{"meta":{"query_hash":"5737e69161e5","filters":{"venue":"Journal of Visual Communication and Image Representation"},"cohort_total":52,"direct_labels_cover":0,"predictions_cover":52,"exported":52,"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/5737e69161e5","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Visual+Communication+and+Image+Representation"},"results":[{"id":"W1721184332","doi":"10.1016/j.jvcir.2015.09.004","title":"Blind single-image super resolution based on compressive sensing","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","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 Victoria","funders":"","keywords":"Compressed sensing; Image (mathematics); Computer science; Kernel (algebra); Superresolution; Artificial intelligence; Point spread function; Computer vision; Function (biology); Minification; Point (geometry); Domain (mathematical analysis); Image restoration; Algorithm; Image processing; Pattern recognition (psychology); Mathematics","score_opus":0.07912307772709411,"score_gpt":0.39555760814459534,"score_spread":0.31643453041750125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1721184332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0091699,0.0003241138,0.9856519,0.0026490598,0.000075694465,0.00013154194,6.050105e-7,0.0000839217,0.0019132274],"genre_scores_gemma":[0.44843572,0.00005378693,0.55123526,0.00020977156,0.0000353067,0.0000011490133,0.0000050719864,0.000008841611,0.000015117402],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982113,0.00051329436,0.00048347906,0.00018405633,0.0004703989,0.00013744678],"domain_scores_gemma":[0.99714047,0.00030668383,0.0006465431,0.00053919764,0.0012375294,0.00012960809],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008634729,0.00012948681,0.00020077609,0.000301495,0.00016817468,0.0004204172,0.00047607886,0.000055542234,0.0000021298197],"category_scores_gemma":[0.00090770976,0.00011995133,0.000055920536,0.00034560007,0.00016499736,0.0025699788,0.00020051547,0.00031052018,0.0000040107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012022863,0.0015482785,0.0004431618,0.00008313897,0.00006173424,0.000082665865,0.0060421783,0.0011530474,0.7641927,0.0018504357,0.008992909,0.21434745],"study_design_scores_gemma":[0.0022126858,0.0008004466,0.00060211687,0.00029791295,0.000023081939,0.00016192943,0.0006205116,0.90285116,0.0842414,0.0071603507,0.0007966426,0.00023178133],"about_ca_topic_score_codex":0.000011375277,"about_ca_topic_score_gemma":0.0000011351634,"teacher_disagreement_score":0.9016981,"about_ca_system_score_codex":0.00011726795,"about_ca_system_score_gemma":0.00009936679,"threshold_uncertainty_score":0.48914734},"labels":[],"label_agreement":null},{"id":"W1981941456","doi":"10.1016/j.jvcir.2014.07.008","title":"A generic, comprehensive and granular decoder complexity model for the H.264/AVC standard","year":2014,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Coding and Compression Technologies","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; Encoder; Motion compensation; Real-time computing; Decoding methods; Software; Video quality; Computational complexity theory; Computer engineering; Algorithm","score_opus":0.09712564571927966,"score_gpt":0.3809208934052589,"score_spread":0.28379524768597925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1981941456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04535037,0.001508228,0.9477407,0.005107826,0.00004503639,0.00015731707,0.0000015223244,0.000037000154,0.00005202335],"genre_scores_gemma":[0.82773966,0.0018619174,0.17009223,0.0002508937,0.000019554715,0.000010660283,0.0000016889101,0.000005238186,0.000018186265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990394,0.00020007548,0.00032546694,0.00012828982,0.00020851579,0.000098211916],"domain_scores_gemma":[0.99804986,0.00060476235,0.00036950456,0.00045036458,0.00047933118,0.000046182624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055094284,0.00008981112,0.00018685707,0.00008978957,0.0003954036,0.00026511968,0.00056493614,0.000039186965,0.0000010548279],"category_scores_gemma":[0.00022525672,0.00006168523,0.000058886188,0.00011831518,0.00021176632,0.00052587164,0.0003253023,0.00016658784,3.3309655e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038314742,0.00023181725,0.0012343582,0.000106854895,0.00014566602,0.0000015872521,0.0049686,0.003420355,0.03649216,0.08611728,0.009838,0.8570602],"study_design_scores_gemma":[0.0007277866,0.00017791882,0.0024268352,0.000031723433,0.000020331072,0.000035065303,0.00042956354,0.9538648,0.0031819982,0.037419822,0.0016032945,0.00008084224],"about_ca_topic_score_codex":0.00000935217,"about_ca_topic_score_gemma":0.0000034453592,"teacher_disagreement_score":0.95044446,"about_ca_system_score_codex":0.0000147653645,"about_ca_system_score_gemma":0.000024007932,"threshold_uncertainty_score":0.30411646},"labels":[],"label_agreement":null},{"id":"W1994231629","doi":"10.1016/j.jvcir.2013.12.020","title":"Hierarchical Implicit Shape Modeling","year":2014,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image and Video Retrieval Techniques","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":"McMaster University","funders":"","keywords":"Computer science; Mathematics","score_opus":0.033974155463836204,"score_gpt":0.38602564112879667,"score_spread":0.3520514856649605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994231629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037594724,0.00032211276,0.959746,0.0012738225,0.000029004832,0.00006212518,1.4720241e-7,0.000040230578,0.0009318136],"genre_scores_gemma":[0.7826912,0.0010556713,0.21598618,0.00019881342,0.00004596694,0.0000015710322,0.000001557246,0.000005406052,0.000013674735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988813,0.00026250485,0.00041078427,0.00011756751,0.00022820121,0.000099689576],"domain_scores_gemma":[0.9986933,0.00022729248,0.0002844665,0.00036236417,0.00035323805,0.000079365505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072961254,0.000077734665,0.00016016846,0.00014430298,0.00013072557,0.00018790753,0.0004937659,0.00003684639,0.000005119392],"category_scores_gemma":[0.00040806565,0.00006736184,0.00006058136,0.00021318144,0.00005296559,0.0016518872,0.00022974501,0.00024766417,0.0000024279095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005596228,0.00015811062,0.00035830538,0.000019455712,0.000023738743,0.0000029648418,0.00079626404,0.00012251746,0.06724075,0.024638396,0.00034181357,0.9062417],"study_design_scores_gemma":[0.00047463292,0.00032962678,0.0012049067,0.00005492731,0.000010666194,0.000119295226,0.00010463551,0.9392773,0.0178188,0.03948573,0.0009965278,0.00012294219],"about_ca_topic_score_codex":0.0000062978856,"about_ca_topic_score_gemma":3.426091e-7,"teacher_disagreement_score":0.9391548,"about_ca_system_score_codex":0.000019368372,"about_ca_system_score_gemma":0.000021241474,"threshold_uncertainty_score":0.2746936},"labels":[],"label_agreement":null},{"id":"W2003292342","doi":"10.1016/j.jvcir.2014.05.008","title":"Expert content-based image retrieval system using robust local patterns","year":2014,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"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 Windsor","funders":"Javna Agencija za Raziskovalno Dejavnost RS; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Robustness (evolution); Search engine indexing; Local binary patterns; Computer science; Operator (biology); Artificial intelligence; Image retrieval; Pattern recognition (psychology); Computer vision; Mathematics; Image (mathematics); Histogram","score_opus":0.07333388033476512,"score_gpt":0.34991951239567165,"score_spread":0.2765856320609065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003292342","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027447408,0.00021402679,0.9708533,0.0009946886,0.00010034174,0.00014011009,0.0000011712897,0.00008237425,0.0001665764],"genre_scores_gemma":[0.8041119,0.00017839114,0.19544505,0.00016296496,0.000064074484,0.0000021896055,0.000006554987,0.000011552127,0.000017354685],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99782425,0.00068544626,0.0006974835,0.00019142804,0.0004508916,0.00015048138],"domain_scores_gemma":[0.9972939,0.00026038344,0.00081776443,0.00057217595,0.00093634694,0.00011945957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011952905,0.0001408346,0.00026526165,0.00021908984,0.00021734006,0.00039485603,0.0006375199,0.00007314294,0.00000749765],"category_scores_gemma":[0.0002426919,0.00012294402,0.000113458635,0.00031127772,0.00015269495,0.0015001491,0.00015650904,0.00024202192,0.0000037387827],"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":[0.0002476539,0.00041268347,0.0011108044,0.00016134116,0.00005934058,0.000013125342,0.0008574889,0.000041333562,0.93271595,0.006542628,0.00018263404,0.057654984],"study_design_scores_gemma":[0.001078819,0.00029346085,0.003140281,0.00026475205,0.000025234387,0.00015393703,0.0018265359,0.48747408,0.5051982,0.00009829062,0.0002449702,0.000201436],"about_ca_topic_score_codex":0.00006238409,"about_ca_topic_score_gemma":7.3758343e-7,"teacher_disagreement_score":0.77666444,"about_ca_system_score_codex":0.00013235107,"about_ca_system_score_gemma":0.000069336944,"threshold_uncertainty_score":0.5013512},"labels":[],"label_agreement":null},{"id":"W2015919738","doi":"10.1016/j.jvcir.2014.01.007","title":"Bandlet-based sparsity regularization in video inpainting","year":2014,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","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":"Concordia University","funders":"","keywords":"Inpainting; Regularization (linguistics); Computer vision; Computer science; Artificial intelligence; Mathematics; Image (mathematics)","score_opus":0.017537634160742957,"score_gpt":0.33913948506798486,"score_spread":0.3216018509072419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015919738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017531538,0.00013857307,0.980044,0.001633451,0.000025843123,0.00007136554,7.419765e-8,0.000041927527,0.0005132053],"genre_scores_gemma":[0.5571614,0.00006846492,0.44260466,0.00013335403,0.0000135007995,0.0000015505352,0.0000020589296,0.000003882603,0.000011099212],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986955,0.00045468894,0.00042262662,0.00012394793,0.00021306222,0.00009017915],"domain_scores_gemma":[0.9984276,0.00027262495,0.0005623437,0.0003388077,0.00035733322,0.000041272913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014136718,0.00007361907,0.00014758228,0.00026272392,0.00010773914,0.00019867758,0.0004261866,0.000038346727,0.0000018031313],"category_scores_gemma":[0.00096842984,0.00007273589,0.00003183684,0.00038058904,0.00006956158,0.0018458933,0.00014672229,0.00020105779,7.7314337e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001537374,0.00075662666,0.027196629,0.00015679689,0.000024514695,0.000009612796,0.00311968,0.0007219249,0.28435746,0.022113066,0.0005926954,0.66079724],"study_design_scores_gemma":[0.0010853545,0.00019208645,0.020417023,0.00024868807,0.000008987588,0.000032828488,0.00011950953,0.88362926,0.052057296,0.041600253,0.00044316368,0.00016553348],"about_ca_topic_score_codex":0.000008853577,"about_ca_topic_score_gemma":0.0000024354893,"teacher_disagreement_score":0.88290733,"about_ca_system_score_codex":0.000045100747,"about_ca_system_score_gemma":0.000037942962,"threshold_uncertainty_score":0.29660836},"labels":[],"label_agreement":null},{"id":"W2017380972","doi":"10.1016/j.jvcir.2015.02.007","title":"TapTell: Interactive visual search for mobile task recommendation","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image and Video Retrieval Techniques","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Human–computer interaction; Variety (cybernetics); Context (archaeology); Task (project management); Mobile phone; Mobile device; Vocabulary; Phone; Visual search; Object (grammar); Perspective (graphical); Tree (set theory); Multimedia; World Wide Web; Information retrieval; Artificial intelligence","score_opus":0.06362312191228862,"score_gpt":0.4560461672885127,"score_spread":0.3924230453762241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017380972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017039593,0.00031912103,0.9806452,0.00090911915,0.00009057857,0.0003242427,0.0000016979008,0.00003985003,0.0006306367],"genre_scores_gemma":[0.835534,0.0010392099,0.16305496,0.00014903284,0.00007503988,0.00002341134,0.000027531212,0.000011214134,0.000085626016],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99873495,0.00031748792,0.0004524992,0.00014768812,0.00023067876,0.000116704],"domain_scores_gemma":[0.99746823,0.00044897356,0.00045186302,0.00023883193,0.001277767,0.000114360395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011647476,0.000094934636,0.00017891392,0.00020945634,0.00011627891,0.00024302288,0.0003722828,0.000042587923,0.0000056370645],"category_scores_gemma":[0.0003974353,0.00008652435,0.00006806083,0.00027467284,0.00006518806,0.0031694754,0.00022083835,0.00021524396,0.0000034584125],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036678775,0.00042165502,0.00027628764,0.000023907081,0.000051473184,0.0000025890226,0.004702705,0.000029120467,0.04084602,0.00094668043,0.002549416,0.9497834],"study_design_scores_gemma":[0.0043477905,0.0058346624,0.0017003213,0.00018297703,0.000062092455,0.00027739565,0.009685858,0.14639577,0.783045,0.013807099,0.03413211,0.00052892976],"about_ca_topic_score_codex":0.000011600112,"about_ca_topic_score_gemma":5.8114335e-7,"teacher_disagreement_score":0.94925445,"about_ca_system_score_codex":0.00008676646,"about_ca_system_score_gemma":0.00007715193,"threshold_uncertainty_score":0.35283604},"labels":[],"label_agreement":null},{"id":"W2021305240","doi":"10.1016/j.jvcir.2006.09.001","title":"Grayscale true two-dimensional dictionary-based image compression","year":2006,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lossless compression; Computer science; Lossy compression; Image compression; Lossless JPEG; Grayscale; JPEG; Data compression; Artificial intelligence; Color Cell Compression; Data compression ratio; JPEG 2000; Pixel; Computer vision; Texture compression; Compression (physics); Image (mathematics); Image