{"meta":{"query_hash":"d3fa214faf59","filters":{"venue":"Series on language processing, pattern recognition, and intelligent systems"},"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/d3fa214faf59","api":"https://metacan.xera.ac/api/v1/cohort?venue=Series+on+language+processing%2C+pattern+recognition%2C+and+intelligent+systems"},"results":[{"id":"W3217430695","doi":"10.1142/9789811239014_0007","title":"Gender Detection from Handwritten Documents Using Transfer Learning Method","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Handwritten Text Recognition 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":"Concordia University","funders":"","keywords":"Computer science; Transfer of learning; Transfer (computing); Artificial intelligence; Natural language processing; Speech recognition","score_opus":0.042310516388248585,"score_gpt":0.2810272819976789,"score_spread":0.2387167656094303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217430695","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":[0.0020209283,0.009123338,0.9708169,0.000024650704,0.00074054225,0.0007035017,0.00017247158,0.0006788719,0.015718779],"genre_scores_gemma":[0.61307997,0.017117923,0.07579399,0.0036248618,0.006609345,0.001143677,0.0074232332,0.0015852312,0.27362177],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9961961,0.00029394982,0.00096940104,0.0013781507,0.000697363,0.00046505727],"domain_scores_gemma":[0.99816203,0.000113156115,0.00044913872,0.0004789164,0.00057780545,0.00021894941],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005095832,0.00078443123,0.000842285,0.00047559288,0.00046941068,0.0013320216,0.00044046505,0.00064160704,0.0005602898],"category_scores_gemma":[0.000036693353,0.00077816227,0.0002342394,0.00013354476,0.00009770392,0.0009866586,0.00014785465,0.0008695448,0.00014097402],"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":[0.000029806246,0.00004912876,0.000022205688,0.0008144305,0.00024689513,0.00014410159,0.0045326794,0.000012130677,0.0014572431,0.00014134972,0.000039902545,0.99251014],"study_design_scores_gemma":[0.0046611708,0.0037110099,0.00009219903,0.052367426,0.0027210175,0.00515153,0.021305252,0.0783939,0.5672018,0.041494243,0.206129,0.016771466],"about_ca_topic_score_codex":0.0005566388,"about_ca_topic_score_gemma":0.000109125,"teacher_disagreement_score":0.97573864,"about_ca_system_score_codex":0.0001836604,"about_ca_system_score_gemma":0.00011744145,"threshold_uncertainty_score":0.9997047},"labels":[],"label_agreement":null},{"id":"W3217733771","doi":"10.1142/9789811239014_0002","title":"Intensive Survey on Peripheral Blood Smear Analysis Using Deep Learning","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"AI in cancer detection","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":"Concordia University","funders":"","keywords":"Peripheral blood; Medicine; Peripheral; Internal medicine","score_opus":0.042900420511525934,"score_gpt":0.26307256702431625,"score_spread":0.22017214651279032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217733771","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":[0.04793669,0.035725422,0.8852695,0.00009412317,0.004357624,0.0015320673,0.0004670109,0.0010104418,0.023607114],"genre_scores_gemma":[0.86377734,0.0042302343,0.0014533227,0.0015034139,0.002040939,0.00012446698,0.002502493,0.00040132715,0.12396645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666727,0.0002735972,0.00075781025,0.0012512569,0.00064100727,0.00040908996],"domain_scores_gemma":[0.9970316,0.00011923053,0.00084187917,0.0005304155,0.001312723,0.00016412916],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004952592,0.000655217,0.000886813,0.00061635143,0.00043256787,0.0009978823,0.00039298253,0.00040745363,0.00026589105],"category_scores_gemma":[0.00011706748,0.0006423811,0.00025236426,0.0003437925,0.00013200208,0.00044029314,0.00015313338,0.0007808667,0.00006097558],"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":[0.00023339293,0.0001764268,0.0028432542,0.0023634322,0.004370836,0.0007219064,0.029352613,0.002980549,0.00018955569,0.00029710264,0.00015849943,0.9563124],"study_design_scores_gemma":[0.0062051155,0.01803691,0.0055546216,0.06871333,0.016925143,0.005693002,0.08515386,0.6758814,0.03486828,0.003290614,0.05009315,0.029584588],"about_ca_topic_score_codex":0.00090388657,"about_ca_topic_score_gemma":0.0009969657,"teacher_disagreement_score":0.92672783,"about_ca_system_score_codex":0.00023759395,"about_ca_system_score_gemma":0.000117062475,"threshold_uncertainty_score":0.99960274},"labels":[],"label_agreement":null},{"id":"W4200523749","doi":"10.1142/9789811239014_bmatter","title":"BACK