"about_ca_system_score_gemma":0.0020577875,"threshold_uncertainty_score":0.030907214},"labels":[],"label_agreement":null},{"id":"W2997722046","doi":"","title":"Sports Field Localization using Memory Networks","year":2019,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Video Analysis and Summarization","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; Field (mathematics); Analytics; Data science; Artificial intelligence","score_opus":0.0053501856968487735,"score_gpt":0.24696866767634768,"score_spread":0.2416184819794989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997722046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09522878,0.0009063888,0.88845646,0.00026256722,0.000108611,0.00008136094,0.0009176613,0.0049495855,0.009088582],"genre_scores_gemma":[0.79452413,0.0006878711,0.18757425,0.00013261952,0.00015446702,0.0001223258,0.0019680276,0.00015687468,0.014679391],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998074,0.000023905584,0.000008131782,0.0000748145,0.000037804777,0.00004802254],"domain_scores_gemma":[0.99972814,0.000069065914,0.00004827246,0.000049564653,0.000082671955,0.000022243896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020219496,0.00085753336,0.0003990466,0.0015834845,0.00046377763,0.0009820879,0.0011227888,0.00050032086,0.0030061544],"category_scores_gemma":[0.0009302177,0.00031007736,0.00027816248,0.0012751707,0.0002455106,0.0012980736,0.00084270496,0.00041237648,0.0012614526],"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":[0.0006813837,0.00015879083,0.003937245,0.00011261085,0.00010794848,0.00023927409,0.000119667675,0.1386847,0.033920415,0.005205174,0.00908958,0.8077432],"study_design_scores_gemma":[0.000016602848,0.000060514805,0.0014784767,0.0000138523,0.000031300464,0.000080672056,0.0000741006,0.9758641,0.013418271,0.0053117606,0.0036346235,0.000015734468],"about_ca_topic_score_codex":0.014439559,"about_ca_topic_score_gemma":0.021021184,"teacher_disagreement_score":0.014439559,"about_ca_system_score_codex":0.0006485219,"about_ca_system_score_gemma":0.00053734065,"threshold_uncertainty_score":0.028711021},"labels":[],"label_agreement":null},{"id":"W3015873503","doi":"10.1109/wacv45572.2020.9093581","title":"Optimizing Through Learned Errors for Accurate Sports Field Registration","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"McGill University; University of Victoria","funders":"","keywords":"Computer science; Field (mathematics); Artificial intelligence; Image registration; Template; Image (mathematics); Programming language; Mathematics","score_opus":0.06509233449340282,"score_gpt":0.29840757103115584,"score_spread":0.23331523653775302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015873503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012467819,0.00020574109,0.9837096,0.00017732776,0.0000423049,0.000034828972,0.000111287874,0.0021104305,0.0011406713],"genre_scores_gemma":[0.37273455,0.00039067876,0.61381835,0.00024244832,0.00014846082,0.00014707584,0.0011775602,0.0012408821,0.010099967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991478,0.00016626617,0.00005286501,0.0003216325,0.00020236358,0.000109077155],"domain_scores_gemma":[0.9990339,0.00037203412,0.00016098304,0.00020200368,0.0001830166,0.00004796974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013197466,0.0015273627,0.0013489268,0.0010441318,0.00050792936,0.0011567352,0.001495498,0.0014905538,0.0035534173],"category_scores_gemma":[0.005476942,0.0008267971,0.00064919936,0.0010813582,0.00092151924,0.0018451421,0.001279826,0.0015166779,0.0016713613],"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":[0.00018243512,0.00008567351,0.0009129257,0.00010042293,0.00007279821,0.00006755431,0.000079370955,0.6271627,0.013935291,0.00745795,0.0061452887,0.34379765],"study_design_scores_gemma":[0.000008484009,0.000027706084,0.00026167318,0.000006942618,0.000009955801,0.000022645569,0.0000132451205,0.98907274,0.0054430882,0.0037948254,0.0013296353,0.000009139076],"about_ca_topic_score_codex":0.012503028,"about_ca_topic_score_gemma":0.014320497,"teacher_disagreement_score":0.012503028,"about_ca_system_score_codex":0.0009930988,"about_ca_system_score_gemma":0.0016968676,"threshold_uncertainty_score":0.024860501},"labels":[],"label_agreement":null},{"id":"W3016486495","doi":"10.1109/cvprw50498.2020.00449","title":"Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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; Mitacs","keywords":"Computer science; Event (particle physics); Ice hockey; Task (project management); Field (mathematics); Artificial intelligence; Frame (networking); Analytics; Convolutional neural network; Pattern recognition (psychology); Data mining","score_opus":0.026394177093695764,"score_gpt":0.26766478478053873,"score_spread":0.24127060768684297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016486495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054715686,0.0005899375,0.93913233,0.00027163417,0.00008941721,0.00008433401,0.00053599285,0.0025061513,0.0020744414],"genre_scores_gemma":[0.5788454,0.0006566396,0.40936306,0.00031651065,0.0001391084,0.00013818509,0.00234555,0.0002593136,0.007936218],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996332,0.00006263071,0.000018982611,0.00014063367,0.000073874035,0.000070729344],"domain_scores_gemma":[0.9995572,0.0001733507,0.000056940764,0.00008135933,0.000089604924,0.000041535775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007645011,0.0010204911,0.0007878236,0.0010544177,0.0002602172,0.0006799579,0.0009504071,0.00077156373,0.0021039736],"category_scores_gemma":[0.0016717901,0.0004174065,0.0007707899,0.0007634832,0.0003719903,0.0012240526,0.0009346976,0.0009787976,0.0010223882],"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":[0.0008247533,0.00029585647,0.0023748016,0.00022200272,0.000161288,0.0003024675,0.00015985545,0.16416518,0.15668476,0.0051018232,0.006370116,0.6633372],"study_design_scores_gemma":[0.000013636758,0.00007077622,0.0016935158,0.000012266711,0.000022350134,0.00006942258,0.000021882337,0.97733736,0.01595454,0.003429507,0.0013583037,0.000016379301],"about_ca_topic_score_codex":0.005938178,"about_ca_topic_score_gemma":0.008018052,"teacher_disagreement_score":0.005938178,"about_ca_system_score_codex":0.0006907317,"about_ca_system_score_gemma":0.00057365897,"threshold_uncertainty_score":0.011807203},"labels":[],"label_agreement":null},{"id":"W30174891","doi":"10.21037/jtd.2018.06.23","title":"Modeling Video Data for Content Based Queries: Extending the DISIMA Image Data Model","year":2003,"lang":"en","type":"article","venue":"Conference on Multimedia Modeling","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Video tracking; Video compression picture types; Video post-processing; Semantics (computer science); Key (lock); Salient; Data model (GIS); Data modeling; Data set; Computer vision; Smacker video; Set (abstract data type); Artificial intelligence; Information retrieval; Video processing; Database","score_opus":0.34352421464284294,"score_gpt":0.3526268850288693,"score_spread":0.009102670386026357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W30174891","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046769906,0.00076001504,0.9450756,0.00092016347,0.00009623578,0.00037925426,0.0021238774,0.0025791011,0.0012958212],"genre_scores_gemma":[0.64451647,0.0014805692,0.34272298,0.00037604582,0.00025612395,0.0006053799,0.006400867,0.0003325978,0.003308954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874073,0.00032721405,0.00016656752,0.00022941815,0.0004443173,0.000091770366],"domain_scores_gemma":[0.99459934,0.003354014,0.00029681608,0.0006527025,0.0009976415,0.0000995419],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002508857,0.0010428454,0.0010460922,0.0022272111,0.0003187438,0.002003216,0.0019003286,0.0012596924,0.001509111],"category_scores_gemma":[0.01168249,0.00041067938,0.001115248,0.0017188068,0.00042841936,0.0031468258,0.0007884481,0.0010956957,0.00096496625],"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":[0.00091625674,0.00046953454,0.011669828,0.00040537576,0.00017753353,0.000523947,0.000582721,0.4646263,0.014171293,0.013543893,0.0077320924,0.4851813],"study_design_scores_gemma":[0.000009472042,0.000050221577,0.0003824716,0.000007671154,0.000015139008,0.000058469224,0.000025825708,0.99499464,0.00091163744,0.0025893394,0.0009468971,0.000008097981],"about_ca_topic_score_codex":0.019169932,"about_ca_topic_score_gemma":0.011642668,"teacher_disagreement_score":0.019169932,"about_ca_system_score_codex":0.0011371532,"about_ca_system_score_gemma":0.00096941344,"threshold_uncertainty_score":0.038116693},"labels":[],"label_agreement":null},{"id":"W3022363577","doi":"","title":"Feature-based cut detection with automatic threshold selection.","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Ottawa; Carleton University","funders":"","keywords":"Computer science; Feature selection; Feature (linguistics); Artificial intelligence; Scheme (mathematics); Frame (networking); Tracking (education); Data mining; Selection (genetic algorithm); Boundary (topology); Pattern recognition (psychology); Variety (cybernetics); Computer vision; Mathematics","score_opus":0.004577390034399404,"score_gpt":0.18931909514369805,"score_spread":0.18474170510929863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022363577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018053025,0.0003247514,0.9768966,0.000052685584,0.00008609373,0.00011590725,0.00017369514,0.0036861163,0.0006111134],"genre_scores_gemma":[0.12069117,0.00010615051,0.8768739,0.00006284988,0.000039243605,0.00012212443,0.0006315707,0.00020096835,0.0012719653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986015,0.00023828197,0.000088845816,0.0003287473,0.00063140935,0.00011131876],"domain_scores_gemma":[0.9969471,0.0010312925,0.0003301389,0.00043113946,0.0011408952,0.00011936525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014220658,0.00078723225,0.0009523981,0.0029143197,0.0004154894,0.0013510815,0.0016636321,0.0015335295,0.0022675088],"category_scores_gemma":[0.0053865216,0.00045828862,0.00054749387,0.0014569319,0.00050765765,0.0013181845,0.001066824,0.0008968522,0.0016893679],"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":[0.0006666174,0.00015419564,0.002625747,0.00028238594,0.00010381688,0.00020013879,0.00013656757,0.007871804,0.17186482,0.0024307207,0.006650864,0.8070123],"study_design_scores_gemma":[0.00012117192,0.00038509126,0.008850488,0.00004526301,0.00010235354,0.0011141732,0.00010647684,0.73619646,0.24079004,0.00439701,0.0077973516,0.00009417513],"about_ca_topic_score_codex":0.0011921291,"about_ca_topic_score_gemma":0.0017896804,"teacher_disagreement_score":0.0029143197,"about_ca_system_score_codex":0.00051180494,"about_ca_system_score_gemma":0.0004922023,"threshold_uncertainty_score":0.007585585},"labels":[],"label_agreement":null},{"id":"W3023833469","doi":"","title":"Gradual Transition Detection Based on Fuzzy Logic Using Visual Attention Model.","year":2013,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Transition (genetics); Artificial intelligence; Fuzzy logic; Search engine indexing; Change detection; Visual search; Computer vision; Process (computing); Object (grammar); Pattern recognition (psychology)","score_opus":0.018702936371765636,"score_gpt":0.23440559534395217,"score_spread":0.21570265897218655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023833469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050154146,0.00082567823,0.94378334,0.00023214283,0.00008644097,0.00013861565,0.00011286097,0.00093766063,0.003729191],"genre_scores_gemma":[0.8396573,0.00036768115,0.15748274,0.0001428749,0.00004958936,0.00010226898,0.00015813482,0.000027932,0.002011645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957436,0.000048172697,0.000028788723,0.00014159392,0.00015198039,0.000055053468],"domain_scores_gemma":[0.9993888,0.00027318962,0.00007365432,0.000023793495,0.0002091408,0.000031454714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007152716,0.00048096225,0.0006664174,0.0013638838,0.0004250359,0.0009996807,0.0011437072,0.0007731683,0.0012484684],"category_scores_gemma":[0.0018718517,0.00026907862,0.0009822592,0.0005549515,0.0004437378,0.0011011861,0.0003528629,0.0007207824,0.00021655335],"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":[0.00077951426,0.00039579804,0.0080677755,0.0005624676,0.00027183248,0.0008037875,0.0006153494,0.2515749,0.07480176,0.017730054,0.004358733,0.640038],"study_design_scores_gemma":[0.000021190528,0.000107372965,0.001528585,0.00001850177,0.000056188394,0.0001397427,0.00003811619,0.9881216,0.00562801,0.003676653,0.0006419857,0.000022033255],"about_ca_topic_score_codex":0.015948186,"about_ca_topic_score_gemma":0.010794308,"teacher_disagreement_score":0.015948186,"about_ca_system_score_codex":0.0012606982,"about_ca_system_score_gemma":0.0007849528,"threshold_uncertainty_score":0.031710684},"labels":[],"label_agreement":null},{"id":"W3034817275","doi":"10.24963/ijcai.2020/108","title":"k-SDPP: Fixed-Size Video Summarization via Sequential Determinantal Point Processes","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Novelis (Canada)","funders":"Fundamental Research Funds for the Central Universities; Civil Aviation Administration of China; Nanjing University of Aeronautics and Astronautics; National Natural Science Foundation of China","keywords":"Automatic summarization; Computer science; Probabilistic logic; Point process; Partition (number theory); Frame (networking); Statistical model; Key (lock); Artificial intelligence; Algorithm; Theoretical computer science; Mathematics; Combinatorics; Statistics","score_opus":0.014144046677161452,"score_gpt":0.22472585206013304,"score_spread":0.21058180538297158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034817275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043188604,0.0002055419,0.9947135,0.0000450169,0.000016014665,0.000032534794,0.000041561943,0.0002743319,0.0003526538],"genre_scores_gemma":[0.38309065,0.00088033575,0.610373,0.00017921579,0.00016754173,0.00027895012,0.0009037579,0.0002626536,0.0038638555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926263,0.00017227927,0.00005737991,0.00019206187,0.00024443312,0.000071179755],"domain_scores_gemma":[0.998744,0.00069365447,0.00011599218,0.000117832606,0.00025510165,0.00007332235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009872458,0.0007639709,0.0012088357,0.00089570344,0.0005139161,0.0010031287,0.0014390559,0.0008566048,0.0019345776],"category_scores_gemma":[0.0033140744,0.00042318326,0.00083348644,0.0011249755,0.0005594736,0.0017823032,0.0013821003,0.0013107837,0.00073213974],"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":[0.00034684295,0.00014096237,0.0011036557,0.00039515135,0.00010598662,0.0003108084,0.00030358593,0.5366973,0.02673072,0.03375175,0.0052286936,0.39488453],"study_design_scores_gemma":[0.000010339121,0.00006103152,0.0001203131,0.0000051571983,0.000012639132,0.0000408111,0.00001571511,0.98950267,0.0028581857,0.0062947255,0.0010706233,0.000007703479],"about_ca_topic_score_codex":0.0028419772,"about_ca_topic_score_gemma":0.0030185664,"teacher_disagreement_score":0.0028419772,"about_ca_system_score_codex":0.00060899026,"about_ca_system_score_gemma":0.0009607583,"threshold_uncertainty_score":0.0064718127},"labels":[],"label_agreement":null},{"id":"W3042990756","doi":"10.48550/arxiv.2007.09598","title":"Adaptive Video Highlight Detection by Learning from User History","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Analysis and Summarization","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 Manitoba","funders":"","keywords":"Computer science; Normalization (sociology); Artificial intelligence; Affine transformation; User modeling; Machine learning; Encoder; Multi-user; Human–computer interaction; User interface; Computer network","score_opus":0.05813559320292488,"score_gpt":0.15241399352239338,"score_spread":0.09427840031946849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042990756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32046363,0.0030046732,0.6644814,0.00051368686,0.00019722866,0.00022591963,0.0017090504,0.0051451544,0.0042592147],"genre_scores_gemma":[0.88040316,0.0010979024,0.10968806,0.00015810179,0.00021659203,0.000097368764,0.0019648343,0.0001541961,0.0062197503],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997423,0.00003263746,0.000009964329,0.00012162864,0.00005505642,0.000038383747],"domain_scores_gemma":[0.9994338,0.00020621027,0.00010198146,0.00007387332,0.0001322346,0.00005189495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048104385,0.00091289333,0.000540016,0.001050618,0.00019987543,0.00050534337,0.0007531888,0.00045838763,0.0010107793],"category_scores_gemma":[0.0018893786,0.00027960812,0.00036461352,0.0005187604,0.00023119808,0.0012245687,0.00052219944,0.0007155437,0.0006585035],"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":[0.0010460236,0.0002646066,0.018443545,0.00026480376,0.00018070795,0.00036603172,0.00026196142,0.042772055,0.09994873,0.0018198924,0.011755012,0.82287675],"study_design_scores_gemma":[0.000020663298,0.00027858978,0.015181943,0.00003512928,0.00009275338,0.00028299718,0.00012172857,0.93340117,0.04316079,0.002489086,0.0049063964,0.00002877358],"about_ca_topic_score_codex":0.0032211365,"about_ca_topic_score_gemma":0.008280909,"teacher_disagreement_score":0.0032211365,"about_ca_system_score_codex":0.00048025738,"about_ca_system_score_gemma":0.0003450635,"threshold_uncertainty_score":0.0064047575},"labels":[],"label_agreement":null},{"id":"W3084224228","doi":"10.1145/3249796","title":"Session details: Interacting with images","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Session (web analytics); Computer science; Artificial intelligence; Computer graphics (images); Multimedia; World Wide Web","score_opus":0.00916555759497377,"score_gpt":0.23979854654526472,"score_spread":0.23063298895029094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084224228","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011110842,0.0034430993,0.02582991,0.006901133,0.01761394,0.0037899169,0.02618439,0.028678227,0.87644863],"genre_scores_gemma":[0.039126463,0.0026992178,0.0064912173,0.0020662488,0.0031917086,0.0017499506,0.011862075,0.0029197305,0.92989326],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99970007,0.00005591019,0.000016458129,0.00008295323,0.000069175374,0.00007550744],"domain_scores_gemma":[0.9977465,0.0009017296,0.00004093915,0.00035333564,0.00033240675,0.0006250353],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00069895043,0.0018892409,0.0022447798,0.00051915343,0.0014208859,0.0027201702,0.0013114999,0.0033872505,0.8669199],"category_scores_gemma":[0.0036038992,0.0003595023,0.0012528569,0.00061796553,0.00033455118,0.0019832482,0.0019502364,0.001508231,0.69085175],"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":[0.0016469568,0.00040649177,0.0002509133,0.0006354343,0.00002973213,0.00021935551,0.00012011647,0.00011858343,0.014669747,0.00086594815,0.88241225,0.098624386],"study_design_scores_gemma":[0.00064153306,0.0010136139,0.0052871825,0.00028745199,0.00010432935,0.00043826262,0.00020107669,0.002052442,0.009689887,0.002904508,0.9773005,0.00007923875],"about_ca_topic_score_codex":0.0008085531,"about_ca_topic_score_gemma":0.0016633556,"teacher_disagreement_score":0.13308012,"about_ca_system_score_codex":0.00027357036,"about_ca_system_score_gemma":0.0003298309,"threshold_uncertainty_score":0.18982255},"labels":[],"label_agreement":null},{"id":"W3092937404","doi":"","title":"Methodology of Measure of Similarity in Student Video Sequence of Interactions.","year":2020,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Analysis and Summarization","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":"McGill University; Polytechnique Montréal","funders":"","keywords":"Measure (data warehouse); Computer science; Sequence (biology); Similarity (geometry); Similarity measure; Artificial intelligence; Data mining","score_opus":0.05869953715184739,"score_gpt":0.3070060560618234,"score_spread":0.24830651890997602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092937404","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015526956,0.00077437464,0.9798135,0.000069536305,0.000101562,0.00040278758,0.0010275953,0.0008363661,0.0014472011],"genre_scores_gemma":[0.13344412,0.0005069148,0.8589144,0.000047127134,0.0001229451,0.001058219,0.0034862263,0.00017410381,0.0022459836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967173,0.0008315269,0.00033475077,0.0009778225,0.0009553489,0.00018340057],"domain_scores_gemma":[0.99630153,0.0011301019,0.000433013,0.00037403352,0.0015611029,0.0002002059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024864848,0.00069511484,0.0009053581,0.005516186,0.0006976244,0.0014537189,0.0014042191,0.00093878526,0.0030227953],"category_scores_gemma":[0.00975669,0.00024304021,0.00075384404,0.0030233746,0.0005843772,0.0013727209,0.0013834356,0.00085645297,0.0016009643],"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":[0.00055788015,0.00022888785,0.006998903,0.0009974934,0.00030839493,0.00021701833,0.00066154764,0.0065587885,0.062391967,0.012619611,0.0076122065,0.9008472],"study_design_scores_gemma":[0.00019115214,0.002690901,0.08771109,0.00036105872,0.00079785543,0.0033038035,0.0025647073,0.6232813,0.16141888,0.04098966,0.0763627,0.00032699326],"about_ca_topic_score_codex":0.0021339385,"about_ca_topic_score_gemma":0.0023765664,"teacher_disagreement_score":0.005516186,"about_ca_system_score_codex":0.000701971,"about_ca_system_score_gemma":0.0014068815,"threshold_uncertainty_score":0.013149917},"labels":[],"label_agreement":null},{"id":"W3093487217","doi":"10.1111/tgis.12696","title":"Spatiotemporal retrieval of dynamic video object trajectories in geographical scenes","year":2020,"lang":"en","type":"article","venue":"Transactions in GIS","topic":"Video Analysis and Summarization","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":"National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Computer science; Computer vision; Artificial intelligence; Trajectory; Discontinuity (linguistics); Focus (optics); Object (grammar); Image retrieval; Range (aeronautics); Image (mathematics); Mathematics","score_opus":0.013842712881405213,"score_gpt":0.244042790866038,"score_spread":0.2302000779846328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093487217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122274615,0.0014514403,0.87007815,0.00024387005,0.00009986325,0.00022124486,0.001170987,0.001572471,0.0028873333],"genre_scores_gemma":[0.75190616,0.0016815913,0.23955661,0.00009544623,0.00014632555,0.00013608106,0.0027890177,0.0001579461,0.0035307468],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99935895,0.00008565432,0.00005732205,0.00016744444,0.00026621675,0.00006438103],"domain_scores_gemma":[0.9989785,0.00017413536,0.00016900957,0.00016419585,0.00046111742,0.000053164742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045486994,0.00059724884,0.0006790539,0.0026389882,0.00038366357,0.0010129161,0.0006359572,0.00045689323,0.0013060252],"category_scores_gemma":[0.0032051527,0.00020219942,0.0004706642,0.0030089556,0.00025036428,0.0017095406,0.00066285883,0.00038872,0.00070047675],"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":[0.0006609658,0.00013607125,0.008379518,0.00061402144,0.0001659382,0.00075604906,0.0008108674,0.07103115,0.12672211,0.009641662,0.009247189,0.77183455],"study_design_scores_gemma":[0.000042542044,0.00028409806,0.011352528,0.000051949268,0.00012735858,0.0007127799,0.001066218,0.9109986,0.056683064,0.0052837017,0.013324974,0.00007226507],"about_ca_topic_score_codex":0.009047083,"about_ca_topic_score_gemma":0.0068062,"teacher_disagreement_score":0.009047083,"about_ca_system_score_codex":0.000654015,"about_ca_system_score_gemma":0.00075953815,"threshold_uncertainty_score":0.01798886},"labels":[],"label_agreement":null},{"id":"W3094916564","doi":"10.18653/v1/2020.inlg-1.20","title":"Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Bar chart; Transformer; Margin (machine learning); Natural language processing; Artificial intelligence; Chart; Natural language; Language model; Natural language understanding; Encoder; Metric (unit); Natural language generation; Architecture; Information retrieval; Machine learning","score_opus":0.034023924085931044,"score_gpt":0.26972657323003385,"score_spread":0.2357026491441028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094916564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07811197,0.001003043,0.855496,0.0007220336,0.00037073754,0.00085607235,0.016501732,0.040713858,0.0062245294],"genre_scores_gemma":[0.38093498,0.0006463709,0.56909764,0.00031190322,0.00010485949,0.000875874,0.03938682,0.0012253174,0.007416293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994085,0.0001853082,0.00005371603,0.000205275,0.000112228445,0.00003495869],"domain_scores_gemma":[0.9972494,0.001541024,0.00014623888,0.00046793808,0.0005080936,0.0000873089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011358779,0.0011207573,0.00041788866,0.001290885,0.0002922439,0.0009068694,0.0013591794,0.0007520632,0.0046294015],"category_scores_gemma":[0.0075733415,0.00023807409,0.00073136715,0.00080590736,0.0004115106,0.0024188084,0.0009609043,0.0013358697,0.0023239728],"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":[0.0007195602,0.0004169391,0.003140546,0.0011802062,0.000114628405,0.000446343,0.0007292721,0.07828469,0.037015323,0.013387801,0.05253473,0.8120299],"study_design_scores_gemma":[0.00010019185,0.00021190516,0.000868541,0.000042188436,0.000052594663,0.00017411837,0.00013883701,0.9372546,0.032301255,0.012656442,0.016155187,0.000044109078],"about_ca_topic_score_codex":0.0059749326,"about_ca_topic_score_gemma":0.00857755,"teacher_disagreement_score":0.0059749326,"about_ca_system_score_codex":0.0008505908,"about_ca_system_score_gemma":0.0012041372,"threshold_uncertainty_score":0.015486896},"labels":[],"label_agreement":null},{"id":"W3105616012","doi":"","title":"Optimisation using Natural Language Processing: Personalized Tour Recommendation for Museums","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Polytechnique Montréal","funders":"","keywords":"Computer science; Natural language; Natural (archaeology); World Wide Web; Multimedia; Natural language processing; History","score_opus":0.06439452609041729,"score_gpt":0.32221952330593945,"score_spread":0.25782499721552216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105616012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112494305,0.001939769,0.8611086,0.0009333147,0.00022203395,0.00031248268,0.003951604,0.0129046645,0.0061333086],"genre_scores_gemma":[0.41383943,0.00073961634,0.55942035,0.00036403956,0.00025027426,0.0002727022,0.010911277,0.00085160823,0.013350613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994598,0.00013180342,0.00004175134,0.00018271209,0.00010906128,0.00007500727],"domain_scores_gemma":[0.999243,0.00038035173,0.000056072382,0.000088405,0.00020033066,0.000031800708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000616052,0.0010489944,0.0013265889,0.0017315496,0.0005873831,0.0010090229,0.001175411,0.0010858228,0.0066319653],"category_scores_gemma":[0.0023490274,0.00049555814,0.0014358412,0.0020069696,0.00033586877,0.0013173913,0.0005606881,0.00082954735,0.0021321347],"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":[0.00086231576,0.0006516883,0.0020637112,0.000767771,0.00023323025,0.00030645437,0.00023969586,0.1469276,0.03949732,0.0028824932,0.03354436,0.7720235],"study_design_scores_gemma":[0.00007358826,0.00016709545,0.0013831335,0.000014869085,0.00008478571,0.00007019523,0.0001308379,0.9835148,0.0063502295,0.0038266573,0.004358981,0.000024825984],"about_ca_topic_score_codex":0.02351097,"about_ca_topic_score_gemma":0.035538897,"teacher_disagreement_score":0.02351097,"about_ca_system_score_codex":0.00083262316,"about_ca_system_score_gemma":0.0010814355,"threshold_uncertainty_score":0.04674822},"labels":[],"label_agreement":null},{"id":"W3106645934","doi":"10.1007/978-3-030-58589-1_16","title":"Adaptive Video Highlight Detection by Learning from User History","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada); University of Manitoba","funders":"","keywords":"Computer science; Normalization (sociology); Artificial intelligence; Affine transformation; User modeling; Machine learning; Human–computer interaction; User interface","score_opus":0.0141309733185947,"score_gpt":0.19343315935093133,"score_spread":0.17930218603233664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106645934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1355967,0.0029639783,0.849987,0.00020613837,0.00028337757,0.0001825529,0.0009895047,0.005548965,0.00424185],"genre_scores_gemma":[0.6848925,0.0023292857,0.2956779,0.00017550858,0.0004959773,0.00016659567,0.0023228405,0.0005177168,0.0134216705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977547,0.000021524505,0.00000907348,0.00009277643,0.000059606748,0.00004159571],"domain_scores_gemma":[0.99953914,0.00018998202,0.000048958613,0.000048403504,0.00011820318,0.00005531424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033514656,0.0009996245,0.0010029768,0.0018005179,0.0002468426,0.00057833036,0.00073239655,0.0005118884,0.0020713443],"category_scores_gemma":[0.0009295639,0.00028567723,0.00050925306,0.0011253292,0.0001590802,0.00077661703,0.0006251232,0.0006898219,0.0020689897],"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":[0.00061750016,0.00021980659,0.004011163,0.00016406189,0.0000819975,0.0001466135,0.000056669134,0.007223255,0.10544226,0.0003724603,0.005431354,0.87623274],"study_design_scores_gemma":[0.000045435892,0.00059237116,0.020246867,0.000042147556,0.00023186223,0.00072147243,0.00011444784,0.8798753,0.08907747,0.0018027413,0.007197032,0.000052895033],"about_ca_topic_score_codex":0.002224083,"about_ca_topic_score_gemma":0.0049370877,"teacher_disagreement_score":0.002224083,"about_ca_system_score_codex":0.00025169263,"about_ca_system_score_gemma":0.0003093309,"threshold_uncertainty_score":0.006929338},"labels":[],"label_agreement":null},{"id":"W3107586392","doi":"10.1007/978-3-030-63403-2_51","title":"Soccer Field Lines Determination and 3D Reconstruction","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Video Analysis and Summarization","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":"Ontario Tech University","funders":"","keywords":"Artificial intelligence; Computer vision; Perspective distortion; Hough transform; Computer science; Invariant (physics); Perspective (graphical); Field (mathematics); Mathematics; Image (mathematics)","score_opus":0.016093989829501328,"score_gpt":0.2556267510694312,"score_spread":0.2395327612399299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107586392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036197554,0.0008646693,0.9708329,0.00010816301,0.00012701581,0.00009725248,0.00031953034,0.0027771832,0.021253506],"genre_scores_gemma":[0.072730325,0.0029645993,0.836834,0.00017753083,0.00015773559,0.0001124808,0.0023984062,0.0012911608,0.08333385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963725,0.000024573652,0.000012050882,0.00010039659,0.00019556121,0.000030207433],"domain_scores_gemma":[0.99982786,0.00003090523,0.000010499865,0.00003909859,0.00008090429,0.000010705486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032517407,0.0011109202,0.0006935744,0.002081948,0.00047472346,0.0019755042,0.0014222005,0.0009077295,0.023216948],"category_scores_gemma":[0.0006075004,0.0009758705,0.00068022625,0.0016226602,0.00044539265,0.001218505,0.0010517291,0.0008086701,0.013816381],"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":[0.0000658389,0.000029521176,0.0002731863,0.00015100038,0.000022065451,0.000071941824,0.00007895155,0.010091732,0.028331365,0.0074693356,0.0129629355,0.9404521],"study_design_scores_gemma":[0.00003634521,0.0002635853,0.006096303,0.0002278173,0.00010987693,0.0033590961,0.00047323067,0.43811315,0.15384717,0.030654276,0.36667895,0.0001401518],"about_ca_topic_score_codex":0.002926017,"about_ca_topic_score_gemma":0.004415332,"teacher_disagreement_score":0.023216948,"about_ca_system_score_codex":0.00036533707,"about_ca_system_score_gemma":0.000655177,"threshold_uncertainty_score":0.07766837},"labels":[],"label_agreement":null},{"id":"W3108842862","doi":"10.1109/cvprw53098.2021.00508","title":"SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Kootenay Association for Science & Technology","funders":"King Abdullah University of Science and Technology; Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture; Aalborg Universitet; Waalse Gewest","keywords":"Computer science; Task (project management); Field (mathematics); Implementation; Benchmark (surveying); Artificial intelligence; Human–computer interaction; Video editing; Realm; Segmentation; Multimedia; Annotation; Machine learning; Software engineering","score_opus":0.11025223124926356,"score_gpt":0.3189543174245202,"score_spread":0.20870208617525662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3108842862","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12992509,0.0109009165,0.052318342,0.0011236714,0.0021302681,0.0035254154,0.68189985,0.075740434,0.042435996],"genre_scores_gemma":[0.039235126,0.0007862336,0.04153178,0.0002635809,0.00012220148,0.0009641351,0.9110756,0.0015741648,0.004447258],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969047,0.00046010246,0.0002814384,0.0012484324,0.000749679,0.00035567716],"domain_scores_gemma":[0.9972589,0.0006477078,0.00025600748,0.0007361243,0.0007511375,0.00035020665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019529529,0.005794777,0.0014519603,0.0076484685,0.0017552768,0.0022631956,0.0046266424,0.0038765601,0.011374904],"category_scores_gemma":[0.009092436,0.00077170716,0.0017907636,0.00484074,0.0013093463,0.0033718098,0.0034785662,0.0028441537,0.012689289],"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":[0.0015796869,0.0009705356,0.005934338,0.004362946,0.000471424,0.0010133967,0.00079909834,0.011381108,0.018689917,0.002582302,0.70576036,0.24645491],"study_design_scores_gemma":[0.0011815714,0.0014334745,0.06930689,0.0019058831,0.00058996223,0.004287861,0.0036897727,0.17341782,0.03870679,0.012921186,0.6921073,0.00045152643],"about_ca_topic_score_codex":0.042434,"about_ca_topic_score_gemma":0.08722933,"teacher_disagreement_score":0.042434,"about_ca_system_score_codex":0.0023540861,"about_ca_system_score_gemma":0.0026593516,"threshold_uncertainty_score":0.08437401},"labels":[],"label_agreement":null},{"id":"W3110716366","doi":"10.48011/asba.v2i1.1541","title":"Determinação das Linhas do Campo de Futebol para sua Reconstrução 3D","year":2020,"lang":"pt","type":"article","venue":"Anais do Congresso Brasileiro de Automática 2020","topic":"Video Analysis and Summarization","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":"Humanities; Mathematics; Philosophy","score_opus":0.03580794036423302,"score_gpt":0.305834750218657,"score_spread":0.270026809854424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110716366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09831461,0.0020521195,0.8900925,0.00037423984,0.00017824396,0.00014462168,0.00054123363,0.0015517714,0.006750581],"genre_scores_gemma":[0.43721163,0.0024768836,0.5519141,0.00011689848,0.000094616094,0.0001751642,0.0011824154,0.00086817046,0.005960069],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989588,0.00016561618,0.00005501904,0.00022678124,0.00045913635,0.00013454129],"domain_scores_gemma":[0.9980046,0.0007644062,0.00019061305,0.00024502078,0.0007125108,0.000082841376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010871248,0.0012640902,0.00074767624,0.0029191815,0.0005788658,0.002548304,0.0007583636,0.00077551143,0.0053624543],"category_scores_gemma":[0.0053463285,0.00067802443,0.0009747433,0.0018977391,0.00068180176,0.0019119777,0.0010457161,0.0010985939,0.0018664482],"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":[0.00060448295,0.00009189489,0.009072292,0.00060694607,0.0001032736,0.00027578528,0.00081187225,0.03205913,0.111688055,0.008279233,0.0039683944,0.83243865],"study_design_scores_gemma":[0.00006679793,0.00075374555,0.03072776,0.00044871276,0.00031211105,0.0016270683,0.00240558,0.6614261,0.21998562,0.017206684,0.064749755,0.00028997398],"about_ca_topic_score_codex":0.0039801504,"about_ca_topic_score_gemma":0.0050958786,"teacher_disagreement_score":0.0053624543,"about_ca_system_score_codex":0.0007822276,"about_ca_system_score_gemma":0.0011566839,"threshold_uncertainty_score":0.01793921},"labels":[],"label_agreement":null},{"id":"W3121479196","doi":"10.15353/jcvis.v6i1.3542","title":"A Tool for Annotating Homographies from Hockey Broadcast Video","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Computer science; Frame (networking); Computer vision; Ground truth; Artificial intelligence; Overhead (engineering); Point (geometry); Ice hockey; Reference frame; Computer graphics (images); Telecommunications; Mathematics","score_opus":0.0076050378153431865,"score_gpt":0.2551083343398905,"score_spread":0.2475032965245473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121479196","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016942937,0.0009024673,0.805424,0.00016028737,0.00026724386,0.0006737855,0.06585087,0.102611855,0.0071665877],"genre_scores_gemma":[0.077470474,0.00086241984,0.7451191,0.0001523761,0.00011802763,0.0010485263,0.1635339,0.006609405,0.005085866],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986551,0.00015134805,0.00009544966,0.0005948169,0.0003775309,0.00012570409],"domain_scores_gemma":[0.9982022,0.00040193103,0.00024364406,0.0006519687,0.00040853204,0.00009165731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009927361,0.0020439685,0.0010474005,0.0062242234,0.0008520282,0.0014743522,0.0013480695,0.001541388,0.012281081],"category_scores_gemma":[0.0042743855,0.00074492174,0.0010085175,0.003922136,0.0006204584,0.0022706718,0.00260297,0.0015865142,0.009610909],"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":[0.0006608402,0.00028167156,0.0038782256,0.0019946333,0.00029973435,0.00093938573,0.0013038411,0.009095275,0.04961826,0.007826232,0.18362692,0.74047494],"study_design_scores_gemma":[0.00023208602,0.0004915117,0.03748203,0.0010548419,0.00032911086,0.0031459166,0.003670667,0.26263297,0.09844245,0.03670551,0.55542,0.00039294673],"about_ca_topic_score_codex":0.008220844,"about_ca_topic_score_gemma":0.016421791,"teacher_disagreement_score":0.012281081,"about_ca_system_score_codex":0.00055527483,"about_ca_system_score_gemma":0.0009878818,"threshold_uncertainty_score":0.04108435},"labels":[],"label_agreement":null},{"id":"W3121880205","doi":"10.15353/jcvis.v6i1.3535","title":"BenderNet and RingerNet: Highly Efficient Line Segmentation Deep Neural Network Architectures for Ice Rink Localization","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Artificial neural network; Ice hockey; Market segmentation; Deep neural networks; Line (geometry); Architecture; Computer vision; Geography","score_opus":0.008292615145022532,"score_gpt":0.2582707516403677,"score_spread":0.2499781364953452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121880205","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06538318,0.0020902075,0.91519344,0.000912973,0.00043282297,0.00013785492,0.001303303,0.008144717,0.0064014723],"genre_scores_gemma":[0.50554186,0.0013075412,0.45303997,0.0007873083,0.00019971123,0.00031364473,0.007097767,0.0007659139,0.030946225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981815,0.00003351242,0.000008772872,0.00006464723,0.00003987391,0.00003495608],"domain_scores_gemma":[0.9997671,0.00007123309,0.00002481313,0.000038299826,0.000077103046,0.00002139723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000439396,0.0012181927,0.00055488094,0.000653656,0.00036081322,0.0007621508,0.0012534442,0.0011226011,0.002414548],"category_scores_gemma":[0.0013543804,0.0005095119,0.00055281154,0.00074218,0.00036078054,0.0012121779,0.0007966829,0.0016718777,0.0011872794],"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":[0.0005236545,0.00017864633,0.0016305397,0.00016217654,0.00022030779,0.00015979246,0.0001362806,0.3785957,0.021477079,0.0071544345,0.034369167,0.5553922],"study_design_scores_gemma":[0.000017826198,0.000051920426,0.00033792015,0.00000972,0.000017897844,0.000020390391,0.000018347702,0.99012685,0.0048929034,0.0023806149,0.0021156871,0.000009866198],"about_ca_topic_score_codex":0.010053745,"about_ca_topic_score_gemma":0.018269729,"teacher_disagreement_score":0.010053745,"about_ca_system_score_codex":0.0007721886,"about_ca_system_score_gemma":0.00086411193,"threshold_uncertainty_score":0.019990444},"labels":[],"label_agreement":null},{"id":"W3123589309","doi":"10.1109/tvcg.2021.3052167","title":"Shape-Driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Analysis and Summarization","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Silhouette; Computer science; Artificial intelligence; Encoder; Artificial neural network; Pattern recognition (psychology); Context (archaeology)","score_opus":0.01895791875084982,"score_gpt":0.26864313318034133,"score_spread":0.24968521442949151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123589309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06703352,0.00019522637,0.9296334,0.00019052817,0.000039659353,0.00006296196,0.00007182662,0.0007712713,0.002001571],"genre_scores_gemma":[0.83275497,0.00010411114,0.16320167,0.00013743273,0.000028114495,0.00014484908,0.00016022801,0.00011236439,0.0033563334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977523,0.000046728117,0.000010434808,0.000081086655,0.000047731246,0.00003882644],"domain_scores_gemma":[0.99934834,0.0003007559,0.00010065831,0.00006211168,0.00012244654,0.00006565132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063956744,0.00083195185,0.00091453316,0.00043432918,0.00028363228,0.0006013093,0.0011640836,0.0009843264,0.002286688],"category_scores_gemma":[0.0022438692,0.00044991504,0.0005890777,0.00034006248,0.00073501404,0.0009861257,0.00079169864,0.0011750567,0.00036428275],"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":[0.00008080138,0.00007136996,0.0009722291,0.00004040853,0.00002160268,0.00006525747,0.00004980555,0.9422743,0.004676011,0.0043620747,0.0008385668,0.046547536],"study_design_scores_gemma":[0.0000032020937,0.000010189119,0.00003376843,0.0000014822981,0.0000012734802,0.0000032431064,0.0000013221265,0.99898213,0.00022603468,0.00068071915,0.000055179018,0.0000014139642],"about_ca_topic_score_codex":0.007479119,"about_ca_topic_score_gemma":0.007450513,"teacher_disagreement_score":0.007479119,"about_ca_system_score_codex":0.0011020425,"about_ca_system_score_gemma":0.0009111272,"threshold_uncertainty_score":0.01487118},"labels":[],"label_agreement":null},{"id":"W3139062549","doi":"10.1109/iv51561.2020.00077","title":"ConVisQA: A Natural Language Interface for Visually Exploring Online Conversations","year":2020,"lang":"en","type":"article","venue":"2020 24th International Conference Information Visualisation (IV)","topic":"Video Analysis and Summarization","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":"Computer science; Conversation; Human–computer interaction; Natural language user interface; Asynchronous communication; User interface; Natural language; Interface (matter); World Wide Web; User needs; Natural (archaeology); Multimedia; Artificial intelligence; Linguistics; Programming language","score_opus":0.08330247308624492,"score_gpt":0.33141390089316286,"score_spread":0.24811142780691794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3139062549","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017724773,0.0010938235,0.7834083,0.0007684172,0.00034084165,0.0016482089,0.012057686,0.16499582,0.01796211],"genre_scores_gemma":[0.11368283,0.0010660748,0.8272523,0.0012644494,0.00017478022,0.0044807172,0.015542921,0.012443345,0.024092637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992944,0.00028488046,0.000056513614,0.0001454497,0.0001719725,0.000046742898],"domain_scores_gemma":[0.9970355,0.0021475083,0.00010810358,0.00014316753,0.0003871659,0.00017854347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015622653,0.0017404353,0.0007402026,0.0014412792,0.0005195125,0.002093979,0.0020294017,0.0014756367,0.03637634],"category_scores_gemma":[0.0059677577,0.0006284482,0.00087970553,0.0005927639,0.000619652,0.002657162,0.002663071,0.0011716009,0.0070663043],"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":[0.0026654317,0.00039591818,0.0022219634,0.0056430046,0.00020822514,0.0016769612,0.014415704,0.0030201152,0.1684143,0.015646098,0.3279009,0.45779145],"study_design_scores_gemma":[0.0010635245,0.00096423045,0.007161077,0.0011606556,0.00016998687,0.0024408854,0.0041480465,0.096222535,0.04550878,0.03170718,0.80889714,0.0005559847],"about_ca_topic_score_codex":0.0047630416,"about_ca_topic_score_gemma":0.0068752584,"teacher_disagreement_score":0.03637634,"about_ca_system_score_codex":0.00059352344,"about_ca_system_score_gemma":0.0008927534,"threshold_uncertainty_score":0.12169093},"labels":[],"label_agreement":null},{"id":"W3141426434","doi":"10.1007/978-3-642-35725-1","title":"Advances in Multimedia Modeling","year":2013,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Computer science; Multimedia","score_opus":0.010845832095693208,"score_gpt":0.24117189074660064,"score_spread":0.23032605865090744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141426434","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039110836,0.06180926,0.8649144,0.0025878751,0.0016993062,0.00005997156,0.00068894826,0.002316712,0.062012352],"genre_scores_gemma":[0.17950156,0.14259396,0.5136959,0.0016047683,0.0049749757,0.00025255157,0.004219876,0.0016989947,0.15145744],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995716,0.00008410431,0.000023532666,0.00010523401,0.0001872876,0.000028220891],"domain_scores_gemma":[0.99927217,0.00032582955,0.000032680622,0.00016931951,0.0001733358,0.000026759135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007425029,0.0010598757,0.00094687985,0.0017455311,0.00037996983,0.0021708815,0.0015472304,0.0009205723,0.010951672],"category_scores_gemma":[0.0025574262,0.0005050176,0.0007807799,0.0023239527,0.00047573663,0.0033513696,0.0010194669,0.0013512045,0.005207058],"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":[0.000035545458,0.00007816538,0.00037318864,0.00040209142,0.000064095424,0.0001274291,0.000105735766,0.022214834,0.0033261625,0.17032793,0.066198006,0.7367468],"study_design_scores_gemma":[0.0000097311095,0.000046277673,0.0006278543,0.00024224436,0.00009274625,0.00044724037,0.000104391045,0.3143705,0.0068648746,0.33041865,0.34672752,0.000047914822],"about_ca_topic_score_codex":0.002810888,"about_ca_topic_score_gemma":0.0020644963,"teacher_disagreement_score":0.010951672,"about_ca_system_score_codex":0.0008229355,"about_ca_system_score_gemma":0.00061582425,"threshold_uncertainty_score":0.03663701},"labels":[],"label_agreement":null},{"id":"W3145633659","doi":"10.1109/iros.2011.6048862","title":"Application of locality sensitive hashing to realtime loop closure detection","year":2011,"lang":"en","type":"article","venue":"2011 IEEE/RSJ International Conference on Intelligent Robots and Systems","topic":"Video Analysis and Summarization","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":"Computer science; Locality-sensitive hashing; Locality; Hash function; Closure (psychology); Loop (graph theory); Parallel computing; Hash table; Computer security; Mathematics","score_opus":0.07474543312596213,"score_gpt":0.2846509775916407,"score_spread":0.20990554446567858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145633659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03483003,0.00054007606,0.9607325,0.00010684596,0.000099918274,0.000055368346,0.000059148937,0.0025853077,0.0009907628],"genre_scores_gemma":[0.54738563,0.0002426891,0.4499762,0.00012910207,0.00014464291,0.00006460267,0.00018373759,0.00024376655,0.0016296554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985367,0.00027880946,0.00008552232,0.00028780004,0.00069113524,0.00012008337],"domain_scores_gemma":[0.9970312,0.0011354554,0.0004763319,0.0005987797,0.00060978706,0.00014843639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009952551,0.00048240527,0.00088978995,0.001574978,0.0003892495,0.00079443894,0.0012273578,0.00089754246,0.0015553405],"category_scores_gemma":[0.0053744162,0.0004284871,0.00035205847,0.0008800862,0.00068323436,0.0018985673,0.0012003251,0.0009410219,0.00057447125],"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":[0.0005458543,0.0002045767,0.0036814031,0.0002668575,0.00008166365,0.0004737058,0.00037765375,0.06588598,0.14141971,0.005824645,0.0042180456,0.77701986],"study_design_scores_gemma":[0.0000468556,0.0003512823,0.0027753268,0.000024044186,0.000023864583,0.0008615044,0.00013160103,0.91821104,0.06371017,0.007729132,0.0060687745,0.00006633453],"about_ca_topic_score_codex":0.0012398461,"about_ca_topic_score_gemma":0.0010628115,"teacher_disagreement_score":0.001574978,"about_ca_system_score_codex":0.0004703415,"about_ca_system_score_gemma":0.0004885843,"threshold_uncertainty_score":0.0052634478},"labels":[],"label_agreement":null},{"id":"W3163527296","doi":"10.1145/3411763.3451544","title":"Tagbly: Enhanced Multimedia","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Multimedia; Graphics; Zoom; Timestamp; Interactive media; World Wide Web; Computer graphics (images)","score_opus":0.008005965077863985,"score_gpt":0.22343811223661167,"score_spread":0.21543214715874767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163527296","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0101446295,0.0025711062,0.5600098,0.000961342,0.0016065271,0.0008915949,0.012682076,0.3034585,0.10767436],"genre_scores_gemma":[0.1263661,0.0033345069,0.4582714,0.003342543,0.001416814,0.0013561805,0.0553113,0.04325659,0.30734462],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998892,0.0001378735,0.0000650923,0.00014666622,0.0006308616,0.00012749426],"domain_scores_gemma":[0.9986162,0.00024776658,0.00008183927,0.00046123384,0.00043478378,0.0001582772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009077685,0.0016672004,0.0006185477,0.0023800645,0.0006608611,0.0030405165,0.00274316,0.0014288301,0.08335056],"category_scores_gemma":[0.0037470683,0.0006086082,0.0006009549,0.0017846122,0.00052129605,0.005347891,0.003665486,0.0014864178,0.035635166],"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":[0.0011978201,0.00015601501,0.00045993502,0.0008256446,0.00004990536,0.0006082396,0.00035725717,0.0016663852,0.05984416,0.028331216,0.47959667,0.42690668],"study_design_scores_gemma":[0.00015845426,0.0002334179,0.0009012971,0.00018791694,0.00004191167,0.0010601691,0.00009774595,0.018048417,0.055796534,0.011251151,0.9120759,0.00014718379],"about_ca_topic_score_codex":0.0013099124,"about_ca_topic_score_gemma":0.0018164754,"teacher_disagreement_score":0.08335056,"about_ca_system_score_codex":0.0006642906,"about_ca_system_score_gemma":0.0006369232,"threshold_uncertainty_score":0.27883542},"labels":[],"label_agreement":null},{"id":"W3164449659","doi":"10.1145/3252084","title":"Session details: Full - F15/applications/human-centered multimedia track/automatic generation of media content","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Session (web analytics); Computer science; Multimedia; Track (disk drive); Media content; Computer graphics (images); World Wide Web; Operating system","score_opus":0.0611479262729866,"score_gpt":0.27747073268542566,"score_spread":0.21632280641243906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164449659","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01917418,0.0027299288,0.13852641,0.006120827,0.013409012,0.0034993042,0.07728831,0.09318439,0.6460676],"genre_scores_gemma":[0.04794532,0.0015947137,0.046339102,0.0009874501,0.0025173926,0.00072968006,0.060943846,0.005783348,0.83315927],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997056,0.000039131708,0.00001073286,0.000083510036,0.00009104739,0.0000699076],"domain_scores_gemma":[0.99882454,0.00021307175,0.000021577021,0.00024188073,0.0004003055,0.00029868365],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009436808,0.0014595739,0.0013602794,0.0011334708,0.0014213184,0.002469055,0.0013750159,0.0034172158,0.60200053],"category_scores_gemma":[0.0010568136,0.00029436802,0.0009894454,0.001059087,0.0002683503,0.0014487127,0.0011590812,0.0012727161,0.45281753],"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":[0.0007165516,0.0002688445,0.0003267944,0.00031542932,0.000030850362,0.00009182283,0.000051619572,0.0002785699,0.024766043,0.0007061826,0.7898086,0.18263873],"study_design_scores_gemma":[0.00037601753,0.0010231184,0.006313341,0.00012796867,0.0000886162,0.0004973657,0.00014638137,0.012163332,0.049672488,0.0022235054,0.92726606,0.00010182683],"about_ca_topic_score_codex":0.002945052,"about_ca_topic_score_gemma":0.005539566,"teacher_disagreement_score":0.39799947,"about_ca_system_score_codex":0.00037926668,"about_ca_system_score_gemma":0.0007096201,"threshold_uncertainty_score":0.5676979},"labels":[],"label_agreement":null},{"id":"W3164854769","doi":"10.1109/cvprw53098.2021.00514","title":"Puck localization and multi-task event recognition in broadcast hockey videos","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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; Mitacs; Compute Canada","keywords":"Computer science; Event (particle physics); Context (archaeology); Artificial intelligence; Task (project management); Computer vision; Engineering; Geography","score_opus":0.027813658556671044,"score_gpt":0.25984939353049896,"score_spread":0.23203573497382793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164854769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48124906,0.0013097848,0.5054499,0.00041262325,0.00037623377,0.00024022696,0.0012105244,0.005044309,0.00470732],"genre_scores_gemma":[0.94458604,0.00025573035,0.049227383,0.00011786353,0.0001231668,0.00009017084,0.0020060365,0.00009904198,0.003494498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936277,0.00009344374,0.000023781125,0.00029820946,0.0000843628,0.00013746569],"domain_scores_gemma":[0.99931335,0.0002981009,0.000086618464,0.000085716514,0.00014017163,0.00007602025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007555284,0.001565923,0.0008308403,0.0012124717,0.0004526314,0.0007261726,0.001321549,0.0009731302,0.0014606647],"category_scores_gemma":[0.002626915,0.00031198908,0.0005383567,0.00068739604,0.00041082958,0.0012230905,0.0010422857,0.0011305156,0.0007947296],"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":[0.0016191803,0.0009783965,0.015982695,0.00038691956,0.00021452873,0.00074134656,0.00038558609,0.23538363,0.05055882,0.001587958,0.010919367,0.68124163],"study_design_scores_gemma":[0.000015068179,0.00018437649,0.005415298,0.000014307195,0.000031658128,0.0000999793,0.00012976762,0.98008704,0.01181559,0.0013410778,0.0008530073,0.000012968936],"about_ca_topic_score_codex":0.011140388,"about_ca_topic_score_gemma":0.010073093,"teacher_disagreement_score":0.011140388,"about_ca_system_score_codex":0.0006938418,"about_ca_system_score_gemma":0.0004411335,"threshold_uncertainty_score":0.022151053},"labels":[],"label_agreement":null},{"id":"W3166937257","doi":"10.5565/rev/elcvia.1286","title":"Video Summarization for Multiple Sports Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"ELCVIA Electronic Letters on Computer Vision and Image Analysis","topic":"Video Analysis and Summarization","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":"University of Calgary","funders":"","keywords":"Timestamp; Automatic summarization; Computer science; Artificial intelligence; Python (programming language); False positive paradox; Key frame; Key (lock); Feature extraction; League; Computer vision; Machine learning; Frame (networking); Real-time computing; Computer