processing","score_opus":0.01519203767192199,"score_gpt":0.3540390804271585,"score_spread":0.33884704275523647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021305240","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038959432,0.0006164544,0.9572706,0.001963934,0.000112322676,0.00017399185,0.0000056237086,0.000118593285,0.00077904377],"genre_scores_gemma":[0.511356,0.00009551185,0.48824665,0.00013472691,0.00005929484,0.0000069334324,0.00005122484,0.0000099691115,0.00003965529],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9980709,0.00043880762,0.0006554408,0.00020798242,0.0004903362,0.0001365274],"domain_scores_gemma":[0.9975017,0.00048633205,0.00073897373,0.0006329382,0.0005514849,0.000088595676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044506736,0.00014162505,0.00021996027,0.0002709123,0.00028411642,0.00020741114,0.00065965066,0.000049227023,0.00002521993],"category_scores_gemma":[0.000098354896,0.00012310821,0.00009040316,0.00030540596,0.00015831404,0.0024376453,0.00035129912,0.0002839818,0.000005380829],"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":[0.00022996488,0.0012411432,0.0019771438,0.000042308482,0.000029242956,0.000031664254,0.00018897232,0.002082036,0.9127284,0.007840938,0.027657783,0.04595035],"study_design_scores_gemma":[0.0046496233,0.0005796716,0.0363615,0.0004942965,0.00004705292,0.000395081,0.00013147219,0.38198295,0.5259344,0.042885378,0.005967766,0.0005708279],"about_ca_topic_score_codex":0.000056080444,"about_ca_topic_score_gemma":0.0000035850146,"teacher_disagreement_score":0.47239658,"about_ca_system_score_codex":0.000057239828,"about_ca_system_score_gemma":0.00007528026,"threshold_uncertainty_score":0.5020207},"labels":[],"label_agreement":null},{"id":"W2021395519","doi":"10.1016/j.jvcir.2012.06.009","title":"Color demosaicking with an image formation model and adaptive PCA","year":2012,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Demosaicing; Artificial intelligence; Computer vision; Image (mathematics); Computer science; Color image; Pattern recognition (psychology); Mathematics; Image processing","score_opus":0.0620850226442393,"score_gpt":0.3827626193821282,"score_spread":0.32067759673788887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2021395519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21778733,0.0004098266,0.7808805,0.0003704765,0.00002106723,0.00008443887,3.866956e-7,0.000014595694,0.00043138838],"genre_scores_gemma":[0.5896468,0.0002095631,0.4100114,0.000087547945,0.000024236711,0.0000017803592,0.0000023606954,0.0000046823247,0.0000115918065],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987176,0.0004937081,0.00032009804,0.00009316326,0.00024276164,0.00013263382],"domain_scores_gemma":[0.99860346,0.00018395901,0.00043272454,0.00025399838,0.0004056104,0.00012021675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012469958,0.00009305764,0.00015426456,0.00014162491,0.00022074969,0.0003022683,0.00024398226,0.000035655772,0.0000016802428],"category_scores_gemma":[0.00009528268,0.00007479323,0.00002628593,0.00017447927,0.000092955335,0.008264432,0.00012208117,0.00017776687,9.675327e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014929413,0.0010841133,0.0025258851,0.00010587379,0.00014876144,0.000017413655,0.048115816,0.0011305233,0.5144353,0.021885656,0.0007467889,0.40831092],"study_design_scores_gemma":[0.0015246026,0.0006975015,0.008752602,0.00007910879,0.000044653687,0.00049729075,0.0018552097,0.9589578,0.024510683,0.0028326923,0.00006112114,0.00018673783],"about_ca_topic_score_codex":0.000012481224,"about_ca_topic_score_gemma":0.0000024287674,"teacher_disagreement_score":0.95782727,"about_ca_system_score_codex":0.00003113372,"about_ca_system_score_gemma":0.000033434833,"threshold_uncertainty_score":0.5991514},"labels":[],"label_agreement":null},{"id":"W2022042586","doi":"10.1016/s1047-3203(03)00018-x","title":"An accurate bit-rate control algorithm for video transcoding","year":2003,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Coding and Compression Technologies","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 Ottawa","funders":"","keywords":"Transcoding; Computer science; Bit rate; Algorithm; Bit (key); Real-time computing; Computer network","score_opus":0.03823681838505216,"score_gpt":0.3761620762443301,"score_spread":0.33792525785927796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022042586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018420765,0.00060746225,0.97951186,0.0010130599,0.0000975296,0.00015522017,0.0000013340353,0.00006130257,0.00013148731],"genre_scores_gemma":[0.79649025,0.00050014735,0.20279868,0.0001603377,0.000017672608,0.000011379886,0.0000015076954,0.0000060225134,0.00001399533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986668,0.00047542603,0.00043283656,0.00014617732,0.00015568132,0.00012302781],"domain_scores_gemma":[0.99822146,0.0004189815,0.00046404658,0.0004249178,0.00040212733,0.0000684518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011548875,0.000095631054,0.0001904135,0.00017990447,0.0002451624,0.0003475562,0.0005802979,0.000045174125,0.000003431455],"category_scores_gemma":[0.00032546197,0.000081158774,0.00007616647,0.00021103432,0.000062551415,0.0016963746,0.000034076475,0.00017177853,9.395818e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006968028,0.00035966525,0.0002632262,0.00002463258,0.0000794289,0.0000057554175,0.0011489048,0.00021677675,0.26210788,0.019293485,0.00083964685,0.7155909],"study_design_scores_gemma":[0.0043024966,0.0011090657,0.001662287,0.00014768087,0.000057904756,0.00017158235,0.0019171302,0.71061826,0.25021428,0.026009208,0.0034567772,0.0003333367],"about_ca_topic_score_codex":0.000004731817,"about_ca_topic_score_gemma":5.282303e-7,"teacher_disagreement_score":0.7780695,"about_ca_system_score_codex":0.000020916405,"about_ca_system_score_gemma":0.000043753687,"threshold_uncertainty_score":0.33514923},"labels":[],"label_agreement":null},{"id":"W2035478145","doi":"10.1016/j.jvcir.2005.12.002","title":"Special issue on emerging H.264/AVC video coding standard","year":2006,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Coding and Compression Technologies","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 Alberta","funders":"","keywords":"Coding (social sciences); Computer science; Context-adaptive variable-length coding; Scalable Video Coding; Context-adaptive binary arithmetic coding; Data compression; Algorithm; Mathematics; Motion compensation; Statistics","score_opus":0.023925156845185692,"score_gpt":0.355031431573626,"score_spread":0.3311062747284403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035478145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18936552,0.0016543439,0.7240834,0.02757048,0.0017245755,0.0004762734,0.000004009154,0.00046439277,0.05465701],"genre_scores_gemma":[0.96300364,0.0011272043,0.034248415,0.00013711001,0.0011908166,0.000003979834,0.0000032204532,0.000009798863,0.00027580166],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986517,0.00021054046,0.0004781306,0.00014766603,0.00038841236,0.00012358424],"domain_scores_gemma":[0.99849224,0.00023568785,0.0005138176,0.00042848245,0.000288396,0.00004139352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005567762,0.00010347853,0.0001916023,0.00027478155,0.00027378034,0.0003456543,0.0006165728,0.000050037186,0.000027605005],"category_scores_gemma":[0.00021822061,0.0000898935,0.000068574656,0.0002939678,0.000077951874,0.0009413026,0.00025079516,0.00027483114,0.000008567847],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020478768,0.0003086689,0.0014122988,0.000030318937,0.00004164652,0.000024444196,0.0012715912,0.00030901525,0.029914176,0.034049433,0.12778303,0.8046506],"study_design_scores_gemma":[0.0070292815,0.0031068828,0.03426836,0.0014838736,0.00010045731,0.00047945854,0.006599327,0.06820783,0.43796298,0.07750749,0.3619435,0.0013105377],"about_ca_topic_score_codex":0.000020965874,"about_ca_topic_score_gemma":0.0000026830949,"teacher_disagreement_score":0.8033401,"about_ca_system_score_codex":0.000049474686,"about_ca_system_score_gemma":0.00002958555,"threshold_uncertainty_score":0.3665751},"labels":[],"label_agreement":null},{"id":"W2038918073","doi":"10.1006/jvci.2000.0461","title":"Encoding DCT Coefficients Based on Rate–Distortion Measurement","year":2001,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Quantization (signal processing); Discrete cosine transform; Algorithm; Rate distortion; Bit rate; Trellis quantization; Distortion (music); Rate–distortion optimization; Mathematics; Encoding (memory); Constraint (computer-aided design); Peak signal-to-noise ratio; Computer science; Coding (social sciences); Computer vision; Artificial intelligence; Image compression; Statistics; Real-time computing; Image (mathematics); Image processing; Bandwidth (computing); Telecommunications; Multiview Video Coding","score_opus":0.06834564411912766,"score_gpt":0.35802171928630555,"score_spread":0.28967607516717786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038918073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08495232,0.00037978584,0.90952903,0.0031313058,0.00016232648,0.000106996056,2.0314816e-7,0.00008583491,0.0016522259],"genre_scores_gemma":[0.990868,0.0005310103,0.008384997,0.00016166612,0.000019213267,0.0000039418987,0.0000014204408,0.000004584568,0.000025214169],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985452,0.00034765102,0.00038754698,0.00013593858,0.00048211118,0.00010154571],"domain_scores_gemma":[0.9984078,0.00016153112,0.0004555028,0.0004406821,0.00048073422,0.000053748336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012262341,0.00008846042,0.00013761921,0.0002696584,0.00022129982,0.00020449402,0.000525327,0.000041187453,0.000006984717],"category_scores_gemma":[0.0005249618,0.00007459426,0.00005880452,0.00032691556,0.00005341713,0.00060611457,0.0001197183,0.00019827994,0.0000046946902],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037205196,0.0014812647,0.005898696,0.00004111767,0.000059652444,0.000032804273,0.0013062477,0.0036696773,0.17310311,0.0039029326,0.0052039833,0.8049285],"study_design_scores_gemma":[0.004042377,0.001864244,0.050071634,0.00094630686,0.000054845717,0.00015106575,0.0014817071,0.7568335,0.17200352,0.0043311473,0.007663104,0.0005565196],"about_ca_topic_score_codex":0.0000075888097,"about_ca_topic_score_gemma":9.4723447e-7,"teacher_disagreement_score":0.9059156,"about_ca_system_score_codex":0.000090003996,"about_ca_system_score_gemma":0.000042805295,"threshold_uncertainty_score":0.30418658},"labels":[],"label_agreement":null},{"id":"W2048330522","doi":"10.1016/j.jvcir.2015.01.007","title":"No-reference blur assessment based on edge modeling","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":60,"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":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Pixel; Computer vision; Metric (unit); Enhanced Data Rates for GSM Evolution; Computer science; Gaussian blur; Parametric statistics; Contrast (vision); Edge detection; Image (mathematics); Image restoration; Mathematics; Image processing; Statistics","score_opus":0.1387914075125798,"score_gpt":0.45551765298090796,"score_spread":0.31672624546832817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048330522","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0110232495,0.00012277755,0.9770371,0.0024497362,0.00012847414,0.000109055196,5.1904976e-7,0.000024190422,0.00910492],"genre_scores_gemma":[0.8453946,0.00013518285,0.15384735,0.0004922744,0.000050757295,0.000004170531,0.000007980169,0.0000057829093,0.00006187414],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981128,0.0005916043,0.0004929747,0.00014575094,0.00054254004,0.00011432488],"domain_scores_gemma":[0.9977082,0.00021618819,0.00040364004,0.00051654124,0.0010170555,0.0001383886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014882794,0.00010243414,0.00017738633,0.00017174934,0.000120543395,0.00036121937,0.0005414869,0.000039659808,0.000007492387],"category_scores_gemma":[0.00025471032,0.00008913594,0.000053720312,0.0001937956,0.000039364553,0.001486203,0.00016794338,0.00029079567,0.000014200879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020286175,0.0148664415,0.013344055,0.00056503795,0.0006343994,0.00018123075,0.019727686,0.08416196,0.12167429,0.16504472,0.033355493,0.54441607],"study_design_scores_gemma":[0.001007221,0.00048809304,0.0010264518,0.000070458074,0.0000118986045,0.000014380304,0.00045637815,0.9938303,0.00095778226,0.0013423752,0.0006863321,0.00010829943],"about_ca_topic_score_codex":0.000039398856,"about_ca_topic_score_gemma":0.000001747732,"teacher_disagreement_score":0.9096684,"about_ca_system_score_codex":0.00009355727,"about_ca_system_score_gemma":0.00023358133,"threshold_uncertainty_score":0.3634858},"labels":[],"label_agreement":null},{"id":"W2056220869","doi":"10.1006/jvci.2001.0488","title":"Nice Perspective Projections","year":2001,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Perspective (graphical); Ambiguity; Parallel projection; Computer graphics; Computer science; Projection (relational algebra); Artificial intelligence; Computer vision; Mathematics; Object (grammar); Graph; Computer graphics (images); Orthographic projection; Theoretical computer science; Algorithm","score_opus":0.032678964945319584,"score_gpt":0.3897377839897251,"score_spread":0.3570588190444055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056220869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090676546,0.00086966885,0.8962935,0.0059876605,0.00015974064,0.00013154281,3.6956948e-7,0.000032197437,0.005848736],"genre_scores_gemma":[0.93398786,0.0015015979,0.0639993,0.00014812342,0.00010129178,0.000003852699,0.0000038744433,0.0000041496633,0.00024996974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990426,0.00023309252,0.00030785825,0.000110867644,0.00023226955,0.00007333227],"domain_scores_gemma":[0.998331,0.00018659761,0.00031639353,0.00021728534,0.0008909632,0.000057774014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004688167,0.00006461133,0.0001029353,0.00024604143,0.00019888156,0.00020419704,0.00026495953,0.000027136808,0.0000124609],"category_scores_gemma":[0.00025672905,0.000060440998,0.000053005577,0.00056375616,0.000045445555,0.0016321522,0.00010011369,0.00014854522,0.0000055995556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035563088,0.002588486,0.009811837,0.00004698371,0.00040935833,0.000042760217,0.025407873,0.00341338,0.11340255,0.39835274,0.013478266,0.43269014],"study_design_scores_gemma":[0.006600875,0.0026636398,0.2166012,0.00024340871,0.00018950744,0.005987516,0.020082677,0.5554254,0.02852555,0.11900237,0.043544095,0.0011337777],"about_ca_topic_score_codex":0.00003215786,"about_ca_topic_score_gemma":0.000005752779,"teacher_disagreement_score":0.8433113,"about_ca_system_score_codex":0.00005582597,"about_ca_system_score_gemma":0.0000721997,"threshold_uncertainty_score":0.24647123},"labels":[],"label_agreement":null},{"id":"W2057792073","doi":"10.1016/j.jvcir.2012.07.001","title":"Model-based