MATTER","year":2021,"lang":"en","type":"paratext","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.03230856072463521,"score_gpt":0.27516790911438616,"score_spread":0.24285934838975093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200523749","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030361786,0.22032876,0.0074324375,0.00033508192,0.016198441,0.004478503,0.019362781,0.00045786327,0.7010443],"genre_scores_gemma":[0.0362138,0.0044263788,0.00011220186,0.0020894695,0.0043186876,0.00071558607,0.027411835,0.0010116295,0.9237004],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99422544,0.0005474069,0.0015295155,0.0018087918,0.00084593584,0.0010429001],"domain_scores_gemma":[0.9965536,0.00008071193,0.0012678965,0.00084608636,0.00085753994,0.00039419252],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.0004170325,0.001420356,0.001556277,0.00060522405,0.0004094951,0.001778709,0.0004987598,0.00091426243,0.104286656],"category_scores_gemma":[0.000041264113,0.0013100242,0.00029226037,0.0004916183,0.00033937616,0.00065712724,0.00018131747,0.0010020358,0.5194399],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024881854,0.00049475854,0.0006135916,0.021168362,0.0006124311,0.00017176203,0.011710548,0.000016517854,0.0005235629,0.00000168813,0.900605,0.063832946],"study_design_scores_gemma":[0.0008739599,0.00052308594,0.000056092988,0.024299715,0.0003977441,0.0013771189,0.024827383,0.00015126154,0.011481635,0.000018609842,0.932313,0.0036803558],"about_ca_topic_score_codex":0.00081231823,"about_ca_topic_score_gemma":0.00016293024,"teacher_disagreement_score":0.4151532,"about_ca_system_score_codex":0.0002654572,"about_ca_system_score_gemma":0.0002610237,"threshold_uncertainty_score":0.9998546},"labels":[],"label_agreement":null},{"id":"W4200571108","doi":"10.1142/9789811239014_fmatter","title":"FRONT MATTER","year":2021,"lang":"en","type":"paratext","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Front (military); Geology; Oceanography","score_opus":0.028583859819508894,"score_gpt":0.2716969246950449,"score_spread":0.24311306487553602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200571108","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042833183,0.4041243,0.014633286,0.00037684434,0.023314966,0.0056553516,0.02636943,0.0011832869,0.48150936],"genre_scores_gemma":[0.08740118,0.00550549,0.00014382503,0.0024951368,0.0061279545,0.0013803215,0.03416461,0.0012767508,0.86150473],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9941194,0.000546073,0.0015300245,0.0018072253,0.00096289197,0.0010343988],"domain_scores_gemma":[0.9965309,0.00008129986,0.0012783064,0.00084236416,0.000874002,0.00039314592],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.00048323016,0.0014114818,0.001593497,0.00062574435,0.00046176187,0.0017062704,0.00049491815,0.00089956366,0.054243937],"category_scores_gemma":[0.000064368076,0.0012912153,0.00029385815,0.0003110423,0.00031408487,0.0006223338,0.0001791442,0.0010590916,0.33476916],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025666156,0.0004969275,0.0005672728,0.015555474,0.0006333063,0.00032895984,0.015513566,0.000020175725,0.0004226324,0.0000013976718,0.8544651,0.11173848],"study_design_scores_gemma":[0.0009811479,0.00064505206,0.00011514933,0.027386738,0.00058096566,0.0014310546,0.038233615,0.00022789124,0.0126320645,0.000043889188,0.9132861,0.0044363206],"about_ca_topic_score_codex":0.0012016399,"about_ca_topic_score_gemma":0.00031005632,"teacher_disagreement_score":0.39861882,"about_ca_system_score_codex":0.00031915013,"about_ca_system_score_gemma":0.00028949787,"threshold_uncertainty_score":0.99986356},"labels":[],"label_agreement":null},{"id":"W4205908004","doi":"10.1142/9789811239014_0011","title":"A Comprehensive Unconstrained, License Plate Database","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Vehicle