security","score_opus":0.004839986775894181,"score_gpt":0.23408612457306444,"score_spread":0.22924613779717026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166937257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02911257,0.0016431246,0.9560295,0.00022146881,0.00018866752,0.00022254308,0.0011135552,0.008687612,0.0027810063],"genre_scores_gemma":[0.30784962,0.0014277011,0.6669391,0.00033846445,0.00031963098,0.00036777693,0.008832953,0.000564793,0.013360021],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960405,0.00003932555,0.000030607323,0.00013449792,0.00012605592,0.00006545741],"domain_scores_gemma":[0.9996594,0.00007169564,0.00005232535,0.00004172401,0.00014524123,0.000029616453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042140495,0.0014071983,0.000807307,0.0021723164,0.00036371403,0.0007592034,0.0011833084,0.000632327,0.0036805542],"category_scores_gemma":[0.00094761414,0.00037256363,0.00091703294,0.00125248,0.00019287819,0.001184061,0.0008127936,0.001002384,0.002156768],"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":[0.0002075938,0.00013870915,0.00059475633,0.00016861554,0.000083344836,0.000093385046,0.00006229412,0.01552598,0.044946708,0.00071588863,0.005154091,0.9323086],"study_design_scores_gemma":[0.00003288523,0.00039015716,0.0035101557,0.00005573114,0.0001517392,0.00018206028,0.00015639518,0.916478,0.06063645,0.0035486089,0.01481981,0.000038169226],"about_ca_topic_score_codex":0.006080377,"about_ca_topic_score_gemma":0.009409181,"teacher_disagreement_score":0.006080377,"about_ca_system_score_codex":0.0007191124,"about_ca_system_score_gemma":0.00051763526,"threshold_uncertainty_score":0.01231271},"labels":[],"label_agreement":null},{"id":"W3174863573","doi":"10.5121/csit.2021.110807","title":"Data-Driven Intelligent Application for Youtube Video Popularity Analysis using Machine Learning and Statistics","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Popularity; Computer science; Pace; Process (computing); Web page; Online video; Support vector machine; World Wide Web; Multimedia; Artificial intelligence","score_opus":0.050470028896841816,"score_gpt":0.3237213830961041,"score_spread":0.2732513541992623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174863573","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08830062,0.0005413726,0.74812347,0.0006433859,0.0002112715,0.00057691603,0.007234469,0.1499902,0.0043783425],"genre_scores_gemma":[0.52936506,0.00041035464,0.45209965,0.0002163471,0.00013778929,0.0006214629,0.0093400655,0.0013679898,0.006441348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972636,0.000044271077,0.000034847497,0.00007544991,0.000099952784,0.000019107714],"domain_scores_gemma":[0.99881834,0.0005420713,0.00007081033,0.000094562405,0.00040649014,0.00006763932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005956287,0.00083340105,0.00059577066,0.0032499493,0.00036633995,0.0005954176,0.0006373947,0.00052968494,0.0041237064],"category_scores_gemma":[0.0027807017,0.00029960065,0.00046816067,0.0018217077,0.000114194234,0.00070674263,0.000312117,0.00050161994,0.0016694424],"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":[0.0007622009,0.0006341355,0.027680388,0.00052399345,0.00033966918,0.0008746981,0.00034007436,0.07868486,0.028662898,0.0020263426,0.04622059,0.8132502],"study_design_scores_gemma":[0.000028594412,0.00006372137,0.0063259774,0.00001398049,0.000025087531,0.00008627801,0.00006360916,0.9798068,0.008396499,0.0011599993,0.0040016845,0.00002769946],"about_ca_topic_score_codex":0.016176928,"about_ca_topic_score_gemma":0.023114715,"teacher_disagreement_score":0.016176928,"about_ca_system_score_codex":0.00053436065,"about_ca_system_score_gemma":0.00048467395,"threshold_uncertainty_score":0.032165587},"labels":[],"label_agreement":null},{"id":"W3192090591","doi":"","title":"Development of Computer Vision-Enhanced Smart Golf Ball Retriever","year":2020,"lang":"en","type":"article","venue":"Institutional Repositories DataBase (IRDB)","topic":"Video Analysis and Summarization","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":"Ball (mathematics); Computer vision; Labrador Retriever; Artificial intelligence; Computer science; Computer graphics (images); Mathematics; Medicine","score_opus":0.019133194037996577,"score_gpt":0.2444175933444062,"score_spread":0.22528439930640962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192090591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034471598,0.001262447,0.89972824,0.00023291944,0.00029483394,0.0007397472,0.0023758418,0.053039115,0.007855284],"genre_scores_gemma":[0.15280057,0.0010109132,0.8071145,0.0006094473,0.000255621,0.000699278,0.01077075,0.0011600503,0.025578797],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999138,0.000059342106,0.000063367246,0.00024572026,0.0004101003,0.00008343222],"domain_scores_gemma":[0.99937916,0.000057946283,0.000035476958,0.00009990569,0.00037073932,0.000056682016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069382426,0.0008433529,0.0012459245,0.002118279,0.00038161356,0.0010455507,0.0019847949,0.00086605817,0.0125894],"category_scores_gemma":[0.0009931604,0.00049736706,0.0007193318,0.0013094741,0.00016797772,0.0015914585,0.00081482227,0.00047355925,0.00982247],"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":[0.00061352964,0.00033487967,0.0018222525,0.00042323594,0.0001225005,0.0003332175,0.00009907401,0.002669023,0.22174858,0.0012826254,0.035903063,0.7346481],"study_design_scores_gemma":[0.00032603857,0.0012864331,0.01544654,0.00007824642,0.00039860274,0.0019950438,0.00032212044,0.37191018,0.47065693,0.0014495724,0.13589147,0.00023883731],"about_ca_topic_score_codex":0.0030311735,"about_ca_topic_score_gemma":0.0025513638,"teacher_disagreement_score":0.0125894,"about_ca_system_score_codex":0.00032087593,"about_ca_system_score_gemma":0.00075740117,"threshold_uncertainty_score":0.042115748},"labels":[],"label_agreement":null},{"id":"W3194477706","doi":"10.1145/3475722.3482794","title":"Multi-task Learning for Jersey Number Recognition in Ice Hockey","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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; Mitacs","keywords":"Task (project management); Numerical digit; Computer science; Function (biology); Artificial intelligence; Digit recognition; Machine learning; Arithmetic; Artificial neural network; Engineering; Mathematics","score_opus":0.04313193310445002,"score_gpt":0.28731776036226603,"score_spread":0.244185827257816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194477706","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44550398,0.0017698514,0.5395774,0.0008531176,0.00035559433,0.00023415693,0.00084967935,0.0041415,0.0067146295],"genre_scores_gemma":[0.8865711,0.0002813116,0.10272261,0.00026737654,0.00011508125,0.000115554794,0.0022680473,0.00011524489,0.007543744],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996966,0.00006618664,0.000013005011,0.00012190688,0.000040953146,0.000061364546],"domain_scores_gemma":[0.99944276,0.00026535115,0.000051983436,0.000064010695,0.00011934823,0.000056560402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008106337,0.0008584707,0.0005978839,0.00063838443,0.0004342433,0.0005235453,0.00089729275,0.000905177,0.0019833774],"category_scores_gemma":[0.0018668595,0.0001839799,0.0004931608,0.00048357082,0.0003778619,0.0012033002,0.00054879114,0.0010235168,0.000773155],"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":[0.0013587638,0.0005975568,0.0030097004,0.000311229,0.00013341595,0.00040516307,0.00024018239,0.17525686,0.054966774,0.001994461,0.012516004,0.7492099],"study_design_scores_gemma":[0.000019297931,0.00019308842,0.0020491944,0.000009867866,0.000026439051,0.000046288565,0.00010193537,0.98068273,0.013441734,0.0022129284,0.0012022915,0.000014255401],"about_ca_topic_score_codex":0.0062709334,"about_ca_topic_score_gemma":0.009978627,"teacher_disagreement_score":0.0062709334,"about_ca_system_score_codex":0.00065114343,"about_ca_system_score_gemma":0.00056631403,"threshold_uncertainty_score":0.012468874},"labels":[],"label_agreement":null},{"id":"W3198986793","doi":"","title":"Why read when you can watch?: Video articles and knowledge representation within the medical domain","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Canadian Linguistic Association","funders":"","keywords":"Computer science; Representation (politics); Domain (mathematical analysis); Artificial intelligence; Data science; Information retrieval; Political science; Politics; Mathematics","score_opus":0.025194422820327068,"score_gpt":0.25722029517277234,"score_spread":0.23202587235244526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3198986793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32140648,0.04986476,0.5414055,0.042776745,0.0022257343,0.0006472039,0.005712832,0.0025876036,0.033373125],"genre_scores_gemma":[0.76046,0.016219767,0.19935699,0.0014377618,0.0023157387,0.00019579972,0.007324582,0.00023765449,0.012451677],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992193,0.00035214165,0.00006079534,0.00014083697,0.00016734903,0.000059597853],"domain_scores_gemma":[0.9937517,0.0038651174,0.0005758311,0.00022552621,0.0012636236,0.00031812818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016499495,0.00033969164,0.00023844006,0.004814395,0.0007101454,0.003345038,0.0006956334,0.00118011,0.002563803],"category_scores_gemma":[0.009295515,0.00016955151,0.0002903706,0.0038167073,0.0010748416,0.003839271,0.00072010723,0.00082973705,0.00068163866],"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":[0.00053169753,0.00027633735,0.008483122,0.0009697527,0.000113052054,0.0006814928,0.005265771,0.004340261,0.015735261,0.03229111,0.041658103,0.8896539],"study_design_scores_gemma":[0.00013560429,0.0007982816,0.08977679,0.0022163072,0.0007099004,0.0028254369,0.032131102,0.3259551,0.054866392,0.22783813,0.26235422,0.0003928234],"about_ca_topic_score_codex":0.010171347,"about_ca_topic_score_gemma":0.009314724,"teacher_disagreement_score":0.010171347,"about_ca_system_score_codex":0.00094214955,"about_ca_system_score_gemma":0.0008544864,"threshold_uncertainty_score":0.020224273},"labels":[],"label_agreement":null},{"id":"W3203593077","doi":"10.1016/j.eswa.2022.119250","title":"Player tracking and identification in ice hockey","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":50,"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; Ice hockey; Identification (biology); Artificial intelligence; Convolutional neural network; Panning (audio); Tracking (education); Computer vision; Zoom","score_opus":0.012813737861620818,"score_gpt":0.2399609264847559,"score_spread":0.22714718862313507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203593077","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87669027,0.0013541251,0.104675256,0.00027163269,0.00035246933,0.00021106128,0.0017878482,0.0011946254,0.013462692],"genre_scores_gemma":[0.96032083,0.0005898952,0.024560176,0.00007583753,0.000076884506,0.00005210687,0.0014144738,0.00013664197,0.012773097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997204,0.00003483483,0.000011653904,0.0000902028,0.00006732484,0.00007550603],"domain_scores_gemma":[0.9996288,0.00011540314,0.00003692321,0.00001864942,0.00014955926,0.00005066451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032124782,0.0005175731,0.0004353624,0.0018306221,0.0005422108,0.0011174945,0.00056163396,0.00071761454,0.0029681611],"category_scores_gemma":[0.001097252,0.0002532276,0.0001903086,0.001008294,0.00028096003,0.00069641613,0.0005034044,0.00037218237,0.0018728077],"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":[0.0042301393,0.0006930828,0.09866585,0.0005701747,0.00018129699,0.0021831759,0.0018188502,0.028483203,0.15084028,0.0028725727,0.019844888,0.6896165],"study_design_scores_gemma":[0.00009059037,0.0011726011,0.3027525,0.0002321994,0.00022110877,0.0024003512,0.00594164,0.5495801,0.114520155,0.0033104853,0.019649887,0.00012835888],"about_ca_topic_score_codex":0.015964162,"about_ca_topic_score_gemma":0.020524845,"teacher_disagreement_score":0.015964162,"about_ca_system_score_codex":0.00045010934,"about_ca_system_score_gemma":0.0005046235,"threshold_uncertainty_score":0.031742454},"labels":[],"label_agreement":null},{"id":"W3206421219","doi":"10.18280/ts.400214","title":"Statistical Evaluation of Video Summarization Models from an Empirical Perspective","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Analysis and Summarization","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":"Automatic summarization; Perspective (graphical); Computer science; Empirical research; Artificial intelligence; Statistics; Mathematics","score_opus":0.07721719914288039,"score_gpt":0.35311690462512685,"score_spread":0.27589970548224646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206421219","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5824028,0.011631887,0.38713348,0.0021444175,0.0004952104,0.0008246815,0.0058585093,0.0031361782,0.006372836],"genre_scores_gemma":[0.93903244,0.0015358628,0.048733864,0.00016913222,0.0002770523,0.0004356442,0.008659173,0.00017202126,0.00098471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99309313,0.0034468998,0.00063765247,0.0011189901,0.0014769698,0.00022641836],"domain_scores_gemma":[0.9047916,0.07681961,0.0054524397,0.004363589,0.007876844,0.0006958596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019391406,0.0013890716,0.0010842036,0.004165366,0.0004371748,0.0017858299,0.0015104768,0.0012211413,0.0013555766],"category_scores_gemma":[0.08010544,0.00028479885,0.00094729144,0.0028596176,0.00082052697,0.0029240726,0.00082259567,0.0013736122,0.00047781513],"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":[0.0014822091,0.0008055584,0.05475869,0.001283841,0.0011189245,0.00018257268,0.0004656811,0.63799083,0.0029692282,0.0063598366,0.012402153,0.28018042],"study_design_scores_gemma":[0.000038657516,0.0007712357,0.01443359,0.000099028475,0.00014260276,0.00010053788,0.000240851,0.97748476,0.0017553318,0.003262349,0.0016252993,0.000045818586],"about_ca_topic_score_codex":0.004911671,"about_ca_topic_score_gemma":0.0035245991,"teacher_disagreement_score":0.019391406,"about_ca_system_score_codex":0.0020118016,"about_ca_system_score_gemma":0.0009927645,"threshold_uncertainty_score":0.10255277},"labels":[],"label_agreement":null},{"id":"W3208587582","doi":"10.1109/icassp43922.2022.9746208","title":"Fast Graph Sampling for Short Video Summarization Using Gershgorin Disc Alignment","year":2022,"lang":"en","type":"article","venue":"ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","topic":"Video Analysis and Summarization","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":"Combinatorics; Automatic summarization; Lambda; Graph; Laplacian matrix; Clustering coefficient; Discrete mathematics; Eigenvalues and eigenvectors; Upper and lower bounds; Mathematics; Algorithm; Computer science; Artificial intelligence; Cluster analysis","score_opus":0.07640611591903274,"score_gpt":0.32209126369903646,"score_spread":0.24568514778000372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208587582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019128015,0.00034477343,0.97844326,0.0002184196,0.000031124597,0.00005681868,0.0001311695,0.0008279832,0.0008184196],"genre_scores_gemma":[0.32457638,0.00070455717,0.6685728,0.00025857426,0.00016587779,0.00022388177,0.0014296089,0.00044574312,0.003622669],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993605,0.00016026915,0.000028335358,0.00016569447,0.00020291284,0.00008229252],"domain_scores_gemma":[0.9985689,0.0008873318,0.00014499259,0.00016570474,0.00015286561,0.000080203274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082620524,0.0014272688,0.0016322115,0.0016470223,0.0005976523,0.0010838902,0.0013796745,0.0014541481,0.0029953497],"category_scores_gemma":[0.004861907,0.0005704035,0.00072035077,0.001781874,0.0007978871,0.0021739376,0.0011306949,0.001096693,0.0011070304],"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":[0.0007223016,0.00013935531,0.0009780539,0.00036039762,0.000092994225,0.00033528675,0.00034583278,0.5388329,0.032260172,0.033304747,0.008087397,0.38454056],"study_design_scores_gemma":[0.000015225459,0.000044529374,0.00012754127,0.0000057397815,0.000007734689,0.0000450468,0.000026550251,0.98625666,0.004382221,0.008210061,0.0008712233,0.000007462129],"about_ca_topic_score_codex":0.004962695,"about_ca_topic_score_gemma":0.005482516,"teacher_disagreement_score":0.004962695,"about_ca_system_score_codex":0.001208902,"about_ca_system_score_gemma":0.000923804,"threshold_uncertainty_score":0.010020435},"labels":[],"label_agreement":null},{"id":"W3215263462","doi":"","title":"Traitement intégré d'informations vidéo pour la vidéosurveillance : le projet CAnADA","year":2008,"lang":"fr","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Video Analysis and Summarization","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":"Computer science","score_opus":0.01444961602860954,"score_gpt":0.21033021865252222,"score_spread":0.1958806026239127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215263462","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4246475,0.009898541,0.4824112,0.011795259,0.0015892028,0.0024381422,0.012896762,0.014359326,0.03996404],"genre_scores_gemma":[0.50841516,0.004324463,0.34234318,0.0006966041,0.00041053494,0.00068923994,0.015073198,0.0010835245,0.126964],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99827445,0.00019882707,0.000046779605,0.00030963356,0.0009199753,0.00025027632],"domain_scores_gemma":[0.99698037,0.00033021573,0.00009341534,0.00019361611,0.0020366742,0.00036561937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002036174,0.0013792155,0.00078111445,0.002076799,0.0018003491,0.002722897,0.0009902238,0.0013764349,0.0045812735],"category_scores_gemma":[0.0028932928,0.0003765539,0.0006262169,0.0020008627,0.000851988,0.0014059332,0.00087675,0.0012068426,0.0012587245],"study_design_candidate":"not_applicable","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":[0.0019349615,0.00070161046,0.0068397014,0.0007078514,0.0002446198,0.0006026999,0.0015593924,0.026773492,0.13485676,0.016826196,0.059165508,0.7497873],"study_design_scores_gemma":[0.0010442082,0.0014073566,0.05317807,0.00026437963,0.0005475921,0.0007378308,0.0034867057,0.3200673,0.20351756,0.0059484565,0.4094359,0.00036462545],"about_ca_topic_score_codex":0.7479471,"about_ca_topic_score_gemma":0.59472823,"teacher_disagreement_score":0.7479471,"about_ca_system_score_codex":0.006965158,"about_ca_system_score_gemma":0.021576544,"threshold_uncertainty_score":0.5070746},"labels":[],"label_agreement":null},{"id":"W3216849838","doi":"","title":"Automatic extraction of textual informations in a video by a robust morphological approach","year":2003,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Video Analysis and Summarization","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; Artificial intelligence; Extraction (chemistry); Feature extraction; Computer vision; Information retrieval","score_opus":0.01684226556048665,"score_gpt":0.2262047851482644,"score_spread":0.20936251958777774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216849838","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04733285,0.0028047715,0.9369359,0.00066038826,0.0003572181,0.00032358023,0.0017962585,0.0057320073,0.004057197],"genre_scores_gemma":[0.11389903,0.00221404,0.8723134,0.00016581641,0.0003749165,0.00017718071,0.0045422674,0.0007586685,0.00555462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993699,0.00009559599,0.00010155151,0.00017930848,0.00016968364,0.00008403562],"domain_scores_gemma":[0.9983656,0.00040710063,0.00024912466,0.0002403727,0.0006551472,0.0000827292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000697936,0.0014056286,0.0010515397,0.0057153185,0.00066990795,0.0020090528,0.0009404437,0.001300815,0.0035464528],"category_scores_gemma":[0.0022033667,0.00063201913,0.0012071886,0.003212365,0.00067693065,0.0020938325,0.0011304326,0.0010100809,0.004368446],"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":[0.00053255376,0.00008301413,0.00047470682,0.00057045167,0.00009000509,0.00062951865,0.00020939915,0.0023308047,0.45558378,0.0019040371,0.005366098,0.5322256],"study_design_scores_gemma":[0.00015120553,0.000708201,0.015811408,0.00030674523,0.0007753043,0.0024507195,0.0012642862,0.32826412,0.5803119,0.010842387,0.05886875,0.00024502576],"about_ca_topic_score_codex":0.0015605482,"about_ca_topic_score_gemma":0.0024606567,"teacher_disagreement_score":0.0057153185,"about_ca_system_score_codex":0.00037006103,"about_ca_system_score_gemma":0.00061402674,"threshold_uncertainty_score":0.011864066},"labels":[],"label_agreement":null},{"id":"W40980533","doi":"10.1007/978-3-642-12900-1_14","title":"YouTube Scale, Large Vocabulary Video Annotation","year":2010,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":21,"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","funders":"","keywords":"Computer science; Annotation; Vocabulary; Scale (ratio); Multimedia; World Wide Web; Artificial intelligence; Geography; Linguistics; Cartography","score_opus":0.04981920182564551,"score_gpt":0.3281246007468974,"score_spread":0.2783053989212519,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W40980533","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086254604,0.019900708,0.5411555,0.0035302946,0.0034524263,0.0028423052,0.17616865,0.06188759,0.10480793],"genre_scores_gemma":[0.16628379,0.009557977,0.38580215,0.00068269874,0.0008805108,0.0014943273,0.374602,0.0034279898,0.0572685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991358,0.00011824115,0.000063029234,0.0002387298,0.00038876708,0.000055524684],"domain_scores_gemma":[0.99787796,0.0006266606,0.0000918558,0.000438225,0.0009001668,0.000065226646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079118164,0.001299308,0.00091269804,0.003913857,0.0009705875,0.0018181595,0.0013122401,0.0007410121,0.008553724],"category_scores_gemma":[0.004850148,0.00036558794,0.0003968804,0.0058273114,0.00036238012,0.0040969416,0.0015270697,0.0008602098,0.008153348],"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":[0.0002189943,0.00012675942,0.0014866219,0.0012665399,0.00007407079,0.00024329117,0.00033388473,0.0038775639,0.020998284,0.005043704,0.3408288,0.6255014],"study_design_scores_gemma":[0.00013597636,0.0002843642,0.023365563,0.0007261328,0.00026443272,0.0010682524,0.0027091831,0.32361946,0.061293676,0.041359622,0.544926,0.00024731143],"about_ca_topic_score_codex":0.025235638,"about_ca_topic_score_gemma":0.05179621,"teacher_disagreement_score":0.025235638,"about_ca_system_score_codex":0.0011506178,"about_ca_system_score_gemma":0.0011602787,"threshold_uncertainty_score":0.050177515},"labels":[],"label_agreement":null},{"id":"W4206936894","doi":"10.1109/ssci50451.2021.9660059","title":"Procedural Content Generation of Levels with Increased Connectedness using Complex String Generators","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Video Analysis and Summarization","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":"University of Guelph","keywords":"Social connectedness; Doors; Adjacency list; Computer science; Graph; Representation (politics); String (physics); Theoretical computer science; Variety (cybernetics); Mathematics; Algorithm; Artificial intelligence; Operating system","score_opus":0.13517017012862165,"score_gpt":0.29044921739083296,"score_spread":0.1552790472622113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206936894","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01968722,0.000043220323,0.97427136,0.00008154575,0.000050529197,0.0001227592,0.00014043023,0.0018286497,0.003774366],"genre_scores_gemma":[0.25389254,0.000100864585,0.73717594,0.00008723893,0.000038241185,0.00032629177,0.0007744025,0.0011230855,0.0064814026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993862,0.00017588731,0.00003978628,0.00014890808,0.00019444365,0.000054727483],"domain_scores_gemma":[0.997778,0.0012153595,0.00012333244,0.0005079922,0.000289612,0.00008571314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007001798,0.0007560959,0.00052172656,0.0011177959,0.00053100707,0.0013622333,0.0011597057,0.00066941045,0.007185293],"category_scores_gemma":[0.0050761416,0.00037086767,0.00083191856,0.0009097332,0.0008807795,0.002048779,0.0018507051,0.000808084,0.0015872265],"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":[0.00053136516,0.00028715728,0.0018738584,0.00081624655,0.00010575851,0.0012890317,0.0031460512,0.15151656,0.10206149,0.20096551,0.011372422,0.5260346],"study_design_scores_gemma":[0.00009690461,0.00027629943,0.00095372635,0.00008891542,0.0001069738,0.000449994,0.0007170835,0.7672728,0.06820623,0.11665627,0.04509915,0.00007558665],"about_ca_topic_score_codex":0.00076183886,"about_ca_topic_score_gemma":0.0010927608,"teacher_disagreement_score":0.007185293,"about_ca_system_score_codex":0.0005119011,"about_ca_system_score_gemma":0.0004541936,"threshold_uncertainty_score":0.024037182},"labels":[],"label_agreement":null},{"id":"W4210324837","doi":"10.1145/3462777","title":"MMSUM Digital Twins: A Multi-view Multi-modality Summarization Framework for Sporting Events","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Automatic summarization; Event (particle physics); Computer science; Social media; Leverage (statistics); Popularity; Categorization; Focus (optics); Information retrieval; Data science; Artificial intelligence; World Wide Web; Psychology; Social psychology","score_opus":0.0474307124631043,"score_gpt":0.32408344649669374,"score_spread":0.2766527340335894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210324837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009620873,0.0016104816,0.97812337,0.000323877,0.00022069101,0.00038563303,0.0028594711,0.0045738467,0.0022817778],"genre_scores_gemma":[0.14256246,0.0018049115,0.8309051,0.00037228665,0.00057708035,0.00086829555,0.014124299,0.0007391289,0.008046505],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938786,0.0001298659,0.00005750089,0.00020558345,0.00015808409,0.00006108587],"domain_scores_gemma":[0.99944144,0.00015598201,0.00007733668,0.00006814661,0.00020522991,0.000051763964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090226874,0.001895995,0.0010437877,0.0026151189,0.00064318953,0.0016636773,0.0011950111,0.0011590348,0.0050099655],"category_scores_gemma":[0.0023139985,0.00039344843,0.0014559826,0.0013870173,0.00036240346,0.0023254966,0.001856906,0.0012341699,0.001922578],"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":[0.00091043505,0.00019056811,0.001573243,0.0012910911,0.00034549375,0.0006660488,0.0012586592,0.034798335,0.075021446,0.009448924,0.034596514,0.83989924],"study_design_scores_gemma":[0.00012093602,0.00077671424,0.0062852413,0.00024828545,0.0006045746,0.0006913502,0.0016421023,0.81933516,0.044482496,0.03368646,0.09190463,0.00022206848],"about_ca_topic_score_codex":0.0038774973,"about_ca_topic_score_gemma":0.008372061,"teacher_disagreement_score":0.0050099655,"about_ca_system_score_codex":0.0005452766,"about_ca_system_score_gemma":0.0005384478,"threshold_uncertainty_score":0.016760051},"labels":[],"label_agreement":null},{"id":"W4220766706","doi":"10.5539/elt.v15n4p16","title":"The Reproduction of Visual Pictures in Poetry Translation","year":2022,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Video Analysis and Summarization","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":"Poetry; Harmony (color); Beauty; Psychology; Reproduction; Soul; Competence (human resources); Aesthetics; Linguistics; Cognitive psychology; Literature; Art; Social psychology; Epistemology; Visual arts; Philosophy","score_opus":0.006689557396546511,"score_gpt":0.25159391638213296,"score_spread":0.24490435898558646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220766706","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2579453,0.007861286,0.122542895,0.005203454,0.0020928513,0.00046879207,0.00064693356,0.00060102827,0.60263747],"genre_scores_gemma":[0.9364634,0.0027330483,0.025703784,0.00048968435,0.00043398404,0.0001792545,0.00027680205,0.00032413605,0.0333958],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99879456,0.00073872844,0.00004620217,0.00007973646,0.00028866914,0.00005214295],"domain_scores_gemma":[0.9977641,0.00145862,0.0001567477,0.00028411372,0.0002892952,0.000047173748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009547594,0.00040905498,0.0001612574,0.001390507,0.0012092625,0.0030272119,0.00042489613,0.0006258759,0.006459085],"category_scores_gemma":[0.00548477,0.00014990328,0.00018610737,0.0013072747,0.0041790856,0.0020734456,0.0010248013,0.0011013236,0.00094286655],"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":[0.00048176525,0.00011079702,0.0017409952,0.0025464343,0.000027756318,0.0031938038,0.18798915,0.0011812511,0.052519295,0.39460528,0.021998232,0.33360526],"study_design_scores_gemma":[0.000037721395,0.0005446611,0.011852326,0.0015020571,0.00005085194,0.008144381,0.09607937,0.004083957,0.050794944,0.060217705,0.7666028,0.00008915248],"about_ca_topic_score_codex":0.0004617333,"about_ca_topic_score_gemma":0.0006292234,"teacher_disagreement_score":0.006459085,"about_ca_system_score_codex":0.00067096605,"about_ca_system_score_gemma":0.0004734924,"threshold_uncertainty_score":0.021607816},"labels":[],"label_agreement":null},{"id":"W4224008805","doi":"10.1109/icacta54488.2022.9753219","title":"Soft Support: Specially Abled Communication","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Horizon College and Seminary","funders":"","keywords":"Active listening; Computer science; Inefficiency; Multimedia; Mobile telephony; Internet privacy; Disabled people; Mobile computing; Human–computer interaction; Telecommunications; Mobile radio; Psychology; Applied psychology; Life style","score_opus":0.011536717040727338,"score_gpt":0.22112050893965476,"score_spread":0.20958379189892742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224008805","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058722053,0.0032770454,0.5417501,0.001722868,0.0025571135,0.0012443315,0.0040083043,0.061419092,0.32529908],"genre_scores_gemma":[0.37683102,0.0018456607,0.11221923,0.0017484784,0.0011828243,0.0007540206,0.0058073006,0.002333056,0.4972784],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99963593,0.000035994268,0.000024485822,0.00007469795,0.00015948727,0.000069517926],"domain_scores_gemma":[0.99934524,0.00012393761,0.00005475458,0.00020613478,0.00017125148,0.00009861041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025250023,0.0011413253,0.0004117005,0.0009115687,0.0005133406,0.0015099137,0.0015024944,0.0012257781,0.13271548],"category_scores_gemma":[0.0012043162,0.0002349077,0.000327708,0.00067259255,0.00029544515,0.001596489,0.0019347518,0.00060332054,0.05890883],"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":[0.0006363699,0.00021334106,0.00092852715,0.00092745054,0.00003332684,0.0011482823,0.0003817661,0.0010086697,0.15408808,0.0068481755,0.091472566,0.7423134],"study_design_scores_gemma":[0.0001413526,0.00076525856,0.0039024372,0.00038876955,0.00007799245,0.0035414994,0.00035684792,0.021215666,0.07779568,0.006000335,0.8856621,0.00015199106],"about_ca_topic_score_codex":0.0006183594,"about_ca_topic_score_gemma":0.00069064583,"teacher_disagreement_score":0.13271548,"about_ca_system_score_codex":0.00018880375,"about_ca_system_score_gemma":0.000263503,"threshold_uncertainty_score":0.44397753},"labels":[],"label_agreement":null},{"id":"W4224984048","doi":"10.1145/3491102.3517741","title":"Katika: An End-to-End System for Authoring Amateur Explainer Motion Graphics Videos","year":2022,"lang":"en","type":"article","venue":"CHI Conference on Human Factors in Computing Systems","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Amateur; Animation; Graphics; Motion (physics); 3D computer graphics; Computer graphics (images); Session (web analytics); Multimedia; World Wide Web; Artificial intelligence","score_opus":0.09179661729610653,"score_gpt":0.31150921295841205,"score_spread":0.21971259566230553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224984048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041342936,0.00043105386,0.4987923,0.00027977716,0.00031844023,0.0028521465,0.0051065143,0.43527088,0.015605957],"genre_scores_gemma":[0.26472315,0.0006788331,0.6019741,0.0006433505,0.00027699044,0.006303285,0.016241033,0.033990063,0.07516926],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99897516,0.00031979883,0.00012195909,0.00025132773,0.00023522494,0.00009655186],"domain_scores_gemma":[0.9951385,0.002095249,0.00025463104,0.0010576522,0.00065719674,0.00079677784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002298357,0.0017412928,0.00068647886,0.0015802642,0.00077530567,0.0019425091,0.002386502,0.0013564581,0.042079702],"category_scores_gemma":[0.010123767,0.00090451003,0.0008541348,0.00051935884,0.00056437956,0.0035946595,0.0051104673,0.0013611523,0.016642973],"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":[0.0061392747,0.0015002042,0.010642953,0.0030950524,0.00038838986,0.00340618,0.014354284,0.004023897,0.1528385,0.008331707,0.23608574,0.55919373],"study_design_scores_gemma":[0.0015349048,0.0024916683,0.024591288,0.0008581,0.00034306082,0.0037841403,0.004062832,0.16172774,0.11664567,0.018631265,0.66421026,0.0011191553],"about_ca_topic_score_codex":0.0008594999,"about_ca_topic_score_gemma":0.001635726,"teacher_disagreement_score":0.042079702,"about_ca_system_score_codex":0.00045400162,"about_ca_system_score_gemma":0.0006762752,"threshold_uncertainty_score":0.14077067},"labels":[],"label_agreement":null},{"id":"W4225096548","doi":"10.1145/3491102.3517521","title":"Enhanced Videogame Livestreaming by Reconstructing an Interactive 3D Game View for Spectators","year":2022,"lang":"en","type":"article","venue":"CHI Conference on Human Factors in Computing Systems","topic":"Video Analysis and Summarization","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Computer science; Rendering (computer graphics); Multimedia; Video game development; Human–computer interaction; Video game; Computer graphics (images); Immersion (mathematics); Game design; Augmented reality","score_opus":0.05338186329477262,"score_gpt":0.3140956370676891,"score_spread":0.2607137737729165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225096548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14262609,0.0003205112,0.8070699,0.0002656252,0.00015456781,0.0006577096,0.0010391815,0.018453043,0.029413372],"genre_scores_gemma":[0.43749794,0.00044158782,0.5336311,0.00036076352,0.00013514435,0.0005233613,0.002337069,0.0028687736,0.022204222],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997197,0.00004988071,0.000013436431,0.000053819636,0.00011066624,0.00005239052],"domain_scores_gemma":[0.999522,0.00013071901,0.000027648062,0.00010614425,0.0001290535,0.0000844396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044995215,0.0011151901,0.00058623764,0.0007437216,0.00028293332,0.0016565582,0.0011481477,0.00065343396,0.012828103],"category_scores_gemma":[0.0015385477,0.00060692715,0.000917251,0.0002441853,0.00042889352,0.0011623966,0.0031481136,0.00078794785,0.0031279484],"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":[0.0019361359,0.0007140949,0.007843476,0.0008275975,0.00028278294,0.0018503038,0.0071205655,0.013061555,0.46646583,0.0093671605,0.025907198,0.4646233],"study_design_scores_gemma":[0.0006403756,0.0028329352,0.036561087,0.0005531795,0.00064700784,0.0103527075,0.0028832268,0.37502304,0.28762203,0.010818475,0.27122545,0.00084052567],"about_ca_topic_score_codex":0.0014656682,"about_ca_topic_score_gemma":0.004249852,"teacher_disagreement_score":0.012828103,"about_ca_system_score_codex":0.00022338249,"about_ca_system_score_gemma":0.00034865196,"threshold_uncertainty_score":0.04291433},"labels":[],"label_agreement":null},{"id":"W4229747326","doi":"10.32920/ryerson.14644944","title":"Large-scale Content-based Multimedia Analysis And Applications Using Bag-Of-Words Model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Search engine indexing; Scalability; Categorization; Information retrieval; Domain (mathematical analysis); Frame (networking); Bag-of-words model; Topic model; Scale (ratio); Visual Word; Identification (biology); Multimedia; Image retrieval; Artificial intelligence; Image (mathematics); Database","score_opus":0.040321420088593324,"score_gpt":0.27732524190912794,"score_spread":0.23700382182053462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229747326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018582828,0.003311013,0.9735908,0.00040185536,0.00016530737,0.00015470314,0.00047796956,0.0014432626,0.0018722903],"genre_scores_gemma":[0.44822103,0.0070592286,0.52692795,0.00068937,0.0008194364,0.00062205363,0.0041302047,0.00043091684,0.0110998405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907446,0.00011519734,0.000084183455,0.00029434124,0.00033527808,0.00009651496],"domain_scores_gemma":[0.9990081,0.00035181816,0.0001430411,0.00012047727,0.00032917922,0.000047403682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078972935,0.001374529,0.001462419,0.0046253605,0.00062311476,0.0021581673,0.0014403597,0.0013230791,0.0020003212],"category_scores_gemma":[0.0026126625,0.00053778564,0.0020855048,0.004221764,0.0006286387,0.0036850683,0.0011858586,0.0011649261,0.001696659],"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":[0.00032788984,0.00032536796,0.0041380576,0.00076139276,0.00034387116,0.0006246847,0.00032341745,0.16524845,0.045696933,0.024402596,0.013187536,0.74461967],"study_design_scores_gemma":[0.000008610737,0.00007733841,0.0011479356,0.000026277836,0.00006672947,0.00019537074,0.00008174619,0.97355926,0.0053815464,0.015246821,0.0041748188,0.000033647033],"about_ca_topic_score_codex":0.0049576326,"about_ca_topic_score_gemma":0.0036929562,"teacher_disagreement_score":0.0049576326,"about_ca_system_score_codex":0.0010272465,"about_ca_system_score_gemma":0.0008252737,"threshold_uncertainty_score":0.009857595},"labels":[],"label_agreement":null},{"id":"W4230719295","doi":"10.1007/978-1-4939-7131-2_100498","title":"Influence Propagation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Computer science","score_opus":0.011719497213695779,"score_gpt":0.21335918906379464,"score_spread":0.20163969185009886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230719295","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031025647,0.003015368,0.84915364,0.0010329077,0.0007032593,0.00019022376,0.00069777854,0.00228271,0.13982148],"genre_scores_gemma":[0.2417791,0.010786926,0.40282845,0.0011279273,0.0022786986,0.00067554443,0.004050515,0.0020672595,0.3344055],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9989396,0.00021074664,0.000040471725,0.00035475037,0.00037634093,0.00007818237],"domain_scores_gemma":[0.9981331,0.0008197645,0.00007479043,0.00041988556,0.00047738117,0.00007502746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095894304,0.0016902792,0.0009811593,0.0025476145,0.0009848933,0.0025801002,0.0015914985,0.0014136301,0.024068914],"category_scores_gemma":[0.005760738,0.0005322123,0.0010727369,0.002174241,0.0009748353,0.0025481065,0.0015743738,0.0016629938,0.013177784],"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":[0.00008156638,0.0000843394,0.0005717173,0.00038660096,0.00012722317,0.00017831985,0.0001852607,0.03552882,0.005653261,0.276686,0.09024972,0.5902672],"study_design_scores_gemma":[0.00003261124,0.00008540255,0.0008388537,0.00017396385,0.00015010843,0.0005907045,0.00008497862,0.37595534,0.0129532935,0.37838513,0.23067364,0.000076062766],"about_ca_topic_score_codex":0.0034351938,"about_ca_topic_score_gemma":0.0038697731,"teacher_disagreement_score":0.024068914,"about_ca_system_score_codex":0.0010518733,"about_ca_system_score_gemma":0.00093906705,"threshold_uncertainty_score":0.08051854},"labels":[],"label_agreement":null},{"id":"W4231645116","doi":"10.32920/ryerson.14647533.v1","title":"Video content analysis using the video time density function and statistical models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Video tracking; Video compression picture types; Video content analysis; Video processing; Artificial intelligence; Similarity measure; Video quality; Video post-processing; Multiview Video Coding; Smacker video; Block-matching algorithm; Pattern recognition (psychology); Metric (unit)","score_opus":0.06155042285748146,"score_gpt":0.26030775148508234,"score_spread":0.19875732862760087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231645116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007329983,0.0008430047,0.9900718,0.00016109087,0.00004043153,0.000045465284,0.00019104597,0.00025651522,0.0010606936],"genre_scores_gemma":[0.434158,0.008683587,0.5467043,0.00026944492,0.00045156165,0.00038078357,0.0023404884,0.00021602082,0.0067958185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922526,0.00014577208,0.00004929376,0.00020560765,0.00031239563,0.000061588595],"domain_scores_gemma":[0.99852127,0.00068791985,0.00022370002,0.00013813285,0.00038867176,0.000040382696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010760721,0.0009455244,0.00081983284,0.0039572394,0.00038647233,0.0017778237,0.0009788232,0.0010582116,0.0012019096],"category_scores_gemma":[0.004301401,0.00031928456,0.0012578688,0.0029850306,0.0007585688,0.0033387074,0.00059608044,0.0011321293,0.00079433294],"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":[0.00020898458,0.00015901364,0.0071875057,0.0005786881,0.0002488126,0.0005681034,0.00042863353,0.2966213,0.03483544,0.14376344,0.0070877913,0.50831234],"study_design_scores_gemma":[0.0000065561567,0.00006314788,0.0019120335,0.000038829963,0.000051063565,0.00020644821,0.000097280325,0.966471,0.0043917927,0.022242602,0.0044856323,0.00003359168],"about_ca_topic_score_codex":0.0067849625,"about_ca_topic_score_gemma":0.002380607,"teacher_disagreement_score":0.0067849625,"about_ca_system_score_codex":0.0012735843,"about_ca_system_score_gemma":0.0007954168,"threshold_uncertainty_score":0.013490975},"labels":[],"label_agreement":null},{"id":"W4233690695","doi":"10.1109/mmsp53017.2021.9733496","title":"An Action-Aware Combat Model for Efficient Video Compression of Massively Multiplayer Online Role-playing Games on Cloud Gaming Platforms","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Cloud computing; Action (physics); Video game; Multimedia; Data compression; Artificial intelligence; Operating system","score_opus":0.042913738426367885,"score_gpt":0.3053340406149368,"score_spread":0.26242030218856893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233690695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09465091,0.00041871562,0.895362,0.00029351207,0.00011077943,0.00016844904,0.00020910868,0.0020637026,0.006722865],"genre_scores_gemma":[0.85432,0.00047542554,0.13780837,0.00019450283,0.00005760618,0.00016938779,0.0003482317,0.00013327923,0.0064931647],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998895,0.000012994763,0.0000054526986,0.00002519086,0.00004872249,0.000018117737],"domain_scores_gemma":[0.99988174,0.000040259638,0.000013392601,0.000012268781,0.000036104648,0.000016305346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001496258,0.0005877328,0.00033430217,0.00031832382,0.00022775735,0.00045540338,0.000779476,0.00033635763,0.0016986349],"category_scores_gemma":[0.00044477513,0.00016210329,0.00037686506,0.00019334584,0.00024127949,0.00054132054,0.00044650966,0.0005163601,0.0003262995],"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":[0.00044774206,0.0003320915,0.0026836458,0.0001154602,0.000061673745,0.00039735556,0.00018115602,0.77082276,0.045714706,0.010001277,0.003979035,0.1652631],"study_design_scores_gemma":[0.000005177056,0.000032980086,0.00018121829,0.0000024983854,0.000006336846,0.000032017753,0.000008666466,0.9973564,0.0014725616,0.00038884973,0.0005094237,0.000003871103],"about_ca_topic_score_codex":0.006959461,"about_ca_topic_score_gemma":0.007829356,"teacher_disagreement_score":0.006959461,"about_ca_system_score_codex":0.0004333505,"about_ca_system_score_gemma":0.0005092941,"threshold_uncertainty_score":0.013837934},"labels":[],"label_agreement":null},{"id":"W4233779472","doi":"10.32920/ryerson.14654436","title":"The impact of colour visual attention for video summarization","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Pipeline (software); Frame (networking); Feature (linguistics); Artificial intelligence; Visualization; Ground truth; Key (lock); Computer vision","score_opus":0.016544992591527617,"score_gpt":0.3165963928970975,"score_spread":0.30005140030556987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233779472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15091555,0.0052604433,0.82597125,0.0010696187,0.0004765063,0.00041076133,0.0006377472,0.009399526,0.005858591],"genre_scores_gemma":[0.7283957,0.0011747358,0.26413965,0.0004224581,0.00030170518,0.00016661981,0.0016137776,0.00047735285,0.0033078436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721956,0.0010019931,0.00015176745,0.00077532866,0.0006565981,0.00019478038],"domain_scores_gemma":[0.990607,0.0051339646,0.0007899204,0.0010183044,0.002061505,0.00038933576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044218595,0.001476878,0.00085310015,0.0018478103,0.000533069,0.0022301788,0.0012458629,0.0011393229,0.0018670593],"category_scores_gemma":[0.021170726,0.00025813386,0.00070306903,0.00081220677,0.00065424846,0.002660866,0.0017674539,0.0014567516,0.0009863348],"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":[0.0018556541,0.00032052994,0.004689757,0.00093274866,0.00025471128,0.00021193456,0.00076285494,0.050799746,0.11909715,0.004525992,0.007655588,0.8088933],"study_design_scores_gemma":[0.00015942144,0.0022504348,0.016260205,0.00019600216,0.00041985355,0.0004651805,0.0005651927,0.8270417,0.12602666,0.013172046,0.0133008985,0.00014243463],"about_ca_topic_score_codex":0.0050538443,"about_ca_topic_score_gemma":0.0045148884,"teacher_disagreement_score":0.0050538443,"about_ca_system_score_codex":0.0011066071,"about_ca_system_score_gemma":0.00094624853,"threshold_uncertainty_score":0.023385286},"labels":[],"label_agreement":null},{"id":"W4237464509","doi":"10.32920/ryerson.14664657.v1","title":"Robust video event recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Event (particle physics); Video quality; Quality (philosophy); Artificial intelligence; Feature (linguistics); Feature extraction; Video tracking; Computer vision; Machine learning; Video processing","score_opus":0.050721007965597704,"score_gpt":0.2456321487700163,"score_spread":0.1949111408044186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237464509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017018577,0.0014313173,0.9669316,0.00020058568,0.0002947196,0.0003216104,0.0023447298,0.007731586,0.003725255],"genre_scores_gemma":[0.3062655,0.0023640548,0.6634872,0.00042139913,0.0005853368,0.00038940334,0.015276285,0.000845155,0.010365646],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99859756,0.00013514294,0.000106995714,0.0005806495,0.00043684628,0.0001427899],"domain_scores_gemma":[0.99853075,0.00026234816,0.00022470386,0.0004286355,0.00048849283,0.000065012566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008853117,0.0013742801,0.0015058502,0.0027295751,0.00035303095,0.0013907288,0.0016340417,0.0013916482,0.0035666977],"category_scores_gemma":[0.003914024,0.0003244705,0.0011334993,0.0019004082,0.00035977524,0.0020829272,0.0009925347,0.0012381863,0.0051505044],"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":[0.00048743916,0.00017464018,0.0017805655,0.0004166752,0.00013912591,0.00035197078,0.00006047906,0.017774712,0.11264381,0.0042783604,0.018604305,0.843288],"study_design_scores_gemma":[0.000068649155,0.0004497347,0.0103741735,0.000100385856,0.00021648011,0.001372998,0.00019547596,0.71207327,0.21918035,0.0130997095,0.042753175,0.00011576399],"about_ca_topic_score_codex":0.002059117,"about_ca_topic_score_gemma":0.0018225766,"teacher_disagreement_score":0.0035666977,"about_ca_system_score_codex":0.00048017548,"about_ca_system_score_gemma":0.00051371206,"threshold_uncertainty_score":0.011931837},"labels":[],"label_agreement":null},{"id":"W4237595491","doi":"10.1007/978-0-387-78414-4_134","title":"Multimedia Content Repurposing","year":2008,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Multimedia","topic":"Video Analysis and Summarization","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":"Repurposing; Content (measure theory); Computer science; Multimedia; Mathematics; Engineering; Waste management","score_opus":0.029249722564758815,"score_gpt":0.22761765408602772,"score_spread":0.19836793152126891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237595491","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014282353,0.006161976,0.86480886,0.00045572588,0.0009844458,0.0010437928,0.0027780507,0.021777142,0.08770769],"genre_scores_gemma":[0.087113746,0.009739845,0.71456885,0.00047389485,0.0011320253,0.0006023237,0.011652239,0.0042861225,0.17043093],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965143,0.00003153896,0.000020910033,0.000084973064,0.00017433362,0.00003684662],"domain_scores_gemma":[0.99936265,0.00010689862,0.000028031327,0.00016587046,0.00029868077,0.00003794335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003806496,0.0016344103,0.00081951846,0.0040617883,0.0007273028,0.0017408469,0.0012231062,0.000697022,0.04153911],"category_scores_gemma":[0.0010769289,0.00038294718,0.00095194136,0.0032018533,0.00040441498,0.0018932618,0.001385573,0.00076507137,0.0272388],"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":[0.00011932953,0.000049857077,0.000106136205,0.00039331513,0.00002612704,0.00024379327,0.000104896884,0.0013196985,0.091860645,0.004350381,0.02891658,0.8725093],"study_design_scores_gemma":[0.00004666176,0.00035441888,0.0024873696,0.00023667164,0.00029775227,0.002377759,0.0005669923,0.07651003,0.4200798,0.015255823,0.48167312,0.00011365251],"about_ca_topic_score_codex":0.0008083012,"about_ca_topic_score_gemma":0.0012668142,"teacher_disagreement_score":0.04153911,"about_ca_system_score_codex":0.00032165973,"about_ca_system_score_gemma":0.00038376197,"threshold_uncertainty_score":0.13896215},"labels":[],"label_agreement":null},{"id":"W4238189704","doi":"10.1145/604079.604106","title":"EduNuggets","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 science; Leverage (statistics); World Wide Web; Multimedia; Subject (documents); Artificial intelligence","score_opus":0.006952546326383278,"score_gpt":0.2023671715094025,"score_spread":0.19541462518301922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238189704","genre_codex":"software","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012675328,0.0052301865,0.13220358,0.002019595,0.0015397212,0.0012020873,0.17180856,0.3737454,0.29957557],"genre_scores_gemma":[0.066303894,0.005003365,0.11728951,0.0017165411,0.00074810983,0.0011431528,0.4561042,0.05692762,0.29476362],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99880195,0.00021699307,0.00011365389,0.00019246992,0.0005513796,0.00012346548],"domain_scores_gemma":[0.99758816,0.0004294699,0.0001522301,0.0009713562,0.00054212345,0.00031668105],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011016374,0.0015881418,0.00087149855,0.005962948,0.0013095121,0.004531898,0.0028600213,0.0015333483,0.100507975],"category_scores_gemma":[0.0066084275,0.00063640164,0.0007750154,0.0058890893,0.00050160417,0.0056210174,0.0048684035,0.0011990794,0.0824762],"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":[0.0004822175,0.00010371418,0.000950343,0.0005963912,0.000034397042,0.00028343985,0.0005134366,0.0010381918,0.0028417115,0.015963659,0.66463864,0.31255382],"study_design_scores_gemma":[0.000038089067,0.000038452326,0.0005874658,0.00010602508,0.000014695906,0.00021230461,0.00013295999,0.0020210382,0.0030065503,0.0056771194,0.98813254,0.000032726595],"about_ca_topic_score_codex":0.0049276776,"about_ca_topic_score_gemma":0.0069750724,"teacher_disagreement_score":0.899492,"about_ca_system_score_codex":0.00086611795,"about_ca_system_score_gemma":0.0012861033,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4239670497","doi":"10.1007/978-0-387-39940-9_1030","title":"Video Querying","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science","score_opus":0.014088388741265018,"score_gpt":0.22440614202184886,"score_spread":0.21031775328058383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239670497","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065486687,0.006546299,0.43121767,0.0013831948,0.00073970814,0.0009463782,0.01668909,0.042684507,0.49324444],"genre_scores_gemma":[0.103049636,0.011465242,0.3106385,0.0018611869,0.00064247026,0.0005930375,0.06431122,0.005757161,0.5016816],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958175,0.000028529832,0.000024281517,0.000121932404,0.00020218384,0.000041247367],"domain_scores_gemma":[0.99963367,0.00006409495,0.000012135745,0.00011524698,0.00014276506,0.000032083237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040179153,0.0010234782,0.0007183443,0.0019661037,0.0005488877,0.0029413984,0.001992395,0.0009793647,0.09641885],"category_scores_gemma":[0.0013818075,0.0003214856,0.00052364636,0.0023334248,0.00029694606,0.0031691785,0.0016378656,0.0006201929,0.05487135],"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":[0.00020647258,0.000084164436,0.0002930037,0.00047662965,0.000023788714,0.00015824477,0.00016268918,0.0011610584,0.022979464,0.032861415,0.2628962,0.6786968],"study_design_scores_gemma":[0.000041145886,0.000057496134,0.00066515367,0.00017515401,0.000036483627,0.00073320785,0.0002402527,0.020287812,0.030789087,0.02248694,0.92444164,0.000045613284],"about_ca_topic_score_codex":0.003945973,"about_ca_topic_score_gemma":0.0041949074,"teacher_disagreement_score":0.09641885,"about_ca_system_score_codex":0.000705084,"about_ca_system_score_gemma":0.00049289083,"threshold_uncertainty_score":0.32255322},"labels":[],"label_agreement":null},{"id":"W4241338441","doi":"10.1093/oso/9780199646715.003.0008","title":"Learn","year":2013,"lang":"en","type":"book-chapter","venue":"Oxford University Press eBooks","topic":"Video Analysis and Summarization","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":"","keywords":"Watson; Surprise; Battle; Art history; Artificial intelligence; Computer science; Art; History; Psychology; Communication","score_opus":0.018962645069589935,"score_gpt":0.1806020465689826,"score_spread":0.16163940149939265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241338441","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019770386,0.0024847866,0.011614544,0.018068762,0.005854234,0.00022598673,0.012738649,0.00836472,0.9386713],"genre_scores_gemma":[0.013330661,0.0022187957,0.0062164166,0.0099398745,0.0009153475,0.00028353464,0.012970292,0.001924269,0.9522009],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887365,0.00017407957,0.000068485664,0.00028572514,0.00042622155,0.00017184569],"domain_scores_gemma":[0.9984824,0.00025006745,0.000060237177,0.00030856804,0.0005550031,0.000343745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090661197,0.0009776489,0.00071226916,0.0011129762,0.0018950468,0.0059505813,0.0020250443,0.0026927115,0.59621376],"category_scores_gemma":[0.006442502,0.00037292662,0.0008494622,0.00137494,0.0006998789,0.006356507,0.0045399293,0.0020711883,0.49376917],"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":[0.00005460451,0.00004192702,0.0005679254,0.00017939115,0.000007942655,0.00009565958,0.00023226632,0.00012658077,0.0001762324,0.015873166,0.8059803,0.17666402],"study_design_scores_gemma":[0.000006462168,0.000012650827,0.00017347005,0.00006258671,0.000002659393,0.000058479985,0.00012647806,0.000087085915,0.00010977209,0.0065066223,0.9928477,0.0000059036674],"about_ca_topic_score_codex":0.0027263863,"about_ca_topic_score_gemma":0.0055464203,"teacher_disagreement_score":0.59621376,"about_ca_system_score_codex":0.0014438686,"about_ca_system_score_gemma":0.002151027,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4242454189","doi":"10.1007/978-0-387-39940-9_3930","title":"Video