adaptive resolution upconversion of degraded images","year":2012,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","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":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Upsampling; Pixel; Computer science; Deconvolution; Autoregressive model; Piecewise; Algorithm; Artificial intelligence; Mathematics; Image (mathematics); Mathematical optimization; Computer vision","score_opus":0.054583968003163924,"score_gpt":0.3791078921176934,"score_spread":0.32452392411452946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057792073","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011986312,0.0012536127,0.98575795,0.0005706301,0.00003311323,0.00008659727,7.5363295e-7,0.000035050874,0.00027600944],"genre_scores_gemma":[0.53072894,0.00020368276,0.4690087,0.00003232211,0.0000104946475,0.0000023154316,0.0000015667214,0.0000038584367,0.000008080804],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882877,0.00026224327,0.00042476808,0.000092410875,0.0002743717,0.00011745536],"domain_scores_gemma":[0.9979823,0.00017568046,0.00081079523,0.00034474584,0.0006135546,0.00007290996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074298936,0.00008755128,0.00016834331,0.00022077652,0.00010819571,0.00005679645,0.00041168169,0.00004288267,0.000002466743],"category_scores_gemma":[0.0002426199,0.00008053501,0.000060763145,0.00026263786,0.00014340857,0.0032636146,0.00016488624,0.00017843502,7.623782e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042185618,0.0011915383,0.0042442684,0.00012685325,0.000061134386,0.000002401799,0.0041542267,0.0008177702,0.8195411,0.008162424,0.0026246079,0.1586518],"study_design_scores_gemma":[0.00058298087,0.00021095984,0.0032016898,0.000118572505,0.000023482178,0.000033576616,0.0002845813,0.6998093,0.29090086,0.0046791956,0.000043664837,0.00011113157],"about_ca_topic_score_codex":0.000007917335,"about_ca_topic_score_gemma":2.0983975e-7,"teacher_disagreement_score":0.69899154,"about_ca_system_score_codex":0.000052397885,"about_ca_system_score_gemma":0.000061415085,"threshold_uncertainty_score":0.32841223},"labels":[],"label_agreement":null},{"id":"W2061566419","doi":"10.1016/j.jvcir.2012.01.005","title":"Novel wavelet-based QIM data hiding technique for tamper detection and correction of digital images","year":2012,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":41,"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; Digital watermarking; Artificial intelligence; Dither; Computer vision; Wavelet; Quantization (signal processing); Pattern recognition (psychology); Image (mathematics); False alarm; Noise shaping","score_opus":0.05246300325692648,"score_gpt":0.3697223709175066,"score_spread":0.3172593676605801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061566419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01606441,0.00026090455,0.98314005,0.0001491098,0.000086848784,0.0001998103,0.0000060076395,0.000026980926,0.000065875596],"genre_scores_gemma":[0.75626177,0.00017971576,0.24348919,0.000012415894,0.00002718696,0.00000798762,0.0000124699345,0.000005090259,0.0000041547387],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992459,0.00008042816,0.00034324848,0.000108545595,0.00013084452,0.000091034206],"domain_scores_gemma":[0.99848825,0.0003116965,0.0005058372,0.0003834493,0.0002652709,0.000045512857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007527142,0.00007728873,0.00013624573,0.00020732808,0.0001297647,0.0001235551,0.00032225554,0.000044179513,3.8171223e-7],"category_scores_gemma":[0.00026155548,0.000069668684,0.00004084257,0.00017940815,0.000084852116,0.0035667648,0.00019033565,0.00012300204,5.070002e-8],"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":[0.00007738737,0.00017591003,0.0011869607,0.000039967985,0.00001808345,1.3854157e-7,0.00029567425,0.000004132555,0.8586973,0.00014425542,0.000099067496,0.13926113],"study_design_scores_gemma":[0.00067642593,0.00034255363,0.008342002,0.0001353246,0.000032961012,0.00013559066,0.00026069552,0.05630662,0.93222135,0.0008677197,0.0005284787,0.00015028399],"about_ca_topic_score_codex":0.000007875097,"about_ca_topic_score_gemma":6.296701e-7,"teacher_disagreement_score":0.74019736,"about_ca_system_score_codex":0.000018233275,"about_ca_system_score_gemma":0.000016597844,"threshold_uncertainty_score":0.28410065},"labels":[],"label_agreement":null},{"id":"W2087662994","doi":"10.1006/jvci.2001.0503","title":"On Computing General Position Views of Data in Three Dimensions","year":2002,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Computational Geometry and Mesh Generation","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":"McGill University","funders":"","keywords":"Degenerate energy levels; Position (finance); Graphics; Algorithm; Transformation (genetics); Computer science; Computational complexity theory; Mathematics; Theoretical computer science; Physics; Computer graphics (images)","score_opus":0.11724995543605311,"score_gpt":0.40865313869727543,"score_spread":0.29140318326122233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087662994","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5183909,0.0008872223,0.47889885,0.0012221329,0.00007798114,0.00010879343,0.0000017779253,0.000008148379,0.0004042217],"genre_scores_gemma":[0.89296174,0.0004623364,0.10638494,0.00011434555,0.000036621554,6.2703214e-7,0.000022903865,0.000003458259,0.0000130539065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986004,0.0003266055,0.0005654309,0.00014515842,0.00028748732,0.00007491424],"domain_scores_gemma":[0.99831593,0.00039704467,0.0005073572,0.0004886071,0.00024821775,0.000042832795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008328291,0.00007241256,0.00016279549,0.00029884803,0.00009243083,0.000078671874,0.0004959618,0.00003072593,0.000011351823],"category_scores_gemma":[0.00019114673,0.00006767368,0.00003515845,0.00043571347,0.000039735543,0.0013337263,0.00026405594,0.00015887296,0.0000029468606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017535538,0.002862964,0.005162314,0.00009051305,0.00012186391,0.000025865043,0.0052899527,0.018687297,0.13916542,0.10268135,0.0043661105,0.721371],"study_design_scores_gemma":[0.0007681121,0.00025123666,0.026274359,0.0000978619,0.000010819533,0.00006158065,0.00006192299,0.96370435,0.0024022353,0.006165828,0.00011491042,0.00008676388],"about_ca_topic_score_codex":0.000017348915,"about_ca_topic_score_gemma":0.000011441051,"teacher_disagreement_score":0.9450171,"about_ca_system_score_codex":0.000026140071,"about_ca_system_score_gemma":0.000018563056,"threshold_uncertainty_score":0.27596524},"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":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25065944,0.00041780222,0.7403745,0.0070936847,0.00004679642,0.0001538147,2.8335023e-7,0.000010301225,0.0012433968],"genre_scores_gemma":[0.9695283,0.0015399313,0.028461324,0.0003945324,0.00003308159,0.0000048448833,0.0000126815385,0.000003635665,0.000021678507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815917,0.0007954808,0.0005123391,0.000103871724,0.00034239856,0.00008671926],"domain_scores_gemma":[0.9986842,0.00028659138,0.00042117003,0.00034706297,0.0002277309,0.000033235716],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013728538,0.00007251631,0.00014336625,0.00021201378,0.00014257552,0.0003296767,0.000531194,0.00003047355,0.00001032429],"category_scores_gemma":[0.00011529203,0.000052758955,0.000057357407,0.0005891145,0.000047119895,0.0014813592,0.00008413521,0.00016666463,0.000002565251],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036680922,0.002284791,0.08521283,0.00006460462,0.00023319846,0.00011350743,0.0568837,0.0013094941,0.16399568,0.048482627,0.008000323,0.63305247],"study_design_scores_gemma":[0.006916109,0.0008392704,0.4455428,0.00023654064,0.00011895894,0.00078816,0.022670548,0.46109968,0.008111489,0.041495204,0.011553985,0.00062720955],"about_ca_topic_score_codex":0.00004646063,"about_ca_topic_score_gemma":0.000026019823,"teacher_disagreement_score":0.71886885,"about_ca_system_score_codex":0.000029953895,"about_ca_system_score_gemma":0.00002365068,"threshold_uncertainty_score":0.31790802},"labels":[],"label_agreement":null},{"id":"W2113598111","doi":"10.1016/j.jvcir.2011.06.003","title":"Efficient video sequences alignment using unbiased bidirectional dynamic time warping","year":2011,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Vision and Imaging","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":"Computer science; Dynamic time warping; Image warping; Computer vision; Frame (networking); Artificial intelligence; Sequence (biology); Resolution (logic); Reference frame; Algorithm","score_opus":0.04974990218697384,"score_gpt":0.375172297094229,"score_spread":0.32542239490725516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113598111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22629261,0.0007013843,0.77169114,0.0003866863,0.00012467035,0.00009668797,5.2209936e-7,0.000032234933,0.00067405315],"genre_scores_gemma":[0.73140293,0.00022412172,0.26824203,0.00008753518,0.000014590945,0.0000011048203,0.0000015347684,0.0000055042424,0.000020623074],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987128,0.00027053585,0.00044872242,0.00014548094,0.00030777737,0.00011470652],"domain_scores_gemma":[0.99868757,0.00013146269,0.0005228956,0.00027356556,0.00030324407,0.00008125334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060498324,0.0000932798,0.00014961515,0.00021548498,0.0002208166,0.0001248171,0.0003574005,0.000024937763,0.00003089431],"category_scores_gemma":[0.00011999872,0.000083555424,0.00006334789,0.00028473383,0.000094987394,0.00084840687,0.00017533776,0.0001448059,0.0000071291147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014627865,0.0008401757,0.0016459638,0.000036086985,0.00017200771,0.00002953875,0.009094329,0.0039068745,0.7880781,0.0016329231,0.00021693639,0.19420078],"study_design_scores_gemma":[0.00046290492,0.00010600052,0.004523343,0.00010842345,0.000019716303,0.00018231188,0.00050278986,0.9779624,0.014790151,0.0011452882,0.00007800426,0.000118704316],"about_ca_topic_score_codex":0.000029311397,"about_ca_topic_score_gemma":6.101706e-7,"teacher_disagreement_score":0.97405547,"about_ca_system_score_codex":0.000079051766,"about_ca_system_score_gemma":0.00005693476,"threshold_uncertainty_score":0.34072915},"labels":[],"label_agreement":null},{"id":"W2121901851","doi":"10.1016/j.jvcir.2004.11.011","title":"Split-domain video transmission protocol for video streaming over hybrid wired–wireless connections","year":2005,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Network Traffic and Congestion Control","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 Waterloo","funders":"","keywords":"Computer science; Computer network; Wireless; Wireless network; Transmission (telecommunications); Live streaming; Protocol (science); Video streaming; The Internet; Domain (mathematical analysis); Telecommunications","score_opus":0.02297749848471806,"score_gpt":0.35933865029128675,"score_spread":0.33636115180656867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121901851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023477327,0.0001062594,0.96157265,0.004615443,0.00005650081,0.009797054,0.0000016687117,0.000051005263,0.0003220745],"genre_scores_gemma":[0.8286335,0.0001266682,0.16314742,0.00048750365,0.00034331784,0.0070819515,0.000009154716,0.000022541948,0.00014793554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984449,0.00029959076,0.0006388754,0.0001880614,0.00027337857,0.00015519808],"domain_scores_gemma":[0.99803513,0.0005043371,0.0005746946,0.0003477082,0.00041369538,0.00012445038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007563164,0.00012978129,0.000218112,0.0001518628,0.00032957995,0.0002785808,0.00041769855,0.00003992916,0.00002554622],"category_scores_gemma":[0.000074841824,0.00011607167,0.00012420728,0.00019289152,0.000069254325,0.0015939664,0.000066548266,0.00018182177,0.0000020624527],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024910812,0.00032873865,0.00013297827,0.000032585347,0.000045399986,0.0000023636574,0.0006595555,0.00032749015,0.010393657,0.0065274197,0.0036545193,0.9776462],"study_design_scores_gemma":[0.0074054836,0.00059353973,0.002030131,0.0003174429,0.000047871355,0.00022221208,0.0004668744,0.8824865,0.008470417,0.003353459,0.09428884,0.00031719115],"about_ca_topic_score_codex":0.000008812658,"about_ca_topic_score_gemma":0.000006770798,"teacher_disagreement_score":0.977329,"about_ca_system_score_codex":0.000059589274,"about_ca_system_score_gemma":0.000086102,"threshold_uncertainty_score":0.47332653},"labels":[],"label_agreement":null},{"id":"W2141204096","doi":"10.1016/j.jvcir.2007.06.006","title":"Photometric image processing for high dynamic range displays","year":2007,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image Enhancement Techniques","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":"Dolby (Canada); University of British Columbia","funders":"","keywords":"High dynamic range; Computer science; Brightness; Dynamic range; Range (aeronautics); Tone mapping; High-dynamic-range imaging; Computer vision; Computer graphics (images); Artificial intelligence; Image processing; Image (mathematics); Optics; Physics; Materials