License Plate Recognition","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":"Concordia University","funders":"","keywords":"License; Database; Computer science; MIT License; Operating system","score_opus":0.033099675279345236,"score_gpt":0.22988537030068054,"score_spread":0.1967856950213353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205908004","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10601985,0.22933339,0.022272816,0.00021412314,0.009329466,0.006304272,0.023239797,0.0056107654,0.5976755],"genre_scores_gemma":[0.5460969,0.082339734,0.0015634428,0.0027181425,0.008712272,0.000952305,0.0681173,0.0029474217,0.28655246],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974079,0.000053645846,0.0008669222,0.00076394674,0.00042766306,0.00047989903],"domain_scores_gemma":[0.9984729,0.000101685364,0.0003179707,0.0004034473,0.00044857315,0.00025538768],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001345041,0.0008698523,0.00085042947,0.00034101142,0.00018223195,0.00041473215,0.00016267328,0.0005376592,0.0009368237],"category_scores_gemma":[0.000019458419,0.00090280344,0.00015338595,0.000082683335,0.00017103646,0.0004091835,0.00006331612,0.0007695812,0.00075078453],"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":[0.00015961786,0.000103337254,0.000022663584,0.01895351,0.0010279092,0.0018826234,0.0060638227,0.00019379106,0.0017473394,0.00017900762,0.0026396648,0.9670267],"study_design_scores_gemma":[0.005286282,0.0019046749,0.000045608405,0.11185928,0.0026386057,0.015523697,0.044979185,0.030999755,0.040948946,0.0017341156,0.72808534,0.015994482],"about_ca_topic_score_codex":0.00006814942,"about_ca_topic_score_gemma":0.00010523207,"teacher_disagreement_score":0.9510322,"about_ca_system_score_codex":0.00012556775,"about_ca_system_score_gemma":0.000069212045,"threshold_uncertainty_score":0.99997646},"labels":[],"label_agreement":null},{"id":"W4226360438","doi":"10.1142/9789811239014_0013","title":"Predicting US Elections with Social Media and Neural Networks","year":2021,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Computational and Text Analysis Methods","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Social media; Computer science; Artificial neural network; Political science; Artificial intelligence; World Wide Web","score_opus":0.04137972767914375,"score_gpt":0.29116265423457327,"score_spread":0.24978292655542952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226360438","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15723623,0.13942814,0.1405662,0.0024028216,0.0069536907,0.0038493497,0.0011032533,0.0017489229,0.5467114],"genre_scores_gemma":[0.90309507,0.0028437222,0.00022219116,0.00040858326,0.00617571,0.00009281456,0.0011557387,0.00012528755,0.08588089],"study_design_codex":"design_other","study_design_gemma":"qualitative","domain_scores_codex":[0.9982441,0.00017314771,0.00039665398,0.00047913642,0.0004384486,0.00026848895],"domain_scores_gemma":[0.99876356,0.00021976202,0.00038111844,0.0000770319,0.00042331815,0.00013521538],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039374683,0.00030413285,0.00043422935,0.00016041812,0.0010433141,0.0005194856,0.00008934398,0.00025870837,0.00028687582],"category_scores_gemma":[0.00006895038,0.00026703256,0.000082432154,0.00011493686,0.0003298412,0.00020947696,0.00003369948,0.00036828694,0.0000050328385],"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":[0.000074434865,0.000049847233,0.002939245,0.0005617953,0.0003558314,0.000086185035,0.046296198,0.000085509375,0.0000020547411,0.0017915666,0.00018362743,0.9475737],"study_design_scores_gemma":[0.0044465195,0.0032840497,0.0070739896,0.029949605,0.00827699,0.0022178069,0.49301934,0.10620388,0.00023495915,0.02981055,0.29965228,0.015830027],"about_ca_topic_score_codex":0.0006924964,"about_ca_topic_score_gemma":0.0071843555,"teacher_disagreement_score":0.9317437,"about_ca_system_score_codex":0.000060348633,"about_ca_system_score_gemma":0.00010828381,"threshold_uncertainty_score":0.9999782},"labels":[],"label_agreement":null},{"id":"W4402426001","doi":"10.1142/9789811289125_0008","title":"An Encoder–Decoder Approach to Offline Handwritten Mathematical Expression Recognition with Residual