Abstraction","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Abstraction; Programming language; Philosophy; Epistemology","score_opus":0.013361020064738183,"score_gpt":0.22605715751471328,"score_spread":0.2126961374499751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242454189","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007884214,0.0039604953,0.4855455,0.00084583246,0.0016897428,0.0008787393,0.006078928,0.017389154,0.47572738],"genre_scores_gemma":[0.10294337,0.007842677,0.22444679,0.0010306182,0.0010636317,0.00062025146,0.018373992,0.0029147288,0.640764],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998005,0.000014017385,0.000010359781,0.000050103252,0.00009336653,0.000031663403],"domain_scores_gemma":[0.9996934,0.00003446758,0.000013415041,0.00007177443,0.0001388858,0.000048037306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023435602,0.0009784265,0.00045820608,0.0014985828,0.0004971616,0.0018719112,0.0012244013,0.00055258686,0.13618526],"category_scores_gemma":[0.00084270176,0.00025188804,0.00044417116,0.0013942225,0.00021467026,0.0014999856,0.0017906185,0.00072282704,0.06472711],"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":[0.00020605254,0.000048472848,0.0002332271,0.00039996172,0.000015395335,0.00022591114,0.00016344727,0.0008552789,0.037831124,0.016967356,0.18877707,0.75427675],"study_design_scores_gemma":[0.000037955266,0.00010412122,0.0014030712,0.00019606431,0.000041458927,0.00091010233,0.00024748952,0.00893161,0.029080769,0.010359969,0.94864976,0.00003771323],"about_ca_topic_score_codex":0.0020627072,"about_ca_topic_score_gemma":0.0024197474,"teacher_disagreement_score":0.13618526,"about_ca_system_score_codex":0.00040859566,"about_ca_system_score_gemma":0.00055132114,"threshold_uncertainty_score":0.45558506},"labels":[],"label_agreement":null},{"id":"W4244084318","doi":"10.1145/636616.636617","title":"MVIP-II","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Virtual world; Metaverse; Multicast; Human–computer interaction; Protocol (science); Multimedia; World Wide Web; Virtual reality; Computer network","score_opus":0.008358774004886738,"score_gpt":0.20296328174313022,"score_spread":0.19460450773824348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244084318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021393204,0.002404012,0.6021135,0.0020149087,0.0045274654,0.002902661,0.009593798,0.056201126,0.2988494],"genre_scores_gemma":[0.292936,0.0030980762,0.2881544,0.0034638303,0.001748025,0.0037309714,0.046192057,0.013300329,0.34737635],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983437,0.00025776742,0.00015598345,0.00019404605,0.00072640216,0.00032201302],"domain_scores_gemma":[0.99802125,0.00020433973,0.00006111087,0.00078189466,0.0007420933,0.00018932503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014754443,0.0006994235,0.00074942753,0.00084752095,0.0010554292,0.0028711066,0.0023901078,0.001212022,0.03607846],"category_scores_gemma":[0.00440464,0.00049547997,0.00047091057,0.00066634343,0.00041277037,0.002482846,0.003166423,0.0023501185,0.022361038],"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":[0.0022583613,0.00035181886,0.0014478598,0.00095568737,0.0001260749,0.0003545728,0.00045254728,0.006743253,0.040739298,0.15450406,0.29847145,0.49359503],"study_design_scores_gemma":[0.00018408768,0.0004265046,0.0007381197,0.00022742289,0.000064211294,0.00064388604,0.00013981765,0.035306625,0.04292026,0.025214065,0.89402455,0.00011048532],"about_ca_topic_score_codex":0.0013791327,"about_ca_topic_score_gemma":0.00095728715,"teacher_disagreement_score":0.03607846,"about_ca_system_score_codex":0.0008252957,"about_ca_system_score_gemma":0.0012786491,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4244190800","doi":"10.1145/500213.500217","title":"Classification of summarized videos using hidden markov models on compressed chromaticity signatures","year":2001,"lang":"en","type":"article","venue":"Proceedings of the ninth ACM international conference on Multimedia - MULTIMEDIA '01","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Automatic summarization; Storyboard; Uncompressed video; Hidden Markov model; Video compression picture types; Video tracking; Artificial intelligence; Chromaticity; Search engine indexing; Frame (networking); Set (abstract data type); Cluster analysis; Computer vision; Pattern recognition (psychology); Video processing; Information retrieval; Multimedia","score_opus":0.07394451372354137,"score_gpt":0.30255118639192413,"score_spread":0.22860667266838275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244190800","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22197123,0.000513387,0.7739401,0.000408712,0.00007466048,0.0001740309,0.0005778825,0.0012254715,0.0011145346],"genre_scores_gemma":[0.83996636,0.00047773117,0.15405859,0.00011605293,0.00015062817,0.0001411709,0.0022070124,0.000066813314,0.002815698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965787,0.000065213564,0.000029057757,0.00009451814,0.00008731401,0.00006596624],"domain_scores_gemma":[0.99878794,0.0005175513,0.00019047598,0.000106826956,0.00033353464,0.00006375262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072957244,0.00057254545,0.00058842875,0.0017789977,0.00031492996,0.0007871662,0.0006836594,0.0005758069,0.00073326216],"category_scores_gemma":[0.0025963108,0.00021640948,0.000644269,0.00085298065,0.00033910017,0.0010402258,0.00032633342,0.0007392668,0.00034421007],"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":[0.0009564572,0.00043229142,0.014707527,0.00018273082,0.0001676154,0.00027895716,0.00048182468,0.35561624,0.037998646,0.008970319,0.0047996524,0.57540774],"study_design_scores_gemma":[0.000007640474,0.00004301267,0.0016129337,0.00000747963,0.000017049319,0.000018064431,0.000038302187,0.99333364,0.002627614,0.0020367973,0.00024720354,0.000010228649],"about_ca_topic_score_codex":0.00908051,"about_ca_topic_score_gemma":0.009852828,"teacher_disagreement_score":0.00908051,"about_ca_system_score_codex":0.00090810243,"about_ca_system_score_gemma":0.000525235,"threshold_uncertainty_score":0.01805532},"labels":[],"label_agreement":null},{"id":"W4244432613","doi":"10.1007/0-387-30038-4_148","title":"Multimedia Content Repurposing","year":2006,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Multimedia","topic":"Video Analysis and Summarization","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 Ottawa","funders":"","keywords":"Repurposing; Content (measure theory); Computer science; Multimedia; Engineering; Mathematics; Waste management","score_opus":0.021121904631678606,"score_gpt":0.22304937731253102,"score_spread":0.20192747268085243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244432613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015220236,0.005582936,0.871847,0.0004361567,0.0009176832,0.001053609,0.0027799592,0.022666464,0.07949601],"genre_scores_gemma":[0.09029643,0.008818791,0.72168005,0.00043162925,0.0010327275,0.000598193,0.011758803,0.004218568,0.16116486],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99964666,0.0000323127,0.000021492528,0.00008603539,0.00017599827,0.000037514958],"domain_scores_gemma":[0.99934214,0.00010660211,0.000028220657,0.00017313927,0.00031049192,0.000039432103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003790368,0.0016548038,0.0008542984,0.0041836817,0.00074592733,0.0017606743,0.0012230386,0.000695411,0.04124051],"category_scores_gemma":[0.0010968476,0.00038441663,0.0009475599,0.003245479,0.00039642552,0.0018437937,0.0013514973,0.00074719655,0.026216235],"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":[0.00013043512,0.000054541873,0.000112578804,0.0003698657,0.000026744501,0.00025758834,0.00010105088,0.0013606034,0.10071,0.0040521664,0.026922807,0.8659017],"study_design_scores_gemma":[0.000049206767,0.00038196155,0.0025320419,0.00021056978,0.0003055057,0.0024117725,0.0005471683,0.082189545,0.4511555,0.014014824,0.44608983,0.00011218248],"about_ca_topic_score_codex":0.0008624003,"about_ca_topic_score_gemma":0.0013436478,"teacher_disagreement_score":0.04124051,"about_ca_system_score_codex":0.00032552707,"about_ca_system_score_gemma":0.00038785327,"threshold_uncertainty_score":0.13796324},"labels":[],"label_agreement":null},{"id":"W4244574989","doi":"10.1109/mis.2007.12","title":"In the News","year":2007,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Video Analysis and Summarization","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; Artificial intelligence; Context (archaeology); Video game; Information retrieval; Multimedia","score_opus":0.02823232242166147,"score_gpt":0.2800995407494166,"score_spread":0.25186721832775516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244574989","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008358468,0.008969778,0.0008137105,0.09297173,0.17163755,0.00010904861,0.0071665966,0.0016297833,0.715866],"genre_scores_gemma":[0.003215518,0.0036242504,0.000492043,0.02804066,0.018769432,0.000045994322,0.004437673,0.00045294617,0.94092155],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900156,0.000097773096,0.00006155009,0.00016122934,0.0005259171,0.00015194633],"domain_scores_gemma":[0.99752444,0.00039976137,0.00016938434,0.00031014718,0.00096444756,0.0006319193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010049945,0.0007552303,0.0006453777,0.0015055514,0.0019095904,0.008024707,0.0011904683,0.003185698,0.48762348],"category_scores_gemma":[0.007115416,0.00025886463,0.00058985216,0.001723773,0.0007992541,0.004600768,0.0021687176,0.004203577,0.35148504],"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":[0.000022921198,0.000006015253,0.000069711146,0.000047472364,0.000002318169,0.000058579077,0.000020112318,0.0000074103978,0.00006258892,0.0026722504,0.9786099,0.018420648],"study_design_scores_gemma":[0.0000025481288,0.0000039654565,0.00015311108,0.000030397385,0.0000010492474,0.00003431704,0.000037194422,0.000007023543,0.000023875273,0.00026645628,0.9994381,0.0000019301358],"about_ca_topic_score_codex":0.0043885554,"about_ca_topic_score_gemma":0.009276621,"teacher_disagreement_score":0.48762348,"about_ca_system_score_codex":0.001646251,"about_ca_system_score_gemma":0.0016916976,"threshold_uncertainty_score":0.73084295},"labels":[],"label_agreement":null},{"id":"W4245104823","doi":"10.1007/978-0-387-39940-9_3932","title":"Video Annotation","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Annotation; Computer science; Artificial intelligence","score_opus":0.01266205303265917,"score_gpt":0.2244591921086938,"score_spread":0.21179713907603465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4245104823","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005914729,0.003516014,0.38211545,0.0009870976,0.0023558936,0.001651609,0.03312912,0.038128585,0.5322016],"genre_scores_gemma":[0.045045283,0.0055728722,0.29026186,0.0011796117,0.00095414236,0.0012412632,0.09416536,0.0066064824,0.55497307],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996209,0.00003079693,0.000023128303,0.00013199789,0.00015235826,0.0000406997],"domain_scores_gemma":[0.9991555,0.00010164996,0.00003075548,0.000180001,0.00046351625,0.00006852237],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042441298,0.0014039751,0.000570599,0.0031064777,0.0009354321,0.0019494866,0.0013567462,0.00097307406,0.16548346],"category_scores_gemma":[0.0017642723,0.00033847036,0.00045758998,0.0025662808,0.0002774693,0.0016698319,0.001484955,0.00068445865,0.13866016],"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":[0.0001914852,0.000042379605,0.00023463384,0.0005378726,0.00001174554,0.00020325629,0.00013151979,0.00048388782,0.028654257,0.005116187,0.3110497,0.6533431],"study_design_scores_gemma":[0.00001889685,0.000052469735,0.0013927628,0.00024739737,0.000028530618,0.0006256217,0.00022004795,0.005245012,0.025992326,0.0038019514,0.962333,0.000041968942],"about_ca_topic_score_codex":0.004381521,"about_ca_topic_score_gemma":0.006573591,"teacher_disagreement_score":0.16548346,"about_ca_system_score_codex":0.00055082527,"about_ca_system_score_gemma":0.000849024,"threshold_uncertainty_score":0.55359733},"labels":[],"label_agreement":null},{"id":"W4246536554","doi":"10.32920/ryerson.14654436.v1","title":"The impact of colour visual attention for video summarization","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Pipeline (software); Frame (networking); Feature (linguistics); Artificial intelligence; Visualization; Key (lock); Ground truth; Human visual system model; Computer vision; Image (mathematics)","score_opus":0.016544992591527617,"score_gpt":0.3165963928970975,"score_spread":0.30005140030556987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246536554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15091555,0.0052604433,0.82597125,0.0010696187,0.0004765063,0.00041076133,0.0006377472,0.009399526,0.005858591],"genre_scores_gemma":[0.7283957,0.0011747358,0.26413965,0.0004224581,0.00030170518,0.00016661981,0.0016137776,0.00047735285,0.0033078436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721956,0.0010019931,0.00015176745,0.00077532866,0.0006565981,0.00019478038],"domain_scores_gemma":[0.990607,0.0051339646,0.0007899204,0.0010183044,0.002061505,0.00038933576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044218595,0.001476878,0.00085310015,0.0018478103,0.000533069,0.0022301788,0.0012458629,0.0011393229,0.0018670593],"category_scores_gemma":[0.021170726,0.00025813386,0.00070306903,0.00081220677,0.00065424846,0.002660866,0.0017674539,0.0014567516,0.0009863348],"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":[0.0018556541,0.00032052994,0.004689757,0.00093274866,0.00025471128,0.00021193456,0.00076285494,0.050799746,0.11909715,0.004525992,0.007655588,0.8088933],"study_design_scores_gemma":[0.00015942144,0.0022504348,0.016260205,0.00019600216,0.00041985355,0.0004651805,0.0005651927,0.8270417,0.12602666,0.013172046,0.0133008985,0.00014243463],"about_ca_topic_score_codex":0.0050538443,"about_ca_topic_score_gemma":0.0045148884,"teacher_disagreement_score":0.0050538443,"about_ca_system_score_codex":0.0011066071,"about_ca_system_score_gemma":0.00094624853,"threshold_uncertainty_score":0.023385286},"labels":[],"label_agreement":null},{"id":"W4247944724","doi":"10.1007/978-1-4939-7131-2_101021","title":"Schema Matching","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Computer science; Matching (statistics); Schema (genetic algorithms); Schema matching; Information retrieval; Database; Mathematics; Statistics; Data integration","score_opus":0.017138540746246107,"score_gpt":0.22282162202170627,"score_spread":0.20568308127546017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247944724","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074617714,0.0028730102,0.7823548,0.0022932333,0.0010429355,0.00091732125,0.013615554,0.017622272,0.17181924],"genre_scores_gemma":[0.08132081,0.0047172112,0.69886416,0.0017285771,0.00028342957,0.0005239024,0.06503869,0.0042676376,0.14325562],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985776,0.00017249312,0.00013995387,0.0005371626,0.0004826777,0.00009002212],"domain_scores_gemma":[0.9987501,0.00022475088,0.000042689877,0.00062085426,0.00031651216,0.00004507105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011708016,0.0009873544,0.00079967506,0.0031469783,0.0009104516,0.0038059496,0.0022919571,0.0013226136,0.08194811],"category_scores_gemma":[0.00512256,0.0006272233,0.0016969797,0.004397607,0.0005671435,0.007435151,0.0032546502,0.00165238,0.036055528],"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":[0.00010907293,0.0001537861,0.0007714337,0.00047377677,0.00007225593,0.00016211957,0.00024298798,0.0018803043,0.005758518,0.16053787,0.13490495,0.694933],"study_design_scores_gemma":[0.000033449203,0.00004349604,0.0005698229,0.0002719016,0.00009286511,0.0009265899,0.0003427865,0.020893307,0.018941369,0.14108112,0.8167652,0.000038118527],"about_ca_topic_score_codex":0.0027860682,"about_ca_topic_score_gemma":0.0025221235,"teacher_disagreement_score":0.08194811,"about_ca_system_score_codex":0.0012597818,"about_ca_system_score_gemma":0.001972044,"threshold_uncertainty_score":0.2741437},"labels":[],"label_agreement":null},{"id":"W4248046064","doi":"10.1007/978-0-387-39940-9_2467","title":"Digital Video Search","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Digital video; Computer graphics (images); Telecommunications","score_opus":0.015202412813281871,"score_gpt":0.22964684958379075,"score_spread":0.2144444367705089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248046064","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066632964,0.023846975,0.07810052,0.0018487932,0.0011427231,0.00044691737,0.006293248,0.0067832847,0.87487423],"genre_scores_gemma":[0.030570196,0.017689025,0.036908664,0.0006704926,0.0004883721,0.00010869337,0.010113979,0.0006785773,0.902772],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978894,0.000017458213,0.00001159434,0.00004690055,0.000109646564,0.000025543395],"domain_scores_gemma":[0.9997322,0.00006579729,0.000012172018,0.000048495243,0.00010487668,0.000036494348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021974691,0.00060025,0.0005205505,0.0033980059,0.00057869457,0.0024174317,0.00084601337,0.0007940181,0.13469161],"category_scores_gemma":[0.00097087515,0.00023071209,0.00030220085,0.0045776237,0.00024916173,0.0030209278,0.0010311322,0.00041604272,0.08208956],"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":[0.00004282813,0.00004674708,0.00019095006,0.00037231855,0.000011331735,0.00007292004,0.000063315674,0.00029912186,0.004147003,0.014862486,0.2638151,0.71607584],"study_design_scores_gemma":[0.000010435422,0.000035636443,0.00066361704,0.00014934753,0.000018036779,0.0005568994,0.00012472789,0.0032232234,0.00455756,0.0069774576,0.9836669,0.00001621176],"about_ca_topic_score_codex":0.002915994,"about_ca_topic_score_gemma":0.0067384276,"teacher_disagreement_score":0.13469161,"about_ca_system_score_codex":0.00064088753,"about_ca_system_score_gemma":0.00061769586,"threshold_uncertainty_score":0.45058835},"labels":[],"label_agreement":null},{"id":"W4248201467","doi":"10.32920/ryerson.14647533","title":"Video content analysis using the video time density function and statistical models","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Video tracking; Video compression picture types; Video content analysis; Video processing; Artificial intelligence; Similarity measure; Video quality; Video post-processing; Multiview Video Coding; Block-matching algorithm; Smacker video; Pattern recognition (psychology); Metric (unit)","score_opus":0.06155042285748146,"score_gpt":0.26030775148508234,"score_spread":0.19875732862760087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248201467","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007329983,0.0008430047,0.9900718,0.00016109087,0.00004043153,0.000045465284,0.00019104597,0.00025651522,0.0010606936],"genre_scores_gemma":[0.434158,0.008683587,0.5467043,0.00026944492,0.00045156165,0.00038078357,0.0023404884,0.00021602082,0.0067958185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922526,0.00014577208,0.00004929376,0.00020560765,0.00031239563,0.000061588595],"domain_scores_gemma":[0.99852127,0.00068791985,0.00022370002,0.00013813285,0.00038867176,0.000040382696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010760721,0.0009455244,0.00081983284,0.0039572394,0.00038647233,0.0017778237,0.0009788232,0.0010582116,0.0012019096],"category_scores_gemma":[0.004301401,0.00031928456,0.0012578688,0.0029850306,0.0007585688,0.0033387074,0.00059608044,0.0011321293,0.00079433294],"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":[0.00020898458,0.00015901364,0.0071875057,0.0005786881,0.0002488126,0.0005681034,0.00042863353,0.2966213,0.03483544,0.14376344,0.0070877913,0.50831234],"study_design_scores_gemma":[0.0000065561567,0.00006314788,0.0019120335,0.000038829963,0.000051063565,0.00020644821,0.000097280325,0.966471,0.0043917927,0.022242602,0.0044856323,0.00003359168],"about_ca_topic_score_codex":0.0067849625,"about_ca_topic_score_gemma":0.002380607,"teacher_disagreement_score":0.0067849625,"about_ca_system_score_codex":0.0012735843,"about_ca_system_score_gemma":0.0007954168,"threshold_uncertainty_score":0.013490975},"labels":[],"label_agreement":null},{"id":"W4248929594","doi":"10.32920/ryerson.14664657","title":"Robust video event recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Event (particle physics); Video quality; Quality (philosophy); Artificial intelligence; Feature (linguistics); Feature extraction; Video tracking; Computer vision; Machine learning; Video processing; Multimedia","score_opus":0.050721007965597704,"score_gpt":0.2456321487700163,"score_spread":0.1949111408044186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248929594","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017018577,0.0014313173,0.9669316,0.00020058568,0.0002947196,0.0003216104,0.0023447298,0.007731586,0.003725255],"genre_scores_gemma":[0.3062655,0.0023640548,0.6634872,0.00042139913,0.0005853368,0.00038940334,0.015276285,0.000845155,0.010365646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859756,0.00013514294,0.000106995714,0.0005806495,0.00043684628,0.0001427899],"domain_scores_gemma":[0.99853075,0.00026234816,0.00022470386,0.0004286355,0.00048849283,0.000065012566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008853117,0.0013742801,0.0015058502,0.0027295751,0.00035303095,0.0013907288,0.0016340417,0.0013916482,0.0035666977],"category_scores_gemma":[0.003914024,0.0003244705,0.0011334993,0.0019004082,0.00035977524,0.0020829272,0.0009925347,0.0012381863,0.0051505044],"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":[0.00048743916,0.00017464018,0.0017805655,0.0004166752,0.00013912591,0.00035197078,0.00006047906,0.017774712,0.11264381,0.0042783604,0.018604305,0.843288],"study_design_scores_gemma":[0.000068649155,0.0004497347,0.0103741735,0.000100385856,0.00021648011,0.001372998,0.00019547596,0.71207327,0.21918035,0.0130997095,0.042753175,0.00011576399],"about_ca_topic_score_codex":0.002059117,"about_ca_topic_score_gemma":0.0018225766,"teacher_disagreement_score":0.0035666977,"about_ca_system_score_codex":0.00048017548,"about_ca_system_score_gemma":0.00051371206,"threshold_uncertainty_score":0.011931837},"labels":[],"label_agreement":null},{"id":"W4251674830","doi":"10.1007/978-0-387-39940-9_3933","title":"Video Chaptering","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science","score_opus":0.014139766883461012,"score_gpt":0.2200185489226371,"score_spread":0.2058787820391761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251674830","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00043673345,0.005292535,0.0090585835,0.00054908096,0.0025296872,0.0002295445,0.0042846315,0.002732026,0.9748872],"genre_scores_gemma":[0.00094397075,0.002796865,0.002850371,0.0002572096,0.00033313534,0.00006249389,0.0034440302,0.00051622675,0.98879576],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998399,0.000010252427,0.000006101214,0.000028412825,0.00009862188,0.000016716265],"domain_scores_gemma":[0.99963593,0.000061965584,0.000010814892,0.00004050334,0.00018421358,0.000066503526],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00018293646,0.0010110547,0.0006144798,0.0026375542,0.0006390212,0.0018433682,0.0012686634,0.0007253928,0.69948494],"category_scores_gemma":[0.00087912777,0.00028860892,0.00051592494,0.0025696848,0.00019965811,0.0015934608,0.0009760802,0.0009222443,0.50010127],"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":[0.00002544127,0.000036297428,0.000024820634,0.00018872847,0.0000023937232,0.000056259054,0.000028959264,0.000103053164,0.0017047419,0.0025798352,0.8121131,0.18313645],"study_design_scores_gemma":[0.000004074912,0.000010187955,0.0001495953,0.00008129036,0.0000024095486,0.00008689967,0.000026510257,0.000095720534,0.0003611307,0.0008908235,0.99828714,0.0000042109164],"about_ca_topic_score_codex":0.0034750393,"about_ca_topic_score_gemma":0.008573257,"teacher_disagreement_score":0.69948494,"about_ca_system_score_codex":0.0005615879,"about_ca_system_score_gemma":0.0006706643,"threshold_uncertainty_score":0.42864823},"labels":[],"label_agreement":null},{"id":"W4251847535","doi":"10.32920/ryerson.14644944.v1","title":"Large-scale Content-based Multimedia Analysis And Applications Using Bag-Of-Words Model","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Search engine indexing; Scalability; Categorization; Domain (mathematical analysis); Information retrieval; Bag-of-words model; Frame (networking); Visual Word; Scale (ratio); Topic model; Image retrieval; Multimedia; Artificial intelligence; Image (mathematics); Database","score_opus":0.040321420088593324,"score_gpt":0.27732524190912794,"score_spread":0.23700382182053462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251847535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018582828,0.003311013,0.9735908,0.00040185536,0.00016530737,0.00015470314,0.00047796956,0.0014432626,0.0018722903],"genre_scores_gemma":[0.44822103,0.0070592286,0.52692795,0.00068937,0.0008194364,0.00062205363,0.0041302047,0.00043091684,0.0110998405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907446,0.00011519734,0.000084183455,0.00029434124,0.00033527808,0.00009651496],"domain_scores_gemma":[0.9990081,0.00035181816,0.0001430411,0.00012047727,0.00032917922,0.000047403682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078972935,0.001374529,0.001462419,0.0046253605,0.00062311476,0.0021581673,0.0014403597,0.0013230791,0.0020003212],"category_scores_gemma":[0.0026126625,0.00053778564,0.0020855048,0.004221764,0.0006286387,0.0036850683,0.0011858586,0.0011649261,0.001696659],"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":[0.00032788984,0.00032536796,0.0041380576,0.00076139276,0.00034387116,0.0006246847,0.00032341745,0.16524845,0.045696933,0.024402596,0.013187536,0.74461967],"study_design_scores_gemma":[0.000008610737,0.00007733841,0.0011479356,0.000026277836,0.00006672947,0.00019537074,0.00008174619,0.97355926,0.0053815464,0.015246821,0.0041748188,0.000033647033],"about_ca_topic_score_codex":0.0049576326,"about_ca_topic_score_gemma":0.0036929562,"teacher_disagreement_score":0.0049576326,"about_ca_system_score_codex":0.0010272465,"about_ca_system_score_gemma":0.0008252737,"threshold_uncertainty_score":0.009857595},"labels":[],"label_agreement":null},{"id":"W4252183759","doi":"10.1007/s11042-019-7493-8","title":"Editorial Note: Frontiers in Multimedia Analytics Emerging Media Types Technologies and Applications","year":2019,"lang":"en","type":"editorial","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","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":"Computer science; Analytics; Multimedia; Data science; World Wide Web","score_opus":0.008455741136766459,"score_gpt":0.24962510311446812,"score_spread":0.24116936197770167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252183759","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000016481501,0.0029432117,0.00012210278,0.017383251,0.978246,0.000013702924,0.000036873764,0.000031298594,0.0012071657],"genre_scores_gemma":[0.00019422887,0.0021882928,0.00008926024,0.0059644063,0.9853072,0.000011415812,0.000022780609,0.000031879998,0.0061905016],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99116725,0.0012094629,0.0009616648,0.0009989265,0.0051748464,0.00048784763],"domain_scores_gemma":[0.9525073,0.013392953,0.002656124,0.0010967833,0.023624608,0.0067222016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086406935,0.0039330735,0.004104295,0.008306822,0.0046491083,0.013482437,0.0034508593,0.015176137,0.024414876],"category_scores_gemma":[0.03061253,0.0010813619,0.0023346571,0.0032665192,0.0029486122,0.005811821,0.0023254612,0.01562454,0.0176503],"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":[0.000019018195,0.000009045561,0.000014403918,0.000133142,0.0000082134775,0.00006273859,0.000009232689,0.000015197414,0.000061891515,0.00019405941,0.99546033,0.0040127565],"study_design_scores_gemma":[0.00003808076,0.00002067631,0.00020933118,0.00038625908,0.000038377933,0.00022989612,0.000059251473,0.00018406902,0.00014677641,0.0012306919,0.997437,0.00001955476],"about_ca_topic_score_codex":0.0019749422,"about_ca_topic_score_gemma":0.005620071,"teacher_disagreement_score":0.024414876,"about_ca_system_score_codex":0.003451307,"about_ca_system_score_gemma":0.0043949666,"threshold_uncertainty_score":0.08167589},"labels":[],"label_agreement":null},{"id":"W4252293138","doi":"10.1145/1539024.1508946","title":"QuickDraw","year":2009,"lang":"en","type":"article","venue":"ACM SIGCSE Bulletin","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Computer science; Multimedia; Interface (matter); Human–computer interaction; Computer graphics; Graphics; Computer graphics (images)","score_opus":0.009217622111990884,"score_gpt":0.2252774183734643,"score_spread":0.21605979626147342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252293138","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052315434,0.0020538322,0.3795106,0.0006765564,0.0012045754,0.0005252169,0.019267326,0.38779724,0.20373304],"genre_scores_gemma":[0.04864473,0.0027796424,0.3698249,0.0015738813,0.00028574868,0.0012240951,0.0612184,0.11773843,0.3967102],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987785,0.00009011618,0.00008392128,0.0002138691,0.0007181732,0.00011539409],"domain_scores_gemma":[0.9981704,0.00053570216,0.000095345844,0.00039874561,0.00056416384,0.0002356677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091907714,0.0014269495,0.00083846267,0.0029719565,0.0009079212,0.0028295454,0.0039441795,0.001530794,0.30068332],"category_scores_gemma":[0.0036953273,0.0013219531,0.0009965872,0.0015786032,0.00040325336,0.0038360676,0.003265555,0.0017849284,0.12139857],"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":[0.00039502516,0.00011812943,0.00058496685,0.00096410984,0.00003439251,0.00029974733,0.00029394982,0.0011710406,0.015613967,0.013612985,0.48577976,0.48113194],"study_design_scores_gemma":[0.00007168172,0.000067079935,0.00037534512,0.0000740893,0.000020610303,0.0005341144,0.000047433885,0.0022080052,0.010174271,0.0040058414,0.9823668,0.00005470051],"about_ca_topic_score_codex":0.0013254875,"about_ca_topic_score_gemma":0.0032327427,"teacher_disagreement_score":0.30068332,"about_ca_system_score_codex":0.0005714448,"about_ca_system_score_gemma":0.0011019354,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4256153909","doi":"10.1145/1878061","title":"Proceedings of the 2010 ACM workshop on Social, adaptive and personalized multimedia interaction and access","year":2010,"lang":"en","type":"paratext","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"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":"Personalization; Computer science; Multimedia; Adaptation (eye); World Wide Web; Context (archaeology); Scalability","score_opus":0.05229072121084883,"score_gpt":0.3183892015281524,"score_spread":0.2660984803173036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256153909","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028438512,0.07664527,0.3671847,0.066274785,0.054338336,0.0018615196,0.005327193,0.009601794,0.39032793],"genre_scores_gemma":[0.09512093,0.044026624,0.132324,0.005938484,0.010550198,0.0012858264,0.0149096465,0.0022533773,0.69359094],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99822503,0.0005873353,0.00012269759,0.000289852,0.0005364176,0.00023868444],"domain_scores_gemma":[0.99745125,0.0006967336,0.00007520816,0.0004246027,0.0007455414,0.0006065523],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0028986684,0.0012702782,0.0013996935,0.0011872742,0.0014926298,0.006114908,0.0023171657,0.002238803,0.064763986],"category_scores_gemma":[0.0049501765,0.00060431584,0.0008141642,0.0013416781,0.0011590436,0.008152647,0.0039481344,0.0033475396,0.022535235],"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":[0.00026691565,0.000221801,0.0006418986,0.00040422234,0.000050419174,0.00026590002,0.0011352475,0.0005625693,0.0040515596,0.01620684,0.73569524,0.24049747],"study_design_scores_gemma":[0.000019710678,0.000057800622,0.0009407031,0.0002168428,0.000031478052,0.000281537,0.0007099611,0.0048071393,0.0009308375,0.007292152,0.9846773,0.00003459424],"about_ca_topic_score_codex":0.005471659,"about_ca_topic_score_gemma":0.011777649,"teacher_disagreement_score":0.93523604,"about_ca_system_score_codex":0.0012283067,"about_ca_system_score_gemma":0.0020090998,"threshold_uncertainty_score":0.21665716},"labels":[],"label_agreement":null},{"id":"W4281479313","doi":"10.32920/ifmj.v2i2.1600","title":"Diverse Perspective of the Shared History","year":2022,"lang":"en","type":"article","venue":"Interactive Film and Media Journal","topic":"Video Analysis and Summarization","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":"Narrative; Perspective (graphical); Newspaper; Media studies; Visual arts; Digital media; History; Sociology; World Wide Web; Literature; Computer science; Art","score_opus":0.015791257776776407,"score_gpt":0.2297968167678566,"score_spread":0.2140055589910802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281479313","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07113706,0.0063891867,0.016360646,0.020525372,0.0013285264,0.00005625197,0.00016744976,0.00015810176,0.8838774],"genre_scores_gemma":[0.92380536,0.0023386297,0.0029247901,0.001695288,0.00046304025,0.000058043872,0.00013463787,0.00017447413,0.06840574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99669623,0.0019768865,0.000077191646,0.0004721969,0.0004387684,0.00033862804],"domain_scores_gemma":[0.99844474,0.00062271167,0.00011414562,0.00030352772,0.00014632259,0.00036866343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024215046,0.00062651176,0.00037419674,0.002547401,0.012266103,0.014417243,0.0013385192,0.002454634,0.015947571],"category_scores_gemma":[0.0036013848,0.00031381863,0.00041947095,0.0019582496,0.031962175,0.016001705,0.014094565,0.004578017,0.0013004619],"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":[0.000024854398,0.000013403238,0.0007671031,0.00008978301,0.000010768716,0.0007876514,0.3037723,0.00013689189,0.00045466912,0.67112786,0.007019581,0.015795127],"study_design_scores_gemma":[0.000010376821,0.000032734974,0.00084252446,0.00034114756,0.000020318263,0.00094253884,0.15617736,0.00020092436,0.00053410843,0.09464641,0.7462212,0.00003046086],"about_ca_topic_score_codex":0.0055012777,"about_ca_topic_score_gemma":0.008718811,"teacher_disagreement_score":0.015947571,"about_ca_system_score_codex":0.0058266735,"about_ca_system_score_gemma":0.0026408415,"threshold_uncertainty_score":0.05334997},"labels":[],"label_agreement":null},{"id":"W4285058078","doi":"10.1007/978-3-031-05637-6_4","title":"Spell Painter: Motion Controlled Spellcasting for a Wizard Video Game","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Ontario Tech University","funders":"","keywords":"Spell; Computer science; Wizard of oz; Wizard; Motion (physics); Video game; Popularity; Computer graphics (images); Painting; Work (physics); Computer vision; Artificial intelligence; Multimedia; Human–computer interaction; Simulation; Visual arts; Art; World Wide Web; Engineering","score_opus":0.01818388902039307,"score_gpt":0.23907727078724542,"score_spread":0.22089338176685236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285058078","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04933774,0.0019562107,0.77328885,0.00038975864,0.0007432019,0.0007788384,0.0019971156,0.031482764,0.14002548],"genre_scores_gemma":[0.19851957,0.0020959654,0.31782573,0.00038056416,0.00019183202,0.0006261426,0.0042711394,0.00471583,0.47137335],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998919,0.000008827375,0.00000560887,0.000021925569,0.000055956927,0.000015888714],"domain_scores_gemma":[0.9999068,0.000028930372,0.0000051458724,0.000010756602,0.000027935113,0.000020540683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015350325,0.0011486559,0.00040904677,0.00053058664,0.0003000789,0.00069694297,0.0011271181,0.0006569235,0.046503212],"category_scores_gemma":[0.00039448668,0.0002912309,0.0003315418,0.0003287334,0.00018005598,0.0007055155,0.0007751033,0.00066457805,0.008377514],"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":[0.000835806,0.00028920462,0.00029019866,0.00047852608,0.000051256433,0.0004029505,0.00032780477,0.0037324936,0.19718334,0.008515568,0.06534082,0.72255194],"study_design_scores_gemma":[0.00050708663,0.0015906439,0.0063614347,0.00030398095,0.00019990577,0.0027551646,0.00035319623,0.15536931,0.1917158,0.007967553,0.63267744,0.00019844319],"about_ca_topic_score_codex":0.002429893,"about_ca_topic_score_gemma":0.0049102586,"teacher_disagreement_score":0.046503212,"about_ca_system_score_codex":0.00028747667,"about_ca_system_score_gemma":0.0003444837,"threshold_uncertainty_score":0.15556878},"labels":[],"label_agreement":null},{"id":"W4287178698","doi":"10.48550/arxiv.2105.10563","title":"Puck localization and multi-task event recognition in broadcast hockey\\n videos","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Analysis and Summarization","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":"Computer science; Event (particle physics); Context (archaeology); Artificial intelligence; Task (project management); Computer vision; Engineering; Geography","score_opus":0.06679461980108352,"score_gpt":0.18903485160655645,"score_spread":0.12224023180547293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287178698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44039142,0.0015390229,0.5459778,0.0005296591,0.0003546154,0.00021274904,0.0010239865,0.0041737505,0.005797034],"genre_scores_gemma":[0.93889606,0.00031959484,0.05450321,0.00013385518,0.00012440853,0.00007582382,0.0017724824,0.00009253999,0.004081864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994691,0.000080519254,0.000020226124,0.00023738029,0.00008093197,0.000111897956],"domain_scores_gemma":[0.99946815,0.00023597726,0.00006265555,0.00006336093,0.00011148796,0.00005829511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065634033,0.0012546674,0.00068525557,0.0011094735,0.00037673753,0.00072327117,0.0011297141,0.00077880884,0.001417402],"category_scores_gemma":[0.0023795383,0.00026797573,0.00043976077,0.0005764005,0.0003804762,0.001252239,0.00094146625,0.0010114623,0.0006554161],"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":[0.0012346484,0.0006887537,0.010439763,0.00027658106,0.0001646672,0.0004978331,0.00034385832,0.19338958,0.04235084,0.0022278195,0.009128752,0.739257],"study_design_scores_gemma":[0.000010588849,0.00013464819,0.004287186,0.000012179766,0.000025868654,0.00006600841,0.000120066994,0.982754,0.01010348,0.0015081874,0.0009669738,0.000010888362],"about_ca_topic_score_codex":0.012501979,"about_ca_topic_score_gemma":0.011722539,"teacher_disagreement_score":0.012501979,"about_ca_system_score_codex":0.0007195491,"about_ca_system_score_gemma":0.00041333502,"threshold_uncertainty_score":0.024858415},"labels":[],"label_agreement":null},{"id":"W4287815362","doi":"10.48550/arxiv.2004.06172","title":"Event detection in coarsely annotated sports videos via parallel multi\\n receptive field 1D convolutions","year":2020,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Analysis and Summarization","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":"Computer science; Event (particle physics); Ice hockey; Task (project management); Field (mathematics); Frame (networking); Artificial intelligence; Analytics; Convolutional neural network; Pattern recognition (psychology); Data mining","score_opus":0.06676311292263942,"score_gpt":0.19829916927574467,"score_spread":0.13153605635310525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287815362","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07761634,0.00054612965,0.91524786,0.0003074413,0.000103327606,0.000069821006,0.0004045605,0.0024659485,0.0032385604],"genre_scores_gemma":[0.6619801,0.0005394886,0.32676205,0.0002849793,0.00008889159,0.00008194154,0.0012999865,0.00017643237,0.008786175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975306,0.00003131566,0.000010857964,0.000092492,0.000054755295,0.00005753858],"domain_scores_gemma":[0.99974865,0.000095885734,0.000031925065,0.00004335222,0.000049657363,0.000030499357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005540232,0.0007620001,0.00059518433,0.00073395565,0.00023506432,0.0005929908,0.00096495386,0.0006806646,0.0021090615],"category_scores_gemma":[0.0011587108,0.0003622323,0.00071834703,0.0005321905,0.00036647284,0.0010283515,0.0008218842,0.0008437511,0.00073148],"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":[0.0007132622,0.00025877962,0.0023776176,0.00016041107,0.00013964761,0.0003849005,0.00014075483,0.22869371,0.14475073,0.006912551,0.005883123,0.6095845],"study_design_scores_gemma":[0.000010295046,0.000040536142,0.0008318551,0.000007743811,0.0000128421925,0.000061318344,0.000013290661,0.9841651,0.0117328,0.0022393113,0.000875574,0.000009355438],"about_ca_topic_score_codex":0.009413719,"about_ca_topic_score_gemma":0.012522913,"teacher_disagreement_score":0.009413719,"about_ca_system_score_codex":0.0007334781,"about_ca_system_score_gemma":0.00065324036,"threshold_uncertainty_score":0.018717825},"labels":[],"label_agreement":null},{"id":"W4288748398","doi":"10.1007/s11042-022-13418-6","title":"Multimodal movie genre classification using recurrent neural network","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","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 Toronto","funders":"","keywords":"Computer science; Categorization; Classifier (UML); Film genre; Artificial intelligence; Modal; Recurrent neural network; Natural language processing; Machine learning; Artificial neural network","score_opus":0.05102731265886538,"score_gpt":0.27856359270794057,"score_spread":0.2275362800490752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288748398","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54517055,0.008850298,0.41340774,0.0011347379,0.0014494887,0.0005472173,0.0058711404,0.009971538,0.013597365],"genre_scores_gemma":[0.8886729,0.0014037646,0.085474275,0.00018791719,0.0005794675,0.00022443925,0.009110011,0.0001537414,0.014193537],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996536,0.000049673952,0.000025787152,0.00011042764,0.00007508466,0.00008551818],"domain_scores_gemma":[0.99952006,0.00012042089,0.00004641935,0.000050318,0.00021280488,0.000049880644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060046517,0.0012372155,0.0010612392,0.0026840372,0.00043190768,0.0007757831,0.00083439704,0.00071751914,0.0028471574],"category_scores_gemma":[0.0012406401,0.00023823533,0.001158047,0.0017238419,0.00015972556,0.0007718895,0.0006074764,0.0009934453,0.0019019082],"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":[0.00073585974,0.0007606588,0.008565573,0.00018346902,0.00031839946,0.00021443238,0.000084326915,0.024450911,0.023077257,0.0006525676,0.015552412,0.92540413],"study_design_scores_gemma":[0.000017942686,0.00016676713,0.005445556,0.000024652027,0.00015390187,0.000059155354,0.000059860136,0.98599756,0.0056765974,0.00072446937,0.0016532966,0.000020249043],"about_ca_topic_score_codex":0.009102697,"about_ca_topic_score_gemma":0.011382794,"teacher_disagreement_score":0.009102697,"about_ca_system_score_codex":0.0006404479,"about_ca_system_score_gemma":0.0004254989,"threshold_uncertainty_score":0.018099427},"labels":[],"label_agreement":null},{"id":"W4289547719","doi":"","title":"A First Summarization System of a Video in a Target Language","year":2018,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Video Analysis and Summarization","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","funders":"","keywords":"Automatic summarization; Computer science; Natural language processing; Artificial intelligence; Information retrieval","score_opus":0.006159265545334013,"score_gpt":0.20218177949165672,"score_spread":0.1960225139463227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289547719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037847236,0.0026773051,0.87643635,0.00093144,0.0010678628,0.0012513327,0.0077710096,0.063897654,0.008119807],"genre_scores_gemma":[0.13749057,0.0013987732,0.8133747,0.00039124908,0.0007993002,0.0008396112,0.021802459,0.0029657516,0.020937663],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990414,0.0001944396,0.00011235479,0.00032602713,0.00022534147,0.000100553334],"domain_scores_gemma":[0.99728715,0.00062731834,0.00013518872,0.00027239838,0.0015313653,0.00014664902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010536225,0.0022830486,0.0014599687,0.0026474092,0.0012046971,0.0025298197,0.00091809034,0.0016990894,0.012671151],"category_scores_gemma":[0.0035262874,0.0004483981,0.0009837511,0.0015145844,0.00035305234,0.0017136689,0.0010441188,0.0012215129,0.010626536],"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":[0.0014558369,0.00016795535,0.0009201417,0.0014380314,0.00017994129,0.00075892406,0.0009245702,0.002495102,0.27530095,0.0021280432,0.048900075,0.66533047],"study_design_scores_gemma":[0.0003171315,0.0019391731,0.007823999,0.00043871035,0.0010551162,0.0018436036,0.0016129491,0.2190727,0.5390298,0.0047396384,0.22186,0.00026733102],"about_ca_topic_score_codex":0.0039821886,"about_ca_topic_score_gemma":0.003341271,"teacher_disagreement_score":0.012671151,"about_ca_system_score_codex":0.00080002454,"about_ca_system_score_gemma":0.0010182521,"threshold_uncertainty_score":0.042389214},"labels":[],"label_agreement":null},{"id":"W4293223831","doi":"10.11159/mhci22.109","title":"Video Analysis Tool with Template Matching and Audio-Track Processing","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Video Analysis and Summarization","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":"Saint Mary's University","funders":"Universidad del Atlántico","keywords":"Computer science; Track (disk drive); Matching (statistics); Audio signal processing; Computer vision; Artificial intelligence; Speech recognition; Computer graphics (images); Speech coding; Audio signal","score_opus":0.004623011767010912,"score_gpt":0.19016663725312608,"score_spread":0.18554362548611517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293223831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032688205,0.00009548352,0.9351937,0.000059215585,0.000096913944,0.00027972224,0.0009915874,0.057332687,0.0026819743],"genre_scores_gemma":[0.039714105,0.00014178467,0.94482297,0.00012768453,0.0000883815,0.0006396726,0.0033401272,0.0035347377,0.00759059],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989298,0.00007044816,0.000108873784,0.0003520413,0.00044511273,0.00009370178],"domain_scores_gemma":[0.99889094,0.00030832563,0.00009999439,0.00020736319,0.0004235897,0.00006982478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008433476,0.0015319661,0.00090894464,0.004739679,0.0006126013,0.0015954495,0.0026181343,0.0015469297,0.029927878],"category_scores_gemma":[0.00349065,0.000543962,0.0012790663,0.0021451928,0.00035860547,0.0015570904,0.0012313648,0.00074970507,0.013471127],"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":[0.00047590316,0.0002426613,0.0012317097,0.00040315549,0.00013275798,0.0004619479,0.00022955064,0.005176047,0.0718617,0.0033936247,0.037056066,0.8793349],"study_design_scores_gemma":[0.00021601008,0.00044814302,0.0061035743,0.0001290876,0.0001715272,0.0022002738,0.00036386392,0.49877754,0.33445063,0.008429341,0.14844255,0.00026742392],"about_ca_topic_score_codex":0.0030780053,"about_ca_topic_score_gemma":0.0026034368,"teacher_disagreement_score":0.029927878,"about_ca_system_score_codex":0.0005390569,"about_ca_system_score_gemma":0.0008060936,"threshold_uncertainty_score":0.10011876},"labels":[],"label_agreement":null},{"id":"W4293863534","doi":"10.1109/siu55565.2022.9864747","title":"Game Character Generation with Generative Adversarial Networks","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Video Analysis and Summarization","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":"Stantec (Canada)","funders":"","keywords":"Computer science; Generative grammar; Task (project management); Metric (unit); Artificial intelligence; Variation (astronomy); Adversarial system; Character (mathematics); Machine learning; The Internet; Scratch; Process (computing); Generative adversarial network; Generative Design; Deep learning; World Wide Web; Programming language","score_opus":0.028422636247417733,"score_gpt":0.24678876200015162,"score_spread":0.2183661257527339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293863534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037833832,0.00040942704,0.9514914,0.0003630344,0.00014692359,0.00015530956,0.00027596083,0.0023004184,0.007023669],"genre_scores_gemma":[0.7759295,0.00023230255,0.20881215,0.0004816764,0.00006914368,0.0002704957,0.00093444984,0.000467716,0.012802482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997348,0.0000816763,0.0000080509435,0.00008519779,0.00005752096,0.00003260281],"domain_scores_gemma":[0.9993863,0.0004093819,0.000037765403,0.00006760914,0.00007186462,0.000027049009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062778493,0.00095328287,0.0004500015,0.0004471786,0.0002063784,0.0005407182,0.00082998414,0.0007621352,0.0031154985],"category_scores_gemma":[0.0018782787,0.0004711984,0.00066618464,0.00028282788,0.0005407337,0.000703332,0.0008438986,0.0013923012,0.0007718803],"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":[0.00008151632,0.000049985618,0.00040191453,0.000044842855,0.00003545177,0.00008693967,0.00004708301,0.921716,0.003562175,0.0050856494,0.0027056278,0.06618276],"study_design_scores_gemma":[0.0000034204284,0.000009395649,0.00003556247,0.0000027880965,0.0000020018595,0.0000108105,0.0000029278888,0.9975237,0.0006567918,0.0014147852,0.00033566475,0.0000022368229],"about_ca_topic_score_codex":0.003037373,"about_ca_topic_score_gemma":0.0041772365,"teacher_disagreement_score":0.0031154985,"about_ca_system_score_codex":0.00066687644,"about_ca_system_score_gemma":0.0003418266,"threshold_uncertainty_score":0.010422409},"labels":[],"label_agreement":null},{"id":"W4294732557","doi":"10.3384/ecp191010","title":"Evaluating deep tracking models for player tracking in broadcast ice hockey video","year":2022,"lang":"en","type":"article","venue":"Linköping electronic conference proceedings","topic":"Video Analysis and Summarization","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 Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Compute Canada","keywords":"Ice hockey; Tracking (education); Computer science; Video tracking; Artificial intelligence; Computer vision; Video processing; Physical medicine and rehabilitation","score_opus":0.06225302887827722,"score_gpt":0.3167346679960382,"score_spread":0.254481639117761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294732557","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.71986634,0.013749395,0.2349745,0.0010090088,0.0014273742,0.00052719127,0.0042635896,0.0097769685,0.014405665],"genre_scores_gemma":[0.90969336,0.0024113322,0.064222954,0.00036411438,0.00021512824,0.00015107954,0.01329017,0.0003918963,0.009259983],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992316,0.0001523823,0.00005440208,0.0002721726,0.00014052168,0.00014882597],"domain_scores_gemma":[0.9986045,0.0007929125,0.000107850545,0.00012517087,0.00027792205,0.00009168602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022328629,0.001956908,0.0009636479,0.0015122154,0.0004445094,0.0013663248,0.0011990303,0.0013647258,0.0021760415],"category_scores_gemma":[0.004703461,0.00036954312,0.0008341769,0.0008917706,0.0004259601,0.0012440906,0.0008874637,0.0014896578,0.0016899019],"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":[0.0017792413,0.0007215159,0.01008648,0.00062760594,0.00057997165,0.00016845494,0.00015173941,0.39409918,0.008475776,0.0011801299,0.016872479,0.56525743],"study_design_scores_gemma":[0.000035023528,0.000273475,0.0022533885,0.00005340079,0.00007479186,0.00003327565,0.00008056422,0.99165756,0.003910266,0.0005276851,0.001087783,0.000012649275],"about_ca_topic_score_codex":0.0263071,"about_ca_topic_score_gemma":0.026051471,"teacher_disagreement_score":0.0263071,"about_ca_system_score_codex":0.0013820779,"about_ca_system_score_gemma":0.00094865664,"threshold_uncertainty_score":0.052307963},"labels":[],"label_agreement":null},{"id":"W4294739740","doi":"10.3384/ecp191002","title":"Puck and Player Tracking: Challenges and Opportunities","year":2022,"lang":"en","type":"article","venue":"Linköping electronic conference proceedings","topic":"Video Analysis and Summarization","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":"Computer science; Tracking (education)","score_opus":0.06108070508056294,"score_gpt":0.23574272067945923,"score_spread":0.1746620155988963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294739740","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03987401,0.05893666,0.70525503,0.15260538,0.004471328,0.00069839484,0.00336479,0.0033687262,0.031425748],"genre_scores_gemma":[0.3546842,0.027000427,0.5846957,0.00627823,0.005011278,0.0010372307,0.005336934,0.0018189327,0.014137085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.95395,0.023483826,0.002746008,0.0058696927,0.012710303,0.0012401957],"domain_scores_gemma":[0.8502307,0.0883455,0.0058711777,0.011482323,0.039506886,0.0045633446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.046450615,0.0020397117,0.0029500227,0.005980911,0.0031281016,0.011873205,0.0059702876,0.0055408105,0.0032870846],"category_scores_gemma":[0.099921964,0.0012552949,0.0007985146,0.009243775,0.005126333,0.03498612,0.00730921,0.0065661776,0.0031065443],"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":[0.0003412928,0.00025072333,0.023706988,0.002023641,0.0001609331,0.00020245955,0.0043158554,0.007025429,0.002967632,0.09749909,0.07493777,0.78656816],"study_design_scores_gemma":[0.00006698441,0.0006998089,0.022066977,0.0021779074,0.00015417543,0.0014601435,0.026437424,0.13041632,0.009289866,0.45510915,0.3515068,0.00061451475],"about_ca_topic_score_codex":0.009844785,"about_ca_topic_score_gemma":0.009182342,"teacher_disagreement_score":0.046450615,"about_ca_system_score_codex":0.0032401308,"about_ca_system_score_gemma":0.003426934,"threshold_uncertainty_score":0.24565727},"labels":[],"label_agreement":null},{"id":"W4296491960","doi":"10.1145/3546750","title":"Augmented Reality Based Video Shooting Guidance for Novice Users","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Video Analysis and Summarization","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; Augmented reality; Mobile device; Sample (material); Multimedia; Matching (statistics); Computer vision; Artificial intelligence; Video camera; Human–computer interaction","score_opus":0.05871861747895485,"score_gpt":0.3244186896993891,"score_spread":0.26570007222043424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296491960","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.64714843,0.0009963753,0.3349571,0.00028904018,0.00006633434,0.00036404212,0.00027254914,0.0068901097,0.009015952],"genre_scores_gemma":[0.82321185,0.0006801264,0.16840984,0.00013176104,0.000032318298,0.00013908779,0.00033236848,0.00013517076,0.0069275512],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997987,0.000058051435,0.000013533563,0.00003842704,0.000059851307,0.00003137885],"domain_scores_gemma":[0.9992341,0.00030742193,0.00007222935,0.00011052454,0.00018084144,0.00009493219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026715582,0.0006541418,0.00034112652,0.0003149423,0.00020516216,0.00054862903,0.0006174972,0.00072960585,0.005156826],"category_scores_gemma":[0.0014594899,0.0002056818,0.00038654474,0.00012225175,0.00018359121,0.0006471888,0.000693062,0.0004752421,0.0010904046],"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":[0.0016431683,0.0008545857,0.009052003,0.0014314327,0.000086456654,0.0028670703,0.004402665,0.004714556,0.39473313,0.0008190678,0.0048789256,0.57451683],"study_design_scores_gemma":[0.00081931456,0.020878281,0.14418653,0.0008386394,0.0007068884,0.018123519,0.011591134,0.33144945,0.37658694,0.002604354,0.091654815,0.000560109],"about_ca_topic_score_codex":0.0010041379,"about_ca_topic_score_gemma":0.0023016438,"teacher_disagreement_score":0.005156826,"about_ca_system_score_codex":0.00010044468,"about_ca_system_score_gemma":0.00018462307,"threshold_uncertainty_score":0.017251253},"labels":[],"label_agreement":null},{"id":"W4297750301","doi":"","title":"Soccer