{"meta":{"query_hash":"a37a74aa11c7","filters":{"venue":"Applied Computer Science"},"cohort_total":8,"direct_labels_cover":0,"predictions_cover":8,"exported":8,"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/a37a74aa11c7","api":"https://metacan.xera.ac/api/v1/cohort?venue=Applied+Computer+Science"},"results":[{"id":"W1010892572","doi":"","title":"Central-symmetrical property analysis on circularly orthogonal moments","year":2014,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Zernike polynomials; Velocity Moments; Property (philosophy); Image (mathematics); Computer science; Order (exchange); Algorithm; Mathematics; Computer vision; Physics; Optics","score_opus":0.012575601820026715,"score_gpt":0.23020197215223384,"score_spread":0.21762637033220714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1010892572","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":[0.0028582066,0.0000044416915,0.9916622,0.00063186645,0.00021363968,0.00020846607,7.3404425e-7,0.00039900525,0.00402144],"genre_scores_gemma":[0.88868546,0.0000026310283,0.10974484,0.0013617927,0.00008815034,0.000025799593,0.0000022255228,0.0000058448604,0.00008323472],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99670005,0.00005366958,0.00031019922,0.0010756948,0.0012363815,0.00062399334],"domain_scores_gemma":[0.9981632,0.00008285342,0.00013371512,0.0011461149,0.00017612643,0.00029798195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000931505,0.00020659518,0.00026689307,0.0007755482,0.0003779944,0.0006763677,0.0027063708,0.000058338745,0.0000090857875],"category_scores_gemma":[0.000031998847,0.00014018423,0.0001288082,0.0073376303,0.00029342846,0.0004891348,0.00052380905,0.0001873938,0.00009573259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007113332,0.00026981262,0.0012754367,0.000006922315,0.000058754555,0.000004030778,0.00017390572,0.00023374693,0.009850016,0.48378283,0.00020782277,0.5041296],"study_design_scores_gemma":[0.00035725255,0.0002543218,0.06433393,0.000009259622,0.000041131494,0.0000075405896,0.0000024178287,0.87634486,0.044887483,0.007239583,0.005953214,0.00056899275],"about_ca_topic_score_codex":0.0000071059617,"about_ca_topic_score_gemma":3.382002e-7,"teacher_disagreement_score":0.8858273,"about_ca_system_score_codex":0.0001491391,"about_ca_system_score_gemma":0.00015199062,"threshold_uncertainty_score":0.652223},"labels":[],"label_agreement":null},{"id":"W2484935086","doi":"","title":"Color image reconstruction from Charlier moments","year":2015,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Artificial intelligence; Color image; Moment (physics); Computer vision; Image (mathematics); Image processing; Physics","score_opus":0.02289956073373926,"score_gpt":0.24552480933832888,"score_spread":0.22262524860458963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2484935086","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":[0.02256734,0.000012981916,0.9707154,0.0005797543,0.00081856805,0.00020923409,0.0000019951096,0.0005147054,0.0045800116],"genre_scores_gemma":[0.35344005,0.0000046891187,0.6457648,0.00053637393,0.00014498955,0.00003302074,0.0000021211763,0.000006007032,0.00006791689],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979809,0.000021912556,0.00024473766,0.000732781,0.00066830247,0.00035138024],"domain_scores_gemma":[0.9985253,0.000032270396,0.00013389083,0.0007699223,0.00027205842,0.0002665228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005732776,0.00015213978,0.00015401188,0.00016873122,0.00021180855,0.00059975235,0.0019367754,0.000050370134,0.000010023929],"category_scores_gemma":[0.000015674133,0.00013723486,0.000032776145,0.0011381075,0.00041845388,0.0012825311,0.0006384336,0.00011903269,0.00037117107],"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":[0.000014003739,0.00010339238,0.00013095644,0.0000035761577,0.000010006919,0.000007266598,0.0009827962,0.000006446419,0.18286304,0.09444903,0.0017110874,0.7197184],"study_design_scores_gemma":[0.0007597482,0.000135769,0.0022406015,0.000017445125,0.0000056690437,0.000038826027,0.00004580657,0.20697409,0.71916026,0.065203786,0.004876996,0.0005409824],"about_ca_topic_score_codex":0.000020727111,"about_ca_topic_score_gemma":3.6511364e-7,"teacher_disagreement_score":0.7191774,"about_ca_system_score_codex":0.00018271871,"about_ca_system_score_gemma":0.0003096691,"threshold_uncertainty_score":0.57834256},"labels":[],"label_agreement":null},{"id":"W2942136431","doi":"","title":"Color