{"meta":{"query_hash":"489065b6dbde","filters":{"venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)"},"cohort_total":7,"direct_labels_cover":0,"predictions_cover":7,"exported":7,"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/489065b6dbde","api":"https://metacan.xera.ac/api/v1/cohort?venue=TENCON+2022+-+2022+IEEE+Region+10+Conference+%28TENCON%29"},"results":[{"id":"W4312007055","doi":"10.1109/tencon55691.2022.9977897","title":"IEEE Hong Kong Section 50th Anniversary 1972-2022: Advance Technology for Huminity – The Tech-Biz Intelligence","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Age of Information Optimization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University at Buffalo; University of California, San Diego; University of Illinois at Urbana-Champaign; SaskPower; University of Texas at Dallas; Chinese University of Hong Kong; Hong Kong Polytechnic University; University of Hong Kong; University of Toronto; University of Edinburgh; China Computer Federation; Hang Seng Management College; University of Alberta; University of Saskatchewan; Princeton University; State University of New York; University of Maryland, Baltimore County","keywords":"Section (typography); Friendship; China; Work (physics); Telecommunications; Engineering; Library science; Computer science; Sociology; Political science; Business; Advertising; Social science; Law","score_opus":0.028446873405137848,"score_gpt":0.25281485113092955,"score_spread":0.2243679777257917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312007055","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":[0.008400062,0.00033706086,0.97385204,0.0065910853,0.004184853,0.0019665791,0.000060698694,0.0010597064,0.0035479157],"genre_scores_gemma":[0.96418214,0.0009566419,0.018039754,0.0017798336,0.00048516126,0.0021040612,0.00014630477,0.000099327066,0.012206783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99532855,0.00036840304,0.001032307,0.0012607909,0.0010853937,0.0009245415],"domain_scores_gemma":[0.99560857,0.00044118552,0.0011184246,0.0018217744,0.0008192779,0.00019076241],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0013684928,0.0005880073,0.0005688474,0.0009998642,0.0022332878,0.0003109219,0.0037023837,0.00028634968,0.0008361984],"category_scores_gemma":[0.0003272767,0.00055993913,0.000276145,0.0033269431,0.00054957066,0.002711832,0.0009424664,0.0013481206,0.00011190072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008071329,0.0010325155,0.0017426001,0.00047306705,0.00045922407,0.0003786648,0.015612095,0.14594696,0.016381882,0.1868856,0.13017578,0.5001045],"study_design_scores_gemma":[0.0016361674,0.0020599957,0.00025806908,0.00015338404,0.000105626474,0.00089710107,0.021657137,0.82892096,0.019867871,0.024285093,0.097958006,0.0022005765],"about_ca_topic_score_codex":0.00007187438,"about_ca_topic_score_gemma":0.00006250232,"teacher_disagreement_score":0.9558123,"about_ca_system_score_codex":0.00070429745,"about_ca_system_score_gemma":0.00081333396,"threshold_uncertainty_score":0.9996852},"labels":[],"label_agreement":null},{"id":"W4312007190","doi":"10.1109/tencon55691.2022.9977467","title":"Attention-Based Accurate and Robust Facial Landmark Detector","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Landmark; Computer science; Artificial intelligence; Face (sociological concept); Pattern recognition (psychology); Convolutional neural network; Detector; Feature (linguistics); Pooling; Block (permutation group theory); Weighting; Channel (broadcasting); Computer vision; Feature vector; Position (finance); Algorithm; Embedding; Focus (optics); Mathematics","score_opus":0.04175368791083664,"score_gpt":0.2408037305096079,"score_spread":0.19905004259877127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312007190","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":[0.37031758,0.0009450598,0.5933727,0.018176403,0.0046383543,0.0019416093,0.00036021214,0.0018439873,0.008404057],"genre_scores_gemma":[0.9889974,0.00020403003,0.001365528,0.0012986967,0.00013155174,0.00034579248,0.000101822516,0.00003530585,0.0075199027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961093,0.0005603415,0.000598954,0.001179792,0.00089297787,0.0006586207],"domain_scores_gemma":[0.99790466,0.00023637852,0.00041936652,0.00081051304,0.00028685117,0.0003422124],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006769562,0.00043685557,0.00055527425,0.0005740508,0.0010292229,0.00042444782,0.0012600673,0.00012227868,0.0046749343],"category_scores_gemma":[0.000102552054,0.00043775025,0.0003067158,0.0013771841,0.00020131796,0.00067184295,0.00045651002,0.00065370376,0.00022907421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010081653,0.0024787947,0.027475286,0.0006790496,0.0014067596,0.002695774,0.004724962,0.024460591,0.0659437,0.017994992,0.13015948,0.7209724],"study_design_scores_gemma":[0.00376809,0.00092211907,0.0031651442,0.000101251724,0.00019506166,0.00024843146,0.002510753,0.9365768,0.0019124185,0.00242632,0.046144675,0.0020289302],"about_ca_topic_score_codex":0.00018671773,"about_ca_topic_score_gemma":0.00016751324,"teacher_disagreement_score":0.9121162,"about_ca_system_score_codex":0.00018196496,"about_ca_system_score_gemma":0.00049596635,"threshold_uncertainty_score":0.9998074},"labels":[],"label_agreement":null},{"id":"W4312068770","doi":"10.1109/tencon55691.2022.9978010","title":"A Low Error Face Recognition System Based on A New Arrangement of Convolutional Neural Network and Data Augmentation","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Softmax function; Computer science; Normalization (sociology); Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Support vector machine; Convolutional neural network; Face (sociological concept); Word error rate; Feature extraction; Artificial neural network","score_opus":0.09246256890659646,"score_gpt":0.2797022031141003,"score_spread":0.18723963420750386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312068770","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":[0.100133,0.0008045493,0.87163675,0.014949083,0.0040864195,0.003007707,0.0010762443,0.0009309206,0.003375308],"genre_scores_gemma":[0.99342996,0.00008328211,0.0026814705,0.0010284856,0.00016073232,0.0002007942,0.0010273652,0.000025401867,0.0013625082],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99595374,0.0006484094,0.00069552765,0.0011549836,0.0010815823,0.0004657477],"domain_scores_gemma":[0.99755377,0.00024046373,0.00063905725,0.0010742818,0.00023459207,0.00025781148],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009048618,0.00034901625,0.0005074366,0.00039083557,0.00051103235,0.00015797548,0.0012577428,0.00008255493,0.0014838768],"category_scores_gemma":[0.000057770205,0.00036349805,0.000154637,0.0011378702,0.00013220601,0.0007015457,0.0004995952,0.00040493067,0.000069489004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016683084,0.0019669242,0.0021611939,0.0012102559,0.0009249044,0.00045053026,0.0034852603,0.13224319,0.008431922,0.010770823,0.2516649,0.5850218],"study_design_scores_gemma":[0.0017857534,0.00057875464,0.00030654803,0.00019785215,0.00010952111,0.000058913145,0.0021868148,0.9913033,0.00042461275,0.00057856104,0.0019973181,0.0004719976],"about_ca_topic_score_codex":0.00021775652,"about_ca_topic_score_gemma":0.00008173067,"teacher_disagreement_score":0.89329696,"about_ca_system_score_codex":0.00025713153,"about_ca_system_score_gemma":0.00054192875,"threshold_uncertainty_score":0.9998817},"labels":[],"label_agreement":null},{"id":"W4312069169","doi":"10.1109/tencon55691.2022.9977891","title":"DBAUNet: Dual-branch attention U-Net for time-domain speech enhancement","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Speech enhancement; Encoder; Speech recognition; Dual (grammatical number); Benchmark (surveying); Block (permutation group theory); Artificial intelligence; Waveform; Channel (broadcasting); Speech coding; Pattern recognition (psychology); Mathematics; Telecommunications","score_opus":0.024853010955816435,"score_gpt":0.25437247362066423,"score_spread":0.2295194626648478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312069169","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":[0.18673521,0.0011750137,0.77859074,0.011885104,0.0050439215,0.0031373077,0.00011639702,0.0011099284,0.012206371],"genre_scores_gemma":[0.8442785,0.0003974419,0.041560013,0.0043126424,0.0011655316,0.0033081672,0.00049127237,0.00020187728,0.10428451],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.99425584,0.0004055869,0.0009588944,0.0017378732,0.0014080615,0.0012337266],"domain_scores_gemma":[0.99692035,0.00023394483,0.0007522686,0.0013427498,0.00041613108,0.00033456404],"candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0015218933,0.00062594167,0.0007388703,0.0005018644,0.0013677528,0.00047884212,0.0020294013,0.00016338394,0.004142701],"category_scores_gemma":[0.000089381116,0.00066136237,0.00038077796,0.0013664853,0.00019495033,0.0011418437,0.00076480146,0.00068545283,0.0005334888],"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":[0.00037987277,0.00093568466,0.00039105854,0.00029453568,0.00025786407,0.0004283064,0.0029776148,0.00027390904,0.48512006,0.00623123,0.20851584,0.29419404],"study_design_scores_gemma":[0.010533445,0.0052052694,0.00059807807,0.00050958846,0.0002571521,0.0017353033,0.004971762,0.07817211,0.31127876,0.0834656,0.49754485,0.0057280688],"about_ca_topic_score_codex":0.000062577514,"about_ca_topic_score_gemma":0.000029337194,"teacher_disagreement_score":0.73703074,"about_ca_system_score_codex":0.00046077804,"about_ca_system_score_gemma":0.0007615857,"threshold_uncertainty_score":0.99993235},"labels":[],"label_agreement":null},{"id":"W4312102483","doi":"10.1109/tencon55691.2022.9977492","title":"Multi-Wavelength Silicon Photonic Neural Network for WDM Optical Communication Systems","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Shun Hing Institute of Advanced Engineering; Natural Sciences and Engineering Research Council of Canada; Chinese University of Hong Kong; CMC Microsystems","keywords":"Wavelength-division multiplexing; Photonics; Computer science; Artificial neural network; Silicon photonics; Optical communication; Optical fiber; Digital signal processing; Electronic engineering; Wavelength; Optoelectronics; Telecommunications; Materials science; Engineering; Artificial intelligence; Computer hardware","score_opus":0.05757208498813495,"score_gpt":0.27029193629446646,"score_spread":0.2127198513063315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312102483","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":[0.375138,0.010235866,0.5618093,0.015827002,0.020368379,0.009973386,0.00013269256,0.0031247414,0.0033906077],"genre_scores_gemma":[0.9845726,0.0004159672,0.008228102,0.0008112005,0.0005278379,0.0010769691,0.00010672748,0.00007605074,0.0041845385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99400157,0.0010165655,0.0011520574,0.0014741336,0.0009537648,0.0014019031],"domain_scores_gemma":[0.9951812,0.0010391566,0.0007528142,0.0022076427,0.000424086,0.00039507067],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0016865438,0.00061878626,0.00086379924,0.00025637276,0.0020764838,0.0005971359,0.004008148,0.00020774141,0.00019960504],"category_scores_gemma":[0.00010628057,0.0005993831,0.00041768505,0.0013002226,0.00025669573,0.00074463786,0.0018448073,0.001389982,0.00004210459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005925056,0.0013330784,0.0012708167,0.0004282515,0.0004596195,0.0005032927,0.00251622,0.6566438,0.007547879,0.10138583,0.12769794,0.09962076],"study_design_scores_gemma":[0.0013100511,0.00063025847,0.0002133142,0.0000936212,0.00003726433,0.00028208035,0.0006816127,0.9717123,0.00013282041,0.00084165175,0.023313524,0.0007514909],"about_ca_topic_score_codex":0.00024244904,"about_ca_topic_score_gemma":0.00007524434,"teacher_disagreement_score":0.6094346,"about_ca_system_score_codex":0.000351112,"about_ca_system_score_gemma":0.00044581437,"threshold_uncertainty_score":0.99964577},"labels":[],"label_agreement":null},{"id":"W4312102546","doi":"10.1109/tencon55691.2022.9977590","title":"Data Augmentation Methods for Low Resolution Facial Images","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Regularization (linguistics); Pattern recognition (psychology); Face (sociological concept); Set (abstract data type); Data set; Training