video retrival using adaptive time-frequency methods","year":2006,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Video Analysis and Summarization","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":"Computer science; Time–frequency analysis; Real-time computing; Telecommunications; Radar","score_opus":0.016595478436409405,"score_gpt":0.254173638980825,"score_spread":0.2375781605444156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297750301","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037114155,0.00066782197,0.9579936,0.00016578202,0.00026623876,0.00011039515,0.00014886649,0.0009853762,0.0025477419],"genre_scores_gemma":[0.2354237,0.0007083471,0.7529664,0.00013630519,0.00041453846,0.00020244632,0.0006871136,0.00036453456,0.00909655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995484,0.00008627958,0.000029456049,0.00012523877,0.00016317076,0.0000475775],"domain_scores_gemma":[0.9986526,0.00055731763,0.000102946906,0.00012971331,0.00049358985,0.000063790685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076636253,0.00092561194,0.0007365763,0.001927873,0.0005626483,0.00080114516,0.0009379864,0.0010181784,0.004794032],"category_scores_gemma":[0.002811388,0.0003632493,0.00070507126,0.0014523815,0.00031866148,0.0012766905,0.0006959096,0.0007325421,0.0015718207],"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":[0.0007717354,0.00022254442,0.00073759415,0.00017547992,0.000085265216,0.00006752577,0.00010993679,0.016887166,0.09546164,0.0009135571,0.0015996709,0.8829679],"study_design_scores_gemma":[0.00009667036,0.00032898292,0.006592135,0.000038022077,0.00017751004,0.00026452172,0.00015811138,0.92443776,0.057639394,0.0017560862,0.008456404,0.00005441946],"about_ca_topic_score_codex":0.0022404075,"about_ca_topic_score_gemma":0.004427411,"teacher_disagreement_score":0.004794032,"about_ca_system_score_codex":0.0002809391,"about_ca_system_score_gemma":0.00045277074,"threshold_uncertainty_score":0.016037643},"labels":[],"label_agreement":null},{"id":"W4302012097","doi":"10.32920/ryerson.14655225","title":"Statistical models of intelligent video-content analysis for cognition","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Cognition; Artificial intelligence; Ranking (information retrieval); Machine learning; Psychology","score_opus":0.12512991755990366,"score_gpt":0.3124006253717518,"score_spread":0.18727070781184812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302012097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027698211,0.0009193569,0.9652838,0.00070255325,0.0000693565,0.00009932994,0.00056038576,0.0005359264,0.0041310526],"genre_scores_gemma":[0.73554754,0.0024307454,0.24885403,0.0003190073,0.0002984606,0.00055386726,0.0014203809,0.00021755965,0.01035827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991824,0.00029203235,0.000045003868,0.00020655573,0.00020222494,0.000071678245],"domain_scores_gemma":[0.99573094,0.0030872447,0.00043920838,0.00026988203,0.00038950393,0.00008329971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022088268,0.00075657375,0.0006656674,0.0020009552,0.0003647531,0.0020818561,0.0011533573,0.00096135715,0.0027858566],"category_scores_gemma":[0.009350669,0.00042698396,0.0012556788,0.0013955528,0.0008476195,0.002119122,0.00070535747,0.0012310663,0.0010172863],"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":[0.00013345461,0.00023599478,0.011902282,0.00032687825,0.00031429637,0.00018854535,0.0006564225,0.55900955,0.006689032,0.24743497,0.0055904547,0.16751808],"study_design_scores_gemma":[0.0000034286743,0.000023902961,0.0022859355,0.000018573794,0.000016883787,0.000033257355,0.000033695465,0.95457256,0.00040587608,0.041156597,0.0014321884,0.000017026352],"about_ca_topic_score_codex":0.010284185,"about_ca_topic_score_gemma":0.006353147,"teacher_disagreement_score":0.010284185,"about_ca_system_score_codex":0.0021232693,"about_ca_system_score_gemma":0.0008505811,"threshold_uncertainty_score":0.020448625},"labels":[],"label_agreement":null},{"id":"W4302012123","doi":"10.32920/ryerson.14655225.v1","title":"Statistical models of intelligent video-content analysis for cognition","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Cognition; Artificial intelligence; Machine learning; Ranking (information retrieval); Granularity; Psychology","score_opus":0.12512991755990366,"score_gpt":0.3124006253717518,"score_spread":0.18727070781184812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302012123","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027698211,0.0009193569,0.9652838,0.00070255325,0.0000693565,0.00009932994,0.00056038576,0.0005359264,0.0041310526],"genre_scores_gemma":[0.73554754,0.0024307454,0.24885403,0.0003190073,0.0002984606,0.00055386726,0.0014203809,0.00021755965,0.01035827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991824,0.00029203235,0.000045003868,0.00020655573,0.00020222494,0.000071678245],"domain_scores_gemma":[0.99573094,0.0030872447,0.00043920838,0.00026988203,0.00038950393,0.00008329971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022088268,0.00075657375,0.0006656674,0.0020009552,0.0003647531,0.0020818561,0.0011533573,0.00096135715,0.0027858566],"category_scores_gemma":[0.009350669,0.00042698396,0.0012556788,0.0013955528,0.0008476195,0.002119122,0.00070535747,0.0012310663,0.0010172863],"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":[0.00013345461,0.00023599478,0.011902282,0.00032687825,0.00031429637,0.00018854535,0.0006564225,0.55900955,0.006689032,0.24743497,0.0055904547,0.16751808],"study_design_scores_gemma":[0.0000034286743,0.000023902961,0.0022859355,0.000018573794,0.000016883787,0.000033257355,0.000033695465,0.95457256,0.00040587608,0.041156597,0.0014321884,0.000017026352],"about_ca_topic_score_codex":0.010284185,"about_ca_topic_score_gemma":0.006353147,"teacher_disagreement_score":0.010284185,"about_ca_system_score_codex":0.0021232693,"about_ca_system_score_gemma":0.0008505811,"threshold_uncertainty_score":0.020448625},"labels":[],"label_agreement":null},{"id":"W4307205164","doi":"10.1561/0600000099","title":"Video Summarization Overview","year":2022,"lang":"en","type":"article","venue":"Foundations and Trends® in Computer Graphics and Vision","topic":"Video Analysis and Summarization","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 Manitoba","funders":"","keywords":"Automatic summarization; Computer science; Field (mathematics); Multimedia; Data science; World Wide Web; Information retrieval","score_opus":0.022950818022527394,"score_gpt":0.3016034509537627,"score_spread":0.27865263293123527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307205164","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010098363,0.071428865,0.8248706,0.0026548326,0.0020040215,0.0014942323,0.012737162,0.03116122,0.043550733],"genre_scores_gemma":[0.12884472,0.06379668,0.65935135,0.0019020436,0.0031464456,0.0012031394,0.07953985,0.0035939976,0.05862174],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99894553,0.00018983679,0.000115822455,0.00025845913,0.00040661887,0.00008359453],"domain_scores_gemma":[0.99817467,0.00040277868,0.0001467805,0.0002416906,0.00091767835,0.00011634094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001033084,0.0017222851,0.0009410555,0.0035575803,0.0006123439,0.0021455789,0.0016354268,0.0010497385,0.018411543],"category_scores_gemma":[0.0039463923,0.0004197549,0.0007996476,0.003062776,0.000347468,0.0032849964,0.0011623506,0.0015078785,0.012012867],"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":[0.00025811425,0.00011113854,0.0005421124,0.0019763194,0.00010711742,0.00016252193,0.00015315633,0.006865836,0.018891718,0.006602348,0.10605698,0.8582727],"study_design_scores_gemma":[0.00013055609,0.0009916147,0.0042711594,0.0010969193,0.00034129104,0.0017570042,0.0005689977,0.12019525,0.07030739,0.0259329,0.77422196,0.0001850155],"about_ca_topic_score_codex":0.003898745,"about_ca_topic_score_gemma":0.004254092,"teacher_disagreement_score":0.018411543,"about_ca_system_score_codex":0.00087463687,"about_ca_system_score_gemma":0.0008941555,"threshold_uncertainty_score":0.061592758},"labels":[],"label_agreement":null},{"id":"W4309598565","doi":"10.1145/3565516.3565523","title":"The Colour of Horror","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Carleton University","funders":"","keywords":"Cluster analysis; Weighting; Computer science; Palette (painting); Theme (computing); Artificial intelligence; Computer vision; Computer graphics (images); Art; Visual arts; World Wide Web","score_opus":0.005337281085710543,"score_gpt":0.1934269462387848,"score_spread":0.18808966515307426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309598565","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24293156,0.0038566901,0.58760923,0.0012159433,0.0026752052,0.0011210932,0.015385328,0.012358627,0.13284636],"genre_scores_gemma":[0.47812077,0.0016657498,0.4748455,0.000299868,0.0005331988,0.0006892312,0.0082268035,0.003697154,0.03192168],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99965703,0.00006584745,0.000019559708,0.000094579016,0.00012255296,0.000040480754],"domain_scores_gemma":[0.9988949,0.0003249507,0.00007300393,0.00016407474,0.00045984267,0.00008322344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058029493,0.0006949364,0.00036939202,0.0043635936,0.00088803296,0.0018440684,0.00043988027,0.0004316663,0.013167452],"category_scores_gemma":[0.003851807,0.00037389415,0.00071805273,0.0020220522,0.0006921405,0.0013877314,0.0012353943,0.0008593819,0.0032603103],"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":[0.0011814358,0.00006312668,0.014306925,0.002052461,0.00015699894,0.00044701085,0.0063359914,0.006618119,0.07136586,0.017908944,0.05747244,0.82209074],"study_design_scores_gemma":[0.00017743676,0.0004950894,0.13276584,0.0013577888,0.0003733314,0.0018854742,0.007612238,0.08777435,0.08264551,0.034863085,0.6495765,0.00047329828],"about_ca_topic_score_codex":0.0022070927,"about_ca_topic_score_gemma":0.0043561175,"teacher_disagreement_score":0.013167452,"about_ca_system_score_codex":0.0004992763,"about_ca_system_score_gemma":0.00033953995,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4312419729","doi":"10.1007/978-3-031-20212-4_24","title":"A Reusable Methodology for Player Clustering Using Wasserstein Autoencoders","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Western University","funders":"","keywords":"Autoencoder; Cluster analysis; Computer science; Artificial intelligence; Machine learning; Data mining; Deep learning","score_opus":0.08386138389997486,"score_gpt":0.31017521132604137,"score_spread":0.2263138274260665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312419729","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021569348,0.000017930995,0.9982022,0.000008171001,0.000011757763,0.000021436414,0.00003659402,0.001269477,0.00021658851],"genre_scores_gemma":[0.01575973,0.00008426369,0.9784545,0.000037418646,0.00003179067,0.00012966296,0.0005882694,0.0009550831,0.003959351],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985812,0.00021680116,0.0001062912,0.0005056551,0.00048858713,0.000101492566],"domain_scores_gemma":[0.99851805,0.00032652114,0.00007454912,0.00053286465,0.00048739047,0.00006055947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001715502,0.0016742558,0.0013036137,0.0019453948,0.0010540637,0.00202731,0.003730329,0.0016466265,0.009197874],"category_scores_gemma":[0.004597367,0.0012573058,0.0018558662,0.0018150399,0.0008095006,0.0028153586,0.0031323112,0.002490508,0.007174582],"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":[0.000092188944,0.0001373598,0.0006480518,0.00017890919,0.00023435631,0.00012882882,0.00018887712,0.10558829,0.019297505,0.040951047,0.010134345,0.8224203],"study_design_scores_gemma":[0.000018659557,0.00003718857,0.00032570586,0.00003183523,0.00004248591,0.00011929384,0.00005663629,0.9294959,0.014740675,0.04328748,0.011809315,0.00003497196],"about_ca_topic_score_codex":0.0075034187,"about_ca_topic_score_gemma":0.016713908,"teacher_disagreement_score":0.009197874,"about_ca_system_score_codex":0.0009814692,"about_ca_system_score_gemma":0.0014143283,"threshold_uncertainty_score":0.030769885},"labels":[],"label_agreement":null},{"id":"W4312468557","doi":"10.1561/9781638280798","title":"Video Summarization Overview","year":2022,"lang":"en","type":"book","venue":"","topic":"Video Analysis and Summarization","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":"Automatic summarization; Computer science; Multimedia; Field (mathematics); Internet video; The Internet; Video tracking; World Wide Web; Video processing; Artificial intelligence","score_opus":0.021869113215258702,"score_gpt":0.23796544692138866,"score_spread":0.21609633370612996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312468557","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005196945,0.0608321,0.73357695,0.0023759366,0.003986528,0.00094923784,0.0060872054,0.020809647,0.16618542],"genre_scores_gemma":[0.071403615,0.073153935,0.46699575,0.0018126592,0.0042932346,0.0008541358,0.03870264,0.004482613,0.3383015],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99946064,0.00006330151,0.00004104622,0.00013025568,0.00026458089,0.000040047576],"domain_scores_gemma":[0.9990839,0.000166528,0.000055519416,0.00010263848,0.0005332708,0.000058099013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046699695,0.0014523689,0.0007057737,0.0033208935,0.00062276,0.0021639995,0.0016862554,0.0009546694,0.04681717],"category_scores_gemma":[0.0016133708,0.0004034221,0.00069883553,0.0032283203,0.00033955404,0.0028988698,0.0010272611,0.0013967056,0.028765049],"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":[0.00012038036,0.00005208741,0.00012218062,0.0011408908,0.000034695542,0.00013660698,0.00011898993,0.0037250018,0.013718389,0.009697371,0.16455303,0.8065803],"study_design_scores_gemma":[0.000029543666,0.0002511106,0.00095105136,0.000459595,0.000072944356,0.0011082436,0.0002036729,0.030332273,0.024891859,0.01260472,0.929028,0.00006706575],"about_ca_topic_score_codex":0.002476008,"about_ca_topic_score_gemma":0.0030041889,"teacher_disagreement_score":0.04681717,"about_ca_system_score_codex":0.00087839516,"about_ca_system_score_gemma":0.000607905,"threshold_uncertainty_score":0.15661895},"labels":[],"label_agreement":null},{"id":"W4312820677","doi":"10.1145/3555858.3555909","title":"Automatic Interactive Documentation for Emergent Story Discovery","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Concordia University","funders":"Fonds de Recherche du Québec-Société et Culture","keywords":"Documentation; Computer science; Data science; World Wide Web; Programming language","score_opus":0.010359310440989105,"score_gpt":0.2674520180742217,"score_spread":0.2570927076332326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312820677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031916652,0.00042846965,0.8973628,0.0003872514,0.000096116724,0.00033295344,0.003914471,0.05512434,0.01043688],"genre_scores_gemma":[0.18600991,0.0003794456,0.78649217,0.00010157958,0.00007597954,0.00060053216,0.012041324,0.004552522,0.009746564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812394,0.0006137269,0.00021518739,0.00038857452,0.0005761037,0.000082332765],"domain_scores_gemma":[0.9874658,0.0077642486,0.0007830721,0.002111673,0.0014617621,0.00041348278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024737655,0.0009320777,0.00059974444,0.0037375537,0.00094017136,0.0033321006,0.0014831411,0.0010125552,0.013171715],"category_scores_gemma":[0.017880406,0.0005263792,0.00050528604,0.001784405,0.00056826003,0.0041330634,0.004635028,0.0011052428,0.00508396],"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":[0.00090931257,0.00036305984,0.0050410554,0.0018989107,0.000098668475,0.0012118556,0.012278912,0.003915964,0.033476733,0.036866933,0.06755794,0.83638066],"study_design_scores_gemma":[0.00031145726,0.00031310503,0.0063114474,0.0008246451,0.00013050431,0.0019319382,0.006674719,0.28050843,0.08068037,0.07915302,0.5429439,0.00021645134],"about_ca_topic_score_codex":0.0009227559,"about_ca_topic_score_gemma":0.0020871656,"teacher_disagreement_score":0.013171715,"about_ca_system_score_codex":0.0004986951,"about_ca_system_score_gemma":0.00088824873,"threshold_uncertainty_score":0.044063747},"labels":[],"label_agreement":null},{"id":"W4312956563","doi":"10.1007/978-3-031-19836-6_2","title":"Sports Video Analysis on Large-Scale Data","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"York University","funders":"","keywords":"Computer science; Closed captioning; Segmentation; Salient; Artificial intelligence; Transformer; Machine learning; Annotation; Focus (optics); Action recognition; Image (mathematics)","score_opus":0.01675281976667602,"score_gpt":0.25093597353373803,"score_spread":0.234183153767062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312956563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088054135,0.0044753607,0.8807527,0.00086190325,0.0004957473,0.00021739125,0.01059047,0.006371853,0.008180612],"genre_scores_gemma":[0.3860902,0.0044822562,0.54724634,0.00020605896,0.00092640705,0.00027225647,0.041452546,0.0008787527,0.01844513],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99966145,0.000054117892,0.000024515304,0.00011293355,0.00010350762,0.000043383796],"domain_scores_gemma":[0.9992193,0.00037859584,0.00005315352,0.00013231079,0.00017700007,0.00003970228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042746536,0.00090749824,0.0007987581,0.00225432,0.00031686376,0.0010429377,0.000614313,0.00045949113,0.0035482785],"category_scores_gemma":[0.0015995457,0.00026323958,0.00065969385,0.0035429494,0.00018998007,0.0010975929,0.000605735,0.0005674295,0.0031807658],"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":[0.0002062669,0.00012269664,0.004566255,0.00028401028,0.00016741607,0.00019607673,0.00008314614,0.024564888,0.041641656,0.0016212687,0.022209594,0.90433675],"study_design_scores_gemma":[0.000025372932,0.00014854872,0.02511153,0.0000506995,0.00013181953,0.00029265304,0.00036597758,0.90715754,0.02703514,0.013944132,0.025691476,0.000045103778],"about_ca_topic_score_codex":0.0045989035,"about_ca_topic_score_gemma":0.009741466,"teacher_disagreement_score":0.0045989035,"about_ca_system_score_codex":0.00031470397,"about_ca_system_score_gemma":0.00033929144,"threshold_uncertainty_score":0.011870146},"labels":[],"label_agreement":null},{"id":"W4319431216","doi":"10.1109/icacrs55517.2022.10029277","title":"Prediction of YouTube View Count using Supervised and Ensemble Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Automation, Computing and Renewable Systems (ICACRS)","topic":"Video Analysis and Summarization","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":"Computer science; Upload; Random forest; Laptop; Decision tree; Social media; Key (lock); Regression analysis; Linear regression; Variable (mathematics); Variables; Machine learning; Artificial intelligence; World Wide Web; Computer security","score_opus":0.036068480585551614,"score_gpt":0.26011166565705895,"score_spread":0.22404318507150733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319431216","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89287937,0.0020232834,0.090377696,0.0006264332,0.00043875014,0.00023848927,0.0063054757,0.0026179,0.0044925483],"genre_scores_gemma":[0.93712175,0.0005462099,0.04361863,0.00009400163,0.00014064023,0.00014165425,0.015111501,0.00006409265,0.003161448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993845,0.00011060192,0.00006276268,0.00017847633,0.00015633769,0.00010733246],"domain_scores_gemma":[0.998154,0.00069526077,0.00019248249,0.00012713292,0.0007230054,0.00010808014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001147542,0.0010388601,0.0008636502,0.0030342499,0.00037760733,0.00062583556,0.00076350325,0.0007917544,0.0007238145],"category_scores_gemma":[0.0033852113,0.00022590927,0.00081561814,0.0013728053,0.00017235547,0.00079479936,0.00042659685,0.001020585,0.00058961724],"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":[0.0007018805,0.0017876705,0.1611787,0.00036115604,0.00046884918,0.0007185981,0.00024682336,0.30350512,0.007422305,0.0009180716,0.026593622,0.49609715],"study_design_scores_gemma":[0.000005950307,0.00006787391,0.009708452,0.000014451836,0.000018848245,0.000034119737,0.00004906425,0.98838603,0.0010514221,0.0001737484,0.00048051632,0.000009542775],"about_ca_topic_score_codex":0.022739427,"about_ca_topic_score_gemma":0.024723848,"teacher_disagreement_score":0.022739427,"about_ca_system_score_codex":0.00058828684,"about_ca_system_score_gemma":0.00065665314,"threshold_uncertainty_score":0.045214176},"labels":[],"label_agreement":null},{"id":"W4323355188","doi":"10.21203/rs.3.rs-2662848/v1","title":"DMFLC: Short Video Classification Based on Deep Multimodal Feature Fusion and Low Rank Representation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Video Analysis and Summarization","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 Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Modality (human–computer interaction); Modalities; Feature (linguistics); Deep learning; Pattern recognition (psychology); Similarity (geometry); Representation (politics); Consistency (knowledge bases); Domain (mathematical analysis); Feature extraction; Rank (graph theory); Machine learning; Image (mathematics)","score_opus":0.08564821856067076,"score_gpt":0.3908606446143083,"score_spread":0.3052124260536375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323355188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071003795,0.0027534114,0.91249764,0.00072583044,0.0003605423,0.00032208284,0.0020811092,0.0064244973,0.0038310585],"genre_scores_gemma":[0.562069,0.0013500297,0.4048871,0.000583131,0.00071593904,0.00044936253,0.012115185,0.0003129672,0.017517269],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943846,0.000067456676,0.00003991271,0.00015070112,0.00019034045,0.00011309405],"domain_scores_gemma":[0.9993567,0.00012245036,0.00009256218,0.00010146333,0.00025969686,0.00006716917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006508373,0.0014157838,0.001337138,0.0024980488,0.00054954464,0.0010466893,0.0015586606,0.0013076894,0.0035023934],"category_scores_gemma":[0.002157654,0.00023330106,0.0009112417,0.0019042008,0.00035571435,0.0019060367,0.0009802623,0.0015576287,0.0020326898],"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":[0.0004942023,0.00031572685,0.0026511047,0.00012919547,0.00012049073,0.0001364295,0.000059237944,0.03343003,0.018021718,0.0018288662,0.019055856,0.92375714],"study_design_scores_gemma":[0.000025779787,0.00013012132,0.0014549455,0.000020625836,0.000042109303,0.00007390469,0.000045926594,0.98426473,0.009154925,0.0021846371,0.0025806786,0.000021563723],"about_ca_topic_score_codex":0.014188098,"about_ca_topic_score_gemma":0.014939498,"teacher_disagreement_score":0.014188098,"about_ca_system_score_codex":0.0011929298,"about_ca_system_score_gemma":0.0009049838,"threshold_uncertainty_score":0.028211057},"labels":[],"label_agreement":null},{"id":"W4362586868","doi":"10.1007/s11042-023-15128-z","title":"Stacked Bin Convolutional Neural Networks based Sparse Low-Rank Regressor: Robust, Scalable and Novel Model for Memorability Prediction of Videos","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Scalability; Classifier (UML); Pattern recognition (psychology); Machine learning; Data mining; Database","score_opus":0.056808424568736464,"score_gpt":0.254182700682358,"score_spread":0.19737427611362152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362586868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09236776,0.0017657384,0.8965758,0.0005212692,0.00024398591,0.00009185606,0.002085949,0.004225395,0.0021222935],"genre_scores_gemma":[0.82563025,0.0013244115,0.15080996,0.0003795034,0.00028607735,0.0001523161,0.005557381,0.00026042573,0.015599585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997644,0.000032565425,0.000010517037,0.000078196426,0.00005665365,0.000057799924],"domain_scores_gemma":[0.9995158,0.00014348113,0.00007247226,0.00006749195,0.00016501872,0.00003580104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051595253,0.0010603863,0.0009569697,0.00080628024,0.00023200894,0.00057460676,0.0013141525,0.00071563304,0.0021011604],"category_scores_gemma":[0.0015326068,0.000392536,0.0006436687,0.00097144657,0.0003446821,0.0010602721,0.0006786748,0.0014876548,0.0011666236],"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":[0.0006361025,0.0005527696,0.0037459375,0.000184761,0.0002233081,0.00014315262,0.00007691767,0.24956228,0.041890435,0.00488173,0.015697686,0.682405],"study_design_scores_gemma":[0.000004570255,0.00003888672,0.00055159774,0.000005536241,0.000016457665,0.00001505479,0.000005293677,0.9948472,0.0031958178,0.00091222447,0.00040109103,0.0000063751345],"about_ca_topic_score_codex":0.015518947,"about_ca_topic_score_gemma":0.025733082,"teacher_disagreement_score":0.015518947,"about_ca_system_score_codex":0.0006498552,"about_ca_system_score_gemma":0.0009148813,"threshold_uncertainty_score":0.030857205},"labels":[],"label_agreement":null},{"id":"W4377832613","doi":"10.18280/ts.400213","title":"TraViQuA: Natural Language Driven Traffic Video Querying Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Analysis and Summarization","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":"Computer science; Deep learning; Artificial intelligence; Natural (archaeology); Natural language processing; Geology","score_opus":0.014592870128629453,"score_gpt":0.24990855655161567,"score_spread":0.23531568642298623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11302606,0.0016778611,0.46035263,0.0010544878,0.00035483853,0.0014117645,0.027423695,0.38346696,0.011231652],"genre_scores_gemma":[0.44278318,0.00065988034,0.47734094,0.0013788907,0.000080859485,0.0009716463,0.0626443,0.0036619068,0.010478543],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991455,0.0001278231,0.00008660986,0.00032375363,0.00022897267,0.000087331864],"domain_scores_gemma":[0.9992828,0.00029229245,0.0000661453,0.00013281478,0.00016884595,0.000057250538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008787806,0.0016368524,0.00064460497,0.0013059461,0.0004273087,0.0013616651,0.002546992,0.0012610423,0.006396868],"category_scores_gemma":[0.002875049,0.0005230074,0.0010029232,0.0008084136,0.00046800854,0.0029454946,0.0018396167,0.0015713463,0.0022042287],"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":[0.0019003749,0.0014425868,0.009540274,0.0016501189,0.0003909634,0.0014348757,0.0008719792,0.059279915,0.07213529,0.009011096,0.18021776,0.6621247],"study_design_scores_gemma":[0.00011454526,0.00022763608,0.0014267146,0.000037791615,0.000031429347,0.0002791828,0.00020293365,0.9487358,0.022062935,0.0060905474,0.020727543,0.000062937535],"about_ca_topic_score_codex":0.021773167,"about_ca_topic_score_gemma":0.031054439,"teacher_disagreement_score":0.021773167,"about_ca_system_score_codex":0.0014674405,"about_ca_system_score_gemma":0.0011538672,"threshold_uncertainty_score":0.04329288},"labels":[],"label_agreement":null},{"id":"W4379653785","doi":"10.32920/ifmj.v3i1.1679","title":"Reconstructing Transnational History","year":2023,"lang":"en","type":"article","venue":"Interactive Film and Media Journal","topic":"Video Analysis and Summarization","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":"Narrative; Context (archaeology); Sociology; Dialectic; Media studies; Visual arts; World history; History; Aesthetics; Literature; Epistemology; Art","score_opus":0.021358711559583847,"score_gpt":0.23833769318404022,"score_spread":0.21697898162445636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379653785","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30445686,0.0072540753,0.03637001,0.0065330863,0.001077074,0.00020451995,0.0014491279,0.0005628594,0.6420923],"genre_scores_gemma":[0.90840876,0.0059890253,0.019094938,0.0003036516,0.00018704934,0.00012572255,0.001106386,0.00045405774,0.06433045],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99938667,0.00032864796,0.000033922344,0.00008557602,0.00010044277,0.00006468617],"domain_scores_gemma":[0.9986481,0.0007289683,0.000089288864,0.00028258443,0.00016322803,0.00008779984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013080108,0.00045573385,0.00017673941,0.0033658016,0.0029842232,0.006336276,0.0007035834,0.0007808979,0.012671826],"category_scores_gemma":[0.0031854815,0.0002017611,0.00018180026,0.002955433,0.00401185,0.0062942933,0.003360307,0.0013078277,0.0011147717],"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":[0.000097691904,0.000043553242,0.0040694177,0.0005708202,0.000032204684,0.0014441516,0.42214143,0.0011456982,0.004415447,0.33004734,0.021630922,0.2143613],"study_design_scores_gemma":[0.0000066379616,0.00004061338,0.00384732,0.0006962418,0.0000265694,0.00049346604,0.2295463,0.0011278316,0.0026136637,0.03217901,0.72939545,0.000026866395],"about_ca_topic_score_codex":0.0077061704,"about_ca_topic_score_gemma":0.018678013,"teacher_disagreement_score":0.012671826,"about_ca_system_score_codex":0.0029237957,"about_ca_system_score_gemma":0.0015544188,"threshold_uncertainty_score":0.04239148},"labels":[],"label_agreement":null},{"id":"W4380990286","doi":"10.1007/s11042-023-15565-w","title":"AutoTag: automated metadata tagging for film post-production","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Analysis and Summarization","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":"Tamkeen; Swiss Re; York University; New York University Abu Dhabi; National Science Foundation","keywords":"Computer science; Metadata; Shot (pellet); Task (project management); Production (economics); Process (computing); Information retrieval; Multimedia; Human–computer interaction; Artificial intelligence; World Wide Web; Programming language","score_opus":0.03347354137478025,"score_gpt":0.28646826725598884,"score_spread":0.2529947258812086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380990286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03980075,0.0006655071,0.6594859,0.0005292963,0.0005947946,0.00103676,0.012075674,0.27624145,0.0095698545],"genre_scores_gemma":[0.16207339,0.00039271428,0.771299,0.00027284952,0.00025998068,0.00062294793,0.038468193,0.009330079,0.017280856],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980106,0.00047234455,0.0002111082,0.00046142426,0.00069124065,0.00015343663],"domain_scores_gemma":[0.99115795,0.0025606775,0.0006954429,0.002621156,0.0025938114,0.00037084185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025050556,0.0013340495,0.00071924296,0.005208765,0.001281344,0.0025459137,0.0016123012,0.0011233189,0.011517167],"category_scores_gemma":[0.00871374,0.00063406775,0.0006125672,0.0026553595,0.0006128796,0.0038597428,0.0029306249,0.0010316825,0.012901798],"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":[0.00076807174,0.0002971608,0.005368928,0.00083008915,0.000088001885,0.00072981423,0.0020184822,0.0025964137,0.092044145,0.0038963999,0.10168539,0.7896772],"study_design_scores_gemma":[0.00025896105,0.0006971195,0.020018848,0.00040206255,0.00018116104,0.0014018547,0.0039506415,0.25833625,0.36856952,0.01637785,0.32941204,0.00039367276],"about_ca_topic_score_codex":0.005126119,"about_ca_topic_score_gemma":0.0071050515,"teacher_disagreement_score":0.011517167,"about_ca_system_score_codex":0.0008784839,"about_ca_system_score_gemma":0.001036369,"threshold_uncertainty_score":0.03852874},"labels":[],"label_agreement":null},{"id":"W4383424622","doi":"10.1007/978-3-031-36183-8_10","title":"Deep Learning Based Camera Switching for Sports Broadcasting","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Computer science; Broadcasting (networking); Artificial intelligence; Ice hockey; Scheme (mathematics); Computer vision; Tracking (education); Smart camera; Deep learning; Computer security","score_opus":0.016927834695568248,"score_gpt":0.24213651291329982,"score_spread":0.22520867821773158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383424622","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.107000746,0.003032471,0.860308,0.00043917916,0.00049231347,0.000117736236,0.0013051213,0.005977244,0.02132723],"genre_scores_gemma":[0.7305688,0.0013730129,0.22241688,0.0003063695,0.00026570263,0.000071160284,0.0025755544,0.00043409833,0.041988406],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998565,0.000014221227,0.000004065034,0.000045130277,0.000036075824,0.000043938602],"domain_scores_gemma":[0.99987316,0.000038835762,0.000010510299,0.00002248409,0.00003569947,0.000019241585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020470569,0.00054262445,0.00067610276,0.0005012538,0.00025377359,0.0004821667,0.0008045884,0.0005442911,0.007955516],"category_scores_gemma":[0.0004471852,0.00025140218,0.0004272146,0.0006193308,0.00016208708,0.00056027726,0.00041133037,0.001235475,0.0016377377],"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":[0.00051436323,0.0002487386,0.0008492516,0.00010397257,0.00007088596,0.00008255036,0.00004010901,0.081516415,0.028658498,0.0031243246,0.012004989,0.8727859],"study_design_scores_gemma":[0.000017450999,0.00007976878,0.00068862445,0.000011491224,0.00002857138,0.000038218514,0.00002080887,0.9842291,0.009543747,0.0020679096,0.0032653837,0.000009005477],"about_ca_topic_score_codex":0.011787455,"about_ca_topic_score_gemma":0.01961151,"teacher_disagreement_score":0.011787455,"about_ca_system_score_codex":0.00057156733,"about_ca_system_score_gemma":0.00059918396,"threshold_uncertainty_score":0.026613891},"labels":[],"label_agreement":null},{"id":"W4383560150","doi":"10.54254/2755-2721/4/20230435","title":"Commercial video recognition system for short video (TikTok) based on machine learning","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Video Analysis and Summarization","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; Random forest; Mobile phone; Principal component analysis; Decision tree; Machine learning; Artificial intelligence; Multimedia; Data mining; Telecommunications","score_opus":0.013860698875399105,"score_gpt":0.20865850634547656,"score_spread":0.19479780747007747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1845553,0.0024379657,0.7647329,0.00041692174,0.00076764135,0.0009378064,0.003075084,0.03220741,0.010868982],"genre_scores_gemma":[0.6852909,0.001286275,0.2932879,0.00025628394,0.00020787625,0.0005175543,0.0067682005,0.00023216782,0.012152782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956316,0.000035000594,0.000038660786,0.00013703942,0.00017085111,0.000055334884],"domain_scores_gemma":[0.99958545,0.00005493356,0.00005134248,0.00004330312,0.00023075721,0.000034124674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003967951,0.00069683074,0.0007049486,0.0017705011,0.0003216956,0.000603142,0.0007504852,0.00052983366,0.0028863268],"category_scores_gemma":[0.00090962916,0.00015991087,0.0005018086,0.0010022336,0.0001356643,0.0010427933,0.00039679886,0.00053600426,0.0020730165],"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":[0.0005790773,0.00033629235,0.006704106,0.0003342143,0.00011045172,0.000272996,0.00006268,0.0059250393,0.054415364,0.0010165072,0.016788604,0.9134547],"study_design_scores_gemma":[0.000100782694,0.0007122556,0.022022193,0.0000670261,0.00020604873,0.0012579787,0.00012915554,0.84464484,0.11358723,0.0016212469,0.015541706,0.00010945077],"about_ca_topic_score_codex":0.00365058,"about_ca_topic_score_gemma":0.0038715366,"teacher_disagreement_score":0.00365058,"about_ca_system_score_codex":0.0005771891,"about_ca_system_score_gemma":0.0004325628,"threshold_uncertainty_score":0.009655714},"labels":[],"label_agreement":null},{"id":"W4386074526","doi":"10.11159/cist23.107","title":"A Multi-Viewpoint Approach For Semantic Multimedia Documents Adaptation","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":5,"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; Adaptation (eye); Multimedia; Information retrieval; World Wide Web","score_opus":0.014561220331542045,"score_gpt":0.22661866832106808,"score_spread":0.21205744798952603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386074526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034734223,0.00019154254,0.9929501,0.000119759025,0.00004001277,0.000071221235,0.00008395219,0.00063179777,0.002438265],"genre_scores_gemma":[0.11299624,0.00045142978,0.88230294,0.00009063426,0.00004338746,0.00013061769,0.000425897,0.0002815493,0.0032773975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979868,0.00049027987,0.00018139025,0.00045714804,0.00075104693,0.00013324132],"domain_scores_gemma":[0.9989899,0.0002312421,0.00008544013,0.00036349907,0.0002332552,0.00009669095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015316287,0.0008945036,0.00061546045,0.0017848689,0.00097062544,0.0022442054,0.0020019133,0.0011035043,0.001958407],"category_scores_gemma":[0.0026279446,0.0005551481,0.0020835367,0.0013620257,0.0011020938,0.003457808,0.0032036463,0.0018417484,0.00078138174],"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":[0.00033724515,0.0002994059,0.0033114234,0.00078557094,0.00030954453,0.0017887007,0.0070367004,0.037793286,0.0829005,0.3265555,0.01000824,0.5288739],"study_design_scores_gemma":[0.00006763589,0.0001901106,0.003093884,0.0002958827,0.00047824398,0.0017513676,0.0029135647,0.5786557,0.05610181,0.18445705,0.17174396,0.00025080724],"about_ca_topic_score_codex":0.006281521,"about_ca_topic_score_gemma":0.008113004,"teacher_disagreement_score":0.006281521,"about_ca_system_score_codex":0.0011942988,"about_ca_system_score_gemma":0.0011102621,"threshold_uncertainty_score":0.012489915},"labels":[],"label_agreement":null},{"id":"W4386245226","doi":"10.1109/procomm57838.2023.00055","title":"Workshop: LXD - Where UXD Meets ID","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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","score_opus":0.018271220454214282,"score_gpt":0.2527082654903684,"score_spread":0.23443704503615412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386245226","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007164998,0.003136183,0.016095446,0.01385575,0.040926978,0.0008263052,0.005584906,0.006862532,0.9055469],"genre_scores_gemma":[0.014181639,0.00068298675,0.0053524645,0.0018672345,0.0019141061,0.0002802868,0.0021947436,0.0011566231,0.9723698],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99901164,0.00026070952,0.000034656532,0.00022642173,0.00023807016,0.00022846498],"domain_scores_gemma":[0.998367,0.00012917607,0.00003055053,0.00012233926,0.00021218167,0.0011386658],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0024485677,0.0009527054,0.00066354085,0.0006432349,0.00346873,0.006886047,0.0013172163,0.0027945226,0.5326813],"category_scores_gemma":[0.0021083963,0.00054061494,0.0007798225,0.00041930203,0.00069347356,0.0026997572,0.006147486,0.003054976,0.31606758],"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":[0.00024476086,0.00010187166,0.00032203534,0.0001446257,0.000006022434,0.00034800603,0.000611987,0.00009523651,0.0021370992,0.004366233,0.95228356,0.039338514],"study_design_scores_gemma":[0.00001514694,0.00003895779,0.00033123975,0.00006303688,0.0000014174319,0.000076771976,0.00048561336,0.00007434859,0.00032524063,0.00047283075,0.99810743,0.000007939408],"about_ca_topic_score_codex":0.0014710403,"about_ca_topic_score_gemma":0.0063515306,"teacher_disagreement_score":0.5326813,"about_ca_system_score_codex":0.0012857034,"about_ca_system_score_gemma":0.0016673267,"threshold_uncertainty_score":0.6665734},"labels":[],"label_agreement":null},{"id":"W4386261912","doi":"10.2139/ssrn.4556189","title":"Egcnet: Edge-Guided Context Real-Time Semantic Segmentation Network","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Analysis and Summarization","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 Windsor","funders":"","keywords":"Segmentation; Enhanced Data Rates for GSM Evolution; Computer science; Context (archaeology); Artificial intelligence; Natural language processing; Geography","score_opus":0.019365821793836127,"score_gpt":0.2669243362865392,"score_spread":0.2475585144927031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386261912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014048266,0.0015748715,0.94602895,0.0003742867,0.00043669858,0.00034060527,0.005543143,0.026069354,0.0055839233],"genre_scores_gemma":[0.1672639,0.00090360414,0.7963356,0.00062144507,0.00030858233,0.0005197382,0.018833196,0.0019066774,0.013307257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993494,0.000091863694,0.00002850558,0.00030177762,0.00014336123,0.00008517729],"domain_scores_gemma":[0.9994937,0.00014067668,0.000044647837,0.00013591594,0.00013411281,0.000050874554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006308257,0.0021945552,0.0016549093,0.002651329,0.0010629572,0.0014476101,0.0024154808,0.0021974277,0.010293245],"category_scores_gemma":[0.0022671442,0.0007102648,0.0009816468,0.0030436534,0.00068042136,0.002074382,0.002263342,0.0017765269,0.004521037],"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":[0.0014667913,0.00029875734,0.00063496653,0.00043274966,0.00018134275,0.00030520465,0.00019751344,0.0640095,0.036130086,0.013436161,0.06318819,0.81971866],"study_design_scores_gemma":[0.000087662636,0.000188207,0.0007135658,0.00005417798,0.00008136111,0.00017802248,0.0001033406,0.9229277,0.022525346,0.028960966,0.02413249,0.000047174635],"about_ca_topic_score_codex":0.011648464,"about_ca_topic_score_gemma":0.019164199,"teacher_disagreement_score":0.011648464,"about_ca_system_score_codex":0.0010216238,"about_ca_system_score_gemma":0.0014571936,"threshold_uncertainty_score":0.03443432},"labels":[],"label_agreement":null},{"id":"W4386598561","doi":"10.1109/icip49359.2023.10222350","title":"Adopting Self-Supervised Learning into Unsupervised Video Summarization through Restorative Score.","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"","keywords":"Computer science; Automatic summarization; Artificial intelligence; Encoder; Benchmark (surveying); Code (set theory); Machine learning; Unsupervised learning; Transformer; Source code","score_opus":0.02453091521273086,"score_gpt":0.2554153500481267,"score_spread":0.23088443483539584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386598561","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00819387,0.0002216058,0.9885943,0.000093367125,0.000039714796,0.00007948737,0.000093493654,0.0019408942,0.0007434199],"genre_scores_gemma":[0.33491373,0.00029198412,0.6551522,0.0002461303,0.00024165152,0.00028752149,0.001577583,0.00057377433,0.0067155324],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99900824,0.00030034865,0.0000557427,0.0003145463,0.00026212243,0.00005901296],"domain_scores_gemma":[0.99746025,0.00096893916,0.00040633406,0.0005096201,0.00054709363,0.00010774238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014160171,0.0011429829,0.00081285986,0.0011105197,0.00036824087,0.00072047784,0.0014865842,0.00090496737,0.0016380799],"category_scores_gemma":[0.00532948,0.00037206567,0.00064404705,0.0007826858,0.0006614856,0.0015798324,0.0011335155,0.0012556931,0.0011186725],"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":[0.00024110642,0.00023722289,0.001644163,0.00025046797,0.00018649107,0.00012426717,0.00028442705,0.19963856,0.03394494,0.009819535,0.008516488,0.74511236],"study_design_scores_gemma":[0.000013701939,0.00012886427,0.00045278238,0.000013249975,0.000028606055,0.00006060299,0.00003313886,0.976919,0.013613157,0.006295269,0.0024276779,0.000014014383],"about_ca_topic_score_codex":0.0013657166,"about_ca_topic_score_gemma":0.0035658942,"teacher_disagreement_score":0.0016380799,"about_ca_system_score_codex":0.00060011324,"about_ca_system_score_gemma":0.000569244,"threshold_uncertainty_score":0.007488668},"labels":[],"label_agreement":null},{"id":"W4386901263","doi":"10.1007/978-3-031-44137-0_1","title":"Tracking and Identification of Ice Hockey Players","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Identification (biology); Ice hockey; Robustness (evolution); Computer vision; Tracking (education); Video tracking; Deep learning; Object (grammar)","score_opus":0.020896336221615564,"score_gpt":0.24764566623789225,"score_spread":0.22674933001627667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386901263","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.66742414,0.0024171378,0.27542043,0.0002214079,0.00041224834,0.0002936789,0.0021411409,0.001330162,0.05033972],"genre_scores_gemma":[0.827399,0.0017834019,0.10357475,0.00011789129,0.00010991055,0.00010452102,0.0033231303,0.0002222044,0.063365236],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982315,0.000009636965,0.0000053528124,0.000071109534,0.000047846657,0.000043014075],"domain_scores_gemma":[0.9998099,0.000046110235,0.000021002988,0.00001921904,0.00008013205,0.000023721306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020861242,0.00053245993,0.00043716416,0.0020347212,0.0004392442,0.00096852344,0.00069927954,0.0006402755,0.003662593],"category_scores_gemma":[0.00042837462,0.00028662337,0.00023742217,0.001228638,0.00024271324,0.0006554914,0.00056552625,0.0003068846,0.002668907],"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":[0.0011539011,0.00018827202,0.023990752,0.00029619687,0.00008477551,0.00060247217,0.0006423909,0.011169721,0.22944903,0.0028584725,0.011485977,0.7180781],"study_design_scores_gemma":[0.00009502521,0.000968427,0.18829085,0.00035358747,0.00031098493,0.003106673,0.00322807,0.49501774,0.24455439,0.004946338,0.058983847,0.00014418115],"about_ca_topic_score_codex":0.0063736457,"about_ca_topic_score_gemma":0.011971245,"teacher_disagreement_score":0.0063736457,"about_ca_system_score_codex":0.00034975738,"about_ca_system_score_gemma":0.00029860713,"threshold_uncertainty_score":0.01267308},"labels":[],"label_agreement":null},{"id":"W4387055596","doi":"10.1145/3625548","title":"Incomplete Multiview Clustering via Semidiscrete Optimal Transport for Multimedia Data Mining in IoT","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Analysis and Summarization","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":"Brandon University","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Multimedia; Internet of Things; Data mining; Artificial intelligence; World Wide Web","score_opus":0.07656993814537665,"score_gpt":0.3338984757053916,"score_spread":0.25732853756001495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387055596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009913158,0.00028359907,0.9888925,0.0001645447,0.00002453621,0.0000197631,0.000071203474,0.00015876212,0.00047194646],"genre_scores_gemma":[0.5634486,0.0010987248,0.43031648,0.0002628076,0.00012246866,0.00019197912,0.0012634473,0.00023730191,0.0030580808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995672,0.00010621792,0.000041078914,0.00013275645,0.000109379034,0.000043318934],"domain_scores_gemma":[0.9991892,0.0003930385,0.000110473535,0.00008885445,0.00017159074,0.00004686348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013251303,0.0008137321,0.0011684162,0.0010888854,0.00056326744,0.0012304772,0.0016057625,0.0012326174,0.00091937097],"category_scores_gemma":[0.0034257665,0.0005073531,0.0013449525,0.0011931736,0.00087304943,0.0023033768,0.0014876348,0.0015004377,0.00024198998],"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":[0.000093155875,0.000037605976,0.0009854449,0.000116377785,0.000054787022,0.00008590822,0.00013730413,0.91652364,0.0031828317,0.019362792,0.0013020184,0.058118243],"study_design_scores_gemma":[0.0000010941857,0.000005742355,0.00004903962,0.0000025485022,0.00000265757,0.00000696042,0.0000085998245,0.99594384,0.00028972377,0.0035015102,0.00018492238,0.0000034348373],"about_ca_topic_score_codex":0.008449981,"about_ca_topic_score_gemma":0.0052459114,"teacher_disagreement_score":0.008449981,"about_ca_system_score_codex":0.0016213584,"about_ca_system_score_gemma":0.0013500525,"threshold_uncertainty_score":0.016801596},"labels":[],"label_agreement":null},{"id":"W4387124582","doi":"10.1016/j.dib.2023.109627","title":"CineScale2: a dataset of cinematic camera features in movies","year":2023,"lang":"en","type":"article","venue":"Data in Brief","topic":"Video Analysis and Summarization","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":"Surgical Specialties (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Computer vision; Shot (pellet); Video camera; Frame (networking); Feature (linguistics); Camera auto-calibration; Smart camera; Orientation (vector space); Computer graphics (images); Camera resectioning","score_opus":0.03061943435541232,"score_gpt":0.2980896555910983,"score_spread":0.267470221235686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387124582","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02326011,0.001807823,0.0029772709,0.00018505221,0.0002393895,0.0002549384,0.9619998,0.003681909,0.005593753],"genre_scores_gemma":[0.014075861,0.0003528709,0.0048476253,0.000053123385,0.000047880516,0.00020065598,0.97862566,0.00013303036,0.001663284],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993579,0.00006493283,0.00007050819,0.00022343546,0.0001871806,0.00009606587],"domain_scores_gemma":[0.99915123,0.00015331694,0.00011945757,0.00020384253,0.00024743992,0.00012470309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033567785,0.002306921,0.0009455466,0.004423535,0.00070494117,0.0011058404,0.0013258038,0.0015611938,0.011174416],"category_scores_gemma":[0.001790108,0.0003740143,0.0007813057,0.0036107292,0.00031573567,0.001097902,0.0011146505,0.0010018082,0.013936229],"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":[0.00061334996,0.00043180698,0.0077819717,0.0027754675,0.00013865008,0.00071293156,0.0003812081,0.0011217785,0.010571467,0.0009928719,0.8930504,0.08142804],"study_design_scores_gemma":[0.00021519185,0.00029198886,0.11489122,0.00086126575,0.00015725315,0.0020237286,0.0012504464,0.010726826,0.012062203,0.0016044999,0.8557407,0.00017469529],"about_ca_topic_score_codex":0.014482833,"about_ca_topic_score_gemma":0.046141516,"teacher_disagreement_score":0.014482833,"about_ca_system_score_codex":0.0008400733,"about_ca_system_score_gemma":0.00063399103,"threshold_uncertainty_score":0.037382126},"labels":[],"label_agreement":null},{"id":"W4387394641","doi":"10.1609/aiide.v19i1.27523","title":"Reconstructing Existing Levels through Level Inpainting","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Video Analysis and Summarization","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 Alberta","funders":"Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Inpainting; Autoencoder; Artificial intelligence; Computer science; Task (project management); Domain (mathematical analysis); Image (mathematics); Baseline (sea); Deep learning; Computer vision; Machine learning; Mathematics; Engineering","score_opus":0.17813408774234804,"score_gpt":0.32723808374868535,"score_spread":0.1491039960063373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387394641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020801386,0.00013099556,0.9753574,0.00008030319,0.000047054134,0.00011264461,0.00008785455,0.0012683141,0.0021139916],"genre_scores_gemma":[0.23118564,0.0002332986,0.76370275,0.00013030402,0.00004084906,0.0000945389,0.00037740803,0.00045961657,0.0037756173],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996884,0.000041494113,0.00001536775,0.000088057255,0.000121355595,0.000045424316],"domain_scores_gemma":[0.99926704,0.00027646375,0.00006612073,0.00023830846,0.00010817889,0.00004388733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047047267,0.00096839434,0.0005922305,0.00080683286,0.00030230833,0.001088724,0.0009064949,0.0007734623,0.003244682],"category_scores_gemma":[0.0016383148,0.00045482008,0.0007356009,0.00041235544,0.00062361744,0.0010528372,0.0014834299,0.0011008437,0.0009940289],"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":[0.00030896213,0.00017482166,0.0015085015,0.0003827217,0.00008115307,0.0005880463,0.0006277566,0.1801138,0.20951322,0.017299594,0.006124537,0.5832769],"study_design_scores_gemma":[0.000040100513,0.00023901157,0.001075204,0.00004697888,0.00005493576,0.0005784218,0.00017461264,0.85273355,0.115637876,0.015098766,0.014282211,0.000038308466],"about_ca_topic_score_codex":0.0008341383,"about_ca_topic_score_gemma":0.0019651991,"teacher_disagreement_score":0.003244682,"about_ca_system_score_codex":0.00033192136,"about_ca_system_score_gemma":0.000401074,"threshold_uncertainty_score":0.010854483},"labels":[],"label_agreement":null},{"id":"W4387961426","doi":"10.1145/3606038.3616161","title":"Rink-Agnostic Hockey Rink Registration","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"Mitacs","keywords":"Computer science; Pipeline (software); Overhead (engineering); Artificial intelligence; Hash function; Adaptation (eye); Computer vision; Computer security","score_opus":0.017879480682364782,"score_gpt":0.24297057999062985,"score_spread":0.22509109930826507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387961426","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10231281,0.0008515885,0.8285541,0.00017674688,0.0005512739,0.00059034576,0.003754236,0.046511322,0.01669764],"genre_scores_gemma":[0.50601083,0.0009192828,0.4284071,0.00029347342,0.00017826872,0.00036210805,0.02750026,0.003288156,0.03304054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989992,0.00008442473,0.000055316108,0.00044337753,0.00024817334,0.00016959093],"domain_scores_gemma":[0.9989196,0.00009431207,0.00008525303,0.00050562125,0.0003465085,0.00004864724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005864992,0.0012795372,0.0011203048,0.0017287148,0.0004704462,0.0011453955,0.0015538913,0.00087434356,0.0057103783],"category_scores_gemma":[0.0022215934,0.00041520214,0.00088437076,0.0013652178,0.0005790154,0.0017584171,0.0017376654,0.0011492212,0.009596903],"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":[0.00074307946,0.00025661176,0.0040616384,0.00036487717,0.00015923726,0.00029566188,0.00024084844,0.029177608,0.115612105,0.0026467405,0.030777194,0.8156643],"study_design_scores_gemma":[0.00008096689,0.00045844453,0.018601095,0.0000847371,0.00014989352,0.0011635313,0.0008284924,0.7388359,0.17920776,0.005900858,0.054532494,0.00015591797],"about_ca_topic_score_codex":0.004189658,"about_ca_topic_score_gemma":0.00940137,"teacher_disagreement_score":0.0057103783,"about_ca_system_score_codex":0.00043474985,"about_ca_system_score_gemma":0.0008270651,"threshold_uncertainty_score":0.01910317},"labels":[],"label_agreement":null},{"id":"W4387961473","doi":"10.1145/3606038.3616162","title":"Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Identification (biology); Motion blur; Computer vision; Transformer; Context (archaeology); Analytics; Pattern recognition (psychology); Data mining; Image (mathematics)","score_opus":0.041087922563063956,"score_gpt":0.27524396680019936,"score_spread":0.2341560442371354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387961473","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24354039,0.001926794,0.7241093,0.000306584,0.00059380796,0.00047080385,0.0042108214,0.009693191,0.015148379],"genre_scores_gemma":[0.66138464,0.0022975882,0.3087324,0.00020763779,0.00030713607,0.00018828786,0.013785403,0.00032750607,0.012769357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975735,0.000014889796,0.000012710339,0.00008612579,0.00007472533,0.00005416925],"domain_scores_gemma":[0.99974185,0.000044297652,0.000044195705,0.000037933358,0.000106394946,0.0000252628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022260817,0.00098644,0.00059954415,0.002386744,0.00030678223,0.0007101599,0.0006971738,0.0005123954,0.0023991035],"category_scores_gemma":[0.0009368062,0.00018358315,0.00043450104,0.0010523845,0.00021105987,0.0010370727,0.000580389,0.00056088873,0.0024293524],"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":[0.00081454025,0.00025410615,0.0056023896,0.0002940882,0.00008104853,0.0004342783,0.00010196095,0.01433521,0.1367838,0.0020741902,0.015339499,0.8238849],"study_design_scores_gemma":[0.000056862023,0.00043381975,0.024978785,0.00011386538,0.00018242063,0.0009827261,0.00040670703,0.79668915,0.14720695,0.00520212,0.02367741,0.000069135735],"about_ca_topic_score_codex":0.0072027873,"about_ca_topic_score_gemma":0.01824424,"teacher_disagreement_score":0.0072027873,"about_ca_system_score_codex":0.00040657775,"about_ca_system_score_gemma":0.0005434831,"threshold_uncertainty_score":0.014321685},"labels":[],"label_agreement":null},{"id":"W4387993570","doi":"10.1145/3586182.3616707","title":"Democratizing