{"meta":{"query_hash":"e871491136ce","filters":{"venue":"Probabilistic Graphical Models"},"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/e871491136ce","api":"https://metacan.xera.ac/api/v1/cohort?venue=Probabilistic+Graphical+Models"},"results":[{"id":"W2522752090","doi":"","title":"Online Algorithms for Sum-Product Networks with Continuous Variables","year":2016,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Algorithm; Product (mathematics); Mathematics","score_opus":0.023641174479941063,"score_gpt":0.2319493166962864,"score_spread":0.20830814221634533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522752090","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":[0.0064471113,0.00024696998,0.989355,0.002006301,0.00043318555,0.0007715278,0.000025007786,0.0004921781,0.00022271682],"genre_scores_gemma":[0.72819585,0.000047511847,0.27084127,0.00023954394,0.00028618783,0.00017414935,0.000014326979,0.00004159683,0.00015955116],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967452,0.000117635595,0.0005000916,0.0012548004,0.00045466528,0.0009275637],"domain_scores_gemma":[0.9967942,0.0010629838,0.00017636459,0.0011944898,0.00048569325,0.00028621795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005168435,0.00039002084,0.00050101796,0.00013842252,0.00019782201,0.00013907622,0.0012005236,0.00017682891,0.000004101708],"category_scores_gemma":[0.00020512454,0.00023374913,0.00014833013,0.0007565866,0.00036582767,0.0004022778,0.00022423221,0.00021952303,0.0000021038713],"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":[0.00003933532,0.00034321842,0.000099532444,0.000019393838,0.00004136545,0.000009397855,0.000021178712,0.4714956,0.00004584561,0.5071252,0.00022629976,0.020533614],"study_design_scores_gemma":[0.00094192504,0.00028287168,0.00009017899,0.00013289865,0.000031759962,0.000026893493,0.0000020422526,0.87442786,0.00003904928,0.12311715,0.0004922398,0.00041514472],"about_ca_topic_score_codex":0.00002162994,"about_ca_topic_score_gemma":0.00003664274,"teacher_disagreement_score":0.72174877,"about_ca_system_score_codex":0.000057060097,"about_ca_system_score_gemma":0.00008197443,"threshold_uncertainty_score":0.9532013},"labels":[],"label_agreement":null},{"id":"W2527038466","doi":"","title":"Relevant Path Separation: A Faster Method for Testing Independencies in Bayesian Networks","year":2016,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Separation (statistics); Bayesian network; Independence (probability theory); Computer science; Path (computing); Intersection (aeronautics); Range (aeronautics); Bayesian probability; Separation of concerns; Source separation; Artificial intelligence; Machine learning; Mathematics; Engineering; Computer network","score_opus":0.05278634295251541,"score_gpt":0.3002076185333202,"score_spread":0.24742127558080476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527038466","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":[0.0011378763,0.00007153705,0.9959073,0.0016241528,0.00014602384,0.0005219543,0.0000053838107,0.00018553149,0.00040023454],"genre_scores_gemma":[0.6302197,0.000005470092,0.3692847,0.00020697844,0.00004408485,0.00019663817,9.2860336e-7,0.000011614417,0.000029898452],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752384,0.0001934793,0.0005676486,0.0008131099,0.00031254007,0.0005894028],"domain_scores_gemma":[0.99735826,0.0014992264,0.000120575656,0.0005642228,0.0002772106,0.00018052627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012256894,0.00025090572,0.0003288758,0.00015706921,0.00012435224,0.00015265206,0.00070816936,0.00020876485,0.00000323304],"category_scores_gemma":[0.00063156674,0.00017199217,0.0001036813,0.00065166136,0.00009025086,0.00058021303,0.00017860295,0.00023818579,0.0000030686217],"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":[0.000026510832,0.00009374385,0.0004210559,0.000039838666,0.000010259893,0.000008296463,0.00022499288,0.04988179,0.0003504275,0.87264585,0.000048036378,0.07624919],"study_design_scores_gemma":[0.00024205056,0.000083661595,0.00011086579,0.00009917942,0.0000048164825,0.000009603547,0.0000024777594,0.5296846,0.000011326262,0.46958846,0.000008994177,0.00015398694],"about_ca_topic_score_codex":0.000029263376,"about_ca_topic_score_gemma":0.000042654166,"teacher_disagreement_score":0.62908185,"about_ca_system_score_codex":0.000059419366,"about_ca_system_score_gemma":0.00013152856,"threshold_uncertainty_score":0.70136374},"labels":[],"label_agreement":null},{"id":"W2898670591","doi":"","title":"Privacy