science","score_opus":0.020462536995938713,"score_gpt":0.3905687104745346,"score_spread":0.3701061734785959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141204096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05466759,0.0006794658,0.9431672,0.00066164264,0.000064815176,0.0002773002,9.083373e-7,0.000050365576,0.000430679],"genre_scores_gemma":[0.64720505,0.00039947583,0.3522225,0.00007905489,0.000025132556,0.0000071693107,0.000005917264,0.000007956665,0.000047734033],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99872,0.00010865009,0.00055629597,0.00014610951,0.00030694474,0.00016199425],"domain_scores_gemma":[0.9978744,0.00033713342,0.00069085375,0.0003369703,0.00069576886,0.00006490594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017469529,0.00010362333,0.00018052712,0.00051605684,0.00019040317,0.0003233477,0.0005658525,0.000042810723,0.0000048591764],"category_scores_gemma":[0.0003389151,0.000095449315,0.00006253506,0.00066474563,0.00008107268,0.0025053853,0.00016001829,0.00017252304,0.0000015988139],"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":[0.00018046408,0.00044821462,0.0006172686,0.00012932779,0.00003784094,0.000010945257,0.0015598761,0.0000018098648,0.46551684,0.0014773153,0.0013810244,0.5286391],"study_design_scores_gemma":[0.0058883857,0.0017527277,0.10317115,0.00048591604,0.00013491506,0.00037002628,0.0015414166,0.14303471,0.7269335,0.014291914,0.0015754505,0.00081994134],"about_ca_topic_score_codex":0.000013494817,"about_ca_topic_score_gemma":0.0000026786138,"teacher_disagreement_score":0.59253746,"about_ca_system_score_codex":0.000080835685,"about_ca_system_score_gemma":0.00003783734,"threshold_uncertainty_score":0.38923103},"labels":[],"label_agreement":null},{"id":"W2168036182","doi":"10.1016/j.jvcir.2005.04.006","title":"Error resiliency schemes in H.264/AVC standard","year":2005,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Coding and Compression Technologies","field":"Computer Science","cited_by":170,"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; Coding (social sciences); Data compression; Propagation of uncertainty; Real-time computing; Computer engineering; Algorithm","score_opus":0.0309914588743053,"score_gpt":0.38325965245333676,"score_spread":0.35226819357903144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168036182","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6778447,0.004809981,0.29656017,0.018066417,0.00011771957,0.0002036035,9.441325e-7,0.0001397202,0.0022567252],"genre_scores_gemma":[0.9003716,0.0016317976,0.09782824,0.000078414276,0.00002121833,0.0000032746475,8.651557e-7,0.0000037266416,0.00006085509],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99884075,0.00021125408,0.0004565995,0.00011957855,0.00026745023,0.00010439401],"domain_scores_gemma":[0.9988186,0.00015995814,0.0003553686,0.00041544053,0.00020820108,0.00004245163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006440244,0.00007558451,0.00016033347,0.00028059343,0.000104635474,0.00016627673,0.00065930944,0.000051597108,0.000009449391],"category_scores_gemma":[0.0003418751,0.000064619955,0.000038852802,0.00031683012,0.000076636585,0.001448179,0.000270751,0.00028715792,0.000003936021],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013102793,0.00034766615,0.005505042,0.000023118404,0.000022851855,0.000010238966,0.002603253,0.00020976888,0.033440042,0.013981664,0.004387056,0.93933827],"study_design_scores_gemma":[0.010598688,0.0026787298,0.10548236,0.0014573472,0.000060185157,0.0006579695,0.012414198,0.280645,0.44391993,0.052509904,0.088208646,0.0013670307],"about_ca_topic_score_codex":0.000009213184,"about_ca_topic_score_gemma":0.0000068031213,"teacher_disagreement_score":0.93797123,"about_ca_system_score_codex":0.000045387776,"about_ca_system_score_gemma":0.000044971628,"threshold_uncertainty_score":0.26351252},"labels":[],"label_agreement":null},{"id":"W2179228901","doi":"10.1016/j.jvcir.2015.09.007","title":"A novel approach for pain intensity detection based on facial feature deformations","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Department of Science and Technology, Ministry of Science and Technology, India; McMaster University; University of Northern British Columbia","keywords":"Discriminative model; Facial expression; Artificial intelligence; Intensity (physics); Metric (unit); Feature vector; Pattern recognition (psychology); Feature (linguistics); Computer science; Support vector machine; Classifier (UML); Mathematics; Physics; Engineering","score_opus":0.061812186935857674,"score_gpt":0.3472417915356212,"score_spread":0.2854296045997635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179228901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008161209,0.000035230096,0.9894487,0.0016534419,0.000087130924,0.00019998472,0.0000024444605,0.000023226716,0.00038863206],"genre_scores_gemma":[0.75968623,0.000022259177,0.23976147,0.0004085453,0.000047341193,0.000014995885,0.00003570345,0.000004673514,0.000018783932],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907047,0.00022863365,0.00026692788,0.00010342962,0.00024912236,0.0000814278],"domain_scores_gemma":[0.99830866,0.00019563976,0.00035999226,0.00023089717,0.0008134813,0.00009135961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013198446,0.000078388985,0.00012917603,0.00019377963,0.0001762836,0.00018348334,0.00023113385,0.000061339015,8.0583465e-7],"category_scores_gemma":[0.00069719413,0.00006566447,0.0000694816,0.0002054263,0.00003526446,0.0011103717,0.0000544903,0.00018443367,0.0000013487178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019051774,0.0023915137,0.00069265685,0.00014063969,0.000096148,0.0000019947445,0.0075029926,0.008125808,0.17712761,0.0007505439,0.022691984,0.7785729],"study_design_scores_gemma":[0.0015772999,0.00052089937,0.0010040645,0.000042890944,0.000014114689,0.000036384292,0.0012890317,0.97946274,0.014481458,0.0005567106,0.0009178331,0.00009656697],"about_ca_topic_score_codex":0.000010831273,"about_ca_topic_score_gemma":0.000002044463,"teacher_disagreement_score":0.97133696,"about_ca_system_score_codex":0.00004952482,"about_ca_system_score_gemma":0.000051547984,"threshold_uncertainty_score":0.26777193},"labels":[],"label_agreement":null},{"id":"W2538968370","doi":"10.1016/j.jvcir.2016.10.008","title":"Gaussian-Hermite moment-based depth estimation from single still image for stereo vision","year":2016,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Depth map; Pixel; Focus (optics); Rendering (computer graphics); Computer science; Gaussian; Binocular disparity; Mathematics; Stereopsis; Computer stereo vision; Laplace operator; Image (mathematics); Optics","score_opus":0.03319345638644171,"score_gpt":0.3859866673868233,"score_spread":0.3527932110003816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2538968370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020145513,0.00025888672,0.97377986,0.0052616065,0.000109419,0.00020205141,0.000005199934,0.000037537287,0.00019990772],"genre_scores_gemma":[0.5100803,0.00015685857,0.489451,0.00020182252,0.00003317878,0.000005834778,0.0000122680585,0.000010567558,0.000048180016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849373,0.00024692953,0.0005847743,0.00021297183,0.00031435152,0.00014721388],"domain_scores_gemma":[0.9974889,0.0006670152,0.00075662736,0.00048981636,0.00048611267,0.000111512345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004721568,0.00013245556,0.00020626058,0.0002044631,0.00014982664,0.00033193085,0.00045103606,0.000054183103,0.000017291204],"category_scores_gemma":[0.00033557342,0.000096467076,0.000095289346,0.0001911578,0.00009552478,0.0034820973,0.00014079749,0.00013575211,0.000007350664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000937043,0.00019615376,0.0002137769,0.000011224001,0.000014467053,0.0000015971651,0.0002870121,0.000028135217,0.34442496,0.0002559562,0.0005353032,0.6539377],"study_design_scores_gemma":[0.0057014236,0.0009319976,0.015486108,0.00063142367,0.000047810146,0.000028936383,0.00033873378,0.756706,0.20821224,0.007980787,0.00358239,0.00035216726],"about_ca_topic_score_codex":0.000009478402,"about_ca_topic_score_gemma":0.000002666775,"teacher_disagreement_score":0.75667787,"about_ca_system_score_codex":0.00008301611,"about_ca_system_score_gemma":0.000048660477,"threshold_uncertainty_score":0.39338133},"labels":[],"label_agreement":null},{"id":"W2567927519","doi":"10.1016/j.jvcir.2017.01.001","title":"Spectral shape classification: A deep learning approach","year":2017,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":33,"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":"Artificial intelligence; Pattern recognition (psychology); Deep learning; Computer science; Mathematics","score_opus":0.04888501279126743,"score_gpt":0.34937353450695996,"score_spread":0.3004885217156925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567927519","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7759793,0.0018552402,0.20375405,0.0006326522,0.000078600344,0.00008128567,5.304944e-7,0.00006595092,0.017552402],"genre_scores_gemma":[0.986241,0.0028611976,0.010707021,0.000008459073,0.00008765814,0.0000021326807,0.000010049457,0.000012192255,0.00007032799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992817,0.000090439295,0.00031706566,0.00007126837,0.00015925655,0.00008024539],"domain_scores_gemma":[0.99912745,0.00004583519,0.0002988741,0.00030054708,0.0001702561,0.00005701626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038779218,0.000075687065,0.00015402513,0.00010402446,0.00035003197,0.0003124011,0.00026050917,0.000040142866,0.000022473201],"category_scores_gemma":[0.00015602965,0.000071408154,0.000074390984,0.00005885784,0.00007202233,0.00065956474,0.00004168295,0.00028643582,0.0000043228856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015002476,0.0005317668,0.031903114,0.00025492333,0.0007567043,0.000013513207,0.008133369,0.08583483,0.14065427,0.0012338345,0.0011896774,0.729344],"study_design_scores_gemma":[0.00030703295,0.000031579384,0.02706661,0.000024540312,0.000046499637,0.000029244915,0.0010600529,0.9704196,0.0006419087,0.00016186023,0.00013251815,0.000078585115],"about_ca_topic_score_codex":0.000007990086,"about_ca_topic_score_gemma":0.0000016831012,"teacher_disagreement_score":0.8845847,"about_ca_system_score_codex":0.000025802314,"about_ca_system_score_gemma":0.000007848323,"threshold_uncertainty_score":0.30124912},"labels":[],"label_agreement":null},{"id":"W2751875267","doi":"10.1016/j.jvcir.2017.09.005","title":"A chordiogram image descriptor using local edgels","year":2017,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Thresholding; Pixel; Image (mathematics); Computer vision; Matching (statistics); Pattern recognition (psychology); Mathematics; Enhanced Data Rates for GSM Evolution; Computer science; Set (abstract data type); Statistics","score_opus":0.058667143057724545,"score_gpt":0.4278940106807082,"score_spread":0.3692268676229837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2751875267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021411274,0.0006615167,0.9763006,0.00069683086,0.0000920874,0.000118545875,5.9179234e-7,0.00003514986,0.00068343204],"genre_scores_gemma":[0.6300413,0.0013112827,0.3684873,0.00006336459,0.000056253863,0.000001668327,0.0000011358626,0.000007665304,0.000030048875],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881124,0.00019890716,0.00043697213,0.00014807086,0.00027015046,0.00013467346],"domain_scores_gemma":[0.99742347,0.000101266654,0.00090089766,0.00086349074,0.00061057636,0.000100298144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067378866,0.000109907254,0.00021322466,0.0001585276,0.0005361534,0.00091275084,0.0010283829,0.000048630074,0.000005795203],"category_scores_gemma":[0.0004970378,0.00009875727,0.00009970023,0.00013207711,0.00029513973,0.005332187,0.0004822459,0.00025353328,0.0000026225794],"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":[0.000085376305,0.00026311472,0.0015493003,0.000031237527,0.0000578836,0.00003247222,0.0007792,0.0000071761956,0.16749468,0.0018925068,0.0010332455,0.8267738],"study_design_scores_gemma":[0.0036086263,0.001393795,0.06263387,0.0006308209,0.00014428464,0.0011640372,0.0016685084,0.15579407,0.7307837,0.034848705,0.006454942,0.00087466266],"about_ca_topic_score_codex":0.00005145371,"about_ca_topic_score_gemma":0.0000014654802,"teacher_disagreement_score":0.8258991,"about_ca_system_score_codex":0.000052231495,"about_ca_system_score_gemma":0.00005254819,"threshold_uncertainty_score":0.8801677},"labels":[],"label_agreement":null},{"id":"W2782981605","doi":"10.1016/j.jvcir.2018.01.002","title":"Early event detection based on dynamic images of surveillance videos","year":2018,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; McGill University; Nvidia","keywords":"Computer science; Event (particle physics); Artificial intelligence; Set (abstract data type); Spotting; CLIPS; Computer vision; Detector; Pattern recognition (psychology)","score_opus":0.012166063302968181,"score_gpt":0.34896424195507647,"score_spread":0.3367981786521083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782981605","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14113863,0.00006154283,0.8576719,0.00060703803,0.000043655888,0.000117243,0.0000012174828,0.000029985014,0.000328788],"genre_scores_gemma":[0.9647757,0.00019418787,0.034901977,0.000059811315,0.000026394511,0.0000072754324,0.0000018680801,0.000005912019,0.000026917807],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989013,0.00023040245,0.00043839033,0.00012324376,0.00023284365,0.000073787254],"domain_scores_gemma":[0.99806696,0.00017716545,0.0006580916,0.00044192607,0.0006091037,0.00004675823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056481647,0.00007653681,0.00013970284,0.00021363284,0.00014989411,0.000082754,0.0003482794,0.000039247883,0.0000071218547],"category_scores_gemma":[0.00010970344,0.00007021964,0.000071662,0.00035847878,0.00013077719,0.0005318372,0.000073720534,0.00013721835,0.000004487772],"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":[0.00021309959,0.0005726546,0.002696021,0.000034851593,0.0000416309,0.0000014287659,0.0006396482,0.000104385916,0.56108063,0.0012508295,0.00033869263,0.4330261],"study_design_scores_gemma":[0.0010476216,0.002566144,0.2262954,0.000115458155,0.000019587307,0.000049914644,0.00017730896,0.3057859,0.460077,0.0029154231,0.0007243404,0.00022593923],"about_ca_topic_score_codex":0.00003107883,"about_ca_topic_score_gemma":0.0000063215984,"teacher_disagreement_score":0.823637,"about_ca_system_score_codex":0.00003785718,"about_ca_system_score_gemma":0.00002933952,"threshold_uncertainty_score":0.2863474},"labels":[],"label_agreement":null},{"id":"W2808276994","doi":"10.1016/j.jvcir.2018.06.001","title":"Multiple disjoint dictionaries for representation of histopathology images","year":2018,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image Retrieval and Classification Techniques","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":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Pattern recognition (psychology); Artificial intelligence; Histogram; Bag-of-words model; Computer science; Histogram of oriented gradients; Bag-of-words