image analysis via Racah moments","year":2015,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Image Retrieval and Classification 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 Winnipeg","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Computer vision","score_opus":0.01892711714496467,"score_gpt":0.26391730061713187,"score_spread":0.2449901834721672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2942136431","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":[0.0036335317,0.000014652736,0.9913806,0.00043855442,0.0002391945,0.0002092442,8.1706384e-7,0.00052883493,0.003554607],"genre_scores_gemma":[0.55359715,0.0000018620766,0.44568372,0.0005502166,0.00004717816,0.000027886397,0.0000017068834,0.0000043545083,0.00008593359],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974038,0.000028794404,0.00029397718,0.0008288592,0.0009792261,0.000465334],"domain_scores_gemma":[0.9980089,0.000032874323,0.00015370024,0.0010784777,0.00037041496,0.00035563632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010935311,0.00017845213,0.00024240326,0.0005196888,0.0002484824,0.0006614302,0.002992371,0.00004753522,0.0000065677177],"category_scores_gemma":[0.00001488033,0.00015620117,0.00008267778,0.005907259,0.0004228441,0.0010203945,0.0009982778,0.00011977153,0.0002577323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035531255,0.0006073739,0.00069379417,0.00001840848,0.00022060097,0.000045774785,0.0028406305,0.0005190609,0.1474082,0.4438994,0.0037311118,0.39998013],"study_design_scores_gemma":[0.00036810432,0.00010692961,0.0021978158,0.000002827553,0.00003561069,0.000011258156,0.000013317104,0.7806243,0.201683,0.012193793,0.0023634871,0.00039959032],"about_ca_topic_score_codex":0.000014454675,"about_ca_topic_score_gemma":6.650782e-7,"teacher_disagreement_score":0.7801052,"about_ca_system_score_codex":0.00017746516,"about_ca_system_score_gemma":0.00027477957,"threshold_uncertainty_score":0.63781863},"labels":[],"label_agreement":null},{"id":"W317671949","doi":"","title":"Image analysis by orthogonal Fourier-Mellin moments","year":2013,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Mellin transform; Fourier transform; Velocity Moments; Computer science; Order (exchange); Image (mathematics); Harmonic; Set (abstract data type); Harmonic analysis; Process (computing); Algorithm; Applied mathematics; Artificial intelligence; Mathematical analysis; Mathematics; Physics; Optics; Zernike polynomials","score_opus":0.006273600722623171,"score_gpt":0.22325104300830875,"score_spread":0.21697744228568558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W317671949","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":[0.006512772,0.000022652142,0.98948395,0.0008247827,0.00012739406,0.0002935549,0.000002296283,0.000423534,0.0023090478],"genre_scores_gemma":[0.442065,0.000009204011,0.55676466,0.00082913117,0.000042545485,0.00007466336,0.0000054017114,0.0000064700125,0.00020291006],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971749,0.000026788168,0.0003467331,0.0009418196,0.00095141155,0.0005583256],"domain_scores_gemma":[0.9981375,0.000054601514,0.00016266547,0.0010959561,0.0002751439,0.00027415372],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005956393,0.00021093772,0.0002464125,0.0004097807,0.0003649531,0.0011405826,0.0029786157,0.00005287949,0.00008378794],"category_scores_gemma":[0.0000075714647,0.00018291658,0.00011031375,0.004542181,0.00044767815,0.0013558926,0.0007250396,0.0001510024,0.0005410338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037773912,0.0003453421,0.0007093312,0.00001433588,0.00015188858,0.00000611747,0.00050380465,0.00002941555,0.2565326,0.13070318,0.011553519,0.59944665],"study_design_scores_gemma":[0.00026617452,0.000076529934,0.008882566,0.0000047154026,0.000037263366,0.000006399056,0.000008384072,0.791932,0.1785794,0.015848642,0.00373591,0.0006220415],"about_ca_topic_score_codex":0.000021892816,"about_ca_topic_score_gemma":3.4201656e-7,"teacher_disagreement_score":0.79190254,"about_ca_system_score_codex":0.00008394223,"about_ca_system_score_gemma":0.00013677267,"threshold_uncertainty_score":0.99989635},"labels":[],"label_agreement":null},{"id":"W4312314366","doi":"10.35784/acs-2020-04","title":"UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS","year":2020,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Pharmacovigilance and