Content Creation and Consumption through Human-AI Copilot Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Modalities; Content creation; Multimedia; Content (measure theory); Consumption (sociology); Media consumption; World Wide Web; Quality (philosophy); Human–computer interaction; Advertising; Sociology; Business","score_opus":0.10826407665873823,"score_gpt":0.32035321676568207,"score_spread":0.21208914010694385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387993570","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13760032,0.0015820776,0.81765825,0.0012764107,0.00014018483,0.0010043761,0.00043002807,0.010715266,0.029593013],"genre_scores_gemma":[0.5721625,0.000851222,0.4079449,0.0003246771,0.000137872,0.00070062705,0.0012615215,0.0012852218,0.015331516],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755126,0.0012000533,0.00013054491,0.0004422549,0.0005312786,0.00014452678],"domain_scores_gemma":[0.99440056,0.0032705755,0.00037010814,0.0009456984,0.00069676124,0.00031631225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027428232,0.0009174061,0.00056917995,0.0012242193,0.0010667167,0.0040126615,0.0017945981,0.0009096742,0.007402313],"category_scores_gemma":[0.009093416,0.00038928702,0.0004883096,0.0008345951,0.00158292,0.0050181686,0.0037911064,0.0010393081,0.002023343],"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":[0.0012453236,0.0012447038,0.0045049456,0.0024361575,0.00030761413,0.0004834938,0.011753024,0.037720535,0.19032893,0.058070604,0.01802514,0.6738795],"study_design_scores_gemma":[0.00026454625,0.0013480805,0.008279928,0.00032418384,0.00032436568,0.0005770391,0.0067147543,0.53581715,0.1576218,0.06857278,0.21986754,0.00028782958],"about_ca_topic_score_codex":0.0014749953,"about_ca_topic_score_gemma":0.002793128,"teacher_disagreement_score":0.007402313,"about_ca_system_score_codex":0.00088335515,"about_ca_system_score_gemma":0.0007460002,"threshold_uncertainty_score":0.024763167},"labels":[],"label_agreement":null},{"id":"W4389315296","doi":"10.1109/cog57401.2023.10333193","title":"Joint Level Generation and Translation Using Gameplay Videos","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Alberta","funders":"","keywords":"Computer science; Representation (politics); Translation (biology); Artificial intelligence; Machine translation; Machine learning; Natural language processing; Human–computer interaction","score_opus":0.275387679553905,"score_gpt":0.29811414105041656,"score_spread":0.022726461496511585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389315296","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038882084,0.00065082085,0.92522365,0.00029433533,0.00040495276,0.0006065499,0.0018235226,0.026712362,0.0054017706],"genre_scores_gemma":[0.36646748,0.00042886735,0.60700995,0.00038126615,0.00014733855,0.0005035152,0.011362766,0.0027008797,0.010997957],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988733,0.00022707778,0.00006578742,0.00050536904,0.0002346404,0.00009377416],"domain_scores_gemma":[0.998723,0.00046599918,0.00009544723,0.00035639826,0.0002785096,0.000080618665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008087345,0.0021441886,0.0009674675,0.0014718504,0.00046857836,0.001757213,0.001555385,0.0011731236,0.0067524184],"category_scores_gemma":[0.005046101,0.00045073067,0.0009879816,0.00078458857,0.0005290151,0.0018390514,0.0016670512,0.0012269012,0.003711683],"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":[0.0006375968,0.00032380346,0.001838933,0.0006024855,0.00014798346,0.000512185,0.0004982066,0.054411158,0.06933577,0.0074490425,0.021647569,0.8425953],"study_design_scores_gemma":[0.00014452402,0.0004609806,0.0018336689,0.000087480366,0.000114417235,0.00044030277,0.00049704546,0.8638316,0.085124634,0.02180663,0.025550181,0.00010854937],"about_ca_topic_score_codex":0.0032095343,"about_ca_topic_score_gemma":0.0051214276,"teacher_disagreement_score":0.0067524184,"about_ca_system_score_codex":0.000716876,"about_ca_system_score_gemma":0.0007444701,"threshold_uncertainty_score":0.022589147},"labels":[],"label_agreement":null},{"id":"W4390055400","doi":"10.18280/mmep.100613","title":"A Novel Context-Aware Deep Learning Algorithm for Enhanced Movie Recommendation Systems","year":2023,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Video Analysis and Summarization","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":"","keywords":"Computer science; Context (archaeology); Recommender system; Deep learning; Algorithm; Artificial intelligence; Machine learning; History","score_opus":0.029996726576642645,"score_gpt":0.22641889959021114,"score_spread":0.19642217301356849,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390055400","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01805161,0.00046465124,0.979143,0.00019018631,0.000053126638,0.000048693568,0.00008973953,0.0007030137,0.001255971],"genre_scores_gemma":[0.4987465,0.00041347631,0.49412882,0.0003228468,0.00010874234,0.00019463417,0.0004086161,0.000080417354,0.0055959797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997428,0.000043809974,0.000020887346,0.00006824326,0.00007390652,0.000050407994],"domain_scores_gemma":[0.9997273,0.0000824357,0.000026979698,0.00002962756,0.0001136099,0.000020082642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056334824,0.0006455339,0.00081089017,0.0004918827,0.0003919252,0.0004948131,0.0012167947,0.0010764453,0.0021155565],"category_scores_gemma":[0.0015216215,0.00039142076,0.0006013345,0.00048745947,0.0002649015,0.0009476447,0.0008969201,0.0011822494,0.0005588037],"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":[0.00016250735,0.000108477696,0.0011000806,0.00011213439,0.00006931484,0.00009125882,0.0000745343,0.5296764,0.01184024,0.0076464606,0.003694726,0.4454239],"study_design_scores_gemma":[0.000003857153,0.000015055866,0.00005376781,0.000003164567,0.0000036026893,0.000007408725,0.0000020706407,0.998577,0.00043211045,0.0006259264,0.0002738767,0.0000021914732],"about_ca_topic_score_codex":0.014736699,"about_ca_topic_score_gemma":0.02152442,"teacher_disagreement_score":0.014736699,"about_ca_system_score_codex":0.0008624072,"about_ca_system_score_gemma":0.0011161813,"threshold_uncertainty_score":0.029301882},"labels":[],"label_agreement":null},{"id":"W4390873040","doi":"10.1109/iccv51070.2023.00279","title":"GePSAn: Generative Procedure Step Anticipation in Cooking Videos","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"","keywords":"Computer science; Anticipation (artificial intelligence); Generative grammar; Artificial intelligence; Domain (mathematical analysis); Generative model; Machine learning; Natural language processing","score_opus":0.023010052749854824,"score_gpt":0.2740819549110001,"score_spread":0.2510719021611453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390873040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.104357876,0.0015785168,0.87280095,0.0011290052,0.00018271177,0.00022644343,0.0036485295,0.0075537856,0.008522092],"genre_scores_gemma":[0.7844929,0.0007797203,0.19058257,0.0005633782,0.00011833033,0.00042849727,0.00902612,0.00079744915,0.013211055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995832,0.00013253314,0.0000126830155,0.00017545535,0.000049375376,0.00004686573],"domain_scores_gemma":[0.9984744,0.0011937289,0.000067739944,0.00012012685,0.00008278826,0.00006117787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009468009,0.00094301655,0.0005832938,0.0007901035,0.0004214604,0.0007728168,0.0016136543,0.0011510443,0.005855637],"category_scores_gemma":[0.0045941556,0.00060352474,0.0011336359,0.000578267,0.00064336084,0.0014545878,0.0009330698,0.0018760783,0.0014766734],"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":[0.00059958914,0.0002109782,0.0081013115,0.0003686378,0.00017702841,0.00071366475,0.00062930794,0.72676235,0.0085595185,0.029243857,0.014585364,0.21004838],"study_design_scores_gemma":[0.000016357611,0.000033192933,0.0006703149,0.000019148403,0.000013374781,0.00006526554,0.00003506792,0.9851461,0.0013266634,0.010488674,0.0021731795,0.00001268801],"about_ca_topic_score_codex":0.01353663,"about_ca_topic_score_gemma":0.02746245,"teacher_disagreement_score":0.01353663,"about_ca_system_score_codex":0.001051049,"about_ca_system_score_gemma":0.0008153986,"threshold_uncertainty_score":0.02691567},"labels":[],"label_agreement":null},{"id":"W4391274453","doi":"10.1007/978-3-031-53311-2_30","title":"Multi-modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Computer science; Domain adaptation; Segmentation; Artificial intelligence; Modal; Domain (mathematical analysis); Adaptation (eye); Semantics (computer science); Computer vision; Dual (grammatical number); Adaptability; Natural language processing; Linguistics","score_opus":0.01309883878653341,"score_gpt":0.23572622984253436,"score_spread":0.22262739105600093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391274453","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02335659,0.0016385936,0.9652918,0.00022700119,0.0003423395,0.00013275773,0.0006193963,0.0039575086,0.0044339835],"genre_scores_gemma":[0.24405412,0.0014097943,0.73497033,0.0003669276,0.0005321533,0.00031901212,0.003903282,0.0011718615,0.013272564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946254,0.00010985995,0.000025274558,0.00021843571,0.00010339459,0.00008044136],"domain_scores_gemma":[0.9993673,0.00021558288,0.000032984273,0.00010954943,0.00022197443,0.000052586962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087731227,0.00097476,0.0009270691,0.0013894989,0.0003846122,0.00093454885,0.0009857978,0.0010017467,0.0028979299],"category_scores_gemma":[0.0016709404,0.00031946055,0.0010178365,0.0017135891,0.00036145217,0.0011003154,0.001357157,0.0014203597,0.0027101377],"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":[0.00079937617,0.00024579925,0.00057868083,0.00025556787,0.00013075481,0.00010089697,0.00012091967,0.01566585,0.15752332,0.0018084676,0.008572187,0.81419826],"study_design_scores_gemma":[0.00006142246,0.00022282379,0.004029406,0.000042276966,0.00018240856,0.00029771856,0.0001354828,0.89212835,0.084299155,0.0043619173,0.014181423,0.000057739268],"about_ca_topic_score_codex":0.0026003702,"about_ca_topic_score_gemma":0.0045702425,"teacher_disagreement_score":0.0028979299,"about_ca_system_score_codex":0.00036005792,"about_ca_system_score_gemma":0.0005827986,"threshold_uncertainty_score":0.009694517},"labels":[],"label_agreement":null},{"id":"W4391307073","doi":"10.1109/vcip59821.2023.10402737","title":"Efficient Multi-purpose Video Annotation for Fast Labeling","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Kelowna General Hospital; Nova Chemicals (Canada); University of British Columbia","funders":"","keywords":"Computer science; Annotation; Artificial intelligence; Computer vision","score_opus":0.02953756837828295,"score_gpt":0.28269766429797244,"score_spread":0.25316009591968947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307073","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0042625507,0.0003230964,0.9822192,0.00013666431,0.00014224793,0.00016287371,0.00041706485,0.010876696,0.001459529],"genre_scores_gemma":[0.05769856,0.000397444,0.93271816,0.00020523406,0.00021414156,0.0004594161,0.0019167181,0.0016967519,0.0046936786],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982558,0.0003301526,0.0001159734,0.00056356465,0.000554159,0.00018030827],"domain_scores_gemma":[0.9963103,0.000911554,0.00034895082,0.0007466719,0.0014916266,0.00019095169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012749473,0.0018819412,0.0011712534,0.0036928635,0.0011191581,0.0017457969,0.0024116188,0.0013328658,0.0071638464],"category_scores_gemma":[0.004893802,0.0007323163,0.0007995173,0.0021574085,0.0005182894,0.0023000685,0.0022896423,0.0015025839,0.0052226544],"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":[0.0005774792,0.00012391864,0.00058563583,0.0004241545,0.000042889795,0.0002111464,0.00050271983,0.0033710848,0.1512378,0.003201601,0.027351439,0.81237006],"study_design_scores_gemma":[0.00010828888,0.0004147433,0.004942875,0.0002155257,0.0001506725,0.0009506498,0.0009988449,0.5513025,0.2998353,0.015629617,0.12520935,0.00024167725],"about_ca_topic_score_codex":0.0033211524,"about_ca_topic_score_gemma":0.0060929894,"teacher_disagreement_score":0.0071638464,"about_ca_system_score_codex":0.0007254802,"about_ca_system_score_gemma":0.00085537106,"threshold_uncertainty_score":0.023965478},"labels":[],"label_agreement":null},{"id":"W4391328244","doi":"10.18280/mmep.110113","title":"Football Player Tracking and Performance Analysis Using the OpenCV Library","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":7,"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":"Football; Computer science; Tracking (education); Artificial intelligence; Computer graphics (images); Computer vision; Psychology; Geography","score_opus":0.02994029726871116,"score_gpt":0.21327798192574865,"score_spread":0.18333768465703748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391328244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009295883,0.0003524715,0.80751985,0.00012513445,0.00021490734,0.00041055406,0.008143396,0.16694024,0.0069974633],"genre_scores_gemma":[0.07631018,0.00061709486,0.86092746,0.00014539354,0.0001119451,0.0014222619,0.027061071,0.012973921,0.020430615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921525,0.000042400916,0.000043759424,0.00035367694,0.00023283105,0.00011202637],"domain_scores_gemma":[0.9995214,0.00010135983,0.00004070803,0.000082093946,0.00021478206,0.000039632785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068986433,0.0021787,0.001751131,0.0045428458,0.00094185513,0.0020546948,0.0022514337,0.0015954351,0.03132496],"category_scores_gemma":[0.0018148074,0.0009811182,0.0017907183,0.0026556626,0.00034500659,0.0013108061,0.0015061472,0.0013915123,0.01893865],"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":[0.00038736826,0.00031466217,0.0010231356,0.00071666006,0.00019157438,0.00012522904,0.00017306331,0.014144529,0.04041869,0.003043446,0.10129452,0.83816713],"study_design_scores_gemma":[0.00014525611,0.00027330127,0.010678291,0.00023387132,0.00022588519,0.0005158105,0.00022610502,0.7208291,0.14365108,0.010879186,0.11212757,0.00021454999],"about_ca_topic_score_codex":0.016510095,"about_ca_topic_score_gemma":0.019183166,"teacher_disagreement_score":0.03132496,"about_ca_system_score_codex":0.00075244164,"about_ca_system_score_gemma":0.0014932376,"threshold_uncertainty_score":0.104792416},"labels":[],"label_agreement":null},{"id":"W4391932663","doi":"10.1145/3648681","title":"Meetor: A Human-Centered Automatic Video Editing System for Meeting Recordings","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Analysis and Summarization","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 Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Non-linear editing system; Computer graphics (images); Video editing; Multimedia; Speech recognition; Human–computer interaction; Video capture; Artificial intelligence; Video processing; Smacker video","score_opus":0.031505484425718364,"score_gpt":0.30219555761313627,"score_spread":0.2706900731874179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391932663","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0483634,0.00079744525,0.86956507,0.00016319774,0.00024385899,0.0009511948,0.0014126275,0.07229272,0.0062105926],"genre_scores_gemma":[0.27544156,0.000652468,0.7006623,0.00035530355,0.00024815116,0.00083473016,0.0045767305,0.0017545111,0.01547432],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992561,0.00014206895,0.00005930533,0.00021982026,0.0002654519,0.000057239566],"domain_scores_gemma":[0.9991066,0.00021778143,0.000109237335,0.00016371715,0.00025189065,0.00015083107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007180719,0.00091897906,0.00073814084,0.0008997194,0.0003215285,0.00065181317,0.0015331958,0.0007677425,0.008050187],"category_scores_gemma":[0.0020064025,0.00029939902,0.0003969243,0.0003506309,0.00022922963,0.0009600016,0.0010237333,0.0006554739,0.0035740463],"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":[0.0020114286,0.0003928257,0.0014501855,0.0008495698,0.00016975752,0.0008056241,0.0006929571,0.0031898562,0.35317332,0.0017541556,0.046071295,0.58943903],"study_design_scores_gemma":[0.0012013654,0.0043815193,0.019842258,0.00018969183,0.00031069235,0.0057738097,0.00088379387,0.32931325,0.44549498,0.0025986775,0.18951672,0.0004932202],"about_ca_topic_score_codex":0.0008715039,"about_ca_topic_score_gemma":0.0012176856,"teacher_disagreement_score":0.008050187,"about_ca_system_score_codex":0.00023549794,"about_ca_system_score_gemma":0.00044537036,"threshold_uncertainty_score":0.026930511},"labels":[],"label_agreement":null},{"id":"W4392904756","doi":"10.1109/icassp48485.2024.10446480","title":"Look, Listen and Recognise: Character-Aware Audio-Visual Subtitling","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Geomechanica (Canada)","funders":"Engineering and Physical Sciences Research Council; Royal Society","keywords":"Computer science; Metadata; Character (mathematics); Timestamp; Set (abstract data type); Speech recognition; Key (lock); Subtitle; Identity (music); Multimedia; Artificial intelligence; Natural language processing; World Wide Web","score_opus":0.011276232106698838,"score_gpt":0.25321209114483045,"score_spread":0.2419358590381316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392904756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042994477,0.00083487894,0.8988212,0.00030761687,0.00053928874,0.00061856385,0.0036049697,0.043410584,0.008868445],"genre_scores_gemma":[0.28102076,0.00051014795,0.6802828,0.00039503732,0.00040999168,0.0006632738,0.013008608,0.0040458073,0.019663576],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999374,0.00013109575,0.000033700297,0.00019293456,0.00018316411,0.00008525098],"domain_scores_gemma":[0.9987324,0.00043829245,0.00009378633,0.00021927239,0.00037175004,0.00014453531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046853392,0.0013262831,0.0007160266,0.0013991705,0.00044537443,0.0011739854,0.0013195316,0.001092512,0.008401036],"category_scores_gemma":[0.0032431337,0.00030352242,0.00061796646,0.0005835195,0.00043589456,0.0013051709,0.0015446749,0.00086820597,0.007485528],"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":[0.0013703004,0.00013446572,0.0009888756,0.0006797817,0.0000678818,0.0007681559,0.0007578871,0.0054264646,0.3018542,0.0023435846,0.031989276,0.65361917],"study_design_scores_gemma":[0.0002069804,0.0010802959,0.010024934,0.00023182573,0.00015058469,0.0028275764,0.0018270931,0.47920275,0.37389556,0.01679071,0.11349623,0.00026545778],"about_ca_topic_score_codex":0.0022390287,"about_ca_topic_score_gemma":0.0037880172,"teacher_disagreement_score":0.008401036,"about_ca_system_score_codex":0.00033620358,"about_ca_system_score_gemma":0.0003213759,"threshold_uncertainty_score":0.028104246},"labels":[],"label_agreement":null},{"id":"W4393160465","doi":"10.1609/aaai.v38i16.29736","title":"Talk Funny! A Large-Scale Humor Response Dataset with Chain-of-Humor Interpretation","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Video Analysis and Summarization","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":"Université de Montréal","funders":"Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Interpretation (philosophy); Humor research; Scale (ratio); Psychology; Cognitive psychology; Philosophy; Social psychology; Linguistics; Geography; Cartography","score_opus":0.03418745627289088,"score_gpt":0.28866807901994296,"score_spread":0.2544806227470521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393160465","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27098697,0.0028520685,0.023530114,0.0024311817,0.0010646643,0.0016258818,0.64096135,0.018797962,0.037749767],"genre_scores_gemma":[0.1789035,0.0005183792,0.031625953,0.0007956592,0.00020349839,0.0015626879,0.7634001,0.00057141343,0.022418825],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991731,0.00027690083,0.000073095616,0.00018189255,0.00020841458,0.00008655238],"domain_scores_gemma":[0.99823594,0.0005856037,0.00011526521,0.0003963608,0.00045697443,0.00020976455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007696611,0.0012498236,0.0004002408,0.0014353476,0.0011460411,0.0005282704,0.0011776626,0.0015473467,0.008435833],"category_scores_gemma":[0.003688948,0.00016658907,0.0004879257,0.0010637242,0.0005413066,0.0008518906,0.0014305825,0.0011130213,0.0059697805],"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":[0.001380022,0.00070459896,0.016094605,0.0032216911,0.00016708726,0.0014823298,0.0038145115,0.0029958638,0.026798276,0.0035020069,0.7694027,0.17043619],"study_design_scores_gemma":[0.00038523154,0.0004568074,0.087850265,0.00040799304,0.000107948734,0.0012492292,0.004388598,0.033668667,0.027950604,0.003039045,0.84022886,0.00026668768],"about_ca_topic_score_codex":0.008094587,"about_ca_topic_score_gemma":0.024823051,"teacher_disagreement_score":0.008435833,"about_ca_system_score_codex":0.0006418488,"about_ca_system_score_gemma":0.00088513875,"threshold_uncertainty_score":0.028220713},"labels":[],"label_agreement":null},{"id":"W4393617660","doi":"10.5281/zenodo.7661190","title":"Streaming video course data","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Video Analysis and Summarization","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":"Mount Saint Vincent University","funders":"","keywords":"Course (navigation); Computer science; Astronomy; Physics","score_opus":0.06512804354256505,"score_gpt":0.2877018561942905,"score_spread":0.22257381265172546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393617660","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044821394,0.00017429034,0.00038281872,0.00009920011,0.0000616814,0.0001624918,0.99150956,0.0005965728,0.0025312076],"genre_scores_gemma":[0.00349242,0.000081021346,0.0007843372,0.000031500604,0.000013962481,0.0002422207,0.99392223,0.000046168185,0.0013860835],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986979,0.00016222341,0.00014376639,0.00033158588,0.0004337752,0.00023067372],"domain_scores_gemma":[0.9970662,0.00051021716,0.0002295281,0.0005641275,0.0013360317,0.00029401586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009219328,0.0014271764,0.0007313636,0.003902292,0.0008700606,0.0012830416,0.0016035058,0.0015420744,0.01678043],"category_scores_gemma":[0.004942259,0.00034769665,0.00061327167,0.0045498097,0.00044747646,0.0008093447,0.0012274571,0.0012678565,0.02751104],"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":[0.0003777632,0.00034990668,0.0072042695,0.001617052,0.000059386497,0.00037704624,0.00028999598,0.0011763299,0.002062535,0.0011158659,0.95273525,0.032634657],"study_design_scores_gemma":[0.00017627885,0.000091327536,0.031584345,0.00042124966,0.000043384698,0.00034685704,0.000695233,0.0019293473,0.0030162605,0.00077392464,0.96085155,0.0000701122],"about_ca_topic_score_codex":0.058443304,"about_ca_topic_score_gemma":0.09429624,"teacher_disagreement_score":0.058443304,"about_ca_system_score_codex":0.0017510661,"about_ca_system_score_gemma":0.0022606838,"threshold_uncertainty_score":0.11620629},"labels":[],"label_agreement":null},{"id":"W4394058320","doi":"10.5281/zenodo.7661189","title":"Streaming video course data","year":2023,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Video Analysis and Summarization","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":"Mount Saint Vincent University","funders":"","keywords":"Course (navigation); Computer science; Physics; Astronomy","score_opus":0.06512804354256505,"score_gpt":0.2877018561942905,"score_spread":0.22257381265172546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394058320","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044821394,0.00017429034,0.00038281872,0.00009920011,0.0000616814,0.0001624918,0.99150956,0.0005965728,0.0025312076],"genre_scores_gemma":[0.00349242,0.000081021346,0.0007843372,0.000031500604,0.000013962481,0.0002422207,0.99392223,0.000046168185,0.0013860835],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986979,0.00016222341,0.00014376639,0.00033158588,0.0004337752,0.00023067372],"domain_scores_gemma":[0.9970662,0.00051021716,0.0002295281,0.0005641275,0.0013360317,0.00029401586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009219328,0.0014271764,0.0007313636,0.003902292,0.0008700606,0.0012830416,0.0016035058,0.0015420744,0.01678043],"category_scores_gemma":[0.004942259,0.00034769665,0.00061327167,0.0045498097,0.00044747646,0.0008093447,0.0012274571,0.0012678565,0.02751104],"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":[0.0003777632,0.00034990668,0.0072042695,0.001617052,0.000059386497,0.00037704624,0.00028999598,0.0011763299,0.002062535,0.0011158659,0.95273525,0.032634657],"study_design_scores_gemma":[0.00017627885,0.000091327536,0.031584345,0.00042124966,0.000043384698,0.00034685704,0.000695233,0.0019293473,0.0030162605,0.00077392464,0.96085155,0.0000701122],"about_ca_topic_score_codex":0.058443304,"about_ca_topic_score_gemma":0.09429624,"teacher_disagreement_score":0.058443304,"about_ca_system_score_codex":0.0017510661,"about_ca_system_score_gemma":0.0022606838,"threshold_uncertainty_score":0.11620629},"labels":[],"label_agreement":null},{"id":"W4394814068","doi":"10.1007/s40747-024-01417-z","title":"Keyframe recommendation based on feature intercross and fusion","year":2024,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Video Analysis and Summarization","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":"Science and Technology Program of Guizhou Province; Petroleum Technology Research Centre; National Natural Science Foundation of China","keywords":"Computer science; Feature extraction; Redundancy (engineering); Artificial intelligence; Frame (networking); Computational intelligence; Feature (linguistics); Pattern recognition (psychology); Computer vision; Data mining","score_opus":0.03847584680883407,"score_gpt":0.28893033416033814,"score_spread":0.25045448735150405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394814068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039340347,0.0010030875,0.95501065,0.00014524639,0.00012104421,0.00020299545,0.00037945635,0.0020044516,0.0017928148],"genre_scores_gemma":[0.5256699,0.00081739336,0.4655659,0.00015455436,0.00017633855,0.00019081969,0.0018450546,0.00016035195,0.0054197195],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99854505,0.00014081027,0.000107660184,0.00044959228,0.00059615116,0.00016084213],"domain_scores_gemma":[0.99847883,0.0002612348,0.0001276802,0.0003343166,0.00070823234,0.000089646644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083284057,0.0010899319,0.0014422752,0.0025333755,0.00072510005,0.0009256309,0.0012411582,0.000982988,0.0019229237],"category_scores_gemma":[0.002849697,0.00035869252,0.0013124145,0.0024462254,0.00027379006,0.0019695575,0.0007151346,0.0007936561,0.0011396349],"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":[0.00050046085,0.00029013993,0.0048274477,0.00022591488,0.0002000432,0.00016576146,0.00017147324,0.033497017,0.042343594,0.0026341905,0.009445192,0.90569884],"study_design_scores_gemma":[0.00006783428,0.0003728861,0.005111269,0.00004074536,0.00023516825,0.0005065269,0.00019847302,0.9330534,0.047987185,0.0037657383,0.008559046,0.00010169757],"about_ca_topic_score_codex":0.013385229,"about_ca_topic_score_gemma":0.012916733,"teacher_disagreement_score":0.013385229,"about_ca_system_score_codex":0.0006865695,"about_ca_system_score_gemma":0.0009788931,"threshold_uncertainty_score":0.026614666},"labels":[],"label_agreement":null},{"id":"W4396832368","doi":"10.1145/3613904.3642711","title":"Piet: Facilitating Color Authoring for Motion Graphics Video","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"","keywords":"Theme (computing); Palette (painting); Computer science; Motion (physics); Workflow; Multimedia; Narrative; Graphics; Focus (optics); Computer graphics (images); Human–computer interaction; Domain (mathematical analysis); Artificial intelligence; World Wide Web; Art","score_opus":0.025132031566784035,"score_gpt":0.2778246097236333,"score_spread":0.2526925781568493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396832368","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052971058,0.00048147675,0.87747145,0.00038764914,0.00037595932,0.0010543095,0.0015736073,0.050641727,0.015042669],"genre_scores_gemma":[0.19312415,0.0005121397,0.78231275,0.0003804054,0.0001934605,0.0015861152,0.001861742,0.0068884226,0.013140806],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989619,0.00041152525,0.000071035414,0.00018818874,0.0002612356,0.000105935454],"domain_scores_gemma":[0.99255365,0.0054255677,0.0002828525,0.0007575772,0.00053221144,0.00044811057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002158684,0.0016315978,0.00039759628,0.0015659328,0.00050713867,0.0017867279,0.0017904217,0.00102548,0.021094153],"category_scores_gemma":[0.017512495,0.0005387361,0.0008753342,0.00062830775,0.0007148805,0.002883586,0.0032125928,0.0011399665,0.003760957],"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":[0.0018859523,0.00063607533,0.0041231145,0.0031279908,0.0000997233,0.0030494486,0.015575638,0.008690078,0.14977783,0.02104806,0.08058798,0.7113982],"study_design_scores_gemma":[0.0008635483,0.0016114631,0.009964711,0.0012707231,0.00019359803,0.0054379883,0.0051422487,0.18125018,0.15997684,0.04144453,0.59226793,0.00057619263],"about_ca_topic_score_codex":0.0003769017,"about_ca_topic_score_gemma":0.00075232406,"teacher_disagreement_score":0.021094153,"about_ca_system_score_codex":0.00036934603,"about_ca_system_score_gemma":0.0005375056,"threshold_uncertainty_score":0.07056695},"labels":[],"label_agreement":null},{"id":"W4396833732","doi":"10.1145/3613904.3642575","title":"CollageVis: Rapid Previsualization Tool for Indie Filmmaking using Video Collages","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Indie film; Filmmaking; Computer science; Computer graphics (images); Animation; Multimedia; Usability; Avatar; Movie theater; Human–computer interaction; Art; Visual arts","score_opus":0.03797936468736887,"score_gpt":0.3208949816811606,"score_spread":0.28291561699379175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396833732","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056826737,0.0014042791,0.7297874,0.00066145416,0.00041183716,0.0025721742,0.007274963,0.16406478,0.0369963],"genre_scores_gemma":[0.1783531,0.0010683133,0.7750093,0.00032924995,0.00024061465,0.0029287448,0.009953687,0.010546937,0.021570085],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989285,0.00029937326,0.00009253848,0.00028458066,0.00032160975,0.00007343289],"domain_scores_gemma":[0.99642974,0.0017681088,0.00031484105,0.00071802543,0.00047505496,0.0002942707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021597329,0.0015638585,0.00060476124,0.0028261193,0.00079627155,0.0023634634,0.0013511274,0.00089351204,0.02317254],"category_scores_gemma":[0.00857224,0.00079691707,0.00068589486,0.0009990128,0.00063804805,0.0029948957,0.0035047939,0.0010207677,0.0046971617],"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":[0.001373516,0.00035235,0.0049455445,0.002058699,0.00015920009,0.0011378186,0.009799063,0.0015884729,0.08776091,0.009584875,0.09737088,0.78386873],"study_design_scores_gemma":[0.0005615161,0.0009330303,0.01727342,0.001012479,0.00022232367,0.0020932127,0.0064764363,0.05656533,0.1607148,0.014008033,0.739747,0.00039231815],"about_ca_topic_score_codex":0.0008809302,"about_ca_topic_score_gemma":0.0013848565,"teacher_disagreement_score":0.02317254,"about_ca_system_score_codex":0.00043236188,"about_ca_system_score_gemma":0.0006554486,"threshold_uncertainty_score":0.077519834},"labels":[],"label_agreement":null},{"id":"W4396913662","doi":"10.48550/arxiv.2405.07407","title":"PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Analysis and Summarization","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; Major League Baseball","keywords":"Analytics; Business; Computer science; Data science","score_opus":0.04201349580639284,"score_gpt":0.17910994105469705,"score_spread":0.13709644524830422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396913662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08415953,0.001206553,0.8167529,0.00069864927,0.0005205148,0.00042662513,0.004657216,0.07823822,0.013339717],"genre_scores_gemma":[0.5640695,0.0008947611,0.40272707,0.000610049,0.00025914868,0.00032505335,0.014218662,0.003434533,0.013461239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995522,0.000061154904,0.00001952493,0.00014732436,0.00017403245,0.000045696273],"domain_scores_gemma":[0.9995833,0.00013179732,0.000040833853,0.00006180617,0.00012215164,0.000060066137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066037476,0.0009501622,0.00040820672,0.0012899485,0.0003409466,0.0013796701,0.0010312771,0.00048912223,0.0044433903],"category_scores_gemma":[0.0026122448,0.00029319947,0.00032330147,0.00045617064,0.00039061613,0.0013546253,0.0020169446,0.00073204946,0.0021729667],"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":[0.0021183824,0.00032565606,0.014860089,0.000547908,0.00023741176,0.00080002553,0.0012499648,0.038193993,0.08699178,0.009175934,0.07057872,0.7749202],"study_design_scores_gemma":[0.00009577542,0.0003737089,0.01301903,0.00013683314,0.000060722698,0.00042369534,0.00054532505,0.8730755,0.04624113,0.017183425,0.048758794,0.00008607527],"about_ca_topic_score_codex":0.0038801061,"about_ca_topic_score_gemma":0.006579494,"teacher_disagreement_score":0.0044433903,"about_ca_system_score_codex":0.00039777978,"about_ca_system_score_gemma":0.00044044442,"threshold_uncertainty_score":0.014864564},"labels":[],"label_agreement":null},{"id":"W4398290291","doi":"10.7910/dvn/eaxeet/5lqdqr","title":"goetheUnread.R","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Video Analysis and Summarization","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; Environmental science","score_opus":0.019200470967990834,"score_gpt":0.2444548204028579,"score_spread":0.22525434943486708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398290291","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016803217,0.00016068965,0.0003062822,0.00008864077,0.00007871477,0.000027579228,0.99319,0.0043448713,0.0016351985],"genre_scores_gemma":[0.00034191817,0.00007019958,0.00058768253,0.00004157221,0.00001593184,0.00009373482,0.99751294,0.00042475938,0.0009112628],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99823415,0.00030432356,0.00015067452,0.00066347193,0.0003576174,0.00028979496],"domain_scores_gemma":[0.99696785,0.0005925799,0.00024197278,0.0012500669,0.00060043304,0.0003471638],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016309206,0.0055155973,0.0021248085,0.0063144905,0.0014711637,0.0042126407,0.0054461616,0.0027016336,0.12828484],"category_scores_gemma":[0.008882313,0.0009482608,0.0018398209,0.0067384187,0.0008618544,0.0024095948,0.0039535495,0.0022939818,0.31158236],"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":[0.00005266913,0.000016275155,0.00015325507,0.00036895715,0.000020184298,0.000010252773,0.00001277343,0.000097615826,0.0001224726,0.00028011965,0.99635834,0.0025069793],"study_design_scores_gemma":[0.0002669414,0.000038638344,0.0014044977,0.00030807863,0.00004253847,0.0000756034,0.00007075173,0.00076861685,0.00090345653,0.0023397803,0.9937389,0.000042244203],"about_ca_topic_score_codex":0.014338096,"about_ca_topic_score_gemma":0.022198996,"teacher_disagreement_score":0.8717152,"about_ca_system_score_codex":0.0013210136,"about_ca_system_score_gemma":0.0024011172,"threshold_uncertainty_score":0.42915553},"labels":[],"label_agreement":null},{"id":"W4399369484","doi":"10.21428/d82e957c.800cda62","title":"Domain-guided Masked Autoencoders for Unique Player Identification","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Identification (biology); Computer science; Domain (mathematical analysis); Artificial intelligence; Machine learning; Pattern recognition (psychology); Biology; Mathematics","score_opus":0.020065066839269816,"score_gpt":0.284111717730447,"score_spread":0.2640466508911772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399369484","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09864467,0.0012453578,0.89291674,0.00034113202,0.0001530234,0.00006677484,0.00054180727,0.0029319855,0.0031585007],"genre_scores_gemma":[0.6820843,0.0006284664,0.306325,0.00039651906,0.00010228824,0.00008979689,0.0019951984,0.00017870017,0.008199675],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996978,0.000053959066,0.000016408521,0.00011402367,0.00006746196,0.000050386694],"domain_scores_gemma":[0.9995634,0.00017358585,0.000044787725,0.0000835842,0.00010892851,0.000025759879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061298884,0.0010719406,0.00066711643,0.00042399994,0.00025602084,0.0004151792,0.0010131829,0.00066387985,0.0016414368],"category_scores_gemma":[0.0019548982,0.00035223004,0.0005779925,0.00037580795,0.00040925623,0.0012482003,0.0007704521,0.0012857933,0.0008681166],"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":[0.00060555094,0.000300696,0.0029407963,0.00014210024,0.00016412075,0.00026122094,0.00015001623,0.39547962,0.059788156,0.00817375,0.010272183,0.5217218],"study_design_scores_gemma":[0.0000053880726,0.000036832877,0.00037670508,0.0000063133202,0.000012219541,0.00003450105,0.000014982706,0.9910492,0.0057986025,0.0017533656,0.0009053549,0.0000065594695],"about_ca_topic_score_codex":0.0058752727,"about_ca_topic_score_gemma":0.010833859,"teacher_disagreement_score":0.0058752727,"about_ca_system_score_codex":0.0005201128,"about_ca_system_score_gemma":0.00077962194,"threshold_uncertainty_score":0.011682153},"labels":[],"label_agreement":null},{"id":"W4400184706","doi":"10.1007/978-3-031-64573-0_7","title":"GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":7,"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; Inference; Image (mathematics); Image quality; Artificial intelligence; Quality (philosophy); Computer vision","score_opus":0.024207800828140755,"score_gpt":0.2774683416743956,"score_spread":0.2532605408462548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400184706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014470473,0.00045204745,0.9114885,0.0002222677,0.00020791992,0.00012867058,0.00072350184,0.06414285,0.008163803],"genre_scores_gemma":[0.120232664,0.00016106604,0.84786135,0.00022461466,0.00009282541,0.00015210398,0.0021246176,0.009517104,0.019633522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994252,0.000096807,0.000027081449,0.00017328333,0.00018928495,0.00008841015],"domain_scores_gemma":[0.99876,0.00062313076,0.000052133786,0.00027695054,0.00023084872,0.000056944205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007812574,0.0012005549,0.0007123635,0.00097529835,0.0005251041,0.0011408341,0.0023317223,0.00125473,0.03357546],"category_scores_gemma":[0.0032259538,0.0007722173,0.0007662873,0.00087262696,0.00049944146,0.0016722119,0.0014473225,0.0013885524,0.0076091522],"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":[0.0012595782,0.00019400628,0.0007082453,0.0002174772,0.00009214678,0.00014555156,0.00010369009,0.03430046,0.04422101,0.010155067,0.060806904,0.8477958],"study_design_scores_gemma":[0.00023760567,0.00011335958,0.0005115731,0.000022220194,0.00004636106,0.00009017848,0.000027791486,0.9068526,0.069350824,0.010039444,0.012681658,0.000026370984],"about_ca_topic_score_codex":0.008335747,"about_ca_topic_score_gemma":0.015711922,"teacher_disagreement_score":0.03357546,"about_ca_system_score_codex":0.00084839965,"about_ca_system_score_gemma":0.00092805363,"threshold_uncertainty_score":0.11232108},"labels":[],"label_agreement":null},{"id":"W4400434575","doi":"10.1109/tcsvt.2025.3623074","title":"Reinforcement Learning for Unsupervised Video Summarization With Reward Generator Training","year":2025,"lang":"en","type":"preprint","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Analysis and Summarization","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":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Reinforcement learning; Computer science; Reinforcement; Unsupervised learning; Artificial intelligence; Machine learning; Natural language processing; Psychology; Social psychology","score_opus":0.031514088451706106,"score_gpt":0.2520957730003055,"score_spread":0.2205816845485994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400434575","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01000377,0.00010795063,0.9883745,0.000096425574,0.000016550883,0.000050232644,0.000027550219,0.0006902251,0.00063277734],"genre_scores_gemma":[0.6431222,0.00015265097,0.35198736,0.00019449276,0.000077937126,0.00030481204,0.00025866806,0.00023422137,0.0036676365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948704,0.00019044358,0.00002462055,0.00014408748,0.00010429676,0.000049463135],"domain_scores_gemma":[0.99782807,0.0014788868,0.00022091148,0.0001773292,0.00021693471,0.00007788482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014215743,0.0008099241,0.0007388896,0.0004207196,0.00031274848,0.00047888688,0.0012021535,0.00076013146,0.0020319857],"category_scores_gemma":[0.0051704356,0.0003488027,0.00036944766,0.0003250944,0.0007513862,0.0009728956,0.00092129444,0.0013027467,0.00049598416],"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":[0.00015926517,0.00010966321,0.0006437402,0.00011620439,0.000050452072,0.00009622103,0.00012755656,0.8170818,0.011171139,0.012828579,0.0018114588,0.15580386],"study_design_scores_gemma":[0.0000080359805,0.00003380413,0.000054401367,0.0000036335102,0.0000035433743,0.000012155697,0.0000038302414,0.99418736,0.0017229842,0.0036631178,0.00030346843,0.0000036448314],"about_ca_topic_score_codex":0.0012787392,"about_ca_topic_score_gemma":0.0018286139,"teacher_disagreement_score":0.0020319857,"about_ca_system_score_codex":0.0007879006,"about_ca_system_score_gemma":0.00056649814,"threshold_uncertainty_score":0.007518053},"labels":[],"label_agreement":null},{"id":"W4400975256","doi":"10.1109/access.2024.3433395","title":"GenVis: Visualizing Genre Detection in Movie Trailers for Enhanced Understanding","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Analysis and Summarization","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; Visualization; Film genre; Timeline; Usability; Depiction; Natural language processing; Information retrieval; sort; Artificial intelligence; Human–computer interaction; Movie theater","score_opus":0.09257614238437267,"score_gpt":0.358068763946263,"score_spread":0.26549262156189035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400975256","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113134444,0.0033229887,0.6330247,0.0015148261,0.00084362313,0.0010010795,0.039712366,0.18172565,0.02572027],"genre_scores_gemma":[0.23258768,0.0014552786,0.72363055,0.00030594043,0.00024225075,0.0006533143,0.022419473,0.0068792338,0.011826311],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997243,0.00006754164,0.00002435456,0.000064140084,0.000089348614,0.000030300926],"domain_scores_gemma":[0.9984321,0.0005826435,0.0001751544,0.00018313114,0.00048513152,0.00014175862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011009969,0.0015007342,0.00045770252,0.004216827,0.0004887736,0.00180949,0.000657298,0.0006619305,0.013487881],"category_scores_gemma":[0.0036287366,0.0003162068,0.00061594276,0.0013542398,0.00021027248,0.0017573445,0.0011627848,0.00082123873,0.002646299],"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":[0.0015889924,0.00029678654,0.012423018,0.0021596078,0.00019761463,0.00060953526,0.0038542226,0.00570613,0.07401992,0.0070971753,0.22756302,0.66448396],"study_design_scores_gemma":[0.00041900593,0.0008993166,0.046167687,0.0014160256,0.00027642804,0.0016227368,0.0037462593,0.4122364,0.10517395,0.027082356,0.40059358,0.0003662726],"about_ca_topic_score_codex":0.0034557462,"about_ca_topic_score_gemma":0.0071935323,"teacher_disagreement_score":0.013487881,"about_ca_system_score_codex":0.00041634645,"about_ca_system_score_gemma":0.0004674742,"threshold_uncertainty_score":0.04512143},"labels":[],"label_agreement":null},{"id":"W4401024496","doi":"10.24963/ijcai.2024/75","title":"Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China; Ant Group","keywords":"Shot (pellet); Consistency (knowledge bases); Computer science; Computer vision; Zero (linguistics); Artificial intelligence; One shot; Computer graphics (images); Engineering","score_opus":0.008550603560191898,"score_gpt":0.2859933010397318,"score_spread":0.2774426974795399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401024496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008454358,0.0003435394,0.9870235,0.00010848955,0.000056385823,0.00008035416,0.0005315046,0.0025194944,0.00088233146],"genre_scores_gemma":[0.28014642,0.0009017856,0.70740646,0.00026048097,0.00021561458,0.00034167588,0.004936644,0.0018660697,0.0039248927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988256,0.00017807262,0.000057544166,0.0004800463,0.00032871758,0.00012998373],"domain_scores_gemma":[0.9988644,0.00024652274,0.00014251009,0.0003245939,0.0003178031,0.00010412251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009243794,0.0018029397,0.0015344117,0.0026654177,0.0005356289,0.0021671928,0.0021124743,0.0016377143,0.004313076],"category_scores_gemma":[0.0050847507,0.0007016664,0.0012279956,0.0021169963,0.0012257185,0.00283283,0.0029285983,0.001718792,0.0028029853],"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":[0.00055537355,0.00013079046,0.0022438546,0.00043844344,0.00014906687,0.0002444518,0.00055351,0.28519243,0.036658242,0.029695584,0.021790287,0.622348],"study_design_scores_gemma":[0.000023587221,0.00009135002,0.0011723385,0.00004942691,0.000030617368,0.00027684701,0.00013430888,0.94212866,0.013971582,0.0333058,0.00876734,0.000048045935],"about_ca_topic_score_codex":0.005050827,"about_ca_topic_score_gemma":0.006405514,"teacher_disagreement_score":0.005050827,"about_ca_system_score_codex":0.0010557711,"about_ca_system_score_gemma":0.0009049864,"threshold_uncertainty_score":0.014428675},"labels":[],"label_agreement":null},{"id":"W4401024718","doi":"10.24963/ijcai.2024/102","title":"Template-based Uncertainty Multimodal Fusion Network for RGBT Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Computer science; Motion (physics); Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.021106461933425923,"score_gpt":0.27026038424217697,"score_spread":0.24915392230875105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401024718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006231505,0.00027602035,0.991779,0.00008234188,0.00002594954,0.00003705759,0.00010588971,0.00062337006,0.0008387666],"genre_scores_gemma":[0.5884401,0.0007706033,0.40410346,0.00038047438,0.00018056614,0.00027071545,0.0011356759,0.00034163496,0.0043766135],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881244,0.00017961158,0.00007350451,0.00039936413,0.00041192464,0.00012313682],"domain_scores_gemma":[0.9990771,0.00027050968,0.00016160854,0.00013520126,0.00029584428,0.000059738337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001570612,0.0013538006,0.0013025544,0.0018441527,0.00066944683,0.0013963737,0.0019423058,0.001241312,0.0022380415],"category_scores_gemma":[0.0046729604,0.00052754255,0.0015134448,0.0018068417,0.0007392428,0.002489507,0.0021753646,0.0012826028,0.0007050514],"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":[0.00029700337,0.00007759464,0.0017939477,0.00014366802,0.00015937671,0.00016384355,0.00024948598,0.44248334,0.023366606,0.013139243,0.0033867683,0.5147392],"study_design_scores_gemma":[0.000004728008,0.000039692102,0.00043205192,0.000012021746,0.00003335682,0.000060542112,0.000021565329,0.9853317,0.006523659,0.0064614844,0.0010581393,0.000021024463],"about_ca_topic_score_codex":0.007203841,"about_ca_topic_score_gemma":0.0062768646,"teacher_disagreement_score":0.007203841,"about_ca_system_score_codex":0.0016526498,"about_ca_system_score_gemma":0.0011590702,"threshold_uncertainty_score":0.014323831},"labels":[],"label_agreement":null},{"id":"W4401484758","doi":"10.1016/j.softx.2024.101845","title":"Corrigendum to “FT Xtraction: Feature Extraction and Visualization of Conversational Video Data for Social and Emotional Analysis” [SoftwareX Volume 27 (2024), 1-8, 101827]","year":2024,"lang":"en","type":"erratum","venue":"SoftwareX","topic":"Video Analysis and Summarization","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":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Computer science; Volume (thermodynamics); Visualization; Feature extraction; Feature (linguistics); Artificial intelligence; Natural language processing; Linguistics","score_opus":0.04015975592567699,"score_gpt":0.3180027191613948,"score_spread":0.2778429632357178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401484758","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00039661478,0.0011402587,0.0098746745,0.051857993,0.87619376,0.00024562492,0.010214953,0.011427115,0.038649026],"genre_scores_gemma":[0.003293899,0.0019454876,0.00927616,0.027304193,0.060258195,0.00033319177,0.014352153,0.006445369,0.8767914],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952827,0.00068254984,0.00048528198,0.00052174396,0.002686393,0.00034129233],"domain_scores_gemma":[0.97275215,0.0034172216,0.0006644466,0.0014507523,0.020766128,0.0009493767],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002651237,0.0026778905,0.0019370182,0.005279055,0.003917973,0.0052955667,0.0028807963,0.0046913223,0.30152944],"category_scores_gemma":[0.03430772,0.0010295528,0.0017755032,0.0030662958,0.0014493556,0.0021332216,0.0025912726,0.0045561115,0.22232722],"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":[0.000008584744,0.0000036544081,0.000010863555,0.000026597101,0.0000018689507,0.000025966141,0.000004335134,0.000017607714,0.000048376467,0.000157325,0.99565,0.0040446236],"study_design_scores_gemma":[0.00001644981,0.000021665988,0.00051865645,0.000101175974,0.00001390409,0.00010701203,0.000035583373,0.0005148242,0.0005087615,0.00080691953,0.9973231,0.000031996355],"about_ca_topic_score_codex":0.05979101,"about_ca_topic_score_gemma":0.08363694,"teacher_disagreement_score":0.30152944,"about_ca_system_score_codex":0.0039122845,"about_ca_system_score_gemma":0.004364847,"threshold_uncertainty_score":0.9962835},"labels":[],"label_agreement":null},{"id":"W4402423302","doi":"10.24908/iqurcp17962","title":"A Deep Dive Into Current Marker-Less Motion Capture Systems and Integrating Theia3D Outputs Into OpenSim","year":2024,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Video Analysis and Summarization","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":"Queen's University","funders":"","keywords":"Computer science; Motion capture; Motion (physics); Current (fluid); Artificial intelligence; Engineering; Electrical engineering","score_opus":0.077242863611921,"score_gpt":0.3563691227290831,"score_spread":0.2791262591171621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402423302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012961707,0.0006579084,0.8634899,0.0010104971,0.0005671205,0.00057393993,0.006187477,0.09672006,0.017831376],"genre_scores_gemma":[0.11753872,0.0014687538,0.7571209,0.0013914875,0.00034126142,0.0020915053,0.02782095,0.06713424,0.025092203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978302,0.000309966,0.00019909731,0.00044445784,0.0010087986,0.00020739798],"domain_scores_gemma":[0.9960425,0.0011842737,0.00022396038,0.0009805977,0.0013138853,0.00025484266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044185333,0.0014615601,0.0008290293,0.001406063,0.00062528177,0.0029033897,0.00286468,0.00096105214,0.02679181],"category_scores_gemma":[0.011896343,0.0009330483,0.0016626093,0.00091015367,0.0009896471,0.0038444607,0.0044110953,0.0023244072,0.0143601615],"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":[0.0018530173,0.00036160316,0.007787868,0.0028445937,0.00026841956,0.0009697766,0.005531188,0.018894436,0.10177162,0.039688963,0.144254,0.67577446],"study_design_scores_gemma":[0.00017643427,0.0007499662,0.0078044594,0.001133327,0.00012769691,0.0012074251,0.00075705803,0.054766122,0.13880625,0.01681655,0.7772151,0.0004395637],"about_ca_topic_score_codex":0.001684039,"about_ca_topic_score_gemma":0.001613787,"teacher_disagreement_score":0.02679181,"about_ca_system_score_codex":0.00079256925,"about_ca_system_score_gemma":0.0014455657,"threshold_uncertainty_score":0.089627564},"labels":[],"label_agreement":null},{"id":"W4402595177","doi":"10.1109/iciea61579.2024.10664734","title":"Badger Identification Using Handcrafted Image Matching with Learned Convolutional Filter","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Badger; Computer science; Identification (biology); Matching (statistics); Artificial intelligence; Computer vision; Filter (signal processing); Image (mathematics); Pattern recognition (psychology); Geology; Mathematics; Paleontology; Statistics; Biology","score_opus":0.020853622280738372,"score_gpt":0.26608429521587795,"score_spread":0.2452306729351396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402595177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046064463,0.0005303522,0.9468515,0.000089116475,0.0001273887,0.00010973375,0.00031785492,0.0040218676,0.0018877819],"genre_scores_gemma":[0.39852512,0.00055639655,0.5842557,0.00021036832,0.0001076393,0.00011029325,0.0025533172,0.00038423343,0.013296904],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948454,0.00003392434,0.000026819886,0.00021760073,0.00014436977,0.0000926878],"domain_scores_gemma":[0.9996074,0.000059500806,0.00006736001,0.00012271013,0.000115455594,0.00002761161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062075176,0.0008741824,0.0011411917,0.0023941682,0.00043017927,0.0009019021,0.0010507565,0.0010612458,0.0024116533],"category_scores_gemma":[0.00096183224,0.0002721533,0.00082254974,0.0014975122,0.00046501754,0.0015230697,0.0009247392,0.0007389192,0.0021531938],"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":[0.00033062598,0.00015407168,0.0030650955,0.00015935495,0.0001608663,0.00013886558,0.000067054956,0.03346271,0.119883806,0.0022582896,0.0062872185,0.83403206],"study_design_scores_gemma":[0.000026110356,0.00036222735,0.009859643,0.00003516412,0.000121029225,0.0005472204,0.00011483059,0.8637766,0.106465004,0.004687325,0.013938931,0.00006592499],"about_ca_topic_score_codex":0.0044743186,"about_ca_topic_score_gemma":0.009323709,"teacher_disagreement_score":0.0044743186,"about_ca_system_score_codex":0.00060447113,"about_ca_system_score_gemma":0.0007847635,"threshold_uncertainty_score":0.00889653},"labels":[],"label_agreement":null},{"id":"W4402702960","doi":"10.1109/cvpr52733.2024.00796","title":"Scaling Up Video Summarization Pretraining with Large Language Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Automatic summarization; Computer science; Scaling; Natural language processing; Artificial intelligence; Language