model in computer vision; Support vector machine; Representation (politics); Intersection (aeronautics); Image (mathematics); Image retrieval; Mathematics; Visual Word","score_opus":0.04057181106023062,"score_gpt":0.3755460995192327,"score_spread":0.33497428845900207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808276994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018655397,0.00033685396,0.97801554,0.0021796126,0.00012923228,0.00022220073,0.000004113118,0.000036995516,0.00042007826],"genre_scores_gemma":[0.83838475,0.0006962634,0.16065419,0.000055781395,0.00007358746,0.0000132309015,0.000011889526,0.0000068089994,0.00010349049],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99862,0.00025634968,0.0006483286,0.00015455832,0.00022559785,0.000095187745],"domain_scores_gemma":[0.99664515,0.0004815415,0.0009240206,0.00040626424,0.001494406,0.0000486159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064533943,0.000088149376,0.00022271108,0.00021732938,0.00018695039,0.000094411706,0.00039459477,0.00005029914,0.000007549704],"category_scores_gemma":[0.0009603782,0.00007874516,0.00009953458,0.00031235866,0.00037595662,0.0012666264,0.0001388255,0.000103798004,0.0000014300714],"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":[0.000352187,0.00046222573,0.0061182147,0.00006911545,0.000048351183,0.0000017044172,0.0032388398,0.0000022506035,0.8211961,0.020081386,0.0030162926,0.14541337],"study_design_scores_gemma":[0.0012529845,0.0010796459,0.059440963,0.00006847578,0.000044543456,0.00011727014,0.0009899396,0.018767662,0.90148735,0.014030101,0.0025540853,0.00016696239],"about_ca_topic_score_codex":0.00002106963,"about_ca_topic_score_gemma":0.0000016793307,"teacher_disagreement_score":0.8197294,"about_ca_system_score_codex":0.00003634675,"about_ca_system_score_gemma":0.000051667535,"threshold_uncertainty_score":0.32111344},"labels":[],"label_agreement":null},{"id":"W2914387319","doi":"10.1016/j.jvcir.2019.02.004","title":"ChaboNet : Design of a deep CNN for prediction of visual saliency in natural video","year":2019,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Université de Bordeaux; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Residual; Convolutional neural network; Computer vision; Human visual system model; Coding (social sciences); Motion (physics); Affine transformation; Pattern recognition (psychology); Image (mathematics); Algorithm; Mathematics","score_opus":0.026689705027500604,"score_gpt":0.34608032232312924,"score_spread":0.3193906172956286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914387319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38751653,0.0005168477,0.6110143,0.00023017185,0.00022615766,0.00038429492,0.0000017786402,0.00001218676,0.0000977247],"genre_scores_gemma":[0.9762723,0.00036014445,0.023267627,0.000025276031,0.000020779871,0.000008441954,0.000008745226,0.0000065330332,0.00003017992],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818474,0.0003932584,0.00084491033,0.00014790667,0.0003220698,0.00010713389],"domain_scores_gemma":[0.9979056,0.00031493313,0.0009138954,0.000257318,0.0005668227,0.000041431802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011559715,0.00009346338,0.00026405873,0.00039822783,0.000056859717,0.000049239123,0.00032370698,0.00005826867,0.000008236061],"category_scores_gemma":[0.00024147281,0.00008639294,0.00009577832,0.00045206922,0.000062021354,0.0013306999,0.000091185546,0.00016208613,0.0000013828786],"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":[0.00095594546,0.0015672017,0.018954359,0.0003041047,0.00010380321,0.0000015150493,0.0072650434,0.0012216428,0.8109665,0.0044265427,0.0001792546,0.15405412],"study_design_scores_gemma":[0.003115771,0.0022683076,0.12397365,0.00018778277,0.000029133007,0.000046723606,0.001444325,0.80946237,0.057235748,0.002046154,0.00005007399,0.0001399437],"about_ca_topic_score_codex":0.000023595576,"about_ca_topic_score_gemma":0.00000417991,"teacher_disagreement_score":0.8082408,"about_ca_system_score_codex":0.000039946703,"about_ca_system_score_gemma":0.000045832396,"threshold_uncertainty_score":0.35230017},"labels":[],"label_agreement":null},{"id":"W2995552133","doi":"10.1016/j.jvcir.2019.102733","title":"A novel change-detection scheduler for a network of depth sensors","year":2019,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Real-time computing; Change detection; Wireless sensor network; Reliability (semiconductor); Frame (networking); Artificial intelligence; Noise (video); Outlier; Visual sensor network; Computer vision; Power (physics); Key distribution in wireless sensor networks","score_opus":0.07363112622527142,"score_gpt":0.39869360313157814,"score_spread":0.32506247690630674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995552133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2687055,0.00031860964,0.7300072,0.0004212464,0.00016928927,0.00021113656,5.149315e-7,0.000011272722,0.00015530168],"genre_scores_gemma":[0.78631496,0.00029491502,0.21324368,0.000050873292,0.00007149674,0.0000051185048,0.0000015743759,0.000005256035,0.000012148858],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990351,0.00022801232,0.00037401254,0.00010052172,0.00017062092,0.00009172901],"domain_scores_gemma":[0.99810696,0.00042760262,0.00061817764,0.0003055951,0.00050603,0.00003564582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013176455,0.00006519662,0.00018655912,0.000089253735,0.00006645685,0.00007137703,0.00024865213,0.000040432285,0.0000022500842],"category_scores_gemma":[0.00020560005,0.000058632446,0.0000817017,0.00028528314,0.000032263746,0.00090076664,0.00007917405,0.00011761527,0.0000012225295],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003062852,0.00036790853,0.021896392,0.0001566936,0.00013820016,8.3949516e-7,0.0028292572,0.0006538026,0.34172457,0.0040160054,0.00010403129,0.627806],"study_design_scores_gemma":[0.0054981853,0.0018761855,0.5614077,0.00044046133,0.00007483518,0.0002653155,0.0010861225,0.30937305,0.11162807,0.005474918,0.0024492212,0.00042596748],"about_ca_topic_score_codex":0.00002186616,"about_ca_topic_score_gemma":0.0000070306755,"teacher_disagreement_score":0.6273801,"about_ca_system_score_codex":0.000016992499,"about_ca_system_score_gemma":0.00002396387,"threshold_uncertainty_score":0.23909616},"labels":[],"label_agreement":null},{"id":"W3045427645","doi":"10.1016/j.jvcir.2020.102861","title":"No-reference image sharpness assessment based on discrepancy measures of structural degradation","year":2020,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Alberta; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Image (mathematics); Computer science; Computer vision; Pattern recognition (psychology); Image quality; Feature (linguistics); Orientation (vector space); Filter (signal processing); Wavelet; Gabor filter; Image restoration; Image resolution; Image processing; Mathematics","score_opus":0.10237947828302799,"score_gpt":0.4256362074573584,"score_spread":0.32325672917433046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045427645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04116846,0.0001534435,0.94900185,0.007159811,0.000087350476,0.00022895023,0.000005214663,0.000029086174,0.002165846],"genre_scores_gemma":[0.86171997,0.000120556804,0.13771482,0.000356998,0.000038135946,0.0000037166749,0.000024146304,0.0000066372836,0.000015034726],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977283,0.00066236453,0.0006835249,0.00018384331,0.0006327344,0.0001092235],"domain_scores_gemma":[0.9973831,0.00029894002,0.00087560806,0.00042404007,0.00091129955,0.00010698915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006481175,0.00012828088,0.0002618492,0.0001241235,0.00013346165,0.0002588605,0.00061304873,0.00003914564,0.000025207117],"category_scores_gemma":[0.00036685925,0.00010812517,0.00008578504,0.00028339966,0.00008734842,0.0018123656,0.00015733799,0.00027455945,0.0000036426493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010334668,0.0015385395,0.015025049,0.0006484368,0.0002879619,0.000030081379,0.006181456,0.0006003839,0.7095268,0.04173418,0.0023198859,0.2210738],"study_design_scores_gemma":[0.002883348,0.0018816675,0.12628934,0.00028002018,0.00007772071,0.00002208486,0.0011985481,0.8002164,0.06440916,0.0017328052,0.00061756396,0.00039138817],"about_ca_topic_score_codex":0.00003560751,"about_ca_topic_score_gemma":0.0000023340756,"teacher_disagreement_score":0.8205515,"about_ca_system_score_codex":0.00004501058,"about_ca_system_score_gemma":0.00013748308,"threshold_uncertainty_score":0.44092163},"labels":[],"label_agreement":null},{"id":"W3083358456","doi":"10.1016/j.jvcir.2020.102895","title":"Gray-level image denoising with an improved weighted sparse coding","year":2020,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":22,"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":"Noise reduction; Neural coding; Pattern recognition (psychology); Artificial intelligence; Mathematics; Prior probability; Computer science; Coding (social sciences); Sparse approximation; Non-local means; Image (mathematics); Image denoising; Video denoising; Algorithm; Statistics","score_opus":0.07407870049251018,"score_gpt":0.3722560702875082,"score_spread":0.298177369794998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083358456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10508893,0.00021399405,0.89172524,0.0024807462,0.000039379865,0.00011229464,8.4910937e-7,0.000037829093,0.00030073052],"genre_scores_gemma":[0.56088626,0.00018976262,0.43845153,0.00037994882,0.000062320636,0.000001214437,0.0000047977674,0.000010489109,0.000013660517],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981122,0.00071783393,0.00049225247,0.0002059819,0.00032054348,0.00015120785],"domain_scores_gemma":[0.9979594,0.00023514844,0.00058953045,0.00039198357,0.0006263599,0.00019758183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008885431,0.00013807598,0.00025136728,0.0001412161,0.00025102138,0.0006648876,0.00062845665,0.000043237585,0.000007861099],"category_scores_gemma":[0.00023768844,0.0001129601,0.000059503047,0.00043372763,0.00011060779,0.0035122193,0.00018368097,0.00032238578,0.000003026691],"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":[0.0004485428,0.00025055767,0.0002604681,0.000035280726,0.000058325015,0.00004401794,0.0051019075,0.000015901449,0.9113611,0.00082427694,0.0002478052,0.081351794],"study_design_scores_gemma":[0.0074026254,0.003639053,0.00834163,0.0002338679,0.00012477557,0.00067118934,0.0026006852,0.6818749,0.29095122,0.0031620143,0.000386042,0.000612017],"about_ca_topic_score_codex":0.000027156631,"about_ca_topic_score_gemma":0.0000028414076,"teacher_disagreement_score":0.681859,"about_ca_system_score_codex":0.000028250977,"about_ca_system_score_gemma":0.00007680935,"threshold_uncertainty_score":0.6411526},"labels":[],"label_agreement":null},{"id":"W3173180830","doi":"10.1016/j.jvcir.2021.103187","title":"EdgeGAN: One-way mapping generative adversarial network based on the edge information for unpaired training set","year":2021,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Generative Adversarial Networks and Image Synthesis","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":"York University; University of Alberta","funders":"","keywords":"Consistency (knowledge bases); Computer science; Kernel (algebra); Adversarial system; Enhanced Data Rates for GSM Evolution; Convolution (computer science); Image translation; Artificial intelligence; Set (abstract data type); Image (mathematics); Generative adversarial network; Translation (biology); Pattern recognition (psychology); Algorithm; Data mining; Mathematics; Artificial neural network","score_opus":0.078268872872566,"score_gpt":0.3202820429658205,"score_spread":0.24201317009325451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173180830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027144663,0.00017665417,0.9852145,0.010293784,0.00031502004,0.00025350452,0.000003721172,0.000015659336,0.0010127206],"genre_scores_gemma":[0.78947324,0.00019744864,0.2082476,0.0015549123,0.00042078222,0.000019298875,0.000048197493,0.000008037579,0.000030498037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803793,0.00082305266,0.00054849265,0.00013505698,0.000292445,0.00016302541],"domain_scores_gemma":[0.99682426,0.0012042904,0.00063429534,0.00039464148,0.0008763269,0.000066161825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014079274,0.00012369647,0.00022176518,0.000100375124,0.00053007825,0.00052833103,0.0003816892,0.000054140277,0.000013531472],"category_scores_gemma":[0.0008754711,0.00009875474,0.00013006864,0.00040626657,0.00007378678,0.0017785873,0.00011969508,0.00021477697,0.0000021428941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094346964,0.00076046656,0.0004375819,0.00012646486,0.0007926339,0.000013569707,0.044908185,0.16135253,0.056939587,0.048302364,0.07406636,0.6113568],"study_design_scores_gemma":[0.0015335218,0.00025262695,0.0017402926,0.00014746631,0.00004137552,0.000015393243,0.0032152715,0.9713369,0.012222561,0.0032340148,0.00607941,0.00018114438],"about_ca_topic_score_codex":0.0000051808875,"about_ca_topic_score_gemma":0.0000039008764,"teacher_disagreement_score":0.8099844,"about_ca_system_score_codex":0.00004883022,"about_ca_system_score_gemma":0.0001744827,"threshold_uncertainty_score":0.5094708},"labels":[],"label_agreement":null},{"id":"W3174119607","doi":"10.1016/j.jvcir.2021.103185","title":"A generic, cluster-centred lossless compression framework for joint auroral data","year":2021,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Data Compression Techniques","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":"Lossless compression; Computer science; Lossy compression; Cluster analysis; Data compression; Joint (building); Real-time computing; Satellite; Compression (physics); Transmission (telecommunications); Reduction (mathematics); Data mining; Algorithm; Artificial intelligence; Telecommunications; Engineering; Aerospace