Adverse Drug Reactions","field":"Pharmacology, Toxicology and Pharmaceutics","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":"McGill University","funders":"","keywords":"Computer science; Side effect (computer science); Machine learning; Artificial intelligence; Drug; The Internet; Classifier (UML); Great Rift; Data science; World Wide Web; Medicine; Pharmacology","score_opus":0.132740532313952,"score_gpt":0.4273538343456269,"score_spread":0.2946133020316749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312314366","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5135776,0.00007281071,0.4708971,0.010801343,0.0017341778,0.0012158857,0.00012768873,0.00038325813,0.00119016],"genre_scores_gemma":[0.9658927,0.00006331833,0.014234859,0.019208265,0.00037941427,0.00008944022,0.00004686735,0.000017502794,0.00006763339],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981235,0.000038840164,0.00032936083,0.0006870206,0.00021852512,0.00060272694],"domain_scores_gemma":[0.99886745,0.00022471783,0.00018615082,0.0002103307,0.000066677436,0.00044467827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004954487,0.00020582596,0.0002481724,0.00008224266,0.00080025854,0.000037303435,0.0005344782,0.000079310856,0.000031667263],"category_scores_gemma":[0.000015348407,0.0002138526,0.00009167444,0.0004140173,0.00018275318,0.00045034126,0.00014181893,0.00051093695,0.00008348298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039515292,0.0003508795,0.000481981,0.00009420437,0.000103411,0.000008601059,0.010160943,0.12989056,0.6015895,0.004443153,0.0069613927,0.24552022],"study_design_scores_gemma":[0.0013496137,0.00002944548,0.0004144321,0.000007527377,0.00003001023,0.00000227833,0.00019449473,0.97128993,0.009613041,0.00085096684,0.01599408,0.00022415351],"about_ca_topic_score_codex":0.000021704655,"about_ca_topic_score_gemma":0.0000063285324,"teacher_disagreement_score":0.84139943,"about_ca_system_score_codex":0.00019503663,"about_ca_system_score_gemma":0.0004069104,"threshold_uncertainty_score":0.8720656},"labels":[],"label_agreement":null},{"id":"W4312808520","doi":"10.35784/acs-2021-27","title":"ARTIFICIAL NEURAL NETWORK BASED DEMAND FORECASTING INTEGRATED WITH FEDERAL FUNDS RATE","year":2021,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Transport Canada","funders":"University of Moratuwa","keywords":"Computer science; Artificial neural network; Demand forecasting; Stockout; Variable (mathematics); Promotion (chess); Econometrics; Federal funds; Variables; Operations research; Artificial intelligence; Machine learning; Economics; Monetary policy; Macroeconomics","score_opus":0.11581446471629749,"score_gpt":0.32266036708560086,"score_spread":0.2068459023693034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312808520","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":[0.24313398,0.000007091702,0.7536838,0.0006786974,0.00019127241,0.00018493607,0.0000029475157,0.00017595655,0.0019413063],"genre_scores_gemma":[0.7537412,1.790938e-7,0.24501343,0.00093935285,0.00020076921,0.00003437073,0.000005902942,0.000010414518,0.000054347638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966463,0.00007856464,0.000540072,0.0010670876,0.0010298773,0.00063811766],"domain_scores_gemma":[0.9974635,0.00064934447,0.00024277413,0.0008221378,0.00061504444,0.00020718922],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002969649,0.00021425879,0.00027761495,0.00015571027,0.0011746236,0.0016143684,0.0012433779,0.00005441825,0.00006144666],"category_scores_gemma":[0.00014769631,0.00015140798,0.00006424491,0.004647983,0.0005978933,0.0002662669,0.00041859117,0.00023791009,0.00004078663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106077794,0.00015923264,0.003959524,0.0000063401476,0.000012076118,0.00008319053,0.00024717103,0.33295673,0.008329398,0.063845046,0.009073725,0.58122146],"study_design_scores_gemma":[0.00013806658,0.000082568025,0.0017655258,0.000020643978,0.0000048609345,0.000042918884,0.000030178215,0.95805246,0.0094122905,0.028263273,0.001944377,0.00024286832],"about_ca_topic_score_codex":0.0000120675895,"about_ca_topic_score_gemma":0.000059040838,"teacher_disagreement_score":0.6250957,"about_ca_system_score_codex":0.000055036755,"about_ca_system_score_gemma":0.0005293391,"threshold_uncertainty_score":0.9994221},"labels":[],"label_agreement":null},{"id":"W4405933084","doi":"10.35784/acs-2024-37","title":"STUDY ON DEEP LEARNING