Attention","year":2024,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Handwritten Text Recognition 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":"Concordia University","funders":"","keywords":"Computer science; Speech recognition; Encoder; Residual; Artificial intelligence; Pattern recognition (psychology); Expression (computer science); Algorithm; Programming language","score_opus":0.03051297855439314,"score_gpt":0.26037560981552166,"score_spread":0.22986263126112852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402426001","genre_codex":"methods","genre_gemma":"other","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":[0.0022970268,0.0028030036,0.8796311,0.00024197191,0.00048009536,0.0027017442,0.00069457974,0.0019251549,0.1092253],"genre_scores_gemma":[0.25234002,0.0041465308,0.19216256,0.0048284666,0.008431445,0.0066897585,0.021842053,0.0023686136,0.5071906],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99519444,0.00013908846,0.001189821,0.0018639485,0.0010297775,0.0005829376],"domain_scores_gemma":[0.9973161,0.00007248254,0.0005742969,0.00084276707,0.0007511883,0.0004431605],"candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007108685,0.0010032201,0.0009298737,0.0008916216,0.00034834552,0.0017531586,0.00068631617,0.00065625703,0.00022019379],"category_scores_gemma":[0.000032526725,0.0008078955,0.00014421216,0.0002042758,0.00018510563,0.0013303902,0.00019472853,0.00076964474,0.0010399119],"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":[0.00029519812,0.00071242556,0.000014802,0.008011276,0.00027739702,0.00025810953,0.011711713,0.000008707928,0.0011794395,0.0021927638,0.0030559832,0.9722822],"study_design_scores_gemma":[0.0063643064,0.028911285,0.00008787102,0.25871465,0.0034224393,0.01557288,0.034702964,0.047710337,0.17372584,0.33034182,0.071224764,0.029220857],"about_ca_topic_score_codex":0.000039846054,"about_ca_topic_score_gemma":0.000035226032,"teacher_disagreement_score":0.94306135,"about_ca_system_score_codex":0.00014317574,"about_ca_system_score_gemma":0.00011281065,"threshold_uncertainty_score":0.9997379},"labels":[],"label_agreement":null},{"id":"W4402426016","doi":"10.1142/9789811289125_0006","title":"Shop Signboard Detection Using the ShoS Dataset","year":2024,"lang":"en","type":"book-chapter","venue":"Series on language processing, pattern recognition, and intelligent systems","topic":"Vehicle License Plate Recognition","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":"Concordia University","funders":"","keywords":"Computer science; Geography","score_opus":0.03510642452363035,"score_gpt":0.2412932678993078,"score_spread":0.20618684337567744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402426016","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15463789,0.26973635,0.09581271,0.00041248935,0.029102407,0.014302067,0.073213324,0.010112693,0.35267007],"genre_scores_gemma":[0.90615726,0.00930142,0.00010931904,0.00051031174,0.005747165,0.00041040807,0.01885062,0.0012448892,0.057668608],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979632,0.000036255413,0.0006719071,0.0005796322,0.0003865837,0.00036245058],"domain_scores_gemma":[0.99912554,0.000062982304,0.00020698,0.0003407093,0.00014479546,0.00011897619],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002478334,0.00065991015,0.00047665733,0.00030562078,0.0002419441,0.00062145607,0.00018445402,0.0004524166,0.00029277062],"category_scores_gemma":[0.000011984845,0.0005336733,0.00011368278,0.00008348228,0.00012689091,0.00036187275,0.000053367294,0.00074981706,0.00072855595],"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":[0.00010258637,0.000041432944,0.000010446768,0.0128827635,0.00085967866,0.0002834084,0.006375958,0.0007965077,0.0020879945,0.00008059459,0.0022614358,0.9742172],"study_design_scores_gemma":[0.0010837768,0.0011887982,0.000008690379,0.04176414,0.003513388,0.0054482836,0.02162437,0.29838294,0.05336951,0.0054698344,0.56003445,0.0081118345],"about_ca_topic_score_codex":0.000098337034,"about_ca_topic_score_gemma":0.0001781658,"teacher_disagreement_score":0.96610534,"about_ca_system_score_codex":0.0001729306,"about_ca_system_score_gemma":0.000028103555,"threshold_uncertainty_score":0.99971145},"labels":[],"label_agreement":null}]}