engineering; Physics","score_opus":0.14796467633575522,"score_gpt":0.4356767691115531,"score_spread":0.2877120927757979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174119607","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035626208,0.0019971768,0.99081844,0.0031585789,0.00014923334,0.00018921134,0.000016253452,0.00004878359,0.00005972074],"genre_scores_gemma":[0.12243923,0.001955131,0.87502736,0.00030971778,0.00007483946,0.000007368163,0.00014885118,0.000011534511,0.000025987565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981604,0.00046307102,0.0006283168,0.00028325783,0.00032482986,0.00014010961],"domain_scores_gemma":[0.99621385,0.0005462606,0.00078407937,0.0016306747,0.0007191245,0.00010598252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047032195,0.00011964308,0.00026427684,0.00011502386,0.00021131597,0.0003430875,0.0012929791,0.00006780613,0.000011017953],"category_scores_gemma":[0.0008844168,0.000107574306,0.000065629705,0.00026527254,0.00007462221,0.0024925005,0.0019052475,0.00027250036,0.0000013496395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035130378,0.0015304497,0.00050580146,0.0001936891,0.00015511725,0.00004403074,0.0022963148,0.00018616725,0.2978598,0.028322984,0.042409617,0.62614477],"study_design_scores_gemma":[0.0031719878,0.0003978728,0.0032587503,0.00096508226,0.000083382496,0.00048152663,0.0016244332,0.63481873,0.25730944,0.07619878,0.021148158,0.0005418649],"about_ca_topic_score_codex":0.0000055779656,"about_ca_topic_score_gemma":0.0000014125526,"teacher_disagreement_score":0.6346325,"about_ca_system_score_codex":0.000033427867,"about_ca_system_score_gemma":0.00010265466,"threshold_uncertainty_score":0.43867528},"labels":[],"label_agreement":null},{"id":"W3178259030","doi":"10.1016/j.jvcir.2021.103200","title":"A CNN model for real time hand pose estimation","year":2021,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":14,"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; Convolutional neural network; Pose; Artificial intelligence; Joint (building); Pattern recognition (psychology); Machine learning; Computer vision","score_opus":0.03991462739930919,"score_gpt":0.37676524098813585,"score_spread":0.3368506135888267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178259030","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032339334,0.00029507763,0.96437186,0.0018734623,0.000074500065,0.00014852876,0.000002413541,0.000018816481,0.00087599707],"genre_scores_gemma":[0.6718911,0.0006265063,0.32671258,0.00010303982,0.000070995666,0.0000112965345,0.00003642487,0.000008705435,0.00053938583],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884963,0.00025934842,0.0004552878,0.00012473763,0.0002260681,0.00008491847],"domain_scores_gemma":[0.9978421,0.00031217973,0.00044635416,0.00030024763,0.0010263372,0.0000727876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006644857,0.000074872056,0.00017614092,0.00011213652,0.00017507123,0.00039184705,0.00022576278,0.000047074896,0.0000064668943],"category_scores_gemma":[0.00033156102,0.000069984424,0.00007581992,0.00019746648,0.000042053252,0.001273211,0.000094574694,0.00007831579,0.000007319748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020912269,0.00082918105,0.0002804142,0.00017656201,0.00020217842,0.000019732714,0.012757349,0.0029875431,0.6173563,0.007699811,0.009160925,0.3483209],"study_design_scores_gemma":[0.0009062799,0.000094262636,0.0006461994,0.00007262658,0.000023194807,0.00017025444,0.0001746369,0.9759193,0.016950015,0.004748525,0.00020915853,0.0000855498],"about_ca_topic_score_codex":0.0000050901053,"about_ca_topic_score_gemma":0.00000233105,"teacher_disagreement_score":0.97293174,"about_ca_system_score_codex":0.00003048612,"about_ca_system_score_gemma":0.0001217596,"threshold_uncertainty_score":0.37785903},"labels":[],"label_agreement":null},{"id":"W3189878313","doi":"10.1016/j.jvcir.2021.103250","title":"Sequence-tracker: Multiple object tracking with sequence features in severe occlusion scene","year":2021,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Surveillance and Tracking Methods","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":"York University; University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Video tracking; Tracking (education); Object (grammar); Frame (networking); Sequence (biology); Feature (linguistics); Identification (biology); Association (psychology); Trajectory; Pattern recognition (psychology)","score_opus":0.05556893168682955,"score_gpt":0.39456592500009413,"score_spread":0.3389969933132646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189878313","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.84049314,0.0022114227,0.15218924,0.004012516,0.0001649671,0.00018704384,0.000002414325,0.00004628489,0.0006929862],"genre_scores_gemma":[0.82169175,0.0014123757,0.17667729,0.00013790307,0.000038811788,0.0000016822788,0.000008861834,0.000007254113,0.000024090918],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99794114,0.0008829714,0.0004471713,0.0002126258,0.00036449285,0.00015159858],"domain_scores_gemma":[0.9978263,0.00058299996,0.00043978973,0.0004542552,0.00062848715,0.00006819703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012112712,0.0001185911,0.00024157381,0.00016978041,0.00016925986,0.00032988365,0.00041054224,0.000058215748,0.000005101002],"category_scores_gemma":[0.000590608,0.00010044894,0.00005294816,0.0006341746,0.00008241881,0.0019394646,0.00017531948,0.00039326493,0.00000119137],"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":[0.00025950614,0.0005368422,0.16099009,0.000101484315,0.000081570506,0.0006464219,0.0074606836,0.0011661013,0.37250176,0.000827045,0.00026477964,0.45516372],"study_design_scores_gemma":[0.0039752955,0.0005139538,0.7980031,0.0009906658,0.000034314326,0.0060508633,0.0028085704,0.03888169,0.14416127,0.0036713313,0.00036269368,0.0005462643],"about_ca_topic_score_codex":0.00008046886,"about_ca_topic_score_gemma":0.00013104278,"teacher_disagreement_score":0.637013,"about_ca_system_score_codex":0.000067016605,"about_ca_system_score_gemma":0.00017444784,"threshold_uncertainty_score":0.40961888},"labels":[],"label_agreement":null},{"id":"W4221166146","doi":"10.1016/j.jvcir.2023.103800","title":"TransCAM: Transformer attention-based CAM refinement for Weakly supervised semantic segmentation","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":85,"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; University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Transformer; Segmentation; Convolutional neural network; Discriminative model; Pattern recognition (psychology); Pixel; Pascal (unit); Voltage","score_opus":0.04025519472113439,"score_gpt":0.3715744347563467,"score_spread":0.33131924003521235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221166146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086713605,0.00011905586,0.9028779,0.009402143,0.0001066911,0.0005736342,0.000005455351,0.00007660734,0.00012488602],"genre_scores_gemma":[0.8770842,0.0010707445,0.12113113,0.00025566536,0.00009046634,0.00011661999,0.00012543301,0.00001879743,0.000106946085],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985999,0.00016662068,0.0005921722,0.00018156944,0.0003007215,0.00015904556],"domain_scores_gemma":[0.9983785,0.00042970513,0.0003629237,0.00035104068,0.00039778562,0.00008003117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057413726,0.000115276176,0.00017763709,0.00025857412,0.00028229202,0.00014074749,0.00039752203,0.000039226194,0.0000057444267],"category_scores_gemma":[0.00005767221,0.00010946221,0.0001257403,0.00067429786,0.000058352907,0.0010330934,0.000027766902,0.00012774905,0.0000056968665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020208678,0.0004648291,0.0011616467,0.00017102582,0.00010297027,0.0000026442824,0.0017767311,0.0026985814,0.77821046,0.0045703496,0.0054817777,0.20515689],"study_design_scores_gemma":[0.0055520963,0.0008460499,0.029041274,0.00019588077,0.00015269408,0.000045357458,0.001719754,0.8727619,0.080647536,0.005289739,0.0032803582,0.00046736136],"about_ca_topic_score_codex":0.0000075537737,"about_ca_topic_score_gemma":0.000007372734,"teacher_disagreement_score":0.8700633,"about_ca_system_score_codex":0.00004498115,"about_ca_system_score_gemma":0.000049531212,"threshold_uncertainty_score":0.44637397},"labels":[],"label_agreement":null},{"id":"W4225498531","doi":"10.1016/j.jvcir.2022.103526","title":"A brief survey on adaptive video streaming quality assessment","year":2022,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Video Quality Assessment","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Quality of experience; Video quality; Multimedia; Fidelity; Convolutional neural network; Quality (philosophy); Quality of service; Subjective video quality; Dynamic Adaptive Streaming over HTTP; Artificial intelligence; Machine learning; Deep learning; Real-time computing; Metric (unit); Computer network; Telecommunications; Image quality","score_opus":0.12036998730109476,"score_gpt":0.45699117920362164,"score_spread":0.3366211919025269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225498531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14936838,0.00032605033,0.84363866,0.00375483,0.00022686539,0.0002948599,0.000013683037,0.00003893075,0.0023377356],"genre_scores_gemma":[0.97280073,0.00017034922,0.026430994,0.00048680368,0.000025979252,0.000010881118,0.000025357775,0.0000069418693,0.000041972246],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9945561,0.0037059514,0.0006791567,0.0001935061,0.0007352952,0.00012997744],"domain_scores_gemma":[0.99695665,0.0011770792,0.0009012243,0.0005556112,0.00033010356,0.00007934253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003654617,0.00010833283,0.0002384892,0.0001865412,0.00047968133,0.00026724234,0.00065521407,0.000021642167,0.000026611324],"category_scores_gemma":[0.000292603,0.00010641532,0.000084700376,0.00037225152,0.00005927676,0.0011601466,0.0005760033,0.00046540293,0.000001218129],"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":[0.0018001416,0.009349804,0.057603996,0.000115074654,0.00082029076,0.00010576397,0.023561627,0.0017736417,0.10355783,0.16597238,0.011961719,0.62337774],"study_design_scores_gemma":[0.0019458062,0.0018368671,0.94723207,0.000048075388,0.000024164152,0.000068309215,0.004500765,0.036736827,0.003234746,0.0033662438,0.0007141677,0.00029195534],"about_ca_topic_score_codex":0.000489964,"about_ca_topic_score_gemma":0.000018841933,"teacher_disagreement_score":0.88962805,"about_ca_system_score_codex":0.00014416366,"about_ca_system_score_gemma":0.00015843243,"threshold_uncertainty_score":0.43394908},"labels":[],"label_agreement":null},{"id":"W4383904392","doi":"10.1016/j.jvcir.2023.103890","title":"EMHIFormer: An Enhanced Multi-Hypothesis Interaction Transformer for 3D human pose estimation in video","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Human Pose and Action Recognition","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 Ottawa","funders":"","keywords":"Computer science; Transformer; Ambiguity; Monocular; Pose; Artificial intelligence; Exploit; Machine learning; Pattern recognition (psychology)","score_opus":0.07978889878690541,"score_gpt":0.42567759312188447,"score_spread":0.34588869433497904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383904392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4497298,0.000023572633,0.5491802,0.0005063539,0.00009802775,0.00024478132,0.0000015647345,0.000043579603,0.0001721157],"genre_scores_gemma":[0.92162514,0.00037813274,0.077752665,0.00007264235,0.000037939455,0.000028428676,0.000047791335,0.000009646652,0.0000475961],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987637,0.0002180353,0.0005681722,0.00014834532,0.00018143537,0.000120311706],"domain_scores_gemma":[0.9987616,0.00025722096,0.0004139122,0.00021521944,0.00029215755,0.000059893955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007585572,0.0000943112,0.00016599977,0.000526541,0.00020997952,0.00022035815,0.00023454866,0.000052992338,0.000015376356],"category_scores_gemma":[0.00016567347,0.00009239501,0.000069347785,0.0003809239,0.000034505272,0.0038011575,0.000025475309,0.0001602813,0.000010328102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012631335,0.0005039923,0.00017841836,0.00005438241,0.000035113324,0.0000020694192,0.0062837414,0.0003911381,0.30465087,0.00043724655,0.00025743718,0.6870793],"study_design_scores_gemma":[0.0040622996,0.0009344953,0.047188826,0.00024757537,0.000047369274,0.000054871245,0.0034468605,0.79741585,0.13901769,0.0068393894,0.00041368714,0.00033111102],"about_ca_topic_score_codex":0.000027378053,"about_ca_topic_score_gemma":0.000047364443,"teacher_disagreement_score":0.79702467,"about_ca_system_score_codex":0.00005545321,"about_ca_system_score_gemma":0.000026431437,"threshold_uncertainty_score":0.3767759},"labels":[],"label_agreement":null},{"id":"W4384133781","doi":"10.1016/j.jvcir.2023.103896","title":"DB-TASNet for disease diagnosis and lesion segmentation in medical images","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"COVID-19 diagnosis using AI","field":"Medicine","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":"Université de Sherbrooke","funders":"China Scholarship Council; Natural Science Foundation of Hebei Province","keywords":"Segmentation; Computer science; Artificial intelligence; Deep learning; Image segmentation; Convolutional neural network; Medical imaging; Machine learning; Scale-space segmentation; Pattern recognition (psychology); Computer vision","score_opus":0.08127157243748095,"score_gpt":0.4802407696857158,"score_spread":0.3989691972482348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384133781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91687685,0.0014736367,0.0019871646,0.078968145,0.000065817745,0.00056595425,0.000008374848,0.000030103496,0.00002393128],"genre_scores_gemma":[0.96082467,0.034036797,0.0030872968,0.0017199563,0.00008250388,0.000072182826,0.00011979419,0.000018005336,0.000038801718],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99870586,0.00021135043,0.00047164102,0.00013558655,0.00036992846,0.00010560505],"domain_scores_gemma":[0.99791807,0.0012263202,0.00026304033,0.00017308805,0.0002354089,0.00018409228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085868634,0.00008636,0.00021993712,0.00037568566,0.000092866,0.00006605444,0.00007892062,0.000051532803,0.000024958932],"category_scores_gemma":[0.002007199,0.00007861543,0.000056628265,0.00030961866,0.000106099695,0.0004224879,0.00008532909,0.00016552178,0.0000022911245],"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":[0.0020356572,0.0015738265,0.3870536,0.00104317,0.0001295773,0.00014288454,0.0031887607,0.00006498515,0.03774455,0.00014228423,0.056300998,0.5105797],"study_design_scores_gemma":[0.00640434,0.00058698276,0.9505041,0.0011680713,0.0002076386,0.00006653392,0.0020059661,0.025139237,0.009270719,0.000938851,0.0035351133,0.00017242301],"about_ca_topic_score_codex":0.000047734753,"about_ca_topic_score_gemma":0.000009999836,"teacher_disagreement_score":0.5634505,"about_ca_system_score_codex":0.00006526959,"about_ca_system_score_gemma":0.000104405684,"threshold_uncertainty_score":0.3205844},"labels":[],"label_agreement":null},{"id":"W4385431685","doi":"10.1016/j.jvcir.2023.103908","title":"Iterative