MODELS FOR THE CLASSIFICATION OF VR SICKNESS LEVELS","year":2024,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Virtual Reality Applications and Impacts","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":"Optech (Canada)","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Computer science; Motion sickness; Simulator sickness; Virtual reality; Artificial intelligence; Motion (physics); Deep learning; Sensory system; Machine learning; Computer vision; Human–computer interaction; Cognitive psychology","score_opus":0.11792269595158567,"score_gpt":0.32801483825121425,"score_spread":0.21009214229962858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405933084","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":[0.014739254,0.000036514724,0.981974,0.0004906095,0.0002456323,0.0007848153,0.0000015661109,0.00015616465,0.0015714356],"genre_scores_gemma":[0.9813345,0.000002953282,0.018252775,0.00013689509,0.00006941261,0.0001742657,6.107815e-7,0.000007761975,0.000020826283],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824816,0.000025914542,0.00025677713,0.00063356594,0.0005491843,0.00028640046],"domain_scores_gemma":[0.9983148,0.00055697345,0.00008455334,0.00083152886,0.00013470868,0.000077419114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013335225,0.0001303988,0.00013632624,0.00016896742,0.00045500137,0.0005799983,0.0019603684,0.000028219458,8.135916e-7],"category_scores_gemma":[0.000018701425,0.00008930637,0.000048233913,0.0014940373,0.00021322149,0.00052256446,0.0003333531,0.00014983499,0.000021295598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000019146075,0.00006522518,0.0000018176091,0.00000614262,0.0000069375055,3.0302903e-7,0.00241206,0.024146598,0.0019312254,0.5360351,0.00002105473,0.43537158],"study_design_scores_gemma":[0.000111095156,0.00022117718,0.0054763407,0.00001327237,0.000007383972,0.0000023025825,0.00018765997,0.9729727,0.0011348317,0.019046742,0.00071373,0.000112796035],"about_ca_topic_score_codex":0.000008595294,"about_ca_topic_score_gemma":0.0000018874166,"teacher_disagreement_score":0.96659523,"about_ca_system_score_codex":0.0000654014,"about_ca_system_score_gemma":0.0001826906,"threshold_uncertainty_score":0.5592937},"labels":[],"label_agreement":null},{"id":"W981799813","doi":"","title":"Future directions in Multiple Instance Learning","year":2013,"lang":"en","type":"article","venue":"Applied Computer Science","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; University of Guelph","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Set (abstract data type); Machine learning; Independence (probability theory); Context (archaeology); Training set; Mathematics","score_opus":0.011356509190147818,"score_gpt":0.22145462015458997,"score_spread":0.21009811096444214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W981799813","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":[0.01491465,0.000069186375,0.98024946,0.00095396757,0.0003455609,0.00028429678,1.2179105e-7,0.0005492811,0.0026334664],"genre_scores_gemma":[0.7745505,0.000032775606,0.22476956,0.00040239375,0.00009210726,0.00008759066,4.0311096e-7,0.0000046396976,0.000060058763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984146,0.000024583826,0.00021480476,0.0005904194,0.00037120993,0.00038440712],"domain_scores_gemma":[0.9991278,0.000060025184,0.00008011836,0.0004959497,0.00012986829,0.00010623086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036972365,0.0001278937,0.00012464184,0.00026884023,0.0003591735,0.00046491885,0.0014903105,0.000045533576,0.000009698435],"category_scores_gemma":[0.000015722057,0.00011559456,0.000027882044,0.0024926618,0.00021691977,0.001116266,0.0004058343,0.00025403264,0.00018043298],"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":[0.0000010463272,0.00006472946,0.001210154,0.000004976402,0.0000014111497,0.0000018210185,0.0008128951,0.000050355582,0.023450147,0.095665805,0.0001617759,0.8785749],"study_design_scores_gemma":[0.00060848275,0.0001084923,0.1362611,0.00003498466,0.0000016479705,0.000025498144,0.00011858384,0.6734374,0.10115679,0.017088013,0.07035255,0.0008064079],"about_ca_topic_score_codex":0.000037830036,"about_ca_topic_score_gemma":0.0000048837605,"teacher_disagreement_score":0.87776846,"about_ca_system_score_codex":0.000107596905,"about_ca_system_score_gemma":0.000117043484,"threshold_uncertainty_score":0.47138092},"labels":[],"label_agreement":null}]}