model; Mathematics","score_opus":0.012303328784572977,"score_gpt":0.24316772591083868,"score_spread":0.23086439712626572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402702960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08590839,0.0042722817,0.8489717,0.001562416,0.0007378994,0.0006445861,0.008873526,0.04400521,0.0050240266],"genre_scores_gemma":[0.36066404,0.0014158755,0.5756883,0.0012549454,0.00060456846,0.0009064455,0.047313098,0.0010914062,0.011061317],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991991,0.00016996736,0.00005794938,0.00035635452,0.00012781631,0.000088797904],"domain_scores_gemma":[0.99796754,0.0010620486,0.00013104154,0.00025535977,0.00049082906,0.00009321047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010424262,0.002343082,0.0012010164,0.001448136,0.00059933047,0.0010181697,0.0017729788,0.0012762472,0.0040709767],"category_scores_gemma":[0.005236971,0.00047501954,0.00126161,0.0013326647,0.00035593708,0.0023237704,0.0009817381,0.0025799542,0.0037733533],"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":[0.00047021333,0.00048444694,0.0020965175,0.0006334168,0.00026136095,0.00028278754,0.00021285325,0.08341379,0.04126223,0.0018369077,0.05073391,0.8183117],"study_design_scores_gemma":[0.000066178916,0.00030048384,0.0013284757,0.0000430313,0.00010116586,0.00010447381,0.0001807647,0.96652913,0.019479798,0.0028838126,0.008947362,0.000035179382],"about_ca_topic_score_codex":0.011840812,"about_ca_topic_score_gemma":0.025874091,"teacher_disagreement_score":0.011840812,"about_ca_system_score_codex":0.0010618415,"about_ca_system_score_gemma":0.0013638026,"threshold_uncertainty_score":0.023543775},"labels":[],"label_agreement":null},{"id":"W4402716105","doi":"10.1109/cvpr52733.2024.00764","title":"4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology; University of Toronto","funders":"Army Research Office; Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; National Science Foundation","keywords":"Distillation; Computer science; Sampling (signal processing); Natural language processing; Artificial intelligence; Telecommunications; Chemistry; Chromatography","score_opus":0.08414648768217287,"score_gpt":0.3050737473820094,"score_spread":0.2209272596998365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402716105","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017272826,0.00037632757,0.96716475,0.00037987772,0.00029204309,0.00021825237,0.00050261134,0.008981759,0.0048115617],"genre_scores_gemma":[0.27642205,0.00023649244,0.7056555,0.0007057031,0.00016221833,0.0004945047,0.0030305565,0.0019938883,0.011299044],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99945134,0.00011762539,0.000030285255,0.00015476966,0.00019638971,0.00004967679],"domain_scores_gemma":[0.9989802,0.0004889567,0.000057810903,0.00020948937,0.0001809033,0.00008269128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009508667,0.0014377177,0.000952616,0.0006898166,0.00053934316,0.0011096109,0.0017681262,0.0017201115,0.011323092],"category_scores_gemma":[0.0040651383,0.00050180044,0.0009267261,0.00061956345,0.0007708149,0.0014402597,0.002103316,0.0017484465,0.0035644004],"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":[0.0005186817,0.00028592316,0.0012611353,0.00042870914,0.00011766078,0.00053789327,0.0003346717,0.40705597,0.03972129,0.023882564,0.027458182,0.49839726],"study_design_scores_gemma":[0.000053038104,0.00005580361,0.0000856821,0.000010712076,0.000007342249,0.00006422809,0.000021480568,0.9826652,0.0072055883,0.005629801,0.004185036,0.000016155513],"about_ca_topic_score_codex":0.0034676297,"about_ca_topic_score_gemma":0.0073805633,"teacher_disagreement_score":0.011323092,"about_ca_system_score_codex":0.00073831563,"about_ca_system_score_gemma":0.0008570097,"threshold_uncertainty_score":0.037879527},"labels":[],"label_agreement":null},{"id":"W4402721845","doi":"10.1145/3670947.3670948","title":"Interaction Techniques for Comparing Video","year":2024,"lang":"en","type":"article","venue":"Graphics Interface","topic":"Video Analysis and Summarization","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":"Autodesk (Canada); University of Saskatchewan","funders":"","keywords":"Computer science; Computer graphics (images)","score_opus":0.03385481851661155,"score_gpt":0.3181441254091384,"score_spread":0.28428930689252685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402721845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007002548,0.0005512104,0.9713036,0.00024515818,0.00013716632,0.000561511,0.0006880036,0.015216739,0.0042940336],"genre_scores_gemma":[0.05933215,0.0005284985,0.9327158,0.00026793958,0.00012143935,0.001021328,0.0009720371,0.0023700253,0.0026707642],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99593675,0.0015495047,0.00035058588,0.0008264908,0.0011463242,0.00019030427],"domain_scores_gemma":[0.9837404,0.011553223,0.00067195477,0.0018500041,0.0018642419,0.0003201927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004973406,0.0021265806,0.0011088119,0.0037424501,0.00082586246,0.0022949567,0.0020742824,0.0015651003,0.03050827],"category_scores_gemma":[0.025468519,0.00063564244,0.0011685231,0.0024596995,0.00074904267,0.00438392,0.003998143,0.0015973025,0.0067877425],"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":[0.0012930996,0.00018540831,0.0011662985,0.0016419234,0.00017226799,0.00035798733,0.0022283855,0.0023419075,0.07762717,0.00809633,0.026162319,0.8787269],"study_design_scores_gemma":[0.0010677859,0.0024989066,0.01782703,0.0012653074,0.0006800714,0.0044590607,0.0057207635,0.22088031,0.28802255,0.07725626,0.37945095,0.0008710524],"about_ca_topic_score_codex":0.00080962776,"about_ca_topic_score_gemma":0.0010050371,"teacher_disagreement_score":0.03050827,"about_ca_system_score_codex":0.00041626132,"about_ca_system_score_gemma":0.0004142245,"threshold_uncertainty_score":0.10206038},"labels":[],"label_agreement":null},{"id":"W4402753403","doi":"10.1109/cvpr52733.2024.01041","title":"Understanding Video Transformers via Universal Concept Discovery","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Transformer; Electrical engineering; Engineering; Voltage","score_opus":0.03574541325534394,"score_gpt":0.23533694936373864,"score_spread":0.1995915361083947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402753403","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03091264,0.00031858048,0.9668998,0.00027623822,0.00001633502,0.000091332666,0.00023845496,0.000503976,0.0007426278],"genre_scores_gemma":[0.61854947,0.00062735635,0.37783834,0.00013731809,0.000053615076,0.00017189383,0.0012867337,0.00012268237,0.001212566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986216,0.00034357645,0.00008825031,0.00049334887,0.00032714102,0.00012620242],"domain_scores_gemma":[0.9957035,0.002523679,0.0005635612,0.00064034824,0.00042015692,0.00014866161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018349366,0.00079579966,0.00058785663,0.0028249652,0.0005832079,0.0016954069,0.0015768501,0.00073203584,0.0015115178],"category_scores_gemma":[0.010985493,0.0004392555,0.0012086952,0.0015142607,0.0018193941,0.00530479,0.0018714152,0.0015196335,0.00031161186],"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":[0.00047659545,0.00025657023,0.011398368,0.00060488796,0.00021666403,0.0007782481,0.0021041788,0.12179899,0.025038261,0.24994625,0.004828404,0.5825526],"study_design_scores_gemma":[0.00001686953,0.00007286944,0.0014931585,0.00004823483,0.00004488586,0.0002658498,0.00031555467,0.8025034,0.008658352,0.18298781,0.0035669364,0.000026085287],"about_ca_topic_score_codex":0.004190613,"about_ca_topic_score_gemma":0.0036632163,"teacher_disagreement_score":0.004190613,"about_ca_system_score_codex":0.0016656829,"about_ca_system_score_gemma":0.0009993874,"threshold_uncertainty_score":0.012085438},"labels":[],"label_agreement":null},{"id":"W4402754204","doi":"10.1109/cvpr52733.2024.02118","title":"GlitchBench: Can Large Multimodal Models Detect Video Game Glitches?","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Alberta","funders":"National Science Foundation","keywords":"Computer science; Video game; Artificial intelligence; Real-time computing; Computer vision; Human–computer interaction; Multimedia","score_opus":0.014835931349954897,"score_gpt":0.23967088663458275,"score_spread":0.22483495528462785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402754204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3010363,0.003556381,0.56104285,0.002607462,0.0008522287,0.0016077226,0.017189275,0.086571336,0.025536453],"genre_scores_gemma":[0.67382133,0.0005435657,0.29432806,0.00090380356,0.00007811827,0.0007510903,0.020907829,0.002428959,0.0062372326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986475,0.0004570713,0.00007427405,0.00041528718,0.00026071616,0.00014518999],"domain_scores_gemma":[0.9956505,0.0027112903,0.00022620226,0.00068038306,0.000495484,0.00023608554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019997365,0.0021466448,0.0007438808,0.0014330797,0.0005080706,0.0027308434,0.0023786577,0.0020873256,0.008119853],"category_scores_gemma":[0.018653836,0.0005514646,0.0010739573,0.00052521954,0.0008315662,0.0035789995,0.002853321,0.0020039796,0.003310822],"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":[0.0031877363,0.0009271079,0.022725997,0.0022225955,0.0006878163,0.0008908379,0.0016524262,0.13472149,0.031894635,0.012769375,0.08747279,0.70084715],"study_design_scores_gemma":[0.00013994375,0.00048638828,0.005886174,0.0001841546,0.00008019733,0.0003151823,0.0005797316,0.9476622,0.01521019,0.017339094,0.012039344,0.00007746846],"about_ca_topic_score_codex":0.015459924,"about_ca_topic_score_gemma":0.018539479,"teacher_disagreement_score":0.015459924,"about_ca_system_score_codex":0.0010283706,"about_ca_system_score_gemma":0.0011852736,"threshold_uncertainty_score":0.030739903},"labels":[],"label_agreement":null},{"id":"W4402915739","doi":"10.1109/cvprw63382.2024.00346","title":"PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Waterloo","funders":"Mitacs","keywords":"Analytics; Computer science; Computer graphics (images); Data science","score_opus":0.009913028251243891,"score_gpt":0.22973935679546792,"score_spread":0.21982632854422401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402915739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1006326,0.00119999,0.8004384,0.0006517153,0.0005436873,0.00046167016,0.004306624,0.076360606,0.015404703],"genre_scores_gemma":[0.6154352,0.00081894844,0.35567397,0.0006260034,0.00022381496,0.00030100349,0.01090255,0.0031089517,0.012909568],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955755,0.000055815814,0.000017854989,0.0001446801,0.00017785862,0.000046122434],"domain_scores_gemma":[0.9996228,0.000115553994,0.000039983322,0.000051144125,0.00011143399,0.00005902765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006460047,0.00093658577,0.00041227762,0.0011941884,0.0003234667,0.001295554,0.0010464366,0.00044082978,0.0046134507],"category_scores_gemma":[0.0023622192,0.00029071685,0.00031352288,0.00039066045,0.00037702333,0.0012685691,0.0019958797,0.00067758025,0.0020194587],"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":[0.002438076,0.0003666209,0.020326035,0.0005619893,0.00026335436,0.00090558594,0.0012650181,0.038720265,0.09414681,0.008923386,0.06651408,0.7655687],"study_design_scores_gemma":[0.000102105165,0.00042140763,0.015081468,0.00015258306,0.000066731045,0.0005165438,0.0005186441,0.87462056,0.048176732,0.014703208,0.04554366,0.00009631289],"about_ca_topic_score_codex":0.0037413323,"about_ca_topic_score_gemma":0.0067700073,"teacher_disagreement_score":0.0046134507,"about_ca_system_score_codex":0.00039035187,"about_ca_system_score_gemma":0.00044024186,"threshold_uncertainty_score":0.01543355},"labels":[],"label_agreement":null},{"id":"W4402916172","doi":"10.1109/cvprw63382.2024.00333","title":"SoccerNet-Depth: a Scalable Dataset for Monocular Depth Estimation in Sports Videos","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Scalability; Monocular; Artificial intelligence; Estimation; Computer vision; Engineering; Database","score_opus":0.01951265655154812,"score_gpt":0.28250447885210594,"score_spread":0.26299182230055784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916172","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017315084,0.002846154,0.014855348,0.00036276813,0.00040325374,0.0007827409,0.93030256,0.024407594,0.008724539],"genre_scores_gemma":[0.01563074,0.00043590672,0.018197548,0.00012337846,0.000040435316,0.00033055467,0.9633628,0.00046602837,0.0014125755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983966,0.00010896937,0.00013077694,0.0005647129,0.0005897891,0.00020924881],"domain_scores_gemma":[0.99912983,0.00009699405,0.00008812983,0.00023648416,0.00033893695,0.000109622466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057490944,0.0049124076,0.0019528964,0.0042646467,0.0009256812,0.0016403011,0.004117957,0.0022035502,0.011936226],"category_scores_gemma":[0.003217666,0.00083895616,0.0017571594,0.0037083784,0.0005656238,0.0024026462,0.0028998938,0.0021822471,0.013503846],"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":[0.0008141422,0.00053834886,0.004977712,0.00255461,0.00038991822,0.00031341656,0.000116027855,0.0076653906,0.0114118345,0.0018315658,0.8126808,0.15670624],"study_design_scores_gemma":[0.0010628981,0.00071513915,0.053359073,0.0014102735,0.00039373993,0.0024400735,0.0010199726,0.1534989,0.036510915,0.0076443464,0.7414896,0.0004551379],"about_ca_topic_score_codex":0.042038128,"about_ca_topic_score_gemma":0.10938057,"teacher_disagreement_score":0.042038128,"about_ca_system_score_codex":0.0016781284,"about_ca_system_score_gemma":0.0024720938,"threshold_uncertainty_score":0.08358693},"labels":[],"label_agreement":null},{"id":"W4402916283","doi":"10.1109/cvprw63382.2024.00541","title":"Retracted: T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":6,"is_retracted":true,"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":"Benchmarking; Computer science; Dynamics (music); Psychology","score_opus":0.022363549916095397,"score_gpt":0.26768265003523933,"score_spread":0.24531910011914393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916283","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2590488,0.011603008,0.4109094,0.0020888795,0.005399146,0.0048843296,0.054179393,0.21276873,0.039118346],"genre_scores_gemma":[0.56240857,0.0016350007,0.26299432,0.0011907964,0.00042705046,0.0027311414,0.14262483,0.0113094775,0.014678812],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99463755,0.0021681876,0.00046574188,0.0011880337,0.0012338107,0.00030669113],"domain_scores_gemma":[0.98744875,0.0067968457,0.0005171293,0.0019842403,0.0024991457,0.00075389654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00621562,0.0031665282,0.001021764,0.0022640894,0.00076042966,0.0029158636,0.00286059,0.002333359,0.008244858],"category_scores_gemma":[0.036725417,0.00049742253,0.0012129188,0.0011610311,0.00080651155,0.0032266236,0.0023855122,0.002082513,0.0053169015],"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":[0.003326154,0.0016328618,0.011753443,0.0059281285,0.0008775994,0.0011170952,0.0012702105,0.15243046,0.038067482,0.0076583554,0.2351104,0.5408279],"study_design_scores_gemma":[0.00083361706,0.0027470146,0.008929809,0.0004824403,0.00022105027,0.000875656,0.0008642197,0.8626178,0.045176506,0.008812514,0.0682018,0.00023758199],"about_ca_topic_score_codex":0.010784578,"about_ca_topic_score_gemma":0.011832904,"teacher_disagreement_score":0.010784578,"about_ca_system_score_codex":0.0016475078,"about_ca_system_score_gemma":0.0013347778,"threshold_uncertainty_score":0.032871723},"labels":[],"label_agreement":null},{"id":"W4402916709","doi":"10.1109/cvprw63382.2024.00329","title":"A General Framework for Jersey Number Recognition in Sports Video","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Artificial intelligence; Computer vision; Multimedia","score_opus":0.02034975756991226,"score_gpt":0.2802157452102821,"score_spread":0.25986598764036983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003009643,0.00066320976,0.9720781,0.0003691964,0.00015791674,0.000346754,0.0053103603,0.0148191815,0.003245683],"genre_scores_gemma":[0.046429444,0.0011558867,0.9100889,0.00048823236,0.0003305967,0.000700368,0.026771402,0.0014098943,0.012625376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988011,0.00010763697,0.00009080189,0.0005452849,0.0003051275,0.0001499581],"domain_scores_gemma":[0.999295,0.000120145225,0.00007089805,0.0002055669,0.00025104423,0.00005743088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001307556,0.0015768454,0.0011425839,0.0035137935,0.0009768682,0.0030614499,0.0030034662,0.0017756966,0.010534721],"category_scores_gemma":[0.0028815207,0.00070629717,0.0025793347,0.0027334862,0.0008160671,0.0030459852,0.0022147116,0.0023617325,0.015431775],"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":[0.00045064994,0.0003549834,0.004316213,0.0008257796,0.00023723158,0.00063301774,0.00044730757,0.03283969,0.058443703,0.059237044,0.13871217,0.70350206],"study_design_scores_gemma":[0.00007934699,0.00026535732,0.005962343,0.00023687755,0.0001131495,0.0012719489,0.00034151477,0.70631266,0.031028504,0.09029568,0.16392589,0.00016672505],"about_ca_topic_score_codex":0.021487286,"about_ca_topic_score_gemma":0.038424738,"teacher_disagreement_score":0.021487286,"about_ca_system_score_codex":0.0012844305,"about_ca_system_score_gemma":0.0017377887,"threshold_uncertainty_score":0.04272443},"labels":[],"label_agreement":null},{"id":"W4402952315","doi":"10.1007/978-3-031-72649-1_12","title":"MagDiff: Multi-alignment Diffusion for High-Fidelity Video Generation and Editing","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Fidelity; Computer graphics (images); Diffusion; Multimedia; Telecommunications","score_opus":0.021244858952395137,"score_gpt":0.25142125625659617,"score_spread":0.23017639730420103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402952315","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009854503,0.00011065485,0.99120796,0.000039743496,0.00006394795,0.000039712962,0.00011879337,0.00686312,0.0005705383],"genre_scores_gemma":[0.024898622,0.00015845333,0.9681322,0.00008025033,0.00005619472,0.0001291864,0.00060402346,0.0021290577,0.003812063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992429,0.00012263123,0.00004435978,0.00018139128,0.0003533071,0.000055470668],"domain_scores_gemma":[0.9985656,0.00060702086,0.000082025144,0.00031336612,0.0003367591,0.00009526097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151285,0.0014857838,0.0010065329,0.0010854458,0.00057903497,0.0013491131,0.0024308437,0.0016736351,0.01537486],"category_scores_gemma":[0.0037579585,0.0007021105,0.0008465004,0.0011892681,0.0005593717,0.0014442592,0.0020326811,0.0022096827,0.006279934],"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":[0.00027332202,0.00013196685,0.0003379015,0.00027238965,0.00009720034,0.00015276135,0.00019009192,0.027263347,0.07357297,0.017569821,0.027722131,0.8524161],"study_design_scores_gemma":[0.000055606135,0.00009435559,0.00032787147,0.000031280837,0.000023258604,0.00026430195,0.000038367372,0.8835389,0.07469366,0.015160574,0.025712216,0.000059566333],"about_ca_topic_score_codex":0.0020101059,"about_ca_topic_score_gemma":0.0035702207,"teacher_disagreement_score":0.01537486,"about_ca_system_score_codex":0.0005789213,"about_ca_system_score_gemma":0.00060263724,"threshold_uncertainty_score":0.05143404},"labels":[],"label_agreement":null},{"id":"W4402981488","doi":"10.1109/otcon60325.2024.10687926","title":"Harnessing the Capabilities of OpenAI’s CLIP and RNN for Visual Sequence Understanding in Film Editing","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Horizon College and Seminary","funders":"","keywords":"Computer science; Sequence (biology); Artificial intelligence; Human–computer interaction; Programming language; Chemistry","score_opus":0.0681453543976325,"score_gpt":0.31762940717619603,"score_spread":0.24948405277856353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402981488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055669285,0.00040948336,0.9353207,0.00016749225,0.000108959255,0.00011245199,0.00013891915,0.0016020993,0.0064706192],"genre_scores_gemma":[0.4745711,0.00042740215,0.51692766,0.00014820036,0.00011149897,0.00014680914,0.0004709622,0.00021623238,0.00698006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998629,0.00003069547,0.000008616449,0.000048759768,0.000035180215,0.000013827092],"domain_scores_gemma":[0.99968183,0.00015071052,0.000024164026,0.000040577266,0.00007871152,0.000024007737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037765896,0.00045269894,0.00021247407,0.0003813639,0.00021007188,0.00050267705,0.0006045356,0.0002795732,0.0026075917],"category_scores_gemma":[0.0012519885,0.00016435338,0.00032305557,0.000242432,0.00026579405,0.00081469334,0.00046693595,0.00066542224,0.000476651],"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":[0.00023495128,0.0001000706,0.0010658154,0.0002549545,0.00007756432,0.00029185027,0.0006929034,0.041930057,0.16811012,0.0057066926,0.0024428377,0.77909213],"study_design_scores_gemma":[0.000019376912,0.00026138383,0.002639561,0.000028742283,0.00007220643,0.00018816667,0.00018342726,0.9151353,0.067269094,0.004222365,0.009943276,0.000037171874],"about_ca_topic_score_codex":0.0034001411,"about_ca_topic_score_gemma":0.0061124596,"teacher_disagreement_score":0.0034001411,"about_ca_system_score_codex":0.0002597792,"about_ca_system_score_gemma":0.0002714394,"threshold_uncertainty_score":0.008723259},"labels":[],"label_agreement":null},{"id":"W4403053170","doi":"10.14375/np.9782760623347","title":"Radioscopie de l'information télévisée au Canada","year":2000,"lang":"fr","type":"book","venue":"","topic":"Video Analysis and Summarization","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":"Political science; Computer science; Geography","score_opus":0.008473298663157299,"score_gpt":0.19391762267272544,"score_spread":0.18544432400956815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403053170","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061363693,0.08455196,0.09641092,0.00582431,0.00397913,0.00033373086,0.0025590241,0.0019317706,0.74304545],"genre_scores_gemma":[0.10943843,0.026253952,0.020638825,0.00019329459,0.00038190422,0.00005379896,0.0010053285,0.0002446042,0.8417899],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.99976784,0.000013860471,0.000003817636,0.000024430503,0.00015350156,0.000036603178],"domain_scores_gemma":[0.9998833,0.00003103697,0.0000055151554,0.000008383927,0.000060822113,0.000011034788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021173248,0.00072010444,0.0003258186,0.0021660167,0.0016674029,0.0015795572,0.00052033324,0.00062457233,0.019482454],"category_scores_gemma":[0.0004686753,0.00027573222,0.00029431048,0.0017533462,0.0008125737,0.000440768,0.00036242124,0.0006258782,0.0024057215],"study_design_candidate":"qualitative","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":[0.00015033726,0.000025805197,0.0014212438,0.0004819742,0.000028332573,0.0005191651,0.0011522477,0.0083608,0.021974498,0.07028253,0.1698497,0.72575337],"study_design_scores_gemma":[0.000010547637,0.000027985838,0.007044414,0.00010393737,0.000013264455,0.00034858196,0.0003303789,0.0027420723,0.0061291135,0.0026313502,0.98059696,0.000021430757],"about_ca_topic_score_codex":0.75358516,"about_ca_topic_score_gemma":0.79289824,"teacher_disagreement_score":0.24641484,"about_ca_system_score_codex":0.00803589,"about_ca_system_score_gemma":0.006149584,"threshold_uncertainty_score":0.495732},"labels":[],"label_agreement":null},{"id":"W4403722283","doi":"10.1109/tpami.2024.3480702","title":"On the Distillation of Stories for Transferring Narrative Arcs in Collections of Independent Media","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Analysis and Summarization","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":"Narrative; Computer science; Distillation; Artificial intelligence; Information retrieval; Natural language processing; Art; Literature; Chemistry","score_opus":0.023509625057007592,"score_gpt":0.27343796337145077,"score_spread":0.24992833831444317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403722283","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14028567,0.001051286,0.84488004,0.00075974606,0.00007702777,0.00026952795,0.00096651877,0.0013325353,0.010377657],"genre_scores_gemma":[0.5386035,0.0005560294,0.4529885,0.00016068015,0.0000831312,0.00020076764,0.0023173217,0.00032489037,0.004765213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991866,0.0002740771,0.000047083402,0.0003039262,0.00013597451,0.00005236728],"domain_scores_gemma":[0.99681526,0.0018333764,0.000387456,0.0005681456,0.00026608567,0.0001296594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013664186,0.0008712785,0.00038635908,0.0026405097,0.0008097802,0.0016383865,0.0011539178,0.00094585377,0.0035161683],"category_scores_gemma":[0.010403983,0.00034246073,0.00067929435,0.0016167145,0.0012916845,0.004913056,0.001922912,0.0016451244,0.001178896],"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":[0.00041891984,0.00021758991,0.006764739,0.00034998843,0.000121730576,0.00019157703,0.0016490461,0.056795593,0.018581966,0.026251748,0.0036982435,0.88495886],"study_design_scores_gemma":[0.000042045882,0.00024588883,0.008449274,0.00015141824,0.000055315308,0.0003679649,0.001040868,0.88677156,0.019445501,0.06805981,0.015299259,0.00007113079],"about_ca_topic_score_codex":0.0031135858,"about_ca_topic_score_gemma":0.005923385,"teacher_disagreement_score":0.0035161683,"about_ca_system_score_codex":0.00080758665,"about_ca_system_score_gemma":0.0005598318,"threshold_uncertainty_score":0.011762738},"labels":[],"label_agreement":null},{"id":"W4403780507","doi":"10.1145/3664647.3680673","title":"T2VIndexer: A Generative Video Indexer for Efficient Text-Video Retrieval","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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 de Montréal","funders":"","keywords":"Computer science; Generative grammar; Information retrieval; Artificial intelligence; Video retrieval; Multimedia","score_opus":0.01886097318976458,"score_gpt":0.27068786106335574,"score_spread":0.25182688787359114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403780507","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012267255,0.0014488751,0.9146046,0.00021683492,0.00016771434,0.00052076817,0.003973789,0.06295314,0.0038470295],"genre_scores_gemma":[0.16837889,0.0013137803,0.79546815,0.0005348881,0.00018161739,0.0008136505,0.017726045,0.0032343245,0.012348652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993193,0.00009996794,0.000053028307,0.00021541223,0.00024408633,0.000068267196],"domain_scores_gemma":[0.99925774,0.00023775827,0.000070633636,0.00025518006,0.00012436506,0.000054294713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009663786,0.0016033646,0.0013066105,0.0026619164,0.00046340062,0.0012996781,0.002482201,0.0011852736,0.008453849],"category_scores_gemma":[0.0044343336,0.00051054667,0.0013407271,0.0019265172,0.0005290045,0.003440644,0.002189447,0.00133677,0.0059475633],"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":[0.0006766361,0.00027030596,0.0014712512,0.0006064905,0.00019396887,0.00034741167,0.00025429486,0.04088606,0.047534745,0.015946027,0.061982643,0.82983017],"study_design_scores_gemma":[0.00010904946,0.00016826438,0.00061691937,0.000031005027,0.00005382845,0.00045645924,0.000070425085,0.92677516,0.03668449,0.013291255,0.021672763,0.00007038842],"about_ca_topic_score_codex":0.008860662,"about_ca_topic_score_gemma":0.011267407,"teacher_disagreement_score":0.008860662,"about_ca_system_score_codex":0.0012818492,"about_ca_system_score_gemma":0.0010291061,"threshold_uncertainty_score":0.028280914},"labels":[],"label_agreement":null},{"id":"W4403922859","doi":"10.1145/3686215.3688382","title":"Detecting when Users Disagree with Generated Captions","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Universitas Brawijaya","keywords":"Computer science; Natural language processing","score_opus":0.015766819601418885,"score_gpt":0.22086264491274257,"score_spread":0.2050958253113237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403922859","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96703434,0.0002380387,0.026636813,0.0001966472,0.0000733037,0.00033017618,0.00043978044,0.0012752817,0.0037756013],"genre_scores_gemma":[0.986387,0.0000636923,0.0118918065,0.00012733231,0.000028428807,0.00015542154,0.00046486096,0.00008696137,0.00079440896],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99255836,0.003617935,0.00068282516,0.0011062981,0.0017522066,0.00028234767],"domain_scores_gemma":[0.93259126,0.04479806,0.0070127845,0.0040871794,0.0107220365,0.0007886303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065116934,0.0010074207,0.00078951713,0.0013935454,0.00072311336,0.0022724043,0.0009393091,0.0012718042,0.0018234781],"category_scores_gemma":[0.07866663,0.00032509537,0.00029003527,0.0005329392,0.00048572494,0.0020690046,0.0014088423,0.0009369668,0.0011964161],"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":[0.007126282,0.0007085336,0.34165925,0.0018303795,0.000541429,0.0014422542,0.05437861,0.0065499684,0.23211062,0.0025495552,0.010552137,0.3405509],"study_design_scores_gemma":[0.00022891039,0.0033010552,0.5032448,0.00042515594,0.00039732218,0.0022017593,0.028816853,0.3304507,0.10778453,0.005125631,0.017343257,0.0006800195],"about_ca_topic_score_codex":0.0013555714,"about_ca_topic_score_gemma":0.001592453,"teacher_disagreement_score":0.0065116934,"about_ca_system_score_codex":0.00052270514,"about_ca_system_score_gemma":0.0003606772,"threshold_uncertainty_score":0.034437537},"labels":[],"label_agreement":null},{"id":"W4404102668","doi":"10.1109/dsd64264.2024.00074","title":"HW/SW Collaborative Techniques for Accelerating TinyML Inference Time at No Cost","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Inference; Artificial intelligence","score_opus":0.02028628767143444,"score_gpt":0.2970331524061505,"score_spread":0.2767468647347161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404102668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012845629,0.0007320419,0.9657069,0.0005168708,0.00018299486,0.00012148747,0.00014647841,0.012531461,0.007216198],"genre_scores_gemma":[0.35578686,0.00080087606,0.62868184,0.00051700464,0.00014315562,0.00037885935,0.0008716216,0.0024603396,0.010359497],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993548,0.000091544796,0.000042680418,0.00014514726,0.00028554993,0.00008028814],"domain_scores_gemma":[0.99885106,0.00042978482,0.000084283674,0.0003747729,0.00021111278,0.000048980557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007084929,0.0016939823,0.0004803208,0.0006487315,0.00044035705,0.0009827239,0.0020756766,0.00058849825,0.013990622],"category_scores_gemma":[0.0036073131,0.000704158,0.0007063931,0.0004609542,0.00052380917,0.0023381773,0.0015129591,0.0016243769,0.0038039305],"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":[0.00056090276,0.00019276327,0.0021164394,0.0007852997,0.00020049217,0.0005081693,0.00039612607,0.121261165,0.12317491,0.0213332,0.021525875,0.70794463],"study_design_scores_gemma":[0.0000891197,0.00025440578,0.0008308806,0.00006352233,0.000102741695,0.00026471278,0.00014050654,0.8618467,0.074825294,0.014935635,0.046603527,0.00004289116],"about_ca_topic_score_codex":0.0033652163,"about_ca_topic_score_gemma":0.007801329,"teacher_disagreement_score":0.013990622,"about_ca_system_score_codex":0.00068708527,"about_ca_system_score_gemma":0.0010707123,"threshold_uncertainty_score":0.046803236},"labels":[],"label_agreement":null},{"id":"W4404304671","doi":"10.1145/3673791.3698424","title":"Offline Evaluation of Set-Based Text-to-Image Generation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Computer science; Set (abstract data type); Benchmark (surveying); Ranking (information retrieval); Process (computing); Similarity (geometry); Image (mathematics); Artificial intelligence; Machine learning; Information retrieval; Data mining; Data science","score_opus":0.07140135182829305,"score_gpt":0.3354684726010519,"score_spread":0.26406712077275885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404304671","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.807432,0.0034773576,0.14461555,0.0003842297,0.0005369049,0.0019218706,0.0055779675,0.022108207,0.0139459185],"genre_scores_gemma":[0.8823325,0.0004373036,0.09861771,0.00013986665,0.00012567648,0.0008364537,0.0125412615,0.0011474245,0.0038216817],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927492,0.0031986623,0.0006464203,0.0011122174,0.00200137,0.00029210647],"domain_scores_gemma":[0.9495935,0.036009584,0.0025881974,0.004275494,0.005822095,0.0017111821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059621823,0.0018549477,0.001343788,0.0034192204,0.0005909362,0.0022713298,0.001876449,0.0012323527,0.0037682394],"category_scores_gemma":[0.043928795,0.00027750898,0.00071531115,0.0018724834,0.0007464681,0.0023334636,0.0016790167,0.0010816322,0.0018602321],"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":[0.006099046,0.0028569493,0.021685932,0.004686301,0.00074299285,0.0004243249,0.0029432299,0.075989686,0.06082315,0.0028810953,0.028680546,0.7921869],"study_design_scores_gemma":[0.0006529773,0.009332045,0.054860286,0.00021982256,0.00027800447,0.00066626363,0.001954899,0.8236519,0.08949225,0.005003322,0.0136290435,0.00025922607],"about_ca_topic_score_codex":0.0022490646,"about_ca_topic_score_gemma":0.0024205856,"teacher_disagreement_score":0.0059621823,"about_ca_system_score_codex":0.0012351772,"about_ca_system_score_gemma":0.00060614967,"threshold_uncertainty_score":0.031531394},"labels":[],"label_agreement":null},{"id":"W4404330441","doi":"10.3390/s24227238","title":"A Systematic Review of Event-Matching Methods for Complex Event Detection in Video Streams","year":2024,"lang":"en","type":"review","venue":"Sensors","topic":"Video Analysis and Summarization","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 Calgary","funders":"","keywords":"Event (particle physics); STREAMS; Computer science; Matching (statistics); Data mining; Real-time computing; Computer network; Statistics; Mathematics","score_opus":0.051741160579949486,"score_gpt":0.41390458165425353,"score_spread":0.36216342107430405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404330441","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002818887,0.99728453,0.0012690831,0.000195264,0.00013587963,0.00011548705,0.00027205163,0.00002732202,0.00041861873],"genre_scores_gemma":[0.0023134004,0.99310607,0.003541518,0.00024797017,0.00008839962,0.00020380825,0.00028536402,0.000012259274,0.00020112263],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9971666,0.00075877097,0.0009425876,0.00036781488,0.0006980426,0.00006634304],"domain_scores_gemma":[0.98137015,0.015090369,0.0014941838,0.00032637763,0.0015876875,0.00013125596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046353075,0.0015727034,0.0029584863,0.007483375,0.00044437443,0.0018207497,0.0016391722,0.0013703919,0.0055810814],"category_scores_gemma":[0.02731337,0.0006898146,0.0041810763,0.006190801,0.00056930474,0.002484598,0.0010051491,0.0009722697,0.0010655395],"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":[0.00014505685,0.000042180873,0.0004483201,0.39216056,0.0015268759,0.00008731599,0.00015761923,0.0003902751,0.0005066128,0.0010428239,0.0073796352,0.5961127],"study_design_scores_gemma":[0.00017211295,0.00064742565,0.006205079,0.5340662,0.019822462,0.0012015428,0.00044397195,0.0009738531,0.0018393047,0.0030189645,0.4314431,0.00016599255],"about_ca_topic_score_codex":0.004205663,"about_ca_topic_score_gemma":0.0083757,"teacher_disagreement_score":0.007483375,"about_ca_system_score_codex":0.0011041841,"about_ca_system_score_gemma":0.0064451704,"threshold_uncertainty_score":0.024514139},"labels":[],"label_agreement":null},{"id":"W4404445439","doi":"10.1609/aiide.v20i1.31882","title":"Abstraction and Path Computation for Video Game Path Finding with Changing Maps","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Video Analysis and Summarization","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 Alberta; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Path (computing); Video game; Abstraction; Computer science; Computation; Theoretical computer science; Algorithm; Multimedia; Programming language","score_opus":0.03320991618226208,"score_gpt":0.2777714130675556,"score_spread":0.24456149688529352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404445439","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030770075,0.00013256169,0.96592474,0.00007334196,0.000020574429,0.000080476515,0.00006709837,0.0012217396,0.0017094159],"genre_scores_gemma":[0.28217226,0.00016805131,0.71585816,0.0000404075,0.000013218043,0.0001169821,0.00026851334,0.00012556142,0.0012367815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995421,0.00011583892,0.00003824573,0.00009486026,0.00014890815,0.00005997832],"domain_scores_gemma":[0.9986016,0.00076286093,0.000115145784,0.00025003147,0.00020073912,0.00006973183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072438206,0.00067554694,0.00067107216,0.00085162,0.0007109877,0.00095897773,0.0011538677,0.00057417154,0.0023840386],"category_scores_gemma":[0.003917178,0.00035357306,0.00070387416,0.0012458237,0.000685299,0.0021072628,0.0015820068,0.0011654122,0.00040737947],"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":[0.00054850045,0.00015985126,0.0033774078,0.00022455514,0.00011661144,0.00015843811,0.0004332839,0.51755154,0.01017809,0.04134098,0.003617084,0.4222936],"study_design_scores_gemma":[0.000029489314,0.000071647664,0.00040178988,0.000008192825,0.000019025974,0.00005432799,0.00006237378,0.98077035,0.002707149,0.013883594,0.0019800533,0.000012009904],"about_ca_topic_score_codex":0.013275537,"about_ca_topic_score_gemma":0.011751725,"teacher_disagreement_score":0.013275537,"about_ca_system_score_codex":0.0009817142,"about_ca_system_score_gemma":0.0014136482,"threshold_uncertainty_score":0.026396513},"labels":[],"label_agreement":null},{"id":"W4404445945","doi":"10.4018/ijswis.359768","title":"Multi Frame Obscene Video Detection With ViT","year":2024,"lang":"en","type":"article","venue":"International Journal on Semantic Web and Information Systems","topic":"Video Analysis and Summarization","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":"Computer science; Frame (networking); Speech recognition; Multimedia; Telecommunications","score_opus":0.008566037662878046,"score_gpt":0.23351625824088404,"score_spread":0.22495022057800598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404445945","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111556195,0.006238272,0.8515595,0.00065751496,0.0007699208,0.0007896183,0.004565752,0.012938018,0.010925202],"genre_scores_gemma":[0.49066252,0.0042019687,0.47338367,0.0005123038,0.00072367635,0.0003038165,0.014101542,0.00067216303,0.015438238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920326,0.00007594638,0.00005045888,0.00024021123,0.00030232166,0.00012782293],"domain_scores_gemma":[0.999164,0.00014379466,0.00012979361,0.00019053424,0.00031150537,0.000060396716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066289323,0.0012841523,0.001333182,0.0037923693,0.00044831986,0.0014461041,0.0011452362,0.0010427654,0.0021845242],"category_scores_gemma":[0.0023444146,0.00030180524,0.0008482602,0.0016787514,0.00044863517,0.0013616236,0.0011916631,0.0012286367,0.0024925473],"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":[0.0010165119,0.00022442054,0.0052378443,0.0005049198,0.00015380469,0.00040058486,0.00016321479,0.009256488,0.09690007,0.0024682356,0.021560693,0.8621133],"study_design_scores_gemma":[0.000105129075,0.00076788233,0.016283503,0.00020457283,0.00028456433,0.0020815865,0.00047189507,0.75145847,0.1816432,0.006941855,0.03964623,0.0001111519],"about_ca_topic_score_codex":0.0047560213,"about_ca_topic_score_gemma":0.0055198544,"teacher_disagreement_score":0.0047560213,"about_ca_system_score_codex":0.00056426285,"about_ca_system_score_gemma":0.00076788815,"threshold_uncertainty_score":0.009456694},"labels":[],"label_agreement":null},{"id":"W4404915904","doi":"10.1109/vis55277.2024.00009","title":"PyGWalker: On-the-fly Assistant for Exploratory Visual Data Analysis","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Canarie","funders":"","keywords":"On the fly; Computer science; Data visualization; Exploratory data analysis; Computer graphics (images); Visualization; Artificial intelligence; Data mining; Operating system","score_opus":0.07254488742693013,"score_gpt":0.32707416415074025,"score_spread":0.2545292767238101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404915904","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025026912,0.00018908338,0.3884309,0.0004174714,0.00015706597,0.0004963809,0.0098090535,0.591516,0.0064814226],"genre_scores_gemma":[0.050572358,0.0005613543,0.73816407,0.0014542089,0.00014895233,0.0027238065,0.03099373,0.15915155,0.016230036],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9968568,0.00082851556,0.00026813085,0.00082057744,0.000887147,0.00033885607],"domain_scores_gemma":[0.98893297,0.0057677394,0.00048838486,0.0024499218,0.0013846898,0.0009761812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006804415,0.002515228,0.0013787794,0.0028367618,0.0008881866,0.0040599178,0.004655269,0.0011352542,0.06534742],"category_scores_gemma":[0.023170467,0.0016098002,0.0020563107,0.0018757168,0.0014559792,0.0051992885,0.00892792,0.0032598982,0.038824704],"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":[0.0017840128,0.00032978642,0.0035751155,0.0022720308,0.0002732454,0.0010319138,0.0027026462,0.0036277534,0.015211835,0.011030592,0.6221653,0.3359957],"study_design_scores_gemma":[0.0011286384,0.00026540933,0.008252583,0.0009646252,0.00017657064,0.0013967581,0.00099508,0.117557354,0.048309933,0.09439809,0.72596437,0.0005906031],"about_ca_topic_score_codex":0.0021883973,"about_ca_topic_score_gemma":0.0032025776,"teacher_disagreement_score":0.06534742,"about_ca_system_score_codex":0.0007963576,"about_ca_system_score_gemma":0.003287961,"threshold_uncertainty_score":0.21860892},"labels":[],"label_agreement":null},{"id":"W4405438024","doi":"10.1145/3708899.3708900","title":"Students Report from ACM MMsys 2023","year":2023,"lang":"en","type":"article","venue":"ACM SIGMultimedia Records","topic":"Video Analysis and Summarization","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; World Wide Web","score_opus":0.027832643501934293,"score_gpt":0.30455513896449804,"score_spread":0.2767224954625637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405438024","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009212538,0.00994799,0.0032941534,0.1282529,0.09648897,0.0010949406,0.014350836,0.0050759474,0.73228174],"genre_scores_gemma":[0.00795492,0.003832429,0.00075921876,0.007752336,0.008025512,0.00041786968,0.0048712646,0.0009196667,0.9654668],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9939089,0.0004983839,0.00028030327,0.0005335168,0.0038844582,0.00089442404],"domain_scores_gemma":[0.9862731,0.00051036,0.00028652555,0.0007221913,0.0071493527,0.005058388],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0055431337,0.0016717113,0.0009265022,0.0028587324,0.0036156888,0.009199768,0.001347368,0.0032331205,0.3847258],"category_scores_gemma":[0.008449229,0.0005790264,0.00091054913,0.003998177,0.00062033004,0.0034198086,0.0072104163,0.0035716544,0.3840949],"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":[0.000022762675,0.000055219985,0.00037605173,0.000032571188,0.0000017639398,0.000019593912,0.000041664705,0.00001161407,0.00014609426,0.00019739296,0.97337794,0.02571742],"study_design_scores_gemma":[0.000005571805,0.000027905027,0.0007024465,0.00003514484,0.000002337083,0.000014073744,0.00010320445,0.000035202673,0.00012617049,0.00007116298,0.99886984,0.0000068874297],"about_ca_topic_score_codex":0.010674419,"about_ca_topic_score_gemma":0.019394321,"teacher_disagreement_score":0.3847258,"about_ca_system_score_codex":0.0021160678,"about_ca_system_score_gemma":0.006867481,"threshold_uncertainty_score":0.87761396},"labels":[],"label_agreement":null},{"id":"W4405917518","doi":"10.18280/mmep.111221","title":"A Comparison of Deep Learning and Machine Learning Approaches to Video Injection Detection","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Video Analysis and Summarization","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":"Computer science; Artificial intelligence; Machine learning; Deep learning","score_opus":0.058128818012649866,"score_gpt":0.23466194734126136,"score_spread":0.17653312932861148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405917518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05935634,0.004327994,0.92690897,0.0009996681,0.00020301562,0.00010545004,0.00029494008,0.0019269614,0.0058767376],"genre_scores_gemma":[0.6219911,0.003087353,0.3635817,0.00041829786,0.0002319066,0.00007877496,0.0009806601,0.00026211957,0.009368042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988263,0.00033839673,0.00007016303,0.00016621711,0.00044533858,0.00015354475],"domain_scores_gemma":[0.9959506,0.002210111,0.0002080392,0.00035402502,0.0011387487,0.00013856204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032688286,0.00097594777,0.0010619381,0.0022356054,0.00040560125,0.0018623094,0.0013307399,0.0013753212,0.0023055607],"category_scores_gemma":[0.0061009815,0.00035087587,0.0005516549,0.0013612359,0.00041876634,0.0024892834,0.001205956,0.0011330843,0.0007240846],"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":[0.00074106676,0.00034345017,0.0036217098,0.00026013792,0.00018859367,0.00005441514,0.00008324227,0.075257614,0.009490061,0.008770571,0.0047328323,0.8964563],"study_design_scores_gemma":[0.000018983406,0.00011351289,0.0015216387,0.00003844538,0.000037212434,0.0000534456,0.000040031806,0.9867987,0.0055458,0.0042107278,0.0016053856,0.00001608695],"about_ca_topic_score_codex":0.0059299837,"about_ca_topic_score_gemma":0.007655503,"teacher_disagreement_score":0.0059299837,"about_ca_system_score_codex":0.0011053702,"about_ca_system_score_gemma":0.0011112081,"threshold_uncertainty_score":0.017287374},"labels":[],"label_agreement":null},{"id":"W4407352310","doi":"10.2139/ssrn.5131396","title":"V-Sparse: Temporal-Spatial Visual Compression and Coarse-to-Fine Alignment for Text-Video Retrieval","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Analysis and Summarization","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","funders":"","keywords":"Computer science; Artificial intelligence; Video retrieval; Pattern recognition (psychology); Computer vision","score_opus":0.012197633311547298,"score_gpt":0.282626849278032,"score_spread":0.2704292159664847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407352310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069451365,0.0008651184,0.9843196,0.00021460264,0.00020778569,0.00013699562,0.00068176026,0.0052572764,0.001371742],"genre_scores_gemma":[0.13584284,0.0013747565,0.8484643,0.00039895126,0.0006095414,0.00039702276,0.0036659474,0.000816848,0.008429641],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992867,0.000116619594,0.000049405186,0.00014417528,0.00031695204,0.00008612663],"domain_scores_gemma":[0.99903274,0.00032680473,0.00008919446,0.00027511167,0.00020736673,0.00006887604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006177362,0.0012538992,0.0013420417,0.0020855356,0.0005533358,0.001377341,0.0014125509,0.0012990513,0.007482001],"category_scores_gemma":[0.0038983985,0.00047180787,0.00068552175,0.0034437666,0.0006389734,0.0020946853,0.0020892294,0.001433021,0.0033582107],"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":[0.0005317167,0.00016988916,0.00036389276,0.00023421216,0.000060187725,0.00011580474,0.00008553161,0.024592567,0.042663664,0.00877497,0.02097396,0.9014335],"study_design_scores_gemma":[0.00012374776,0.00032412607,0.00091146596,0.000054691503,0.000059727558,0.00038133044,0.00011374299,0.90509826,0.04580909,0.030000243,0.017075667,0.00004790533],"about_ca_topic_score_codex":0.0052914363,"about_ca_topic_score_gemma":0.007876691,"teacher_disagreement_score":0.007482001,"about_ca_system_score_codex":0.0004863284,"about_ca_system_score_gemma":0.0011618237,"threshold_uncertainty_score":0.025029778},"labels":[],"label_agreement":null},{"id":"W4408384594","doi":"10.1007/s00530-025-01690-z","title":"Enhancing interpretability in video-based personality trait recognition using SHAP analysis","year":2025,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Video Analysis and Summarization","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 Alberta","funders":"","keywords":"Interpretability; Trait; Computer science; Personality; Artificial intelligence; Big Five personality traits; Machine learning; Psychology; Social psychology","score_opus":0.027104820375756206,"score_gpt":0.27840632500595025,"score_spread":0.2513015046301941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408384594","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76186025,0.00040408576,0.23047864,0.00022237451,0.00012368405,0.00018623957,0.00071032986,0.0012596644,0.004754717],"genre_scores_gemma":[0.9337136,0.00017750835,0.063725546,0.000050032442,0.00008007309,0.00007985728,0.0005300911,0.000048038703,0.0015953189],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996728,0.00010895517,0.00002127054,0.00007579079,0.00007737789,0.00004394159],"domain_scores_gemma":[0.998002,0.0010475246,0.00017413746,0.00010534306,0.0005769666,0.000093977025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007602879,0.00044639388,0.000317838,0.0012778486,0.00016757938,0.0007008056,0.00021065417,0.0003532971,0.0021109],"category_scores_gemma":[0.004542591,0.000088157285,0.000300054,0.00055575476,0.000097301454,0.0005985273,0.00041270486,0.00033402658,0.0006701611],"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":[0.0014241807,0.00049289205,0.039615143,0.00023708452,0.0001433748,0.00018011394,0.00044697482,0.0036843827,0.17999204,0.0008194184,0.0027525101,0.77021194],"study_design_scores_gemma":[0.00009527663,0.0014515825,0.36843923,0.00008115672,0.0003776326,0.0006018327,0.0011807631,0.5115066,0.10940597,0.003040094,0.0036980812,0.00012164678],"about_ca_topic_score_codex":0.0009731589,"about_ca_topic_score_gemma":0.0014428755,"teacher_disagreement_score":0.0021109,"about_ca_system_score_codex":0.00013711111,"about_ca_system_score_gemma":0.00013165739,"threshold_uncertainty_score":0.0070616603},"labels":[],"label_agreement":null},{"id":"W4408792027","doi":"10.1109/mcg.2025.3554312","title":"Meet-in-Style: Text-Driven Real-Time Video Stylization Using Diffusion Models","year":2025,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Video Analysis and Summarization","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Research Center for Informatics, Czech Technical University in Prague","keywords":"Computer science; Computer graphics (images); Style (visual arts); Diffusion; Computer graphics; Human–computer interaction; Multimedia; Artificial intelligence; Computer vision","score_opus":0.013664187638673858,"score_gpt":0.24952428374501323,"score_spread":0.23586009610633937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408792027","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017768769,0.00015491455,0.9623793,0.00010728919,0.00009205603,0.0001574248,0.00023769414,0.01767664,0.0014258127],"genre_scores_gemma":[0.37234125,0.0002722499,0.6159339,0.0002453526,0.00014412068,0.00030146167,0.0010492781,0.001955783,0.007756591],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991235,0.00019132403,0.00005247215,0.0002559156,0.0003210244,0.00005578072],"domain_scores_gemma":[0.9983125,0.00063558546,0.00017278052,0.0003865126,0.00033549298,0.00015712788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011757907,0.0010173125,0.00091585855,0.0007925655,0.00036181297,0.0010868629,0.0018156082,0.0008832743,0.004636509],"category_scores_gemma":[0.0055188653,0.00051209604,0.000671385,0.0004470955,0.00048207666,0.0015468597,0.0015389622,0.0012035307,0.0024961233],"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":[0.0011177065,0.00035654203,0.002921699,0.00031415402,0.00014353846,0.00040707312,0.0007512525,0.096092746,0.13352707,0.0065486003,0.015837684,0.74198186],"study_design_scores_gemma":[0.00003648728,0.00012660208,0.0005830915,0.000010891734,0.000013164917,0.00013366465,0.000053592437,0.95656204,0.03403814,0.0025880095,0.005812347,0.00004203547],"about_ca_topic_score_codex":0.002291848,"about_ca_topic_score_gemma":0.0032801868,"teacher_disagreement_score":0.004636509,"about_ca_system_score_codex":0.00051566906,"about_ca_system_score_gemma":0.00048483568,"threshold_uncertainty_score":0.015510619},"labels":[],"label_agreement":null},{"id":"W4408989553","doi":"10.1007/s11760-025-03982-3","title":"FUVT: a deep few-shot unsupervised learning-based video-to-video translation scheme using Kalman filtering and relativistic GAN","year":2025,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Analysis and Summarization","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":"Concordia University; University of Toronto","funders":"","keywords":"Kalman filter; Shot (pellet); Scheme (mathematics); Translation (biology); Computer science; Artificial intelligence; Mathematics; Materials science; Mathematical analysis","score_opus":0.024324701393300258,"score_gpt":0.2799605337073091,"score_spread":0.2556358323140089,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408989553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014028099,0.00011688573,0.99643576,0.000045187102,0.000041990064,0.000015643715,0.00006280246,0.0014460541,0.00043280094],"genre_scores_gemma":[0.15084597,0.00037866828,0.8339742,0.0003654946,0.00015087413,0.00014563015,0.0013091768,0.0008906984,0.011939333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996705,0.00007450033,0.000014875673,0.00010317554,0.00009317134,0.00004371436],"domain_scores_gemma":[0.9996439,0.000120278,0.00003455243,0.00008702981,0.0000778758,0.0000362633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007711558,0.0010477628,0.001224726,0.0005071916,0.00041939836,0.00076140853,0.0020993373,0.0012788343,0.004587547],"category_scores_gemma":[0.0013839562,0.00055038836,0.00086721673,0.0007056466,0.000572351,0.0013588519,0.0013785312,0.0022276922,0.002602508],"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":[0.00037418742,0.0001371817,0.00045378026,0.00016305015,0.00016983954,0.00013417934,0.00009457754,0.21745531,0.027636522,0.032055553,0.014240077,0.7070857],"study_design_scores_gemma":[0.000009045908,0.000041004714,0.0000775684,0.0000067569795,0.000009481612,0.000029656121,0.000004873637,0.9887884,0.003819289,0.005307179,0.001896357,0.000010375143],"about_ca_topic_score_codex":0.00634694,"about_ca_topic_score_gemma":0.0103769135,"teacher_disagreement_score":0.00634694,"about_ca_system_score_codex":0.00063569937,"about_ca_system_score_gemma":0.0010168459,"threshold_uncertainty_score":0.015346944},"labels":[],"label_agreement":null},{"id":"W4409093780","doi":"10.1109/iccv51701.2025.01451","title":"VPO: Aligning Text-to-Video Generation Models with Prompt