graph filtering network for 3D human pose estimation","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Human Pose and Action Recognition","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adjacency matrix; Pose; Computer science; Graph; Artificial intelligence; Adjacency list; Normalization (sociology); Algorithm; Pattern recognition (psychology); Laplacian matrix; Theoretical computer science","score_opus":0.05249424612086351,"score_gpt":0.40093842167842164,"score_spread":0.34844417555755813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385431685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11019045,0.00009910312,0.88786757,0.00094947044,0.0001786117,0.00021506476,0.000002059988,0.00006938398,0.00042831732],"genre_scores_gemma":[0.79467165,0.00042140082,0.20431715,0.00014940236,0.00019200715,0.000021811798,0.00009281447,0.000010607244,0.00012313828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999048,0.00018029842,0.0003936065,0.0001088576,0.00016250277,0.00010675769],"domain_scores_gemma":[0.998719,0.00025114403,0.00043059565,0.00019818332,0.000351967,0.00004912354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065502455,0.00007436579,0.00013044906,0.00022899789,0.0003729731,0.00030725857,0.00021741992,0.000032849006,0.00000993354],"category_scores_gemma":[0.0001076405,0.00007166568,0.0000678436,0.00037159584,0.00003675115,0.0016591373,0.00008739869,0.00011628633,0.000007153785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002023045,0.00050584023,0.0011155058,0.00020181485,0.00028530444,0.00001776634,0.012492382,0.0054867077,0.15372115,0.02503257,0.041507743,0.7594309],"study_design_scores_gemma":[0.0025074068,0.0009443283,0.027925327,0.00035911732,0.00006821597,0.00012012776,0.00096464047,0.8711803,0.015886817,0.0772147,0.0024515814,0.00037743105],"about_ca_topic_score_codex":0.0000051781617,"about_ca_topic_score_gemma":0.000002406727,"teacher_disagreement_score":0.86569357,"about_ca_system_score_codex":0.000019867572,"about_ca_system_score_gemma":0.000016569373,"threshold_uncertainty_score":0.29629016},"labels":[],"label_agreement":null},{"id":"W4389937182","doi":"10.1016/j.jvcir.2023.104028","title":"BNDCNet: Bilateral nonlocal decoupled convergence network for semantic segmentation","year":2023,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Convergence (economics); Segmentation; Computer science; Artificial intelligence; Computer vision; Economics","score_opus":0.03614113776747117,"score_gpt":0.3804621385552828,"score_spread":0.34432100078781164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389937182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093373775,0.00022676203,0.9032782,0.0024621012,0.0001811336,0.00036836928,0.000001565469,0.000068876965,0.00003924815],"genre_scores_gemma":[0.78558487,0.0022212283,0.21158122,0.00022863553,0.00016401065,0.00005156059,0.000051862276,0.000015548001,0.000101100086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872035,0.00015956665,0.0005356171,0.0001742845,0.00022853322,0.00018162817],"domain_scores_gemma":[0.99811083,0.00057269365,0.0005327539,0.000320447,0.00037946118,0.00008380292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006197116,0.00010306816,0.00017613832,0.00014255488,0.00027976854,0.00015268206,0.00043196313,0.00004039028,0.00000582648],"category_scores_gemma":[0.00011484693,0.00009708507,0.00007454542,0.0007416816,0.000071719776,0.001231122,0.00017038459,0.00013660682,0.000012617161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000616286,0.0006832453,0.0137571795,0.00026895784,0.00035432124,0.000027719923,0.005510377,0.037812013,0.36176774,0.03240205,0.052314375,0.49448574],"study_design_scores_gemma":[0.000998004,0.00022998209,0.012625384,0.000056282825,0.00002720225,0.00006179965,0.000251702,0.9682257,0.006421657,0.0099857,0.0009558195,0.00016072515],"about_ca_topic_score_codex":0.0000061818328,"about_ca_topic_score_gemma":0.000005718231,"teacher_disagreement_score":0.9304137,"about_ca_system_score_codex":0.000034006316,"about_ca_system_score_gemma":0.000036927602,"threshold_uncertainty_score":0.3959014},"labels":[],"label_agreement":null},{"id":"W4391344378","doi":"10.1016/j.jvcir.2024.104072","title":"Low-complexity ℓ∞-compression of light field images with a deep-decompression stage","year":2024,"lang":"lv","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Data Compression Techniques","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":"McMaster University","funders":"National Natural Science Foundation of China; H2020 Marie Skłodowska-Curie Actions; Innovative Research Group Project of the National Natural Science Foundation of China; Innovationsfonden","keywords":"Scalable Vector Graphics; Computer science; Computer graphics (images); World Wide Web","score_opus":0.03312612410471377,"score_gpt":0.39254932420687016,"score_spread":0.3594232001021564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391344378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03660827,0.011573673,0.9466709,0.0031002483,0.0003200491,0.0004788051,0.000026885395,0.00012506437,0.0010960896],"genre_scores_gemma":[0.8114499,0.008778767,0.17925729,0.000097354256,0.00010843033,0.0000104502105,0.000030740655,0.000037837875,0.00022926874],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9960428,0.00094439316,0.0014028351,0.00046514173,0.0008799214,0.00026490865],"domain_scores_gemma":[0.9949523,0.0011182528,0.001519891,0.0012685749,0.0009364854,0.00020446318],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009815126,0.00034317886,0.0006335996,0.0005929025,0.00027466888,0.00066197396,0.0013275373,0.00015806682,0.00012511123],"category_scores_gemma":[0.00031525397,0.00026195348,0.00018049104,0.0007353249,0.00034650735,0.0044372324,0.0011246143,0.00089627784,0.0000059997164],"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":[0.0010422115,0.0013236877,0.0005666036,0.001673569,0.00021703471,0.00013677886,0.00488027,0.00014206248,0.6186764,0.004857207,0.009820333,0.35666385],"study_design_scores_gemma":[0.0013332295,0.0017594129,0.0027648495,0.007343315,0.00013476856,0.00027516735,0.001214483,0.07136662,0.90440863,0.004396343,0.0045194975,0.00048367158],"about_ca_topic_score_codex":0.00006947686,"about_ca_topic_score_gemma":0.0000067210144,"teacher_disagreement_score":0.7748416,"about_ca_system_score_codex":0.000070966074,"about_ca_system_score_gemma":0.00016424371,"threshold_uncertainty_score":0.99998325},"labels":[],"label_agreement":null},{"id":"W4394064065","doi":"10.1016/j.jvcir.2024.104141","title":"Multi-scale features and attention guided for brain tumor segmentation","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":12,"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":"National Natural Science Foundation of China","keywords":"Segmentation; Scale (ratio); Computer science; Artificial intelligence; Brain tumor; Pattern recognition (psychology); Medicine; Cartography; Pathology; Geography","score_opus":0.06971575188595429,"score_gpt":0.4130054279192344,"score_spread":0.3432896760332801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394064065","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86744446,0.0012862983,0.11910859,0.010554682,0.0004210326,0.0007404607,0.000013610037,0.000098012526,0.00033283263],"genre_scores_gemma":[0.9812986,0.0007956568,0.016617602,0.0004553861,0.00008403449,0.000029421,0.000017030612,0.000018852348,0.00068339007],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987376,0.00034319484,0.00042961544,0.00019707924,0.00020094584,0.00009157975],"domain_scores_gemma":[0.9987404,0.00056540157,0.00030377036,0.00014929187,0.00017431902,0.00006683651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066779274,0.00009749223,0.00012906964,0.00022220981,0.00022499773,0.0003741543,0.000106370004,0.00003895235,0.000012717344],"category_scores_gemma":[0.0005708012,0.000087912376,0.00007202504,0.00022932728,0.00012414639,0.00096702215,0.000038499373,0.00017491919,0.0000033095898],"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":[0.00006093443,0.000066946275,0.00013503268,0.000059669885,0.000008193433,0.0000015147473,0.0006482073,0.0000032877278,0.9717157,0.0003781273,0.0021729986,0.024749413],"study_design_scores_gemma":[0.0038567074,0.0007068213,0.08178385,0.000351434,0.00014979344,0.0019304128,0.006495752,0.13343991,0.7631376,0.0023074152,0.0054270737,0.0004132169],"about_ca_topic_score_codex":0.000009693325,"about_ca_topic_score_gemma":0.0000065128993,"teacher_disagreement_score":0.20857807,"about_ca_system_score_codex":0.0000459851,"about_ca_system_score_gemma":0.000028837803,"threshold_uncertainty_score":0.36079785},"labels":[],"label_agreement":null},{"id":"W4395032745","doi":"10.1016/j.jvcir.2024.104160","title":"3D hand pose estimation and reconstruction based on multi-feature fusion","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Human Pose and Action Recognition","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 Ottawa","funders":"","keywords":"Artificial intelligence; Pose; Feature (linguistics); Computer science; Fusion; Computer vision; Estimation; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.027954240713916043,"score_gpt":0.3589585154341448,"score_spread":0.33100427472022875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395032745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18041821,0.000904573,0.8137371,0.0037179887,0.0004195886,0.00018951323,0.0000022821534,0.00006752515,0.00054321456],"genre_scores_gemma":[0.8738201,0.00087816204,0.12497657,0.00015320996,0.000068998634,0.0000036337208,0.00001627351,0.000007654084,0.00007535426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990358,0.0002330999,0.00029243523,0.00015743222,0.00020997316,0.000071308925],"domain_scores_gemma":[0.9990706,0.0002210866,0.0002247263,0.00019417513,0.00022487821,0.00006452443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046569906,0.00009409572,0.00011829792,0.00035239465,0.00023808685,0.0007048144,0.00012318179,0.000062670275,0.000016548922],"category_scores_gemma":[0.000121313984,0.000081451624,0.000046221667,0.00023337375,0.00007744624,0.001765243,0.000048795016,0.0002631427,0.0000072637363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005106436,0.00012231672,0.00012454284,0.000058192592,0.000020561667,0.000007842702,0.00082096615,0.00014974587,0.030021988,0.0004985649,0.00068921864,0.967435],"study_design_scores_gemma":[0.0006471257,0.00024835608,0.0047572935,0.0003499636,0.00002514298,0.00024094022,0.00015478607,0.9843349,0.0077814395,0.0008743297,0.00048809848,0.000097642434],"about_ca_topic_score_codex":0.0000069721705,"about_ca_topic_score_gemma":0.0000024201759,"teacher_disagreement_score":0.98418516,"about_ca_system_score_codex":0.000037129037,"about_ca_system_score_gemma":0.000042260133,"threshold_uncertainty_score":0.6796542},"labels":[],"label_agreement":null},{"id":"W4396621409","doi":"10.1016/j.jvcir.2024.104165","title":"Category-based depth incorporation for salient object ranking","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Salient; Ranking (information retrieval); Object (grammar); Artificial intelligence; Computer science; Mathematics; Computer vision","score_opus":0.03791612347559929,"score_gpt":0.3774928189511021,"score_spread":0.3395766954755028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396621409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04053148,0.0005823684,0.95577633,0.0021466033,0.00036271373,0.00021423236,8.474934e-7,0.000069477144,0.00031593657],"genre_scores_gemma":[0.9616038,0.00019216037,0.037879646,0.00016620469,0.00007234322,0.000014966195,0.000016172517,0.00000962639,0.000045068846],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987,0.0002605539,0.0004957486,0.00016456979,0.0002812399,0.00009784879],"domain_scores_gemma":[0.99870366,0.00026457076,0.00032247617,0.00023622182,0.00041374206,0.000059350325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010741085,0.00009429797,0.00014063051,0.00034088298,0.00019484546,0.00054514187,0.00026761324,0.00004707623,0.0000053623444],"category_scores_gemma":[0.00014925086,0.00008320639,0.00011814269,0.00041201344,0.00004780782,0.0015137757,0.000057161564,0.00015944344,0.0000047013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033957142,0.0007137369,0.0017988445,0.00046656802,0.00018543752,0.00001856162,0.0052486905,0.0012097199,0.26359382,0.058658976,0.0054728515,0.6622932],"study_design_scores_gemma":[0.0014357215,0.0008821358,0.007633267,0.00022949865,0.00006480019,0.000089432404,0.00060594484,0.92742985,0.044183504,0.014270431,0.0029327055,0.00024268152],"about_ca_topic_score_codex":0.000016376527,"about_ca_topic_score_gemma":0.0000089398245,"teacher_disagreement_score":0.9262202,"about_ca_system_score_codex":0.000063990716,"about_ca_system_score_gemma":0.00010122441,"threshold_uncertainty_score":0.52568156},"labels":[],"label_agreement":null},{"id":"W4396696562","doi":"10.1016/j.jvcir.2024.104163","title":"Convolution-transformer blend pyramid network for underwater image enhancement","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Encoder; Underwater; Transformer; Convolutional neural network; Artificial intelligence; Computer vision; Pattern recognition (psychology); Engineering","score_opus":0.02330316752039279,"score_gpt":0.36545225592502567,"score_spread":0.3421490884046329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396696562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031066926,0.0021208157,0.98953867,0.003602257,0.00023999719,0.00034594626,0.0000016101739,0.000078824094,0.0009652049],"genre_scores_gemma":[0.5900249,0.0034915595,0.40566537,0.00021159543,0.00015519313,0.000040458544,0.000022548467,0.00001657002,0.000371805],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986488,0.00015682513,0.00055767153,0.00018200077,0.00026946826,0.00018518705],"domain_scores_gemma":[0.9987251,0.00025970853,0.0002283584,0.0003263749,0.00040133417,0.00005910714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009985287,0.00012294842,0.00017742305,0.0001596524,0.0001770796,0.0005681633,0.00042500236,0.000044918186,0.000029384119],"category_scores_gemma":[0.00004970005,0.00010712803,0.00010559518,0.0002570746,0.00011256911,0.0024604367,0.00008953564,0.00019319131,0.000008833462],"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":[0.00015297267,0.00038387653,0.00010025031,0.00033294034,0.00028288603,0.000013820874,0.0031751404,0.000048099286,0.72197217,0.03637931,0.05004405,0.18711448],"study_design_scores_gemma":[0.0015665215,0.0012625719,0.0008147306,0.0007124853,0.00012772583,0.00015579633,0.0005037419,0.1860387,0.722764,0.047603942,0.037941504,0.0005082524],"about_ca_topic_score_codex":0.000009954118,"about_ca_topic_score_gemma":0.0000024318913,"teacher_disagreement_score":0.5869182,"about_ca_system_score_codex":0.0000816946,"about_ca_system_score_gemma":0.00007253299,"threshold_uncertainty_score":0.5478812},"labels":[],"label_agreement":null},{"id":"W4396747546","doi":"10.1016/j.jvcir.2024.104174","title":"Multi-hop graph transformer network for 3D human pose estimation","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adjacency matrix; Graph; Convolutional neural network; Transformer; Artificial intelligence; Pattern recognition (psychology); Pose; Theoretical computer science; Algorithm","score_opus":0.04759473798964078,"score_gpt":0.4013701902620755,"score_spread":0.35377545227243473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396747546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019844368,0.0009251833,0.9773331,0.0011079011,0.00023725368,0.00021717114,0.0000020800655,0.000054684868,0.00027821292],"genre_scores_gemma":[0.636263,0.0008651901,0.36237893,0.00013734183,0.00016838004,0.000016436872,0.00004577369,0.0000118822445,0.00011303735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989863,0.0001493997,0.00045351274,0.00013073934,0.0001730697,0.00010697204],"domain_scores_gemma":[0.9990348,0.00020770298,0.00021698052,0.0001845181,0.00029721085,0.000058740385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065372215,0.00008865721,0.00013457146,0.00021481088,0.00027816952,0.00046748077,0.00021759179,0.00004654543,0.00001741837],"category_scores_gemma":[0.00005010866,0.000080172234,0.00010786656,0.0002702984,0.00004733322,0.002131743,0.000026187829,0.00017379764,0.000006027253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000083544684,0.00047158272,0.00017037128,0.00023842853,0.00020752037,0.0000088446395,0.0044527994,0.00062435283,0.084184356,0.013214477,0.010449291,0.8858944],"study_design_scores_gemma":[0.0018884258,0.0006511459,0.005627588,0.0004678054,0.00012891101,0.00019249688,0.00041428418,0.9472721,0.010163209,0.026643954,0.006238799,0.00031126788],"about_ca_topic_score_codex":0.000009233967,"about_ca_topic_score_gemma":0.0000059644835,"teacher_disagreement_score":0.94664776,"about_ca_system_score_codex":0.000027003905,"about_ca_system_score_gemma":0.00003252983,"threshold_uncertainty_score":0.45079276},"labels":[],"label_agreement":null},{"id":"W4403330974","doi":"10.1016/j.jvcir.2024.104302","title":"OODNet: A deep blind JPEG image compression deblocking network using out-of-distribution detection","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Deblocking filter; Computer science; Image compression; JPEG; Image (mathematics); Compression (physics); JPEG 2000; Distribution (mathematics); Pattern recognition (psychology); Mathematics; Image processing; Materials science","score_opus":0.044823066995531104,"score_gpt":0.40936598965021814,"score_spread":0.364542922654687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403330974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022641774,0.0036320512,0.9729095,0.0002442249,0.00023657753,0.00014177395,0.0000010851959,0.00010166467,0.000091361675],"genre_scores_gemma":[0.6238743,0.00064644514,0.3753583,0.000016247473,0.00007912503,0.000002589254,0.00000784404,0.000010584499,0.000004555271],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982832,0.00034737348,0.0006832878,0.00019791158,0.00033547697,0.00015272888],"domain_scores_gemma":[0.9979641,0.00027174078,0.00069945777,0.00036900863,0.0006296473,0.000066080494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088547,0.0001345629,0.00022711685,0.00019913621,0.00024435308,0.00045207806,0.00044016517,0.000071787494,0.0000034919528],"category_scores_gemma":[0.00024877876,0.0001255991,0.00009061512,0.0005693594,0.00012885565,0.0030754071,0.00029867742,0.00036002437,0.000001261634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010398745,0.0001260805,0.00015320686,0.00013280226,0.000049086873,0.000011242381,0.0013746225,0.00036188873,0.76708376,0.0005232276,0.00019265253,0.22988746],"study_design_scores_gemma":[0.00040944412,0.00017277063,0.0005143348,0.00066411466,0.000047884518,0.00015188812,0.00018655289,0.879653,0.10968479,0.007956886,0.0004102784,0.00014805411],"about_ca_topic_score_codex":0.000013403298,"about_ca_topic_score_gemma":0.0000033179147,"teacher_disagreement_score":0.8792911,"about_ca_system_score_codex":0.00010165923,"about_ca_system_score_gemma":0.000066577806,"threshold_uncertainty_score":0.51217824},"labels":[],"label_agreement":null},{"id":"W4405515474","doi":"10.1016/j.jvcir.2024.104367","title":"RCMixer: Radar-camera fusion based on vision transformer for robust object detection","year":2024,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Infrared Target Detection Methodologies","field":"Engineering","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":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; National Natural Science Foundation of China","keywords":"Computer vision; Artificial intelligence; Fusion; Computer science; Transformer; Radar; Object detection; Engineering; Pattern recognition (psychology); Telecommunications; Electrical engineering; Voltage","score_opus":0.03819126634014802,"score_gpt":0.3625904096319306,"score_spread":0.3243991432917826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405515474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09741799,0.0012585444,0.8988125,0.00044639155,0.000639562,0.0003210677,0.0000057149336,0.00017200479,0.00092626427],"genre_scores_gemma":[0.8683161,0.0017399089,0.1296482,0.00005028679,0.00013385908,0.000020733249,0.00001994647,0.000037828766,0.00003310816],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882126,0.0002602483,0.00045874153,0.000117616364,0.00022615767,0.00011600667],"domain_scores_gemma":[0.99864435,0.0008182403,0.00010825184,0.00018974741,0.00019076641,0.00004866036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010113603,0.00013113293,0.00018603251,0.00041889882,0.00015704331,0.00018449085,0.000111255926,0.00009255742,0.000030578445],"category_scores_gemma":[0.00025165873,0.000116389834,0.00013897389,0.00027998717,0.000048773272,0.0007229682,0.0000097668,0.00033582482,0.000004113221],"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":[0.000328973,0.00005044999,0.000013712042,0.00019407616,0.000048160273,0.0000022504903,0.00046994383,0.01937557,0.649023,0.00004122052,0.0006399465,0.32981268],"study_design_scores_gemma":[0.00067850674,0.0006124777,0.0007750358,0.00018440367,0.000053589156,0.000037278303,0.0005231616,0.64288485,0.35132265,0.00048154037,0.0023096718,0.00013684983],"about_ca_topic_score_codex":0.0000042727575,"about_ca_topic_score_gemma":0.000002355919,"teacher_disagreement_score":0.77089816,"about_ca_system_score_codex":0.00010101556,"about_ca_system_score_gemma":0.000024757705,"threshold_uncertainty_score":0.47462395},"labels":[],"label_agreement":null},{"id":"W4412749426","doi":"10.1016/j.jvcir.2025.104535","title":"Dual-Branch Wavelet Diffusion models with Dual-Prior Refinement for Underwater Image Enhancement","year":2025,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"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":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada","keywords":"Dual (grammatical number); Underwater; Wavelet; Image (mathematics); Image enhancement; Computer science; Diffusion; Artificial intelligence; Computer vision; Mathematics; Algorithm; Geology; Physics; Art; Oceanography","score_opus":0.03363734240899182,"score_gpt":0.3688717875114133,"score_spread":0.3352344451024215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412749426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030691061,0.0004051159,0.9624458,0.004589485,0.00009216104,0.0003069704,9.968178e-7,0.00001803779,0.0014503933],"genre_scores_gemma":[0.418376,0.00087283156,0.5792354,0.0004918168,0.000041216685,0.000023631443,0.000012766493,0.000010557342,0.0009357707],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983312,0.00037694097,0.0005863168,0.00020984645,0.0003298264,0.0001659056],"domain_scores_gemma":[0.99795556,0.00034074587,0.00043457435,0.0004907258,0.0007162985,0.000062089384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011737978,0.0001421265,0.00025688478,0.0002476972,0.00030250475,0.00039432815,0.00032408544,0.000045080087,0.000010477136],"category_scores_gemma":[0.00009139786,0.00010890337,0.00008118829,0.00028387256,0.00008463334,0.0014331078,0.0002454777,0.00017772164,0.00000168475],"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":[0.0009530657,0.00086062955,0.000053653774,0.00018404353,0.00020279434,0.000015245504,0.0034950036,0.00017869758,0.6808488,0.012434559,0.0041800365,0.29659352],"study_design_scores_gemma":[0.01326786,0.0020229334,0.0028278106,0.00085537747,0.00021970489,0.00018221796,0.0012494605,0.3292057,0.5799004,0.06511072,0.004537068,0.0006207631],"about_ca_topic_score_codex":0.000022017075,"about_ca_topic_score_gemma":0.0000035276016,"teacher_disagreement_score":0.38768494,"about_ca_system_score_codex":0.000067003544,"about_ca_system_score_gemma":0.00008908947,"threshold_uncertainty_score":0.44409505},"labels":[],"label_agreement":null},{"id":"W4413626726","doi":"10.1016/j.jvcir.2025.104573","title":"MEN-VVDF: Multipath excitation network-based video violence detection framework focusing on human activity in keyframes","year":2025,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Human Pose and Action Recognition","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":"McMaster University","funders":"Sichuan Province Science and Technology Support Program; Natural Science Foundation of Sichuan Province; Ministry of Education; National Natural Science Foundation of China","keywords":"Multipath propagation; Computer science; Computer security; Telecommunications","score_opus":0.03590650212536911,"score_gpt":0.3967314869021954,"score_spread":0.3608249847768263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413626726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44893473,0.00010748369,0.5495291,0.00069424347,0.000107204236,0.0001478007,2.786994e-7,0.000026560692,0.00045262824],"genre_scores_gemma":[0.98343843,0.00032933033,0.015884092,0.00024859977,0.000064749,0.000012186904,0.00000394486,0.000006275571,0.000012402629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826866,0.0006556711,0.0004830412,0.00019707842,0.00025922694,0.00013633867],"domain_scores_gemma":[0.9980967,0.00064666674,0.0005673422,0.00033304034,0.0003090646,0.00004719395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086602254,0.00012056266,0.00019390331,0.00046756232,0.00032654297,0.00029008734,0.00027880198,0.00009608394,0.0000064656724],"category_scores_gemma":[0.0003258079,0.000121241465,0.00007154053,0.00059567357,0.00006258275,0.001365763,0.000074566175,0.0004667066,0.000002852821],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032126842,0.0008187191,0.0039966344,0.00007057966,0.000047893245,0.0000074513655,0.0011313601,0.0024335985,0.11484796,0.00527294,0.00018086753,0.8708707],"study_design_scores_gemma":[0.0033047178,0.00085513265,0.354357,0.0030456323,0.00005544305,0.000017277436,0.00078266347,0.3851159,0.16485478,0.08698902,0.0001867405,0.0004357054],"about_ca_topic_score_codex":0.00007868702,"about_ca_topic_score_gemma":0.000034690034,"teacher_disagreement_score":0.870435,"about_ca_system_score_codex":0.00012249814,"about_ca_system_score_gemma":0.000051695075,"threshold_uncertainty_score":0.49440834},"labels":[],"label_agreement":null},{"id":"W7115593364","doi":"10.1016/j.jvcir.2025.104686","title":"Aligning computational and human perceptions of image complexity: A dual-task framework for prediction and localization","year":2025,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Visual Attention and Saliency Detection","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":"Natural Science Basic Research Program of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computational complexity theory; Image (mathematics); Computational model; Perception; Pattern recognition (psychology); Focus (optics); Human visual system model; Gaze","score_opus":0.034045642789887885,"score_gpt":0.39911005899053315,"score_spread":0.3650644162006453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115593364","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14162356,0.0001706174,0.8569133,0.000867868,0.00006839177,0.00020003608,0.0000067484375,0.00002244017,0.00012698637],"genre_scores_gemma":[0.8675844,0.00028675955,0.13194694,0.000091642156,0.000023200402,0.0000084909825,0.00003316935,0.000005190373,0.000020220634],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986826,0.000254369,0.0005978759,0.00017177619,0.00021019338,0.00008314843],"domain_scores_gemma":[0.9983333,0.00029315046,0.0004815648,0.00018587845,0.00065129704,0.000054824333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006389934,0.000096154276,0.00019863383,0.0003323712,0.00042127966,0.00021149527,0.00013005365,0.00006599884,0.0000042590054],"category_scores_gemma":[0.00021627886,0.000097130694,0.00005532234,0.00032888682,0.00024193956,0.0010401059,0.00013114963,0.0001432384,2.454375e-7],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026771124,0.0011354286,0.038261168,0.0006616407,0.00038560425,0.0000021507115,0.010011084,0.0011427923,0.18341891,0.6871223,0.0016561823,0.07593504],"study_design_scores_gemma":[0.0022565513,0.00078585214,0.24750438,0.00045976188,0.0001352703,0.000103956474,0.0034112681,0.5301514,0.00291772,0.21186441,0.00020734398,0.00020204164],"about_ca_topic_score_codex":0.000023627843,"about_ca_topic_score_gemma":0.0000036351776,"teacher_disagreement_score":0.72596085,"about_ca_system_score_codex":0.000028744449,"about_ca_system_score_gemma":0.000032445732,"threshold_uncertainty_score":0.39608747},"labels":[],"label_agreement":null}]}