Sensitive Construction of Junction Tree Agent Organization for Multiagent Graphical Models.","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Data Mining Algorithms and Applications","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 Guelph","funders":"","keywords":"Computer science; Multi-agent system; Tree (set theory); Graphical model; Computer security; Artificial intelligence","score_opus":0.03175791186320538,"score_gpt":0.2517132354035583,"score_spread":0.2199553235403529,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898670591","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":[0.032533698,0.000010896586,0.965139,0.00086581474,0.00029009162,0.00075530837,0.00004740387,0.00019167377,0.00016614927],"genre_scores_gemma":[0.7026828,0.000014318915,0.29698864,0.00006982849,0.00008066571,0.00007295706,0.000066920824,0.000016137616,0.0000077264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810785,0.00006580272,0.0004997382,0.00070006447,0.00033527965,0.00029124878],"domain_scores_gemma":[0.99776906,0.00016128452,0.0002063472,0.0006738774,0.0010353606,0.00015410007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000312696,0.00020440137,0.00026745643,0.00019495253,0.0002795992,0.000087508735,0.00048043602,0.00015050669,0.000004392093],"category_scores_gemma":[0.00022952931,0.00018891119,0.00011231268,0.0011263948,0.00045681137,0.00057671807,0.00020713135,0.0001326692,0.0000047231024],"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":[0.000019737248,0.00025077557,0.000028798451,0.000039334736,0.00003504481,5.0951866e-7,0.00051260646,0.0020456815,0.0006603971,0.95948833,0.00016477256,0.03675404],"study_design_scores_gemma":[0.00029784712,0.00014007154,0.0002452269,0.0000199791,0.00003165626,0.000012270743,0.000015716514,0.61101735,0.0008296469,0.38718945,0.0000675904,0.00013319329],"about_ca_topic_score_codex":0.00003689076,"about_ca_topic_score_gemma":0.000010264977,"teacher_disagreement_score":0.6701491,"about_ca_system_score_codex":0.000049072514,"about_ca_system_score_gemma":0.00007516016,"threshold_uncertainty_score":0.7703575},"labels":[],"label_agreement":null},{"id":"W2898877291","doi":"","title":"Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Graph Theory and Algorithms","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Computer science; Directed acyclic graph; Directed graph; Theoretical computer science; Graph; Product (mathematics); Graph theory; Artificial intelligence; Algorithm; Mathematics; Combinatorics","score_opus":0.022079408785830146,"score_gpt":0.25352163056128996,"score_spread":0.2314422217754598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898877291","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":[0.05027007,0.0004007735,0.94487906,0.0004005648,0.0009103027,0.0011962308,0.000007981363,0.0012179568,0.00071707263],"genre_scores_gemma":[0.93262726,0.000023509505,0.06639113,0.00015716645,0.00037150466,0.00029485836,0.000013930232,0.00004021941,0.00008044043],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9963272,0.0004024032,0.00054613553,0.0013390839,0.0004201017,0.0009650512],"domain_scores_gemma":[0.99725664,0.00073926133,0.00020000251,0.00097567705,0.0005147148,0.0003137348],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011421342,0.00044780035,0.0005182233,0.00036027725,0.00075167266,0.0002890561,0.0014487936,0.00019965328,0.000012748242],"category_scores_gemma":[0.0008810455,0.00037002433,0.00033069574,0.0019389631,0.0006998782,0.00054475793,0.00030338095,0.0005585772,0.0000048303395],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009038093,0.00015617219,0.0001877006,0.00006379781,0.00009673738,0.0000044647436,0.00048557928,0.013359045,0.00041866794,0.9476379,0.00014508874,0.03735442],"study_design_scores_gemma":[0.0002815855,0.00024268946,0.00043127776,0.00002225178,0.000025825551,0.000010416973,0.000004072274,0.4274815,0.00024937396,0.5707665,0.0001837243,0.00030079807],"about_ca_topic_score_codex":0.000017116032,"about_ca_topic_score_gemma":0.000012358168,"teacher_disagreement_score":0.8823572,"about_ca_system_score_codex":0.000026347027,"about_ca_system_score_gemma":0.000057049863,"threshold_uncertainty_score":0.9998752},"labels":[],"label_agreement":null},{"id":"W2899000040","doi":"","title":"Discriminative