Optimization","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science","score_opus":0.02821746942137299,"score_gpt":0.24671202885465804,"score_spread":0.21849455943328505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409093780","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02121394,0.00086774625,0.962913,0.00036757777,0.00017992736,0.00029992542,0.00048928364,0.011552816,0.0021158713],"genre_scores_gemma":[0.4255518,0.00057993835,0.5588008,0.0010749514,0.00023710309,0.00077794027,0.0036414103,0.0021124296,0.0072235987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861765,0.0004361445,0.00007315077,0.000524982,0.00021710825,0.00013102464],"domain_scores_gemma":[0.99775416,0.0012904645,0.0001602189,0.00033017684,0.00031666245,0.00014836053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023294264,0.0021020393,0.0013784518,0.00080939604,0.00046836087,0.0013066261,0.0027185266,0.0018877364,0.004014714],"category_scores_gemma":[0.008270041,0.0007943132,0.001269977,0.00066761253,0.0009061201,0.0021357313,0.0021945743,0.003035687,0.0014647061],"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":[0.0005090925,0.00043788753,0.002389864,0.0004619548,0.000106332365,0.0003288472,0.00038443384,0.51349986,0.015400951,0.008513569,0.018719789,0.43924746],"study_design_scores_gemma":[0.00006435234,0.000102861035,0.00016011043,0.000015725944,0.00001697279,0.00003642585,0.000029290519,0.98908246,0.002970765,0.0056177415,0.0018887257,0.000014525767],"about_ca_topic_score_codex":0.005740955,"about_ca_topic_score_gemma":0.0071519655,"teacher_disagreement_score":0.005740955,"about_ca_system_score_codex":0.0011628085,"about_ca_system_score_gemma":0.001772714,"threshold_uncertainty_score":0.013430536},"labels":[],"label_agreement":null},{"id":"W4409262277","doi":"10.1109/wacv61041.2025.00843","title":"Unsupervised Video Highlight Detection by Learning from Audio and Visual Recurrence","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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 Saskatchewan","funders":"","keywords":"Computer science; Audio visual; Unsupervised learning; Artificial intelligence; Speech recognition; Multimedia; Computer vision","score_opus":0.0048197297047661075,"score_gpt":0.2185157980074473,"score_spread":0.2136960683026812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409262277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19511728,0.00094448833,0.79468435,0.00024324641,0.00012217058,0.00027732895,0.00072042807,0.0046007736,0.003289941],"genre_scores_gemma":[0.7420719,0.0007000416,0.24562837,0.00017382258,0.0003203991,0.00026732514,0.0028030279,0.00035857034,0.0076765874],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995881,0.000040807612,0.000015574748,0.00017983317,0.000100153724,0.0000756013],"domain_scores_gemma":[0.9990662,0.00032201194,0.00017920938,0.000106541265,0.00025239648,0.00007372498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047625703,0.0011573904,0.0008658857,0.0020533663,0.00035746905,0.0008215631,0.0012571411,0.0006687237,0.0012505888],"category_scores_gemma":[0.0023850673,0.00031976038,0.00081282115,0.00097107084,0.00039756033,0.0010798239,0.00084397505,0.0007828313,0.0011462244],"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":[0.0007185496,0.00028153302,0.0067658196,0.00024777622,0.0001346106,0.00044089791,0.00022314847,0.049535748,0.14306678,0.0018605207,0.008518271,0.78820634],"study_design_scores_gemma":[0.000033623335,0.00033990765,0.008498389,0.000030308644,0.000095954296,0.00034517547,0.0001726158,0.94649744,0.037162945,0.0030520572,0.0037387453,0.00003293186],"about_ca_topic_score_codex":0.0026460877,"about_ca_topic_score_gemma":0.0060307635,"teacher_disagreement_score":0.0026460877,"about_ca_system_score_codex":0.0004310244,"about_ca_system_score_gemma":0.000503915,"threshold_uncertainty_score":0.0052613616},"labels":[],"label_agreement":null},{"id":"W4409324838","doi":"10.1063/4.0000499","title":"ACA Video Library (AVL) Update.","year":2025,"lang":"en","type":"article","venue":"Structural Dynamics","topic":"Video Analysis and Summarization","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, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"","keywords":"Computer science; Information retrieval","score_opus":0.0022737563632321722,"score_gpt":0.20895631732299938,"score_spread":0.2066825609597672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409324838","genre_codex":"other","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017979225,0.004924647,0.021116387,0.008410439,0.020529952,0.0009936809,0.17312686,0.08857203,0.68052816],"genre_scores_gemma":[0.0087617785,0.005004436,0.021565236,0.0041232724,0.009800373,0.0012127405,0.22303505,0.021277493,0.70521957],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990086,0.0001221514,0.000056069443,0.00008647577,0.0005988537,0.00012775221],"domain_scores_gemma":[0.99385697,0.0006825449,0.00020729008,0.00054803316,0.003554396,0.0011507066],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0016883735,0.0013392696,0.00066270476,0.0045906003,0.0011268444,0.0050231772,0.002622895,0.0017667591,0.5290822],"category_scores_gemma":[0.010788185,0.00051561574,0.0005469671,0.0027079517,0.00027986147,0.005275916,0.0033521338,0.0017297922,0.44618538],"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":[0.00002619579,0.000008424427,0.00002503303,0.00006928598,0.0000011448748,0.000015622289,0.000014555687,0.000017427905,0.000119979915,0.0003180293,0.9717458,0.02763847],"study_design_scores_gemma":[0.000009737702,0.000008855479,0.00018459617,0.000061573235,0.0000024768972,0.000054445616,0.000026791928,0.00012762335,0.00022003673,0.00039716397,0.9988979,0.000008855741],"about_ca_topic_score_codex":0.009572406,"about_ca_topic_score_gemma":0.01472108,"teacher_disagreement_score":0.5290822,"about_ca_system_score_codex":0.0014373009,"about_ca_system_score_gemma":0.0016053673,"threshold_uncertainty_score":0.6717071},"labels":[],"label_agreement":null},{"id":"W4409379681","doi":"10.1016/j.autcon.2025.106160","title":"Temporal defect point localization in pipe CCTV videos with transformers","year":2025,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Video Analysis and Summarization","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":"Wave Control Systems (Canada)","funders":"National Key Research and Development Program of China; Natural Science Foundation of Tianjin City","keywords":"Transformer; Computer vision; Point (geometry); Artificial intelligence; Computer science; Engineering; Computer graphics (images); Engineering drawing; Electrical engineering; Mathematics; Geometry; Voltage","score_opus":0.0038751392739097973,"score_gpt":0.21713021418574371,"score_spread":0.21325507491183393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409379681","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.702035,0.00072365225,0.28676295,0.00034649565,0.00013176794,0.000086399865,0.001925793,0.0015982063,0.006389723],"genre_scores_gemma":[0.9570614,0.0004291354,0.03949356,0.000035058074,0.00005755841,0.00001961863,0.00094988453,0.00009265546,0.0018611925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998276,0.000017282973,0.0000062477056,0.000027894012,0.00008488793,0.00003612078],"domain_scores_gemma":[0.9994999,0.00011790605,0.00007774052,0.000048247584,0.00021783206,0.0000383878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020989109,0.00037168316,0.00020581187,0.0015051614,0.00019513346,0.00054500357,0.00026402177,0.00046518844,0.0013851613],"category_scores_gemma":[0.0010836801,0.00014922387,0.0001545463,0.0013451255,0.00022696414,0.0005407994,0.00033445677,0.00040753136,0.00036118852],"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":[0.0016770486,0.00014544878,0.01666865,0.00061400543,0.000054357024,0.0016753597,0.0006734661,0.04795084,0.41817963,0.0028950765,0.009928032,0.499538],"study_design_scores_gemma":[0.00004229964,0.0003244091,0.12364546,0.00013466332,0.00010603305,0.0019361888,0.000984714,0.6775032,0.18437934,0.0024012069,0.008475844,0.00006661552],"about_ca_topic_score_codex":0.006720735,"about_ca_topic_score_gemma":0.007277583,"teacher_disagreement_score":0.006720735,"about_ca_system_score_codex":0.0003111437,"about_ca_system_score_gemma":0.00037378207,"threshold_uncertainty_score":0.013363183},"labels":[],"label_agreement":null},{"id":"W4409784910","doi":"10.1007/978-981-96-5815-2_6","title":"Training-Free Language-Guided Video Summarization via Multi-Grained Saliency Scoring","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","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":"Carleton University","funders":"","keywords":"Automatic summarization; Computer science; Natural language processing; Artificial intelligence; Speech recognition; Information retrieval","score_opus":0.025595787442517382,"score_gpt":0.2684903032608774,"score_spread":0.24289451581836002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409784910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02495233,0.0014322953,0.95201904,0.0002724851,0.00037800628,0.0003872528,0.0010260674,0.016060838,0.0034717508],"genre_scores_gemma":[0.27285582,0.0007783214,0.70273423,0.00037523085,0.00045527593,0.00042664522,0.0071267965,0.0019759685,0.013271815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911445,0.00016530333,0.00006367714,0.00032449813,0.0001941466,0.00013790342],"domain_scores_gemma":[0.9980483,0.00069273514,0.000123741,0.0002509727,0.000734804,0.0001494238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010976949,0.002560005,0.0016679276,0.0016949049,0.0006144315,0.001208022,0.0019490153,0.0013543409,0.008459794],"category_scores_gemma":[0.0040492658,0.00052700954,0.0009833955,0.001230169,0.00039085603,0.0018051689,0.0019058326,0.0016836671,0.0060905926],"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":[0.00060030655,0.00020802165,0.00060114637,0.00040726448,0.00013770939,0.00022814222,0.0001266407,0.015349602,0.12339782,0.0015511031,0.018619033,0.83877325],"study_design_scores_gemma":[0.00010474627,0.00054331357,0.0025039853,0.000061370665,0.00020540267,0.00035402473,0.0001924533,0.894341,0.08492011,0.0056644822,0.011042935,0.00006615847],"about_ca_topic_score_codex":0.004128571,"about_ca_topic_score_gemma":0.010804023,"teacher_disagreement_score":0.008459794,"about_ca_system_score_codex":0.00053997093,"about_ca_system_score_gemma":0.0009836602,"threshold_uncertainty_score":0.028300881},"labels":[],"label_agreement":null},{"id":"W4409886277","doi":"10.1145/3706598.3713305","title":"XCam: Mixed-Initiative Virtual Cinematography for Live Production of Virtual Reality Experiences","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Megagrants","keywords":"Cinematography; Virtual reality; Mixed reality; Computer science; Production (economics); Computer graphics (images); Human–computer interaction; Multimedia; Art; Visual arts; Economics","score_opus":0.020654063763098755,"score_gpt":0.2770337157287754,"score_spread":0.25637965196567664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409886277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097116895,0.00074044056,0.8114928,0.0005091408,0.00020063977,0.0019387483,0.001566247,0.028593274,0.057841763],"genre_scores_gemma":[0.3431098,0.00043271077,0.62678355,0.00019282037,0.00012776702,0.001977256,0.0017470063,0.0031801455,0.022448933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950564,0.00022032877,0.00002403663,0.0000713383,0.00012255859,0.000055990608],"domain_scores_gemma":[0.99852115,0.0007776941,0.00006095372,0.0002908272,0.00011389468,0.00023552243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011699687,0.00075182953,0.00020396884,0.0008002075,0.0004355967,0.0015699989,0.0013819904,0.0005832182,0.026278328],"category_scores_gemma":[0.0032603259,0.00038689387,0.00034595912,0.00031078083,0.0006104518,0.0013269958,0.0031150586,0.0006172483,0.0023531253],"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":[0.0008515427,0.00048345633,0.0027046066,0.0023649882,0.00009082749,0.0015111059,0.017889716,0.005485524,0.16590263,0.033130985,0.06309431,0.7064903],"study_design_scores_gemma":[0.0005963089,0.002080135,0.022893885,0.00078960654,0.00016566877,0.0053551234,0.0061468245,0.07442177,0.09509801,0.018650325,0.7734432,0.00035920367],"about_ca_topic_score_codex":0.00048545882,"about_ca_topic_score_gemma":0.0012092676,"teacher_disagreement_score":0.026278328,"about_ca_system_score_codex":0.00030636016,"about_ca_system_score_gemma":0.00034068988,"threshold_uncertainty_score":0.0879097},"labels":[],"label_agreement":null},{"id":"W4409917309","doi":"10.1109/wacvw65960.2025.00140","title":"Towards Long-Term Player Tracking with Graph Hierarchies and Domain-Specific Features","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Term (time); Computer science; Graph; Tracking (education); Artificial intelligence; Theoretical computer science","score_opus":0.009072562450190764,"score_gpt":0.23286585600661563,"score_spread":0.22379329355642488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409917309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052721456,0.0022952985,0.9204525,0.0004189178,0.00010369373,0.00017214131,0.0069130342,0.013322262,0.003600853],"genre_scores_gemma":[0.4368885,0.0012385085,0.5248917,0.0003969406,0.00016340136,0.00015330665,0.030335153,0.00089181884,0.005040682],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896145,0.00015281986,0.000044828543,0.00053500966,0.00020653743,0.00009940044],"domain_scores_gemma":[0.99817073,0.00054361747,0.0002741428,0.00058346085,0.00030067866,0.00012732629],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086109876,0.001454864,0.0010088495,0.0045311972,0.0007298169,0.0015347447,0.0018543307,0.0011876842,0.0010918608],"category_scores_gemma":[0.0034579507,0.0005733269,0.0010561439,0.004571503,0.00042072817,0.0027330092,0.001395824,0.0014725428,0.0021784056],"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":[0.0005918083,0.0006461997,0.031218296,0.00065400585,0.0004955435,0.00049100304,0.0007117958,0.13580461,0.028797373,0.010686783,0.05106759,0.7388351],"study_design_scores_gemma":[0.000030468105,0.00012405802,0.008812037,0.0000727898,0.000098514094,0.0003213282,0.0003374225,0.94898885,0.004327392,0.023173109,0.013673526,0.000040580984],"about_ca_topic_score_codex":0.01857868,"about_ca_topic_score_gemma":0.052895933,"teacher_disagreement_score":0.01857868,"about_ca_system_score_codex":0.00074056524,"about_ca_system_score_gemma":0.00087708875,"threshold_uncertainty_score":0.03694105},"labels":[],"label_agreement":null},{"id":"W4410296174","doi":"10.1109/isbi60581.2025.10981242","title":"Spatiotemporal Learning with Context-Aware Video Tubelets for Ultrasound Video Analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Columbia College","funders":"","keywords":"Computer science; Context (archaeology); Multimedia; Video tracking; Video processing; Computer vision; Artificial intelligence","score_opus":0.006698234630165074,"score_gpt":0.23753083290615556,"score_spread":0.23083259827599048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410296174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017536681,0.00037423128,0.9800869,0.00009698904,0.00003535129,0.000043750835,0.00015281118,0.0013916146,0.00028156716],"genre_scores_gemma":[0.4196047,0.00086051115,0.5755228,0.00020154145,0.00023167908,0.00016981612,0.0010974986,0.00021184495,0.0020995275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965847,0.00007136009,0.000024344365,0.000110378474,0.00010333639,0.000032134896],"domain_scores_gemma":[0.99917066,0.00037675875,0.00011025919,0.00011139347,0.00017746622,0.00005345329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006950214,0.0006523165,0.0007065244,0.00107509,0.00019154378,0.00056214357,0.00078670727,0.0007029685,0.0011500579],"category_scores_gemma":[0.0030229508,0.0002845014,0.00062920095,0.00078598567,0.00026962563,0.0009564511,0.00073751644,0.00088833465,0.0007014802],"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":[0.00040930012,0.00017939584,0.0030328142,0.00011455467,0.00007432613,0.0002035838,0.00009174202,0.12606466,0.0706149,0.0028482694,0.004740103,0.79162645],"study_design_scores_gemma":[0.000007982228,0.00007732197,0.0010504058,0.000009727319,0.000016209902,0.00010732852,0.000013438212,0.98375213,0.011556259,0.0022200665,0.0011792325,0.000009970995],"about_ca_topic_score_codex":0.0017984665,"about_ca_topic_score_gemma":0.0027360413,"teacher_disagreement_score":0.0017984665,"about_ca_system_score_codex":0.00045092983,"about_ca_system_score_gemma":0.0005653212,"threshold_uncertainty_score":0.0038473606},"labels":[],"label_agreement":null},{"id":"W4410772110","doi":"10.1101/2025.05.22.655542","title":"A Python Toolbox for Representational Similarity Analysis","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Video Analysis and Summarization","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; Université de Montréal","funders":"","keywords":"Toolbox; Python (programming language); Computer science; Programming language; Similarity (geometry); Artificial intelligence","score_opus":0.018294030432216342,"score_gpt":0.2553474819081475,"score_spread":0.23705345147593115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410772110","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010422107,0.0002666059,0.64428806,0.00031869937,0.00015914315,0.00019835209,0.032277096,0.31194732,0.009502487],"genre_scores_gemma":[0.031762592,0.00077666756,0.71915185,0.0009923844,0.00017042876,0.002137411,0.081538185,0.14123294,0.022237577],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989918,0.00015379516,0.00011465375,0.00021942453,0.00040605312,0.00011426471],"domain_scores_gemma":[0.99847895,0.0005419957,0.000103587896,0.000411781,0.0003592986,0.00010434212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014041268,0.0017253335,0.0011531201,0.0019692106,0.00073240494,0.0028002092,0.0033355325,0.001079704,0.11586826],"category_scores_gemma":[0.0061309347,0.0012139555,0.0017553972,0.0016347201,0.0007165116,0.0030936836,0.003748199,0.003328416,0.073982194],"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":[0.00032433047,0.00014416497,0.0012172014,0.0013812344,0.00021808653,0.0002599212,0.00027931505,0.011260075,0.009258513,0.058919486,0.70685965,0.20987803],"study_design_scores_gemma":[0.00024101546,0.000059906128,0.0022186947,0.00029539716,0.00007493045,0.0006125203,0.000116543786,0.18419474,0.019322995,0.2188059,0.5738766,0.00018067031],"about_ca_topic_score_codex":0.0020530098,"about_ca_topic_score_gemma":0.002943259,"teacher_disagreement_score":0.11586826,"about_ca_system_score_codex":0.0008640671,"about_ca_system_score_gemma":0.0018130911,"threshold_uncertainty_score":0.387618},"labels":[],"label_agreement":null},{"id":"W4411235880","doi":"10.18438/eblip30616","title":"An Analysis of Anti-Fat Bias LibGuides: Are Libraries in the Thick of It?","year":2025,"lang":"en","type":"article","venue":"Evidence Based Library and Information Practice","topic":"Video Analysis and Summarization","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; Library science; World Wide Web; Information retrieval","score_opus":0.0221927426291147,"score_gpt":0.27684413344010955,"score_spread":0.25465139081099486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411235880","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92563367,0.007596284,0.0042883903,0.0063506686,0.0001224225,0.00049994886,0.0015201198,0.0003152012,0.053673342],"genre_scores_gemma":[0.9670758,0.0060006706,0.010565956,0.0031192258,0.000089108005,0.0006212083,0.0012198057,0.00017542267,0.011132735],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9761899,0.014106647,0.0018598865,0.0011129268,0.0057154805,0.001015173],"domain_scores_gemma":[0.8830571,0.0747398,0.01638226,0.004903957,0.01874484,0.0021720487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01735577,0.0003883472,0.0007206694,0.019072538,0.0051730117,0.010991674,0.0013868582,0.0015145318,0.0057293014],"category_scores_gemma":[0.075583406,0.0005423617,0.00035283834,0.029335272,0.006068735,0.0076514077,0.007726854,0.0009928916,0.0015918206],"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":[0.00032936153,0.00017332577,0.09294448,0.004964352,0.000084264335,0.0014207341,0.6670896,0.00013056218,0.0013171975,0.017806124,0.01334005,0.20039997],"study_design_scores_gemma":[0.000027183267,0.00021232475,0.056249652,0.0039731204,0.00010236713,0.0009255214,0.7050482,0.0003176778,0.0020994428,0.002861266,0.22809981,0.00008343575],"about_ca_topic_score_codex":0.009112298,"about_ca_topic_score_gemma":0.01772576,"teacher_disagreement_score":0.019072538,"about_ca_system_score_codex":0.005271323,"about_ca_system_score_gemma":0.011016854,"threshold_uncertainty_score":0.09178716},"labels":[],"label_agreement":null},{"id":"W4412396433","doi":"10.1145/3726302.3729896","title":"ARC: Approximate Relevant Clip Query in Large-Scale Video Repositories","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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; Scale (ratio); Query optimization; Web search query; Information retrieval; Database; Search engine","score_opus":0.0059862224471071875,"score_gpt":0.2355125004649821,"score_spread":0.2295262780178749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412396433","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059516076,0.004117467,0.8962775,0.0013571,0.00020354529,0.00072932005,0.006692439,0.02609071,0.0050157933],"genre_scores_gemma":[0.37837553,0.0017833061,0.5937658,0.0005625447,0.00039017684,0.0004859176,0.019278653,0.0011256818,0.004232392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964887,0.0006117327,0.00032448003,0.0007736205,0.0014930587,0.00030833893],"domain_scores_gemma":[0.99543935,0.0022300265,0.00033781934,0.0009895581,0.00073805003,0.00026510662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002552608,0.0017265232,0.0025672978,0.0042211963,0.0012381832,0.0031339955,0.003956779,0.0016593775,0.005109311],"category_scores_gemma":[0.0120073045,0.00067608897,0.0011165981,0.0063564586,0.0009298388,0.0063703065,0.0031108328,0.0014320156,0.0019947896],"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":[0.0035079045,0.00085973635,0.0071583227,0.0021396347,0.00059097237,0.0017400383,0.0011025539,0.16882841,0.03554345,0.026832003,0.115788296,0.6359087],"study_design_scores_gemma":[0.00015657637,0.00022591291,0.0014017549,0.000034509467,0.00011831025,0.0007471728,0.0004846352,0.9583562,0.008893407,0.01972282,0.009813673,0.00004505534],"about_ca_topic_score_codex":0.016111255,"about_ca_topic_score_gemma":0.016565321,"teacher_disagreement_score":0.016111255,"about_ca_system_score_codex":0.0014485057,"about_ca_system_score_gemma":0.002266895,"threshold_uncertainty_score":0.032034934},"labels":[],"label_agreement":null},{"id":"W4413144838","doi":"10.1109/cvpr52734.2025.02200","title":"Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Kootenay Association for Science & Technology","funders":"National Natural Science Foundation of China; National Research Foundation","keywords":"Computer science; Sample (material); Image (mathematics); Artificial intelligence; Multimedia; Natural language processing; Human–computer interaction; Computer vision","score_opus":0.01097425876069688,"score_gpt":0.26643457065466986,"score_spread":0.255460311893973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413144838","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07962724,0.0004917962,0.9105462,0.00041925858,0.00013894791,0.0002007622,0.00020594636,0.0049144593,0.0034554582],"genre_scores_gemma":[0.81549454,0.00020390032,0.1781086,0.0003618443,0.000052955522,0.0002940316,0.000342422,0.00059268565,0.0045490703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99961877,0.00010865658,0.000015567148,0.00015549781,0.000062735795,0.00003876931],"domain_scores_gemma":[0.9982286,0.0010949504,0.00013483931,0.00022129058,0.0001846913,0.00013563494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011556525,0.0010588191,0.0005428872,0.0004005683,0.00029256282,0.0007225947,0.0012237179,0.0010992624,0.0040839734],"category_scores_gemma":[0.00806041,0.00044218896,0.00051164185,0.00023011936,0.0007012516,0.0018970347,0.0012094658,0.0016914561,0.00088947813],"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":[0.0006243308,0.00043658545,0.004959789,0.000396883,0.00007880385,0.00045759394,0.00063784455,0.5207853,0.04992284,0.030194161,0.008200437,0.38330537],"study_design_scores_gemma":[0.000033314784,0.00006558029,0.00033311913,0.000014708533,0.000010591008,0.00004932357,0.00002702255,0.9780768,0.0062293336,0.014028987,0.0011191419,0.000012027433],"about_ca_topic_score_codex":0.0011393673,"about_ca_topic_score_gemma":0.0021951087,"teacher_disagreement_score":0.0040839734,"about_ca_system_score_codex":0.00079993147,"about_ca_system_score_gemma":0.00061845226,"threshold_uncertainty_score":0.013662219},"labels":[],"label_agreement":null},{"id":"W4413145451","doi":"10.1109/iccea65460.2025.11103285","title":"Deep Learning-Based Player Behavior Modeling and Game Interaction System Optimization Research","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Kellogg's (Canada)","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Game theory; Human–computer interaction; Mathematics","score_opus":0.037522497517502564,"score_gpt":0.3292279650417521,"score_spread":0.29170546752424953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145451","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023543183,0.00031752078,0.97420746,0.00013070917,0.000019924924,0.000028850453,0.000051212715,0.00053408864,0.0011670815],"genre_scores_gemma":[0.87806773,0.0004172372,0.11602696,0.00014484924,0.000040271236,0.00013001177,0.0002489739,0.00011017511,0.00481366],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965024,0.00007923539,0.000019689458,0.000112972026,0.000076940676,0.00006087913],"domain_scores_gemma":[0.9996958,0.00012393712,0.00004604302,0.00002535879,0.0000836404,0.000025261057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005489434,0.0009914336,0.0007099474,0.00059789617,0.00022234512,0.0005964365,0.0011004237,0.0005265644,0.0012126878],"category_scores_gemma":[0.0012189838,0.00047303745,0.0006767126,0.00050805503,0.00038495773,0.0010777685,0.0005223346,0.0010143211,0.00026330585],"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":[0.000055340737,0.00011602744,0.0020275514,0.000072397575,0.00008771627,0.000047033594,0.00006690492,0.8893208,0.0060863737,0.004480908,0.0008110236,0.096827865],"study_design_scores_gemma":[6.4829203e-7,0.000006566308,0.00010251119,7.462109e-7,0.0000024417939,0.0000023812931,0.000002159039,0.9991437,0.0002483466,0.0004191912,0.000069971786,0.0000013259154],"about_ca_topic_score_codex":0.0132348845,"about_ca_topic_score_gemma":0.010525952,"teacher_disagreement_score":0.0132348845,"about_ca_system_score_codex":0.0009607999,"about_ca_system_score_gemma":0.0008615584,"threshold_uncertainty_score":0.02631569},"labels":[],"label_agreement":null},{"id":"W4413214197","doi":"10.3998/mij.7626","title":"Investigating Cinephile SVoD Catalogues with Small-Scale and Cobbled Together Methods","year":2025,"lang":"en","type":"article","venue":"Media Industries","topic":"Video Analysis and Summarization","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 du Québec à Montréal","funders":"","keywords":"Computer science; Metaphor; Visualization; Data science; Scale (ratio); Set (abstract data type); Selection (genetic algorithm); World Wide Web; Data mining; Artificial intelligence; Cartography; Geography","score_opus":0.03407779105944712,"score_gpt":0.27367536092886263,"score_spread":0.23959756986941552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413214197","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.53846633,0.0019152834,0.2811232,0.0019774355,0.00025723648,0.012846257,0.0077575822,0.0012264948,0.15443026],"genre_scores_gemma":[0.59062785,0.0010979731,0.36603191,0.0004084907,0.00011420413,0.010494918,0.00571975,0.00057968724,0.02492519],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9821171,0.010223543,0.001356685,0.0020163094,0.00362995,0.00065633596],"domain_scores_gemma":[0.9279136,0.049635313,0.005103259,0.007516931,0.008686439,0.0011444992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020875968,0.00057936704,0.00071125344,0.02786626,0.0041117156,0.008807816,0.0023812582,0.0014064882,0.010578731],"category_scores_gemma":[0.05184665,0.0007654393,0.0004613054,0.02435721,0.0044190935,0.008107247,0.0066775065,0.0013538568,0.0017268634],"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":[0.00020030928,0.00041556812,0.029541763,0.002090126,0.000054266326,0.00073047075,0.51417595,0.00078801654,0.004142582,0.07007513,0.008695588,0.36909023],"study_design_scores_gemma":[0.0000987306,0.00028665995,0.050061468,0.002095271,0.0000738201,0.0007478934,0.5742311,0.006433217,0.0072630458,0.0365881,0.32196093,0.00015985445],"about_ca_topic_score_codex":0.008884735,"about_ca_topic_score_gemma":0.022233387,"teacher_disagreement_score":0.02786626,"about_ca_system_score_codex":0.0058947504,"about_ca_system_score_gemma":0.004403954,"threshold_uncertainty_score":0.110404015},"labels":[],"label_agreement":null},{"id":"W4413611910","doi":"10.1016/j.rmal.2025.100248","title":"Generating synthetic data for CALL research with GenAI: A proof-of-concept study","year":2025,"lang":"en","type":"article","venue":"Research Methods in Applied Linguistics","topic":"Video Analysis and Summarization","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":"Proof of concept; Computer science; Data science","score_opus":0.35657481836243327,"score_gpt":0.5723938787290209,"score_spread":0.21581906036658766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413611910","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35429546,0.0045489026,0.5908659,0.006648195,0.0011769258,0.004096795,0.008204557,0.003239288,0.026923995],"genre_scores_gemma":[0.73396283,0.0012248121,0.24870063,0.0017349866,0.00024003134,0.0031777904,0.006957776,0.00038595998,0.0036151898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9913366,0.0060383943,0.0002232846,0.00063828164,0.0015102754,0.00025314663],"domain_scores_gemma":[0.9409685,0.048006162,0.0013845585,0.0051639057,0.0037916487,0.00068527187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015169852,0.0010489803,0.0006603219,0.0006887354,0.0005520086,0.0015686847,0.0017237164,0.0014004769,0.0041243737],"category_scores_gemma":[0.050902475,0.00026433385,0.00071435404,0.0005957533,0.0012739811,0.00165227,0.0018226277,0.0020693934,0.0010170019],"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":[0.003003762,0.0036087811,0.016679673,0.0038296138,0.0006379237,0.0009074841,0.0013021816,0.5292176,0.013212187,0.09926153,0.049767982,0.2785713],"study_design_scores_gemma":[0.0007176902,0.002115936,0.0033716862,0.00042430172,0.000096186974,0.00060273986,0.00072404207,0.9041681,0.018888233,0.035986874,0.032810185,0.00009395979],"about_ca_topic_score_codex":0.0014174707,"about_ca_topic_score_gemma":0.001462426,"teacher_disagreement_score":0.015169852,"about_ca_system_score_codex":0.0010046724,"about_ca_system_score_gemma":0.0012395851,"threshold_uncertainty_score":0.08022684},"labels":[],"label_agreement":null},{"id":"W4414199029","doi":"10.1109/cvprw67362.2025.00586","title":"An End-To-End Pipeline for Virtual Banner Replacement in Football Broadcasts","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Pipeline (software); Augmented reality; Monetization; Football; Broadcasting (networking); Key (lock); Banner; Virtual reality","score_opus":0.011093902525581923,"score_gpt":0.281975932629891,"score_spread":0.2708820301043091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414199029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13923436,0.002513661,0.64424217,0.0006307569,0.00057701534,0.000915481,0.01595419,0.17758082,0.018351516],"genre_scores_gemma":[0.36065304,0.0010367136,0.5319932,0.0005070684,0.00017710812,0.0005430316,0.071269736,0.0041128653,0.029707318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994854,0.00003842064,0.000022310463,0.00021258554,0.00012593114,0.00011542225],"domain_scores_gemma":[0.99971074,0.00005698461,0.000020675967,0.00007898623,0.00009520364,0.000037451373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035222602,0.0017155119,0.0009341754,0.0011667846,0.000595348,0.0012831222,0.0015664219,0.0008672746,0.009033925],"category_scores_gemma":[0.0012238515,0.00060565065,0.0007985504,0.0007790984,0.0003170874,0.0013369559,0.0012803943,0.0013209287,0.008371863],"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":[0.0011740031,0.00045572018,0.0030487033,0.0004338934,0.00016144938,0.00039844486,0.0003218671,0.020609427,0.07851665,0.0017085342,0.05789438,0.8352768],"study_design_scores_gemma":[0.000155818,0.00049816933,0.006889567,0.00010227041,0.00018846788,0.0004945604,0.0004056149,0.8394104,0.088568956,0.005195514,0.05800141,0.000089235706],"about_ca_topic_score_codex":0.017006086,"about_ca_topic_score_gemma":0.028848073,"teacher_disagreement_score":0.017006086,"about_ca_system_score_codex":0.00076459034,"about_ca_system_score_gemma":0.0008869374,"threshold_uncertainty_score":0.03381425},"labels":[],"label_agreement":null},{"id":"W4414521063","doi":"10.1016/j.eswa.2025.129649","title":"GE-adapter: A general and efficient adapter for enhanced video editing with pretrained text-to-image diffusion models","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Analysis and Summarization","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":"Canada Research Chairs","funders":"Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation, Changsha University of Science and Technology; Shenzhen Fundamental Research Program; Science, Technology and Innovation Commission of Shenzhen Municipality; Hunan Provincial Science and Technology Department","keywords":"Adapter (computing); Consistency (knowledge bases); Fidelity; Key (lock); Coherence (philosophical gambling strategy); Consistency model; Temporal database; Flexibility (engineering)","score_opus":0.008610764350851122,"score_gpt":0.23893384635608278,"score_spread":0.23032308200523166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414521063","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029917434,0.00020775244,0.7607003,0.00007388402,0.00014090224,0.00015677117,0.0027677245,0.23128293,0.0016779288],"genre_scores_gemma":[0.070117794,0.0005788644,0.86406165,0.00031363984,0.00012780222,0.0007061343,0.012705776,0.03531562,0.01607271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996412,0.000051316412,0.00004301256,0.00010924889,0.000107735,0.00004747717],"domain_scores_gemma":[0.99891555,0.00044452012,0.000060583174,0.00028557476,0.00022000936,0.00007372124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082859345,0.0025279748,0.000991862,0.0012830085,0.00032361722,0.0014113024,0.0031565889,0.0017795119,0.041789267],"category_scores_gemma":[0.0057139094,0.00087358366,0.0012767507,0.000841444,0.00023037972,0.0019523314,0.0019720758,0.0015028099,0.0193691],"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":[0.0015031067,0.00026698105,0.00096350873,0.0009721437,0.00034230115,0.00078477233,0.0002838282,0.021528943,0.07130278,0.0074111028,0.13149458,0.763146],"study_design_scores_gemma":[0.00025416748,0.00019045617,0.0011618866,0.00011230316,0.00010892864,0.00087622297,0.00010659015,0.7219271,0.15891175,0.01169929,0.10448314,0.00016814559],"about_ca_topic_score_codex":0.0032735565,"about_ca_topic_score_gemma":0.0049008434,"teacher_disagreement_score":0.041789267,"about_ca_system_score_codex":0.00042674286,"about_ca_system_score_gemma":0.0005573483,"threshold_uncertainty_score":0.139799},"labels":[],"label_agreement":null},{"id":"W4415007618","doi":"10.4018/joeuc.389737","title":"Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Organizational and End User Computing","topic":"Video Analysis and Summarization","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":"McGill University","funders":"","keywords":"Reinforcement learning; Latency (audio); Artificial neural network; Graph; Sensitivity (control systems); Deep learning","score_opus":0.029342183514951558,"score_gpt":0.3212961060749812,"score_spread":0.29195392256002967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415007618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062079456,0.0018201552,0.93092066,0.0011602145,0.00013388862,0.000068600544,0.00016171212,0.00055143936,0.0031038867],"genre_scores_gemma":[0.89938956,0.00085246854,0.09449729,0.000307496,0.00011871271,0.00006285939,0.00021007894,0.00007761403,0.0044839354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994856,0.0001840842,0.000021683087,0.00016611996,0.0000765733,0.000065805274],"domain_scores_gemma":[0.99807894,0.0012456422,0.00014885122,0.00013459107,0.00027060308,0.00012142511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012797511,0.0007888535,0.00078543683,0.00042445003,0.0002741301,0.0008028486,0.0015902672,0.00087246485,0.0020710672],"category_scores_gemma":[0.004819527,0.0003552005,0.00047941512,0.0006300056,0.00046503395,0.0021265836,0.0004903808,0.0012494695,0.0003682609],"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":[0.00020498209,0.00027657414,0.004019046,0.00014182265,0.00015855642,0.00009951424,0.00015997596,0.78785944,0.004331243,0.012827274,0.002555118,0.18736641],"study_design_scores_gemma":[0.0000028353368,0.000021535729,0.00015038025,0.0000030315048,0.000008468749,0.0000067503884,0.000010061272,0.9965803,0.0002984799,0.0026639835,0.0002510123,0.0000031557222],"about_ca_topic_score_codex":0.014556904,"about_ca_topic_score_gemma":0.014302952,"teacher_disagreement_score":0.014556904,"about_ca_system_score_codex":0.0012758067,"about_ca_system_score_gemma":0.000961086,"threshold_uncertainty_score":0.028944373},"labels":[],"label_agreement":null},{"id":"W4415123913","doi":"10.1109/tmc.2025.3620352","title":"Distributed and Controllable Mobile Text-to-Image Generation With User Preference Guarantee","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Video Analysis and Summarization","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":"Mobile edge computing; Adaptability; Reinforcement learning; Transmission (telecommunications); Mobile device; Enhanced Data Rates for GSM Evolution; Image quality; Resource allocation","score_opus":0.011616211442236677,"score_gpt":0.23733211896698778,"score_spread":0.2257159075247511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415123913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0682998,0.00012633484,0.9268198,0.000111646936,0.000021924141,0.00011823562,0.00003784743,0.0012583319,0.0032060072],"genre_scores_gemma":[0.8806418,0.000094245355,0.114648476,0.000087517445,0.000035986417,0.000113026705,0.00008328545,0.00016380206,0.0041319034],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991387,0.00020242679,0.000043227992,0.00024132078,0.00028396,0.000090323025],"domain_scores_gemma":[0.9982126,0.0007558426,0.00016744643,0.00041930028,0.00033055575,0.000114256945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081058237,0.0007578127,0.0005690685,0.00019206645,0.00038271782,0.0007668922,0.0013097841,0.00071895245,0.003101324],"category_scores_gemma":[0.0033316717,0.00030903317,0.00037143767,0.00024848228,0.0004945872,0.0016479281,0.0010734265,0.00075811066,0.00082870707],"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":[0.0015418647,0.0005446616,0.0027851388,0.0003058709,0.00008463196,0.0010785647,0.00068913493,0.22766557,0.4519361,0.024235,0.0030946843,0.28603876],"study_design_scores_gemma":[0.000070040864,0.0002168682,0.000837587,0.0000061564438,0.000023366834,0.0002631807,0.00007762509,0.92726463,0.06339711,0.0054787914,0.0023332187,0.000031445375],"about_ca_topic_score_codex":0.0010170484,"about_ca_topic_score_gemma":0.0010630985,"teacher_disagreement_score":0.003101324,"about_ca_system_score_codex":0.00042953715,"about_ca_system_score_gemma":0.00035232506,"threshold_uncertainty_score":0.010374963},"labels":[],"label_agreement":null},{"id":"W4415287434","doi":"10.1109/iccv51701.2025.02479","title":"VideoRFSplat: Direct Scene-Level Text-to-3D Gaussian Splatting Generation with Flexible Pose and Multi-View Joint Modeling","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Margin (machine learning); Generative model; Mixture model; Generalization; Image (mathematics); Joint (building); Sampling (signal processing); Gaussian; Matching (statistics)","score_opus":0.06000399272185646,"score_gpt":0.2738443375575927,"score_spread":0.21384034483573622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415287434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071838265,0.00019943199,0.972606,0.00014913238,0.000117390955,0.00012818162,0.00071202195,0.016659236,0.0022448045],"genre_scores_gemma":[0.25614318,0.00039660122,0.71722096,0.0007778453,0.00011914307,0.0005236364,0.0070744064,0.0046037575,0.013140494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953353,0.00006134687,0.000016496902,0.00016237324,0.00018463844,0.00004167422],"domain_scores_gemma":[0.9995517,0.00012601243,0.000031334024,0.00014242547,0.00010006588,0.000048369322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056819775,0.0015752128,0.00077664666,0.0005334665,0.00031577723,0.00095480034,0.0026879113,0.0014722239,0.0073684654],"category_scores_gemma":[0.0020662064,0.0007045758,0.0015759855,0.0004523081,0.0007600627,0.0012992127,0.0018695197,0.0017708198,0.004379294],"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":[0.0004468361,0.00022188814,0.0012759529,0.00026565374,0.00013475213,0.00043951874,0.00023401061,0.5449508,0.034176875,0.01163947,0.035243012,0.37097135],"study_design_scores_gemma":[0.000030201543,0.000040941522,0.0001002756,0.0000064776477,0.0000074005497,0.000071560826,0.000010037563,0.9859153,0.0067327376,0.0030106746,0.0040609236,0.0000135043],"about_ca_topic_score_codex":0.0076666684,"about_ca_topic_score_gemma":0.010928912,"teacher_disagreement_score":0.0076666684,"about_ca_system_score_codex":0.00082504837,"about_ca_system_score_gemma":0.0009069437,"threshold_uncertainty_score":0.024649978},"labels":[],"label_agreement":null},{"id":"W4417516630","doi":"10.31224/6030","title":"Interpretable AI Decision-Support System for Early-Stage Hiring","year":2025,"lang":"","type":"preprint","venue":"","topic":"Video Analysis and Summarization","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":"Bottleneck; Pipeline (software); Metric (unit); Modular design; Ranking (information retrieval); Baseline (sea); Python (programming language)","score_opus":0.017188173620568917,"score_gpt":0.28783320059511647,"score_spread":0.27064502697454756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417516630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015993012,0.00019002055,0.8704291,0.00056363305,0.0001191085,0.00053292274,0.0020895994,0.10476331,0.0053193797],"genre_scores_gemma":[0.20136675,0.00022001311,0.77799773,0.0004974948,0.00012332652,0.00078505353,0.0062840465,0.0023286347,0.010396972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854857,0.00039921177,0.00015818485,0.00042402968,0.00037783568,0.000092134396],"domain_scores_gemma":[0.99438065,0.0029531454,0.00043131225,0.0007300943,0.0011611541,0.0003435137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00254243,0.0013640664,0.00070933806,0.0017657629,0.00065548264,0.0023149708,0.002268389,0.0010941505,0.01940238],"category_scores_gemma":[0.011580883,0.00050900393,0.00071663526,0.0006346521,0.0003699404,0.0015798985,0.0017705737,0.0014099765,0.009470533],"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":[0.0019413541,0.0005878102,0.005334454,0.00096853124,0.000120333054,0.0010199642,0.0013123056,0.02987343,0.03985184,0.010220905,0.067371406,0.8413977],"study_design_scores_gemma":[0.00023974307,0.00037091997,0.0033833124,0.00021923611,0.0001243537,0.00032030483,0.00062256417,0.8307042,0.049173933,0.027127277,0.08759447,0.000119693956],"about_ca_topic_score_codex":0.002645237,"about_ca_topic_score_gemma":0.0036615906,"teacher_disagreement_score":0.01940238,"about_ca_system_score_codex":0.0008412977,"about_ca_system_score_gemma":0.0014884837,"threshold_uncertainty_score":0.06490743},"labels":[],"label_agreement":null},{"id":"W642154409","doi":"","title":"So what can we actually do with content-based video retrieval?","year":2009,"lang":"en","type":"article","venue":"Arrow@dit (Dublin Institute of Technology)","topic":"Video Analysis and Summarization","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":"Universidad Rey Juan Carlos; Xi’an Jiaotong University; Queensland University of Technology; Queen Mary University of London; Shanghai Jiao Tong University; University of Southern California; Tsinghua University; University of Glasgow; University of Ottawa; City University of Hong Kong; University of Central Florida; Universidad Carlos III de Madrid","keywords":"Metadata; Benchmarking; Search engine indexing; Computer science; Information retrieval; Complement (music); World Wide Web; Digital video; Video retrieval; Multimedia; Frame (networking)","score_opus":0.01810869339503182,"score_gpt":0.227833925232261,"score_spread":0.2097252318372292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W642154409","genre_codex":"commentary","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009582033,0.10283785,0.3699973,0.4216628,0.0076847705,0.00047660433,0.0014014901,0.0060324525,0.08032469],"genre_scores_gemma":[0.25641027,0.11374778,0.5093344,0.04960295,0.016891658,0.00089991454,0.0027911372,0.0019866275,0.04833525],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99136317,0.004263289,0.00038011489,0.0011078423,0.0023044923,0.0005811165],"domain_scores_gemma":[0.97660476,0.012221833,0.0007256028,0.0032199966,0.006256501,0.0009712811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01571507,0.0015273577,0.0023992278,0.004669072,0.0023033724,0.013884225,0.0039915047,0.0066729505,0.015724521],"category_scores_gemma":[0.064080454,0.0008559219,0.0012094093,0.0039245323,0.0066687106,0.056792874,0.0030343812,0.0055077826,0.023814213],"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":[0.00021302838,0.0003285723,0.0035338106,0.001963629,0.00017911811,0.00011299316,0.0011087158,0.0017031659,0.0030598089,0.083334915,0.15081292,0.75364935],"study_design_scores_gemma":[0.00012821892,0.00039560153,0.0042319247,0.0024207272,0.00027460873,0.0009406402,0.0067222286,0.023613855,0.005631616,0.6673814,0.2879141,0.00034510723],"about_ca_topic_score_codex":0.011167553,"about_ca_topic_score_gemma":0.005062078,"teacher_disagreement_score":0.015724521,"about_ca_system_score_codex":0.0023964755,"about_ca_system_score_gemma":0.0023949358,"threshold_uncertainty_score":0.08311027},"labels":[],"label_agreement":null},{"id":"W6888363896","doi":"10.20380/gi2016.26","title":"An Investigation of Textbook-Style Highlighting for Video","year":2016,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Video Analysis and Summarization","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":"Interface (matter); Online video; Interactive video; Video recording; Digital video; Video tracking; Data collection; Online learning","score_opus":0.026923090463850836,"score_gpt":0.2596852650482575,"score_spread":0.23276217458440668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888363896","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.88458806,0.0004550918,0.10079831,0.0002686248,0.00009917435,0.0017353192,0.00018430188,0.0012553851,0.010615763],"genre_scores_gemma":[0.85826516,0.00035776375,0.1351407,0.00030957517,0.00005299061,0.00078525016,0.00018359539,0.00038271898,0.004522271],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9961719,0.0023448714,0.00022432939,0.00038580506,0.00065072766,0.00022240965],"domain_scores_gemma":[0.95662385,0.034745686,0.001824825,0.0015802764,0.0046639503,0.0005613239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004513022,0.0006627211,0.00031381028,0.0007467883,0.00046305027,0.001575443,0.0010938351,0.000875137,0.0028275333],"category_scores_gemma":[0.0464957,0.00025669028,0.00033579356,0.00043129097,0.00038120107,0.001590043,0.00070841255,0.00051004323,0.00050214905],"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":[0.001731199,0.001528419,0.026559008,0.0039809966,0.000065895125,0.0025544406,0.040209103,0.0019224164,0.48438218,0.0040660817,0.004333195,0.42866707],"study_design_scores_gemma":[0.00058745174,0.029818114,0.17199206,0.002773034,0.00073723076,0.011737657,0.037991393,0.05650882,0.55951935,0.002474852,0.12543003,0.00043002705],"about_ca_topic_score_codex":0.0006375779,"about_ca_topic_score_gemma":0.0010107616,"teacher_disagreement_score":0.004513022,"about_ca_system_score_codex":0.00039348684,"about_ca_system_score_gemma":0.0004168484,"threshold_uncertainty_score":0.023867428},"labels":[],"label_agreement":null},{"id":"W6925015232","doi":"10.1666/0022-3360(2000)074&lt;0123:avfftm&gt;2.0.co;2","title":"A VERTEBRATE FAUNA FROM THE MIDDLE DEVONIAN YAHATINDA FORMATION OF SOUTHWESTERN CANADA","year":2000,"lang":"en","type":"article","venue":"BioOne Complete (BioOne)","topic":"Video Analysis and Summarization","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":"Fauna; Devonian; Paleozoic; Vertebrate; Littoral zone","score_opus":0.19198738968849494,"score_gpt":0.2035938270822489,"score_spread":0.011606437393753971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6925015232","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9927328,0.0010432262,0.000094825096,0.000074376214,0.0000053643844,0.000024369663,0.0011801551,0.000013918985,0.0048310435],"genre_scores_gemma":[0.994013,0.0008510695,0.0004113724,0.0000444028,0.0000031850298,0.00001551516,0.0010814863,0.0000056679905,0.0035742768],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9998847,0.0000042025667,0.000005306795,0.000021168837,0.00004084959,0.000043622273],"domain_scores_gemma":[0.9997588,0.000013583657,0.00002507356,0.0000060085363,0.00012804008,0.00006860607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011183882,0.00019312126,0.00014216428,0.0022245424,0.002602997,0.0007366507,0.0004020412,0.0001462648,0.0024285018],"category_scores_gemma":[0.00023175865,0.00014070883,0.00010013531,0.0027360013,0.0005843745,0.00015699942,0.00042646413,0.00018066632,0.00021300004],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020520404,0.00003745417,0.8606697,0.00022649599,0.00006875874,0.0013490369,0.020935828,0.00031128144,0.014324779,0.00078314013,0.0033672915,0.09772111],"study_design_scores_gemma":[0.0000025349977,0.0000103026305,0.9903944,0.000037304137,0.00000903931,0.00018457125,0.0032817565,0.00009827084,0.00024831516,0.000021551017,0.005706722,0.0000053192543],"about_ca_topic_score_codex":0.97547877,"about_ca_topic_score_gemma":0.9949485,"teacher_disagreement_score":0.024521232,"about_ca_system_score_codex":0.0077713025,"about_ca_system_score_gemma":0.0068196934,"threshold_uncertainty_score":0.05638498},"labels":[],"label_agreement":null},{"id":"W6968865432","doi":"10.5281/zenodo.3790135","title":"Lasionycta gelida Crabo &amp; Lafontaine 2009, sp. n.","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Video Analysis and Summarization","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":"Agriculture and Agri-Food Canada","funders":"","keywords":"Holotype; Seta; Dorsum; Taxonomy (biology); Lobe","score_opus":0.029064253135258365,"score_gpt":0.24322700484618806,"score_spread":0.2141627517109297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6968865432","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040254597,0.015340197,0.0030778395,0.002652111,0.00066197425,0.00044954463,0.009382427,0.0010444474,0.92713696],"genre_scores_gemma":[0.5376607,0.016516943,0.010709569,0.0037327167,0.00060195004,0.0011677177,0.0077389022,0.00046876702,0.42140263],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998273,0.000016542686,0.000011525309,0.0000616163,0.000059020862,0.000023930468],"domain_scores_gemma":[0.9996915,0.00005582148,0.00009707764,0.000027134325,0.00010127012,0.000027164515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015187307,0.000752854,0.00035879304,0.0017070497,0.0023584713,0.00074417895,0.0007368536,0.00072357355,0.05467095],"category_scores_gemma":[0.00054712145,0.00025740764,0.00016910557,0.00152217,0.001052873,0.0014935156,0.0008723846,0.0010553654,0.016338328],"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":[0.00023964937,0.000047327943,0.009907015,0.000809695,0.00003458583,0.0017380379,0.0013918758,0.00029720305,0.0069326074,0.005100652,0.26219475,0.7113066],"study_design_scores_gemma":[0.000035337354,0.00003593072,0.04202802,0.0007206517,0.00002801903,0.0017663041,0.0010245623,0.00012560947,0.0007602016,0.0004925425,0.9529657,0.000017167526],"about_ca_topic_score_codex":0.12090649,"about_ca_topic_score_gemma":0.28363243,"teacher_disagreement_score":0.12090649,"about_ca_system_score_codex":0.0032666277,"about_ca_system_score_gemma":0.0017614237,"threshold_uncertainty_score":0.24040544},"labels":[],"label_agreement":null},{"id":"W6985003850","doi":"","title":"Abstracts","year":2013,"lang":"fr","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Video Analysis and Summarization","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":"Promotion (chess); Public health; Psychological intervention; Field (mathematics); Vulnerability (computing)","score_opus":0.018536580776387934,"score_gpt":0.23214462746756404,"score_spread":0.2136080466911761,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6985003850","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016482894,0.022211913,0.0050681387,0.020258872,0.04847227,0.0006028033,0.012453674,0.0016993291,0.8875847],"genre_scores_gemma":[0.004907835,0.0070636403,0.0014599754,0.0019912347,0.0049970145,0.00020263586,0.0055541988,0.0005350814,0.97328836],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984654,0.00021028874,0.00011961717,0.00024854395,0.0007942794,0.00016194068],"domain_scores_gemma":[0.99536246,0.00042643322,0.0001714086,0.00038503617,0.0027791471,0.00087551505],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001703363,0.0011039575,0.0008934461,0.0031976264,0.0023654813,0.0064982264,0.0017135832,0.002018399,0.63527703],"category_scores_gemma":[0.0064943642,0.00035677382,0.000802368,0.0030746507,0.000631121,0.0032772245,0.0028831132,0.0017780801,0.4016712],"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":[0.000037846334,0.00001965664,0.00013595911,0.00020660904,0.0000033197923,0.000045965582,0.00010204467,0.00003223472,0.00023560166,0.0030687326,0.9218003,0.07431166],"study_design_scores_gemma":[0.0000045778957,0.000008687476,0.00038026235,0.00015426187,0.0000022958245,0.000065679254,0.00010614424,0.000026142201,0.00010107714,0.00072823104,0.99841857,0.0000040623095],"about_ca_topic_score_codex":0.008677442,"about_ca_topic_score_gemma":0.012025472,"teacher_disagreement_score":0.36472297,"about_ca_system_score_codex":0.0031426416,"about_ca_system_score_gemma":0.004302194,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W6991911506","doi":"","title":"I nova del procedimento cautelare societario. la cosiddetta strumentalita attenuata e il cosiddetto giudizio abbreviato","year":2004,"lang":"it","type":"article","venue":"IRIS Research product catalog (Sapienza University of Rome)","topic":"Video Analysis and Summarization","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":"Context (archaeology); Identity (music); Subject (documents); Ethnography; Metis","score_opus":0.0534543333792521,"score_gpt":0.3078585754457615,"score_spread":0.2544042420665094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6991911506","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022827234,0.042589344,0.1179565,0.025179038,0.010201297,0.0007350977,0.0015416363,0.0027727883,0.7761971],"genre_scores_gemma":[0.19882804,0.05390179,0.16214395,0.004311612,0.0055742846,0.00088769867,0.00354855,0.0010401395,0.5697639],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976876,0.00048160448,0.00020364374,0.00042721702,0.0009729049,0.00022708267],"domain_scores_gemma":[0.9964575,0.0007266966,0.00027423113,0.0008647243,0.0010748261,0.0006019714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018758998,0.0008261006,0.00044926384,0.0033215405,0.0023655072,0.006756375,0.0010519922,0.0014589882,0.031979322],"category_scores_gemma":[0.0061233467,0.00034426045,0.00049440784,0.003612112,0.0022560798,0.0039647473,0.0035899845,0.0020622637,0.01277968],"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":[0.000081952974,0.00007987479,0.002967424,0.0011916545,0.000024375213,0.0006174983,0.003906523,0.0004763807,0.006034264,0.11763466,0.12277991,0.74420553],"study_design_scores_gemma":[0.0000034284017,0.00005592374,0.0030126753,0.00034805154,0.000011252633,0.0005008824,0.0013813785,0.00039103386,0.0016901671,0.008851552,0.983734,0.000019646362],"about_ca_topic_score_codex":0.0058067753,"about_ca_topic_score_gemma":0.013300626,"teacher_disagreement_score":0.031979322,"about_ca_system_score_codex":0.0029995565,"about_ca_system_score_gemma":0.0041032755,"threshold_uncertainty_score":0.106981516},"labels":[],"label_agreement":null},{"id":"W6992166424","doi":"","title":"Large