set; Resolution (logic); Image resolution; Machine learning; Data mining; Artificial neural network","score_opus":0.11438446735760073,"score_gpt":0.4027301345308763,"score_spread":0.2883456671732756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312102546","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":[0.00020404521,0.00057938753,0.99135405,0.0029707348,0.0014552736,0.0011787503,0.00024330622,0.0011190729,0.0008953746],"genre_scores_gemma":[0.024952183,0.0003359017,0.96501243,0.0010447829,0.0002683184,0.0017224543,0.0006683733,0.000082171835,0.0059133708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99484795,0.0008458544,0.00081762223,0.0018740455,0.00080782856,0.00080669054],"domain_scores_gemma":[0.9956418,0.00046180096,0.00078122324,0.0024327638,0.00048647635,0.00019590455],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0023484123,0.00048593586,0.00056171464,0.00048935314,0.0013052158,0.00042435367,0.0047078184,0.00013153089,0.00047327037],"category_scores_gemma":[0.0005499344,0.00053260813,0.000164089,0.0011838607,0.00029225938,0.0032867491,0.0022505547,0.0006992021,0.000032213487],"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":[0.00026232,0.00032938362,0.000050570998,0.0001827522,0.0000771457,0.0000671436,0.0011561326,0.000276108,0.11155648,0.009692062,0.11371506,0.7626348],"study_design_scores_gemma":[0.001579924,0.0008162648,0.00008568735,0.000092088434,0.000080208425,0.00020287653,0.0010649749,0.7537446,0.025791053,0.072055794,0.14315157,0.0013349466],"about_ca_topic_score_codex":0.00006643703,"about_ca_topic_score_gemma":0.00001577462,"teacher_disagreement_score":0.7612999,"about_ca_system_score_codex":0.000480857,"about_ca_system_score_gemma":0.00079670025,"threshold_uncertainty_score":0.99999493},"labels":[],"label_agreement":null},{"id":"W4312102722","doi":"10.1109/tencon55691.2022.9977693","title":"Combating Uncertainty and Class Imbalance in Facial Expression Recognition","year":2022,"lang":"en","type":"article","venue":"TENCON 2022 - 2022 IEEE Region 10 Conference (TENCON)","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Computer science; Class (philosophy); Facial expression; Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Block (permutation group theory); Machine learning; Expression (computer science); Facial recognition system; Intersection (aeronautics); Noise (video); Fuzzy logic; Data mining; Mathematics; Image (mathematics); Engineering","score_opus":0.06700933578632123,"score_gpt":0.30304759493473005,"score_spread":0.2360382591484088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312102722","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9560288,0.00035065511,0.0008505643,0.0023183005,0.003418026,0.0011784304,0.00019552595,0.00031947182,0.03534018],"genre_scores_gemma":[0.99106324,0.00024965958,0.00010377436,0.0009547148,0.00020620231,0.00073569483,0.0004234223,0.000046961504,0.006216351],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9958068,0.001169829,0.0007521903,0.0010560049,0.0005539036,0.0006612536],"domain_scores_gemma":[0.99837464,0.00027036064,0.00050163915,0.0004592454,0.00018614129,0.00020795985],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009281017,0.00039830824,0.00052343606,0.00048411687,0.00058287213,0.00007877243,0.00037704868,0.00024149641,0.014535322],"category_scores_gemma":[0.00012911037,0.00042587932,0.00013594756,0.0006588611,0.00022927257,0.0003257625,0.00021525435,0.00113006,0.0002618759],"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":[0.0038876159,0.0030207615,0.038940214,0.0004088873,0.00027423084,0.0013818152,0.028670002,0.00050728215,0.10184185,0.008027741,0.13363852,0.6794011],"study_design_scores_gemma":[0.058165994,0.011755444,0.1087233,0.0028797039,0.00064133655,0.0042765923,0.31011188,0.06642356,0.014131451,0.097317345,0.31282464,0.012748774],"about_ca_topic_score_codex":0.00039728495,"about_ca_topic_score_gemma":0.00029163845,"teacher_disagreement_score":0.6666523,"about_ca_system_score_codex":0.0002522774,"about_ca_system_score_gemma":0.00016653983,"threshold_uncertainty_score":0.9998193},"labels":[],"label_agreement":null}]}