Training of Sum-Product Networks by Extended Baum-Welch.","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Training (meteorology); Product (mathematics); Computer science; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.040581510970118646,"score_gpt":0.26223389838308414,"score_spread":0.2216523874129655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899000040","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":[0.032221805,0.00016200451,0.96314806,0.00080290687,0.00032037342,0.00033118005,0.000012899951,0.00011680581,0.0028839407],"genre_scores_gemma":[0.9837669,0.000023264172,0.015813159,0.00018532308,0.000085702275,0.000055277338,0.000013725942,0.0000122939955,0.0000443356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800754,0.00014191096,0.00040424347,0.0006591608,0.000367643,0.00041951446],"domain_scores_gemma":[0.998634,0.00019988972,0.0001384295,0.00054219714,0.000318361,0.00016717016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042919302,0.00020326657,0.00030448378,0.00010789341,0.00015111656,0.00005976261,0.00065998564,0.00010620738,0.000023983379],"category_scores_gemma":[0.00020952114,0.00016387244,0.000112835136,0.0005638241,0.00048448276,0.0005080662,0.00020017274,0.00022705364,0.0000075381363],"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":[0.0001330184,0.00093666714,0.00007992843,0.00014803598,0.00009399693,0.000008916163,0.0068363696,0.0035767765,0.00845111,0.813007,0.0099137705,0.15681443],"study_design_scores_gemma":[0.00023232687,0.00018608295,0.00013208676,0.00007439406,0.0000147961355,0.000004094659,0.0000496385,0.53475314,0.0017525704,0.46253946,0.00007520823,0.00018619558],"about_ca_topic_score_codex":0.000026461594,"about_ca_topic_score_gemma":0.0000076030456,"teacher_disagreement_score":0.9515451,"about_ca_system_score_codex":0.000017082808,"about_ca_system_score_gemma":0.00004280148,"threshold_uncertainty_score":0.6682524},"labels":[],"label_agreement":null},{"id":"W2899147640","doi":"","title":"An Empirical Study of Methods for SPN Learning and Inference","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Regina","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Machine learning","score_opus":0.11016250390616814,"score_gpt":0.42599450067440925,"score_spread":0.31583199676824114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899147640","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":[0.2475964,0.000012684388,0.7515795,0.000089974674,0.000047282196,0.00035153987,8.279422e-7,0.00007320826,0.00024854843],"genre_scores_gemma":[0.6433181,0.0000026645382,0.3565535,0.000044769342,0.000017801633,0.000054307915,3.8820136e-7,0.000004892453,0.0000035424628],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984918,0.0003901305,0.00029835792,0.00047178078,0.00014418138,0.00020370753],"domain_scores_gemma":[0.9980185,0.0011314868,0.000078538804,0.00030072045,0.0003195232,0.00015121534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009544268,0.00012194559,0.00025519202,0.00013286894,0.0001385695,0.00007492325,0.00035981025,0.000077121804,0.0000111052705],"category_scores_gemma":[0.0009940241,0.000100284575,0.0000490056,0.0003552653,0.00020385976,0.00024724478,0.00011189572,0.00012676005,0.0000010928959],"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":[0.0000847971,0.0018626023,0.010786492,0.00008008005,0.000057637695,0.0000016002535,0.0038914129,0.00015833318,0.0010062909,0.18197003,0.000013997302,0.80008674],"study_design_scores_gemma":[0.000265058,0.0012330582,0.0015802415,0.000008608899,0.000015782196,0.0000026798375,0.000071300296,0.6221981,0.0001607835,0.37431687,0.000045350833,0.00010216954],"about_ca_topic_score_codex":0.000013594103,"about_ca_topic_score_gemma":0.000013474446,"teacher_disagreement_score":0.7999846,"about_ca_system_score_codex":0.0000072024473,"about_ca_system_score_gemma":0.00003258746,"threshold_uncertainty_score":0.40894863},"labels":[],"label_agreement":null},{"id":"W2899427005","doi":"","title":"Bayesian