Data-to-Text Generation","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Video Analysis and Summarization","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":"Blackberry (Canada)","funders":"","keywords":"Correctness; Pipeline (software); Relation (database); Fidelity; Real world data; Precision and recall; Heuristics","score_opus":0.03101533223371428,"score_gpt":0.23532477415622444,"score_spread":0.20430944192251016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992166424","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042297293,0.0010684938,0.89548415,0.0009805574,0.0006102429,0.00095589773,0.014314093,0.03971493,0.004574387],"genre_scores_gemma":[0.16421966,0.00041014823,0.7746104,0.0004346125,0.00026025283,0.00092667167,0.050168328,0.001509863,0.007459948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850416,0.00051868096,0.000107969165,0.00048799548,0.00031986332,0.000061418556],"domain_scores_gemma":[0.99522996,0.0023323887,0.00022556141,0.0011773809,0.0009061908,0.0001285323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015231922,0.0014615435,0.0007495679,0.0018336112,0.00059756514,0.0010618722,0.0017063762,0.0007878693,0.0058346507],"category_scores_gemma":[0.009681177,0.0003648992,0.00096129667,0.0015758744,0.0003571165,0.0018568557,0.0019498727,0.0014679332,0.0040282095],"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":[0.0005063964,0.00035075418,0.0022209778,0.00082969957,0.00012190823,0.00045019347,0.0004940727,0.032104246,0.036092687,0.0057512545,0.068644986,0.8524328],"study_design_scores_gemma":[0.00025700562,0.0007057199,0.0032487118,0.00013280637,0.00012565248,0.00057766575,0.0008055094,0.73462224,0.11483715,0.031989414,0.1125846,0.00011356075],"about_ca_topic_score_codex":0.0016579878,"about_ca_topic_score_gemma":0.0038773261,"teacher_disagreement_score":0.0058346507,"about_ca_system_score_codex":0.0005929391,"about_ca_system_score_gemma":0.00072653353,"threshold_uncertainty_score":0.019518793},"labels":[],"label_agreement":null},{"id":"W6992446059","doi":"","title":"Les réactions émotionnelles aux expériences d’injustice dans le secteur de la restauration : ethnographie de trajectoires professionnelles au Québec","year":2021,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Video Analysis and Summarization","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":"Economic Justice; Promotion (chess); Diversification (marketing strategy)","score_opus":0.009933365565201737,"score_gpt":0.2148511739004491,"score_spread":0.20491780833524736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6992446059","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99443984,0.00030865014,0.0003680731,0.00074240007,0.000015606634,0.00003099553,0.00012647205,0.000003952329,0.0039639254],"genre_scores_gemma":[0.98971295,0.0005555225,0.0002461792,0.0003771754,0.0000072087123,0.000052680072,0.000076201104,0.000007803179,0.008964372],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99896526,0.00044116945,0.000036407797,0.00011966472,0.0001636759,0.0002738659],"domain_scores_gemma":[0.9981316,0.0006517112,0.00031668617,0.000069202266,0.0005739573,0.00025675175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001548037,0.000237655,0.00035269756,0.00068183587,0.006318432,0.0029438748,0.0007008166,0.0007756308,0.006672726],"category_scores_gemma":[0.0018453179,0.0003092218,0.00027876804,0.0011315811,0.003614761,0.0012011839,0.0017673866,0.0012673989,0.0004515344],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005352577,0.000044223474,0.05145764,0.00015280097,0.00001524071,0.0003031129,0.93781877,0.000057982055,0.0022554123,0.0005755468,0.0010070786,0.0062587075],"study_design_scores_gemma":[0.0000023019313,0.00003118039,0.09772215,0.00008296806,0.000006312361,0.00003953965,0.894715,0.000055797394,0.00030557704,0.00006137989,0.0069594923,0.000018267914],"about_ca_topic_score_codex":0.8010022,"about_ca_topic_score_gemma":0.92455083,"teacher_disagreement_score":0.1989978,"about_ca_system_score_codex":0.00975662,"about_ca_system_score_gemma":0.008037984,"threshold_uncertainty_score":0.40033942},"labels":[],"label_agreement":null},{"id":"W7010430322","doi":"","title":"Improving Video Highlight Detection via Unsupervised Learning and Test-Time Adaptation","year":2025,"lang":"en","type":"article","venue":"University Library (University of Saskatchewan)","topic":"Video Analysis and Summarization","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; DeepMind","keywords":"Exploit; Adaptation (eye); Key (lock); Generalization; Mobile device; Unsupervised learning; Modalities; Set (abstract data type); Domain (mathematical analysis)","score_opus":0.002840230608967876,"score_gpt":0.14073001416514952,"score_spread":0.13788978355618164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7010430322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17201068,0.0017063366,0.8108865,0.00038276392,0.00032547096,0.0003129218,0.00069383235,0.01059484,0.003086609],"genre_scores_gemma":[0.7120077,0.0010037402,0.27345738,0.00066596456,0.00031171902,0.00047435923,0.0032325499,0.00086375006,0.007982765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991893,0.00011933595,0.000035420126,0.0003900943,0.0001701264,0.000095752795],"domain_scores_gemma":[0.997652,0.0011102859,0.0002397784,0.0003626607,0.0004915787,0.0001437959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011348883,0.0014600446,0.0011401474,0.001056702,0.00041606472,0.00077036256,0.0019926007,0.000987352,0.0013109834],"category_scores_gemma":[0.0057679634,0.00040147261,0.0009077915,0.0007030586,0.0005384512,0.0014000703,0.0011784177,0.0019019914,0.0016544714],"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":[0.00068161445,0.000749994,0.008513362,0.0002409846,0.00017799756,0.00031696472,0.00018571781,0.087076016,0.09669025,0.0010356661,0.010265032,0.7940664],"study_design_scores_gemma":[0.000024762061,0.00021642009,0.004108759,0.000017296154,0.000049858667,0.00016948525,0.00006607291,0.9686091,0.023818372,0.0009152519,0.0019792267,0.000025368938],"about_ca_topic_score_codex":0.005039928,"about_ca_topic_score_gemma":0.00837305,"teacher_disagreement_score":0.005039928,"about_ca_system_score_codex":0.00054695766,"about_ca_system_score_gemma":0.0008069344,"threshold_uncertainty_score":0.01002121},"labels":[],"label_agreement":null},{"id":"W7016153969","doi":"","title":"Where is the puck? Tiny and fast-moving object detection in videos","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":2,"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":"Doors; Field (mathematics); Object (grammar); Object detection; Entertainment; Key (lock)","score_opus":0.009007837371323024,"score_gpt":0.22257789691863022,"score_spread":0.2135700595473072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7016153969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4003044,0.023794152,0.52976596,0.00499955,0.0016088088,0.00035575908,0.0028720296,0.0030576738,0.033241585],"genre_scores_gemma":[0.6948667,0.01534985,0.24796203,0.0006346064,0.00075389835,0.000103920225,0.0035839656,0.00041963125,0.03632547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998267,0.000021951611,0.000009429581,0.00006177383,0.000054435055,0.000025675563],"domain_scores_gemma":[0.99963105,0.00015305667,0.000035328816,0.000029485665,0.00011743445,0.000033778186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033684555,0.00031229528,0.00023162544,0.0012696559,0.0002959292,0.00092689786,0.00027884726,0.0004176893,0.0016082611],"category_scores_gemma":[0.0013115198,0.00013624717,0.00015339014,0.0008480068,0.00040115148,0.0009139076,0.00033953605,0.00035922704,0.000754676],"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":[0.00019082999,0.000076654534,0.0030352103,0.00054620055,0.000028012884,0.00027843847,0.00095430465,0.0048931506,0.11910925,0.0082640145,0.028361684,0.8342624],"study_design_scores_gemma":[0.00006759683,0.00076700206,0.09209282,0.00096926535,0.00025160343,0.0018950998,0.0069767344,0.26933438,0.25710064,0.041585494,0.32870042,0.00025894694],"about_ca_topic_score_codex":0.004807829,"about_ca_topic_score_gemma":0.0067843227,"teacher_disagreement_score":0.004807829,"about_ca_system_score_codex":0.00038613097,"about_ca_system_score_gemma":0.00035422118,"threshold_uncertainty_score":0.009559691},"labels":[],"label_agreement":null},{"id":"W7017307048","doi":"","title":"Automatic audience-informed video summarization of hockey broadcasts","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Video Analysis and Summarization","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":"McGill University","funders":"McGill University","keywords":"Automatic summarization; Leverage (statistics); Key (lock); Convolutional neural network; Set (abstract data type); Video game","score_opus":0.013222589556819377,"score_gpt":0.2454082945033623,"score_spread":0.23218570494654292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017307048","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6032809,0.0056391186,0.34126467,0.00089137995,0.0010429022,0.0010470315,0.009003237,0.018658828,0.01917181],"genre_scores_gemma":[0.7864322,0.0019436103,0.17448923,0.00015801402,0.00097159704,0.00028065912,0.019323852,0.0007795028,0.015621294],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995074,0.000078081175,0.00003379579,0.0001786507,0.00012607907,0.00007588694],"domain_scores_gemma":[0.9987618,0.00032088213,0.00019501334,0.00009123258,0.0005440936,0.00008695648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000696298,0.0013400926,0.0006676936,0.0034182782,0.00043474796,0.0012389395,0.00060583634,0.00053301646,0.0019661768],"category_scores_gemma":[0.0026122648,0.00023945842,0.00041277474,0.0009639848,0.00019607306,0.0010862863,0.0008372033,0.00063992257,0.0017055698],"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":[0.0015092739,0.0002277329,0.010136077,0.0010084612,0.00020188314,0.0005924154,0.0012191529,0.009333927,0.1736723,0.0013315778,0.024406489,0.7763608],"study_design_scores_gemma":[0.00015959199,0.0013793207,0.116759144,0.00034078548,0.00059429224,0.00095879467,0.004317223,0.6102954,0.19598341,0.005142857,0.063919425,0.00014979519],"about_ca_topic_score_codex":0.0036473938,"about_ca_topic_score_gemma":0.007993472,"teacher_disagreement_score":0.0036473938,"about_ca_system_score_codex":0.00048713197,"about_ca_system_score_gemma":0.00033586205,"threshold_uncertainty_score":0.0072523355},"labels":[],"label_agreement":null},{"id":"W7018922998","doi":"","title":"Electronic Arts - Sports PC CD/DVD Image Collection","year":2025,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Video Analysis and Summarization","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":"Tiger; League; Tournament; The arts; Football","score_opus":0.005814464218594037,"score_gpt":0.22301119157479046,"score_spread":0.21719672735619644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018922998","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00033772844,0.00041405368,0.0007132729,0.0004233493,0.0013789028,0.00030377117,0.020249838,0.005175279,0.9710037],"genre_scores_gemma":[0.00083543605,0.000589894,0.0007917659,0.00024030403,0.0003383205,0.00013112488,0.013996493,0.0013841974,0.98169243],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954563,0.000026838696,0.000019515346,0.00007785678,0.00026682217,0.000063347594],"domain_scores_gemma":[0.99880695,0.00007221975,0.000029460178,0.00010531086,0.0007081091,0.00027804205],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003519479,0.001581967,0.0011604471,0.002548086,0.0016350268,0.0051996238,0.0017908538,0.00091667223,0.9222491],"category_scores_gemma":[0.0015660848,0.0007319001,0.00090472604,0.0032992142,0.0002863827,0.002486486,0.0024861488,0.001517671,0.8969734],"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":[0.00001795185,0.000018429095,0.00003796911,0.000052686744,0.0000012423853,0.000010884527,0.000005919898,0.000013657073,0.00015692333,0.00014558808,0.9830526,0.016486127],"study_design_scores_gemma":[0.000028811546,0.000025745547,0.00073958334,0.0000709197,0.000005467994,0.000043115346,0.000037161375,0.000068015615,0.0002432991,0.00012436509,0.9986034,0.000010085717],"about_ca_topic_score_codex":0.007090017,"about_ca_topic_score_gemma":0.0097518815,"teacher_disagreement_score":0.07775092,"about_ca_system_score_codex":0.0008873093,"about_ca_system_score_gemma":0.0012102183,"threshold_uncertainty_score":0.11090219},"labels":[],"label_agreement":null},{"id":"W7026441537","doi":"","title":"ACM Multimedia, 2008 proceedings of the 16th ACM International Conference on Multimedia, Vancouver, Canada October 26 - 31, 2008","year":2008,"lang":"en","type":"other","venue":"","topic":"Video Analysis and Summarization","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":"Work (physics); Government (linguistics); Cover (algebra)","score_opus":0.01699546646450112,"score_gpt":0.22960229448083436,"score_spread":0.21260682801633324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7026441537","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033066086,0.052531514,0.24308793,0.019944292,0.037145726,0.0012075098,0.02212931,0.036299594,0.5545881],"genre_scores_gemma":[0.018280564,0.016760811,0.029253032,0.0009090215,0.0019968965,0.00013157575,0.013076528,0.001542152,0.9180496],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995716,0.000048654674,0.000017345663,0.000069918155,0.00023869093,0.000053787295],"domain_scores_gemma":[0.9985784,0.00015468523,0.00002858258,0.00016087788,0.0008281849,0.00024928947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009895001,0.0015105307,0.0010602665,0.0015769588,0.0009235255,0.0039823796,0.001321398,0.0010555504,0.109621994],"category_scores_gemma":[0.0015328191,0.00036291426,0.00036083677,0.0013966616,0.0004901853,0.0021174075,0.0010993796,0.0014975636,0.06724865],"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":[0.00009554224,0.00012598881,0.00030142415,0.00012531751,0.000019239995,0.000058740286,0.00006871444,0.00020951898,0.0028643326,0.001673948,0.786149,0.20830825],"study_design_scores_gemma":[0.000044835997,0.00009661108,0.002984155,0.00016527946,0.00007827707,0.00027373567,0.00032117142,0.010667369,0.0058431597,0.0033267231,0.9761643,0.000034467095],"about_ca_topic_score_codex":0.028901776,"about_ca_topic_score_gemma":0.08337507,"teacher_disagreement_score":0.109621994,"about_ca_system_score_codex":0.001150374,"about_ca_system_score_gemma":0.002067723,"threshold_uncertainty_score":0.3667221},"labels":[],"label_agreement":null},{"id":"W7030393693","doi":"","title":"Modes of Listening and their Implications to Audience Experience of Orchestral Concerts, with a Case Study of the Toronto Symphony Orchestra","year":2018,"lang":"en","type":"other","venue":"YorkSpace (York University)","topic":"Video Analysis and Summarization","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":"York University","funders":"","keywords":"Symphony; Active listening; Sociocultural evolution; Audience response; Audience participation; Appreciative listening; Musical","score_opus":0.018866371671503436,"score_gpt":0.22534905241903458,"score_spread":0.20648268074753115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7030393693","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.94865125,0.00092032785,0.0024252404,0.0014496819,0.00004809639,0.0001065111,0.00012070808,0.000027610375,0.04625059],"genre_scores_gemma":[0.9942457,0.0006160622,0.0008282089,0.00007040735,0.00002102385,0.00005935501,0.000038479255,0.000017741953,0.004103186],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.9982222,0.0011468638,0.00006153833,0.00015319206,0.00020632004,0.00020987191],"domain_scores_gemma":[0.9970572,0.0021250204,0.00030566734,0.00010409124,0.00019716048,0.00021080257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029802022,0.00044020178,0.0002176685,0.001755068,0.0076142275,0.007352979,0.00096504117,0.001327483,0.0044336556],"category_scores_gemma":[0.005724137,0.00030167235,0.0003630885,0.0014651874,0.007140236,0.0031621768,0.0032063487,0.0011935584,0.0002920609],"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":[0.00003766687,0.00002936121,0.0062844786,0.00012912073,0.00000811973,0.0014062603,0.9785962,0.00008897396,0.001475749,0.004200188,0.0004991356,0.0072446717],"study_design_scores_gemma":[0.0000070566603,0.00004271683,0.01821561,0.00011343465,0.000018214656,0.000456263,0.97053313,0.00018923446,0.00042684027,0.0008991281,0.0090820985,0.000016288848],"about_ca_topic_score_codex":0.043855533,"about_ca_topic_score_gemma":0.12921801,"teacher_disagreement_score":0.043855533,"about_ca_system_score_codex":0.0046367417,"about_ca_system_score_gemma":0.0020026062,"threshold_uncertainty_score":0.08720058},"labels":[],"label_agreement":null},{"id":"W7037285842","doi":"","title":"Dum Dums S01 E11 Charlie Hebdo &amp; Freedom Of Speech","year":2015,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Video Analysis and Summarization","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":"Key (lock); Free speech; Speech act; Action (physics); Government (linguistics)","score_opus":0.00921088845250131,"score_gpt":0.19077761312490538,"score_spread":0.18156672467240406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7037285842","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025227335,0.0015433651,0.00040481985,0.007730047,0.0056317337,0.00009974725,0.0024152147,0.0009144366,0.9787379],"genre_scores_gemma":[0.0037419319,0.00026608582,0.00007593352,0.0003418058,0.00021115608,0.000013354884,0.000277488,0.00017028434,0.994902],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99953175,0.000064205444,0.0000094175975,0.00007961182,0.00016976162,0.0001452893],"domain_scores_gemma":[0.99855906,0.00013797352,0.000049261464,0.000065902466,0.00041697902,0.0007707568],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007495289,0.00074996083,0.00035146056,0.0011017623,0.004607378,0.0041417275,0.0005092507,0.0014317286,0.6487886],"category_scores_gemma":[0.002204557,0.00030956752,0.00025953978,0.00094036176,0.00066693046,0.0017391564,0.0026953763,0.0017752191,0.24414894],"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":[0.000027643884,0.00000938697,0.000094355935,0.000017650122,5.1539985e-7,0.00007752786,0.000113060094,0.000011131357,0.00008474054,0.0012330711,0.9857734,0.012557484],"study_design_scores_gemma":[0.0000023878413,0.000006929364,0.00049489,0.000020165358,2.5994223e-7,0.000038534537,0.00034203372,0.000020918184,0.00009077498,0.00012804694,0.99885166,0.0000034124555],"about_ca_topic_score_codex":0.024697006,"about_ca_topic_score_gemma":0.0645613,"teacher_disagreement_score":0.6487886,"about_ca_system_score_codex":0.0019380223,"about_ca_system_score_gemma":0.0015728597,"threshold_uncertainty_score":0.50096047},"labels":[],"label_agreement":null},{"id":"W7066271100","doi":"","title":"Frank E. Kirby 120796","year":2015,"lang":"en","type":"other","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"Video Analysis and Summarization","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":"Port (circuit theory); Georgian; Negative; Point (geometry); Line (geometry)","score_opus":0.008035730299070282,"score_gpt":0.1931720944299506,"score_spread":0.1851363641308803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7066271100","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005107225,0.0051385304,0.0006203877,0.01797749,0.011702955,0.000038225004,0.0004166806,0.000493396,0.96310157],"genre_scores_gemma":[0.0012868925,0.0012205836,0.00016587663,0.0028850778,0.00044010876,0.000011103337,0.0000839192,0.000096350566,0.99381024],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99959594,0.00003467475,0.000014109558,0.000113482034,0.00018086984,0.000061000945],"domain_scores_gemma":[0.9995289,0.000046443583,0.000017194241,0.00002659264,0.00016329475,0.00021774948],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003632033,0.00068188366,0.00036877766,0.000751446,0.0020032322,0.002690355,0.0005132371,0.002164773,0.6037478],"category_scores_gemma":[0.0017434368,0.00029820626,0.00022137922,0.00040294975,0.0005627177,0.0016770574,0.0019875746,0.0025381516,0.4289859],"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":[0.00000809448,0.0000105895615,0.000058117974,0.000014836974,4.822447e-7,0.00006485006,0.000021408503,0.000013800503,0.00008744474,0.0022054573,0.95487213,0.04264272],"study_design_scores_gemma":[9.67676e-7,0.0000037740397,0.00006682842,0.00002632591,3.26736e-7,0.000107737214,0.00002144756,0.000009287364,0.000030197458,0.00019356208,0.9995378,0.000001714219],"about_ca_topic_score_codex":0.0060091014,"about_ca_topic_score_gemma":0.018203123,"teacher_disagreement_score":0.39625221,"about_ca_system_score_codex":0.0011531332,"about_ca_system_score_gemma":0.0017239173,"threshold_uncertainty_score":0.5652057},"labels":[],"label_agreement":null},{"id":"W7071556106","doi":"","title":"SoccerNet: Exploring the Game with Computer Vision","year":2023,"lang":"en","type":"article","venue":"ORBi (University of Liège)","topic":"Video Analysis and Summarization","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":"Weyerhauser (Canada)","funders":"","keywords":"Video game; Tracking (education); Video tracking; Feature (linguistics); Eye tracking; Key (lock)","score_opus":0.022725408129961336,"score_gpt":0.19459591106563204,"score_spread":0.1718705029356707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7071556106","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16881382,0.023808666,0.15618832,0.0027358574,0.0021807582,0.0031704267,0.5133737,0.07661987,0.053108674],"genre_scores_gemma":[0.13617411,0.0028203907,0.12408081,0.0007758771,0.00025997602,0.0009516531,0.7233527,0.0018162716,0.009768212],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998985,0.00018739399,0.00004757287,0.0004288029,0.00022281279,0.00012845163],"domain_scores_gemma":[0.9996345,0.00009677263,0.000030434774,0.00008334092,0.00008634695,0.00006862685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007118298,0.004049583,0.0011624298,0.0041689435,0.00079383614,0.002145834,0.0027197304,0.0015432561,0.007235414],"category_scores_gemma":[0.0022551613,0.0006691081,0.0017908607,0.00274283,0.0004632686,0.0020255984,0.0021566695,0.0019918394,0.0070446874],"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":[0.0008526455,0.0012444773,0.010277724,0.0016308611,0.00066049426,0.00050437415,0.00036238827,0.017979793,0.008275496,0.0048805526,0.64891565,0.3044155],"study_design_scores_gemma":[0.00058192824,0.0011300475,0.032535125,0.0006399319,0.00035250426,0.0014901441,0.0013163338,0.41981936,0.014104689,0.024673957,0.5031468,0.00020915201],"about_ca_topic_score_codex":0.048106063,"about_ca_topic_score_gemma":0.11219273,"teacher_disagreement_score":0.048106063,"about_ca_system_score_codex":0.0013574072,"about_ca_system_score_gemma":0.0011900879,"threshold_uncertainty_score":0.0956521},"labels":[],"label_agreement":null},{"id":"W7096344025","doi":"","title":"R&amp;amp;D status of ERIC-7 and MADIS: two systems for MPEG-7 indexing/search of audio-visual content","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Search engine indexing; XML; Schema (genetic algorithms); Encoding (memory); Modalities; Interface (matter); Visualization","score_opus":0.08388920865476372,"score_gpt":0.33099819118989504,"score_spread":0.2471089825351313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096344025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12691851,0.10299588,0.51640797,0.0083023235,0.0018538968,0.0031986735,0.003248466,0.023167983,0.21390639],"genre_scores_gemma":[0.28686473,0.039746564,0.49432224,0.0015218536,0.0013445042,0.0013981345,0.013884706,0.0026001767,0.15831712],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99590683,0.0006419833,0.00025083215,0.00049303303,0.0021617464,0.00054551073],"domain_scores_gemma":[0.98807645,0.0020223558,0.00090031757,0.0013218927,0.0064882766,0.0011906689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012864135,0.0010246324,0.0008485549,0.0037749347,0.00072844606,0.0055650617,0.0033401945,0.0013781505,0.0142127285],"category_scores_gemma":[0.010797873,0.0007428876,0.00040982172,0.002443009,0.0026540551,0.0035934967,0.0011975408,0.0014849841,0.005454182],"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":[0.0010648769,0.00045384228,0.004210048,0.0008982141,0.00009167464,0.0002767347,0.0009618564,0.00176395,0.048032776,0.04465925,0.031007264,0.8665795],"study_design_scores_gemma":[0.00037101604,0.0031427785,0.007382829,0.0006142309,0.0002742435,0.0011975152,0.00079000543,0.02634627,0.18347883,0.003959156,0.7721892,0.00025398572],"about_ca_topic_score_codex":0.020723045,"about_ca_topic_score_gemma":0.010663557,"teacher_disagreement_score":0.020723045,"about_ca_system_score_codex":0.004495054,"about_ca_system_score_gemma":0.0060911644,"threshold_uncertainty_score":0.0680328},"labels":[],"label_agreement":null},{"id":"W7099508283","doi":"","title":"Book Review","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"State (computer science); Key (lock); Imperfect; Public interest; Corporate governance; Taste","score_opus":0.007194831738282847,"score_gpt":0.21899375221554138,"score_spread":0.21179892047725854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099508283","genre_codex":"review","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000433847,0.72338575,0.0016881321,0.034582555,0.05659908,0.00028468654,0.0037021118,0.00044570898,0.17887814],"genre_scores_gemma":[0.003192352,0.68135285,0.0023460018,0.03459025,0.023268824,0.00028584324,0.0055870605,0.00038011777,0.24899668],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99838364,0.00025900072,0.0001610445,0.00020474775,0.00087724417,0.00011430651],"domain_scores_gemma":[0.99548656,0.001444109,0.0004057991,0.0002225037,0.0019848812,0.00045603406],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012025824,0.0007127268,0.0013916588,0.003629342,0.00080693286,0.0043418026,0.0023734064,0.0021364316,0.20649703],"category_scores_gemma":[0.011253302,0.0003992679,0.00095143047,0.005348058,0.0007242986,0.0032448962,0.001691419,0.0031966616,0.1284199],"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":[0.000017624043,0.000016739885,0.000041048545,0.0024487083,0.000014431097,0.000077225865,0.000043973276,0.000038441176,0.000072582414,0.0016715853,0.8943386,0.10121916],"study_design_scores_gemma":[0.000005631383,0.000008532735,0.00008707275,0.0017521216,0.000010476423,0.00028155692,0.000025522822,0.0000069671783,0.000021832491,0.00048367918,0.9973122,0.000004416208],"about_ca_topic_score_codex":0.0022355947,"about_ca_topic_score_gemma":0.0042103566,"teacher_disagreement_score":0.793503,"about_ca_system_score_codex":0.0017758893,"about_ca_system_score_gemma":0.0043605394,"threshold_uncertainty_score":0.6908014},"labels":[],"label_agreement":null},{"id":"W7100579385","doi":"","title":"METIS - A Flexible Database Foundation for the Unified Management of Multimedia Content","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Key (lock); Metis; Foundation (evidence); Content management; Work (physics); Content (measure theory)","score_opus":0.07010572265156714,"score_gpt":0.28573732557565046,"score_spread":0.21563160292408332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100579385","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004901309,0.0008968794,0.9229484,0.00041210148,0.00022428081,0.00026397116,0.0038564024,0.061516043,0.0049805706],"genre_scores_gemma":[0.16035946,0.0020159604,0.7746621,0.00086644123,0.0007595073,0.0011011577,0.032202393,0.010587253,0.017445786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99550027,0.0004892202,0.000762986,0.0007540786,0.002102444,0.0003910883],"domain_scores_gemma":[0.99213165,0.0010360572,0.00038999412,0.0044415784,0.0014700646,0.00053072337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052314317,0.001280565,0.0025640177,0.0042198836,0.0017378276,0.010512109,0.007320827,0.0014462556,0.008634262],"category_scores_gemma":[0.01138171,0.001484827,0.001669728,0.005027779,0.0013026809,0.011318027,0.0072605293,0.0032239088,0.006964926],"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":[0.00211044,0.00033423427,0.0045134197,0.00082766387,0.00048526097,0.0005773095,0.0012300699,0.011932013,0.04090057,0.25746396,0.12970959,0.54991555],"study_design_scores_gemma":[0.00040432406,0.00044292203,0.0022390545,0.00041957136,0.0004429672,0.00093630416,0.00059461896,0.2873497,0.09217252,0.2321257,0.38257012,0.00030224022],"about_ca_topic_score_codex":0.0042051105,"about_ca_topic_score_gemma":0.004078994,"teacher_disagreement_score":0.010512109,"about_ca_system_score_codex":0.0016708369,"about_ca_system_score_gemma":0.0024813192,"threshold_uncertainty_score":0.02888453},"labels":[],"label_agreement":null},{"id":"W7117377234","doi":"10.1016/j.cviu.2025.104627","title":"Boundary-aware semantic segmentation for ice hockey rink registration","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"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; Mitacs","keywords":"Homography; Segmentation; Boundary (topology); Feature (linguistics); Frame (networking); Class (philosophy); Reference frame","score_opus":0.02485716507912637,"score_gpt":0.29644886486215183,"score_spread":0.27159169978302544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117377234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047305632,0.0005137185,0.9345912,0.0001535678,0.00019888242,0.0001750247,0.00071257283,0.008172809,0.008176664],"genre_scores_gemma":[0.39847505,0.00056828104,0.5828852,0.00020095913,0.00013035373,0.00018730183,0.0046453024,0.002396278,0.010511161],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951935,0.00004849879,0.00002699578,0.00014500457,0.00014406684,0.000116033196],"domain_scores_gemma":[0.9997086,0.000037682985,0.000029324374,0.00009102083,0.00010923647,0.000024085712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037340136,0.0009598077,0.0011718823,0.0024914546,0.0007799404,0.0015591623,0.001130906,0.0011161843,0.0050453926],"category_scores_gemma":[0.00091140106,0.0005092868,0.0010859594,0.0015744863,0.0005133408,0.0014267918,0.0016000948,0.00094287685,0.0038368495],"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":[0.0007939094,0.0001964955,0.001877157,0.00030447333,0.00011379589,0.0002691136,0.0002580895,0.04102804,0.17920433,0.005599494,0.014162219,0.75619286],"study_design_scores_gemma":[0.00003946885,0.00021751222,0.0052511785,0.00006143715,0.00010829539,0.0006715668,0.0003909808,0.8321435,0.13103847,0.007560872,0.022443132,0.00007355736],"about_ca_topic_score_codex":0.0046779537,"about_ca_topic_score_gemma":0.011249784,"teacher_disagreement_score":0.0050453926,"about_ca_system_score_codex":0.0004799151,"about_ca_system_score_gemma":0.0014382189,"threshold_uncertainty_score":0.016878486},"labels":[],"label_agreement":null},{"id":"W7117715261","doi":"10.18280/ts.420622","title":"A Deep Image Representation and Temporal Correlation Modeling Approach for Film Scene Style Evolution Analysis","year":2025,"lang":"","type":"article","venue":"Traitement du signal","topic":"Video Analysis and Summarization","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":"Representation (politics); Pattern recognition (psychology); Image (mathematics); Style (visual arts); Correlation","score_opus":0.019221728221765627,"score_gpt":0.262225181804176,"score_spread":0.24300345358241035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7117715261","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009040315,0.00027736457,0.98920983,0.000104160805,0.000033735665,0.0000259254,0.00016162216,0.0006560503,0.0004908777],"genre_scores_gemma":[0.36730546,0.0012404674,0.62023264,0.00024856545,0.00022934866,0.00018976074,0.0018430422,0.0004226197,0.0082881125],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997882,0.000033005057,0.000012185813,0.000064542364,0.00006115616,0.000041000356],"domain_scores_gemma":[0.99971765,0.00007471339,0.000038778173,0.000047501635,0.000098385506,0.000022890952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003788233,0.00074903626,0.00083829375,0.0011018672,0.0003058842,0.0007071492,0.001038692,0.0006590855,0.0017140434],"category_scores_gemma":[0.00072609715,0.0004322582,0.0011845795,0.0012530396,0.00022413964,0.00087978726,0.00067534996,0.0013270318,0.00080555375],"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":[0.0002175285,0.00020109848,0.0011751782,0.000119770986,0.0001600976,0.00014987659,0.00009087042,0.19307338,0.061880596,0.010216101,0.0064061433,0.72630936],"study_design_scores_gemma":[0.0000019859758,0.00001647078,0.00030709745,0.0000026137993,0.000014085962,0.000017473263,0.000005068446,0.99566466,0.0023056075,0.0011177397,0.00054224377,0.0000049525097],"about_ca_topic_score_codex":0.009509452,"about_ca_topic_score_gemma":0.0098678935,"teacher_disagreement_score":0.009509452,"about_ca_system_score_codex":0.00048274506,"about_ca_system_score_gemma":0.0006091723,"threshold_uncertainty_score":0.018908203},"labels":[],"label_agreement":null},{"id":"W7124303873","doi":"10.65109/qykt3464","title":"Simulating Tracking Data to Advance Sports Analytics Research","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Football; Tracking (education); Analytics; Data analysis; Intersection (aeronautics); Schema (genetic algorithms); Data collection; Sensor fusion","score_opus":0.17627152293594653,"score_gpt":0.44142511661652556,"score_spread":0.265153593680579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124303873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08886896,0.00040657594,0.8917834,0.0019144512,0.00033646083,0.00048100372,0.0059985975,0.0021889466,0.00802155],"genre_scores_gemma":[0.539283,0.00094212085,0.44111758,0.00048376265,0.00017020917,0.00052947167,0.015115309,0.00044003045,0.0019185897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976866,0.0012014483,0.00021024478,0.00040977492,0.00040064758,0.00009123402],"domain_scores_gemma":[0.98352665,0.011374293,0.0007008969,0.0027319856,0.0013365019,0.0003296395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038193204,0.0007435204,0.00053690904,0.0012611612,0.0005106462,0.002720037,0.0017487794,0.0012925811,0.0025063704],"category_scores_gemma":[0.026328893,0.0005313086,0.00090754195,0.002188593,0.0008793875,0.0034574117,0.0014616903,0.0017687542,0.0007447763],"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":[0.00027393532,0.00043818972,0.0215801,0.00043429152,0.00016975924,0.00014731675,0.0008254368,0.83818984,0.0044904742,0.063093916,0.00915338,0.061203394],"study_design_scores_gemma":[0.000027855225,0.00006272694,0.0016256504,0.000049694972,0.000020934507,0.00002897532,0.00019456552,0.9612662,0.0018489489,0.026630767,0.008218962,0.000024649853],"about_ca_topic_score_codex":0.016449874,"about_ca_topic_score_gemma":0.01865621,"teacher_disagreement_score":0.016449874,"about_ca_system_score_codex":0.0014648101,"about_ca_system_score_gemma":0.0018501995,"threshold_uncertainty_score":0.032708228},"labels":[],"label_agreement":null},{"id":"W7125581068","doi":"10.1109/ic-cgu67042.2025.11338001","title":"A Hybrid Model for Identifying Manipulated Videos using Advanced Computing Techniques","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Horizon College and Seminary","funders":"","keywords":"Identification (biology); Feature (linguistics); Key (lock); Noise (video); Field (mathematics)","score_opus":0.05835604767402096,"score_gpt":0.3410214286014127,"score_spread":0.28266538092739174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125581068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021995025,0.00028082688,0.9750429,0.00019587812,0.00006010536,0.00012261739,0.0003029223,0.0009379327,0.001061853],"genre_scores_gemma":[0.46203485,0.000668409,0.52531546,0.0002058238,0.0001756646,0.0004956164,0.001307144,0.00021468327,0.009582414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993981,0.00011084863,0.000042518623,0.00020896032,0.00016542392,0.00007409717],"domain_scores_gemma":[0.99865144,0.00059450715,0.00011170847,0.00015321015,0.0004312456,0.00005804795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009220532,0.0010195854,0.001210231,0.0017240954,0.0006009283,0.0020833057,0.0018046553,0.001352586,0.0026975344],"category_scores_gemma":[0.0027297942,0.00044066214,0.0010061939,0.001716698,0.0004917333,0.0025072503,0.0009801207,0.0011902909,0.001475289],"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":[0.0007673718,0.0005963641,0.0044164713,0.00027764507,0.00027732633,0.00024828865,0.00022812666,0.39414653,0.031683568,0.013412068,0.0042581623,0.5496881],"study_design_scores_gemma":[0.000004804092,0.00004057357,0.00031234007,0.000004772233,0.000018015528,0.000020829591,0.00001299295,0.99572796,0.0013083452,0.002193003,0.00034818673,0.000008178982],"about_ca_topic_score_codex":0.01486603,"about_ca_topic_score_gemma":0.012251525,"teacher_disagreement_score":0.01486603,"about_ca_system_score_codex":0.00086900586,"about_ca_system_score_gemma":0.0011699348,"threshold_uncertainty_score":0.029559016},"labels":[],"label_agreement":null},{"id":"W7131067432","doi":"10.1109/iccvw69036.2025.00731","title":"A Survey on Long-Video Storytelling Generation: Architectures, Consistency, and Cinematic Quality","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Storytelling; Categorization; Narrative; Redundancy (engineering); Consistency (knowledge bases); Generative grammar; Key frame","score_opus":0.05412420942725947,"score_gpt":0.3068651428895358,"score_spread":0.25274093346227633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131067432","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031470075,0.18272585,0.7391912,0.0013925462,0.00086955697,0.0006449501,0.0021512813,0.010300976,0.031253595],"genre_scores_gemma":[0.24287543,0.13224268,0.5873673,0.0006662858,0.0012511536,0.00075305597,0.01190348,0.003220358,0.019720314],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850285,0.00052421977,0.0001171644,0.00033328365,0.00045908784,0.00006349024],"domain_scores_gemma":[0.9906928,0.007137444,0.00035092066,0.0008048228,0.0008503018,0.00016378619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021387942,0.0017183349,0.0010523495,0.0032511791,0.00045868725,0.0027378674,0.0023788093,0.0012479626,0.009552377],"category_scores_gemma":[0.013450497,0.00078184204,0.0011946361,0.00329899,0.00044487073,0.0028210953,0.001077127,0.0009919301,0.0034567334],"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":[0.00015441184,0.00010762807,0.0013032557,0.0045924284,0.00010618361,0.00008837595,0.00053322985,0.01079035,0.0052593155,0.0052381773,0.013842115,0.95798457],"study_design_scores_gemma":[0.00025211583,0.0016208662,0.013853755,0.006398035,0.00080122246,0.003172976,0.0025085337,0.40068838,0.064905845,0.03573489,0.46967128,0.0003921657],"about_ca_topic_score_codex":0.0025053048,"about_ca_topic_score_gemma":0.0022511971,"teacher_disagreement_score":0.009552377,"about_ca_system_score_codex":0.0006354683,"about_ca_system_score_gemma":0.0006311899,"threshold_uncertainty_score":0.031955898},"labels":[],"label_agreement":null},{"id":"W7131073923","doi":"10.1109/iccvw69036.2025.00614","title":"STORM: Token-Efficient Long Video Understanding for Multimodal LLMs","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Kootenay Association for Science & Technology","funders":"","keywords":"Security token; Encoder; Inference; Latency (audio); Key (lock); Encoding (memory); Computation; Low latency (capital markets)","score_opus":0.036374504090264985,"score_gpt":0.2849731002703537,"score_spread":0.24859859618008873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131073923","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008433713,0.0004854257,0.9746114,0.0001761289,0.000080376405,0.00009454096,0.0007901819,0.0139674535,0.0013607481],"genre_scores_gemma":[0.2862349,0.0006985586,0.6937041,0.0005498877,0.00014914818,0.0004403956,0.0062316,0.0015809829,0.010410449],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995529,0.00008370297,0.00002789478,0.00015916424,0.000115847346,0.000060548737],"domain_scores_gemma":[0.99946004,0.00022293914,0.00005371865,0.0001116342,0.0001140668,0.00003766442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072213303,0.0012773782,0.0009737683,0.00078521494,0.00040110882,0.00095629616,0.0021159197,0.0009881303,0.0070798164],"category_scores_gemma":[0.0028922171,0.0004199008,0.0010665079,0.00060825475,0.00047640913,0.0028076516,0.001957411,0.0016894437,0.0026133636],"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":[0.0006387297,0.00019182333,0.0009301838,0.00036839547,0.00019008966,0.00037543685,0.00034051583,0.118388094,0.043027066,0.018620498,0.021892315,0.79503685],"study_design_scores_gemma":[0.00002521611,0.00009050609,0.00023094684,0.0000221608,0.000031290387,0.00008094304,0.00008022426,0.96319824,0.013999268,0.016290437,0.0059264847,0.000024177449],"about_ca_topic_score_codex":0.007840109,"about_ca_topic_score_gemma":0.013524595,"teacher_disagreement_score":0.007840109,"about_ca_system_score_codex":0.000980828,"about_ca_system_score_gemma":0.0011828273,"threshold_uncertainty_score":0.023684323},"labels":[],"label_agreement":null},{"id":"W7131081464","doi":"10.1109/iccvw69036.2025.00287","title":"Assessing the Quality of Soccer Shots from Single-Camera Video with Vision-Language Models and Motion Features","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Monocular; Feature (linguistics); Inertial measurement unit; Pipeline (software); Filter (signal processing); Wearable computer; Kinematics; Frame (networking); Video quality; Shot (pellet)","score_opus":0.03450756002903954,"score_gpt":0.335947471395597,"score_spread":0.30143991136655746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131081464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46457946,0.00079252495,0.5195593,0.00018476995,0.00015667574,0.00046025062,0.0018478319,0.007239026,0.0051801167],"genre_scores_gemma":[0.8048219,0.00026335762,0.18826076,0.000111903726,0.00007799454,0.00017053487,0.0029062636,0.00024290419,0.0031442684],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993217,0.00009281873,0.0000323872,0.00022925409,0.0002481517,0.00007568235],"domain_scores_gemma":[0.9988747,0.0002815305,0.00015648417,0.00012773606,0.00046615067,0.00009333631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092487317,0.000982768,0.00066109357,0.0012644732,0.00016582345,0.0008811014,0.0007012325,0.0006035112,0.0020103755],"category_scores_gemma":[0.003301104,0.0002004543,0.0005002736,0.00046556973,0.0002067599,0.000869268,0.0009420763,0.000510745,0.0013938361],"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":[0.0012314903,0.00052016764,0.011160333,0.0006380686,0.00024100441,0.00029309138,0.00025203658,0.019867705,0.1975309,0.0005540721,0.0056245704,0.7620866],"study_design_scores_gemma":[0.00008547143,0.0016346427,0.092773765,0.000085345506,0.00018124197,0.0008371158,0.00044715885,0.7904553,0.10770466,0.0015831243,0.0041053314,0.00010689228],"about_ca_topic_score_codex":0.0020350863,"about_ca_topic_score_gemma":0.004285767,"teacher_disagreement_score":0.0020350863,"about_ca_system_score_codex":0.00032150644,"about_ca_system_score_gemma":0.000333911,"threshold_uncertainty_score":0.006725371},"labels":[],"label_agreement":null},{"id":"W7133374563","doi":"","title":"Creating a User-steerable Media Presentation-system as a Canadian/Australian Distance-Learning Research Project","year":2010,"lang":"en","type":"article","venue":"RUNE (Research UNE)","topic":"Video Analysis and Summarization","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":"Interactivity; Architecture; Social media; Path (computing); Narrative; Interactive media; Systems architecture","score_opus":0.07779062466671911,"score_gpt":0.3840988846446625,"score_spread":0.3063082599779434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133374563","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11317003,0.0004959908,0.75077707,0.0014538548,0.00020131093,0.0021169719,0.0023299102,0.07850323,0.050951608],"genre_scores_gemma":[0.2508772,0.00044500938,0.63865477,0.00027726736,0.00009197854,0.0008506584,0.0038513546,0.003193214,0.10175855],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982881,0.00041857708,0.00010103685,0.00032408812,0.00071070984,0.00015753545],"domain_scores_gemma":[0.9972308,0.0005913286,0.00009393182,0.00048690513,0.00088567473,0.0007113363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039236527,0.0009199191,0.00058702606,0.002125491,0.0023157177,0.0032760235,0.0020066968,0.001005963,0.01665472],"category_scores_gemma":[0.0037953262,0.00054837496,0.0005251184,0.0013059865,0.00090631266,0.002262457,0.0028141045,0.0010720943,0.0046040686],"study_design_candidate":"simulation_or_modeling","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":[0.0013260581,0.0006428909,0.003538125,0.00062911364,0.00009119372,0.0011569813,0.012490503,0.0077066286,0.12978354,0.014046985,0.03524195,0.79334605],"study_design_scores_gemma":[0.00030085674,0.000769203,0.0089177,0.00018446386,0.0002734052,0.0011500338,0.0042186948,0.13145751,0.14896572,0.0070330193,0.69617444,0.0005549727],"about_ca_topic_score_codex":0.10324074,"about_ca_topic_score_gemma":0.15188387,"teacher_disagreement_score":0.8967593,"about_ca_system_score_codex":0.0030059486,"about_ca_system_score_gemma":0.0050228056,"threshold_uncertainty_score":0.20527959},"labels":[],"label_agreement":null},{"id":"W7138025320","doi":"10.1109/icca66035.2025.11430820","title":"Towards Efficient Keyframe Selection for News Video Captioning","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"Algoma University","funders":"","keywords":"Closed captioning; Selection (genetic algorithm); Feature selection; Key (lock)","score_opus":0.01266023895226047,"score_gpt":0.269216809580544,"score_spread":0.2565565706282835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7138025320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015751915,0.0021691793,0.9653729,0.00027510198,0.0003810484,0.00031000178,0.00079745025,0.012761844,0.002180666],"genre_scores_gemma":[0.17970802,0.001667922,0.8044945,0.00047494803,0.00058335596,0.00039306888,0.004963704,0.0018315463,0.005882956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902976,0.00026876404,0.000058561687,0.000291806,0.00022520006,0.00012593389],"domain_scores_gemma":[0.99884546,0.00041149376,0.000086597815,0.00016520807,0.00040384487,0.000087417284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010723042,0.0024834943,0.0014349348,0.0027008667,0.0006969247,0.0014731593,0.0013828323,0.0012831191,0.0047868798],"category_scores_gemma":[0.003902821,0.0005357673,0.0010635812,0.001504248,0.0005080069,0.0016809978,0.001304893,0.0015305993,0.0045078257],"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":[0.0008918395,0.00014794666,0.0006286735,0.00057482015,0.00008822385,0.00034841071,0.00034946826,0.019629898,0.1263507,0.0033181366,0.02709364,0.82057816],"study_design_scores_gemma":[0.00012620958,0.00036717576,0.001126026,0.00008929359,0.0001559683,0.0005012838,0.00043086414,0.7896993,0.16908245,0.008660656,0.029698024,0.00006276158],"about_ca_topic_score_codex":0.004201328,"about_ca_topic_score_gemma":0.0048124716,"teacher_disagreement_score":0.0047868798,"about_ca_system_score_codex":0.00073887483,"about_ca_system_score_gemma":0.0008744193,"threshold_uncertainty_score":0.016013682},"labels":[],"label_agreement":null},{"id":"W7160109171","doi":"10.1109/iccv51701.2025.01159","title":"Sliced Wasserstein Bridge for Open-Vocabulary Video Instance Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Video Analysis and Summarization","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":"China Postdoctoral Science Foundation","keywords":"Segmentation; Image segmentation; Noise (video); Bridge (graph theory); Field (mathematics); Feature (linguistics)","score_opus":0.028510883707929976,"score_gpt":0.31489129064901017,"score_spread":0.2863804069410802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7160109171","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017710196,0.0013206344,0.971199,0.00031021476,0.00014699205,0.00016950708,0.0014194503,0.0058859186,0.0018381195],"genre_scores_gemma":[0.29389372,0.0009928397,0.673183,0.00059952436,0.0003450791,0.0004966642,0.0185133,0.0023143047,0.009661559],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998211,0.00024548994,0.00013102927,0.0008275249,0.0003442056,0.00024071465],"domain_scores_gemma":[0.9981718,0.00071449846,0.00013820955,0.00042353108,0.00040284096,0.00014905955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016869674,0.0019933765,0.0034595237,0.0030104478,0.00088948774,0.0022608603,0.0041006478,0.004065596,0.0070436266],"category_scores_gemma":[0.00598525,0.0009693072,0.0018480441,0.0027739808,0.0010081169,0.004151504,0.0031312585,0.0034595318,0.0036578907],"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":[0.0008418217,0.00037256887,0.0012850555,0.00053296005,0.00021359051,0.00046222002,0.00035175835,0.09001923,0.02942933,0.02138962,0.029975105,0.8251268],"study_design_scores_gemma":[0.000028198414,0.000109054796,0.0003492024,0.000047440666,0.000037487807,0.00012709408,0.00008899945,0.96671414,0.005941332,0.022838837,0.0036970708,0.000021072618],"about_ca_topic_score_codex":0.009744728,"about_ca_topic_score_gemma":0.0142447455,"teacher_disagreement_score":0.009744728,"about_ca_system_score_codex":0.0013605315,"about_ca_system_score_gemma":0.0018215714,"threshold_uncertainty_score":0.023563266},"labels":[],"label_agreement":null},{"id":"W7161819741","doi":"10.82308/14983","title":"Where is the puck? Tiny and fast-moving object detection in videos","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Video Analysis and Summarization","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":"Video recording; Object (grammar); Rumble","score_opus":0.0060183667660925946,"score_gpt":0.22879717721649684,"score_spread":0.22277881045040424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7161819741","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27627823,0.0071763573,0.69915974,0.0016983553,0.00074824656,0.00021558379,0.00089557143,0.0035724088,0.010255525],"genre_scores_gemma":[0.71530896,0.0038982553,0.26602054,0.0006185522,0.00032093065,0.00009138728,0.0015504591,0.00045228013,0.011738584],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994487,0.00007749715,0.000020721272,0.00015895577,0.00017020886,0.00012389124],"domain_scores_gemma":[0.9992606,0.00036291234,0.00005785907,0.00008139378,0.0001729771,0.00006422647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007681608,0.0007013605,0.0006104184,0.0013714384,0.00038892534,0.0013438648,0.0006529481,0.0011519333,0.0018554351],"category_scores_gemma":[0.0029072755,0.00039276356,0.00043484764,0.0006733288,0.0005809751,0.0017381659,0.00094210147,0.0008074617,0.00080718234],"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":[0.0009111001,0.00016401724,0.0064216764,0.00073048443,0.00013139336,0.001318909,0.00077136495,0.030453162,0.24393736,0.00727478,0.012956206,0.6949295],"study_design_scores_gemma":[0.000051515628,0.0004015042,0.027493948,0.0003044527,0.00010845712,0.0024392328,0.0017660551,0.7971324,0.123289175,0.012749652,0.034135625,0.00012802538],"about_ca_topic_score_codex":0.007912246,"about_ca_topic_score_gemma":0.009111075,"teacher_disagreement_score":0.007912246,"about_ca_system_score_codex":0.00043366273,"about_ca_system_score_gemma":0.00052764785,"threshold_uncertainty_score":0.015732408},"labels":[],"label_agreement":null},{"id":"W88001600","doi":"10.1007/978-3-319-05290-8_1","title":"Introduction to Multimedia","year":2014,"lang":"en","type":"book-chapter","venue":"Texts in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":8,"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":"Multimedia; Viewpoints; Computer science; Field (mathematics); Perspective (graphical); Interactive media; Mobile device; World Wide Web","score_opus":0.011462405771035102,"score_gpt":0.23342490800534188,"score_spread":0.22196250223430677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W88001600","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009573797,0.049783353,0.061238978,0.002464087,0.004552425,0.00016105127,0.0016578048,0.0027261653,0.87645876],"genre_scores_gemma":[0.0046569197,0.030415656,0.016659759,0.0010304633,0.0022895606,0.00013854893,0.0015098986,0.0008891439,0.9424101],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997013,0.000031998043,0.000015136921,0.00006122986,0.00016412219,0.000026127005],"domain_scores_gemma":[0.9995291,0.00015847282,0.000014556428,0.000049747363,0.00018485732,0.00006338922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027819086,0.0012065258,0.00058058894,0.0039911144,0.00071093766,0.0029792122,0.0010071789,0.00096106087,0.19769813],"category_scores_gemma":[0.0013050237,0.00031652834,0.00031507472,0.003933053,0.0006031058,0.0031845733,0.0013733879,0.0012716928,0.095701344],"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":[0.000012344896,0.000034721514,0.000051127594,0.00043979535,0.000004400026,0.000053980333,0.00020172575,0.0003338222,0.0017007551,0.05047866,0.35923237,0.5874563],"study_design_scores_gemma":[0.0000012076589,0.0000076186916,0.0000648858,0.00017218383,0.0000022682575,0.0000748236,0.000043779284,0.00015608816,0.0003047394,0.008101246,0.99106663,0.000004507881],"about_ca_topic_score_codex":0.0012530993,"about_ca_topic_score_gemma":0.0027849572,"teacher_disagreement_score":0.19769813,"about_ca_system_score_codex":0.00076476374,"about_ca_system_score_gemma":0.00096264126,"threshold_uncertainty_score":0.6613661},"labels":[],"label_agreement":null},{"id":"W94350540","doi":"10.1007/978-3-642-39056-2_26","title":"Similarity Measures to Compare Episodes in Modeled Traces","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":8,"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 Laval","funders":"","keywords":"Computer science; TRACE (psycholinguistics); Similarity (geometry); Context (archaeology); Task (project management); Measure (data warehouse); Data mining; Similarity measure; Noise (video); Algorithm; Artificial intelligence; Image (mathematics)","score_opus":0.02787805811406547,"score_gpt":0.24896137103988428,"score_spread":0.22108331292581881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W94350540","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04216154,0.0008210725,0.9520164,0.00010723648,0.000121091,0.00024680918,0.0011766705,0.0013533661,0.001995797],"genre_scores_gemma":[0.6260934,0.0007439514,0.36281443,0.000101957354,0.0002751258,0.0005287531,0.006223381,0.0004640455,0.0027550324],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99759775,0.00056175824,0.00035140425,0.00052841095,0.00082962494,0.0001310917],"domain_scores_gemma":[0.99173623,0.0047296504,0.00094641413,0.0010716093,0.0012415276,0.00027465328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002209117,0.0008163681,0.0011366273,0.0065612714,0.00057876133,0.0018859261,0.0014129707,0.0012002611,0.0027489578],"category_scores_gemma":[0.014876411,0.00027707464,0.00097111205,0.005127087,0.00056308304,0.003674624,0.0016253918,0.0010444735,0.0009182991],"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":[0.00091177213,0.0007180762,0.020783072,0.0007289657,0.0007833865,0.0003245008,0.0007940099,0.131558,0.018481558,0.04490389,0.0118738115,0.76813895],"study_design_scores_gemma":[0.000052147556,0.00042208415,0.0074087046,0.000101763704,0.00019034877,0.0004494292,0.00043613775,0.9313009,0.007475177,0.045993403,0.0061047017,0.000065358174],"about_ca_topic_score_codex":0.002047891,"about_ca_topic_score_gemma":0.0021383557,"teacher_disagreement_score":0.0065612714,"about_ca_system_score_codex":0.0008272242,"about_ca_system_score_gemma":0.00074147334,"threshold_uncertainty_score":0.011683047},"labels":[],"label_agreement":null}]}