Network Structure Learning with Side Constraints","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","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":"University of Waterloo","funders":"","keywords":"Bayesian network; Computer science; Artificial intelligence; Bayesian probability; Variable-order Bayesian network; Machine learning; Bayesian inference","score_opus":0.017052654482376164,"score_gpt":0.2298890746640274,"score_spread":0.21283642018165122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899427005","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":[0.010886707,0.000039344293,0.9835595,0.00050708244,0.00016011065,0.00021864776,0.000002746874,0.0003928304,0.004233017],"genre_scores_gemma":[0.8723299,0.0000034151312,0.12702616,0.0003952872,0.00018231015,0.00001554409,0.0000032535725,0.000018064526,0.000026070435],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764067,0.00015455509,0.00032365572,0.0007792332,0.00042000288,0.0006818828],"domain_scores_gemma":[0.9985201,0.00016611502,0.000107750086,0.00059237087,0.00031154396,0.0003021182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030854982,0.00028848383,0.00029769383,0.00008110715,0.00037483656,0.00024129287,0.00080755656,0.00017552632,0.00003926878],"category_scores_gemma":[0.00009681455,0.00022144249,0.00006848968,0.00075531437,0.00087390735,0.00036551565,0.00017779077,0.0005828805,0.00001349962],"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":[0.000020250829,0.000032181215,0.00039481794,0.000017815675,0.000027438608,0.000016266204,0.0003076236,0.0780029,0.00008265176,0.907609,0.0000665193,0.013422537],"study_design_scores_gemma":[0.00014076663,0.00020678934,0.00012570659,0.000049821985,0.000009967983,0.00004023709,0.000004026731,0.50138396,0.000022350127,0.4977891,0.00003113018,0.00019613616],"about_ca_topic_score_codex":0.00001605406,"about_ca_topic_score_gemma":0.000044581928,"teacher_disagreement_score":0.86144316,"about_ca_system_score_codex":0.000025735113,"about_ca_system_score_gemma":0.00015320363,"threshold_uncertainty_score":0.90301627},"labels":[],"label_agreement":null},{"id":"W33601243","doi":"10.1016/j.nedt.2020.104740","title":"A Short Note on Discrete Representability of Independence Models.","year":2006,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Independence (probability theory); Computer science; Theoretical computer science; Mathematical economics; Econometrics; Mathematics; Statistics","score_opus":0.03499973794160964,"score_gpt":0.2755188765652267,"score_spread":0.24051913862361707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W33601243","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":[0.06273543,0.000050885796,0.92932034,0.0003941803,0.000114659364,0.00036432067,0.000015310272,0.00021598967,0.006788887],"genre_scores_gemma":[0.9709784,0.000009368299,0.028775461,0.000089420166,0.000036841193,0.00006769378,0.0000055281444,0.000015964255,0.000021366332],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9965032,0.00017095315,0.0007420849,0.0010955642,0.0009709877,0.0005172291],"domain_scores_gemma":[0.9975299,0.0003145015,0.000117693955,0.0015209495,0.00032663683,0.00019027333],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00074845925,0.0003110443,0.00044211073,0.00016244214,0.00011970727,0.00010039612,0.0013222978,0.00024263555,0.000004302158],"category_scores_gemma":[0.00012248516,0.00026485496,0.00022441792,0.0006918002,0.000362587,0.0006365673,0.00035148996,0.00050317525,0.000005130609],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025165536,0.0002425799,0.00022405968,0.00003805078,0.0000062647637,0.0000054781153,0.00006941256,0.20021671,0.00017177685,0.79529977,0.000024377612,0.0036763393],"study_design_scores_gemma":[0.00008568299,0.00006714937,0.00057895447,0.000033296026,0.000007425228,0.0000041388334,0.0000012018694,0.49724182,0.00023869588,0.50158674,0.0000014076693,0.00015350454],"about_ca_topic_score_codex":0.00045338058,"about_ca_topic_score_gemma":0.00004318707,"teacher_disagreement_score":0.90824294,"about_ca_system_score_codex":0.00005443379,"about_ca_system_score_gemma":0.000112816895,"threshold_uncertainty_score":0.9999804},"labels":[],"label_agreement":null}]}