{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"e871491136ce","filters":{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Algorithm; Product (mathematics); Mathematics","authors":[{"name":"Priyank Jaini","is_ca":true},{"name":"Abdullah Rashwan","is_ca":true},{"name":"Han Zhao","is_ca":false},{"name":"Yue Liu","is_ca":true},{"name":"Ershad Banijamali","is_ca":true},{"name":"Zhitang Chen","is_ca":false},{"name":"Pascal Poupart","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02364117447994106,"gpt":0.2319493166962864,"spread":0.2083081422163453,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004839065,0.002996022,0.00403471,0.001782889,0.001468406,0.004332253,0.007570152,0.003608527,0.01381817],"category_scores_gemma":[0.02053994,0.002263098,0.002162169,0.004079579,0.002819809,0.009967135,0.006754033,0.005667976,0.002320628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003030493,"about_ca_system_score_gemma":0.003029654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003656396,"about_ca_topic_score_gemma":0.005212205,"domain_scores_codex":[0.996217,0.001555439,0.0001859654,0.0009569816,0.0007002382,0.0003844682],"domain_scores_gemma":[0.9768737,0.01877662,0.0009938026,0.001809654,0.0009058964,0.0006403191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003602338,0.0004936281,0.0006935417,0.0005730341,0.0001644449,0.0001162618,0.0002097173,0.7103378,0.000752056,0.1387624,0.01019468,0.1373422],"study_design_scores_gemma":[0.00004543898,0.00003485961,0.00004445362,0.0000201711,0.00002194883,0.00004146442,0.00002665324,0.8666987,0.0002524519,0.1320176,0.0007849543,0.00001119078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006638084,0.0004692455,0.9889214,0.0004709886,0.00009045948,0.0001098778,0.0002013422,0.0005805388,0.002518055],"genre_scores_gemma":[0.3016129,0.001147437,0.6811876,0.0006024879,0.0003518334,0.0009841346,0.00146896,0.000756889,0.0118878],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01381817,"threshold_uncertainty_score":0.04622638,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Priyank Jaini","is_ca":true},{"name":"Amur Ghose","is_ca":true},{"name":"Pascal Poupart","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02207940878583015,"gpt":0.25352163056129,"spread":0.2314422217754598,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002107403,0.001706421,0.001529952,0.001923278,0.0009625205,0.002067582,0.00422436,0.002399816,0.008803271],"category_scores_gemma":[0.01371933,0.001521167,0.002196143,0.001687616,0.001288796,0.005258532,0.004202811,0.004099417,0.003247774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140569,"about_ca_system_score_gemma":0.001957057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003805459,"about_ca_topic_score_gemma":0.008860666,"domain_scores_codex":[0.9987679,0.0005012301,0.00004820981,0.0003665428,0.0002525224,0.00006354872],"domain_scores_gemma":[0.9961135,0.002643093,0.0001461968,0.000698062,0.0002497474,0.0001493472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004590296,0.0004024482,0.001535557,0.0008444414,0.0003513109,0.000258793,0.0002381535,0.4243951,0.003189591,0.1250906,0.04053748,0.4026975],"study_design_scores_gemma":[0.00003503173,0.00002481491,0.000049539,0.00002003323,0.00001960858,0.00003292502,0.00001172599,0.8652967,0.000898694,0.1310236,0.002579411,0.000007961004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005942479,0.0002533202,0.9819686,0.0003173211,0.00006977863,0.0001258839,0.0008405499,0.008499428,0.001982581],"genre_scores_gemma":[0.1688523,0.0004274235,0.8175099,0.0004384119,0.0001026233,0.0005908218,0.004430906,0.00178111,0.005866496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008803271,"threshold_uncertainty_score":0.02944988,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Andrew C. Li","is_ca":false},{"name":"Peter van Beek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01705265448237616,"gpt":0.2298890746640274,"spread":0.2128364201816512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007847304,0.001700021,0.003298297,0.002407605,0.001115199,0.002857416,0.004387983,0.003600399,0.007418103],"category_scores_gemma":[0.0491339,0.002421386,0.001698641,0.003517584,0.002790375,0.009186438,0.003258884,0.006347379,0.001367142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002157808,"about_ca_system_score_gemma":0.002730919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006726538,"about_ca_topic_score_gemma":0.008965935,"domain_scores_codex":[0.9943203,0.003488583,0.0001626292,0.0009793827,0.0008656943,0.0001834883],"domain_scores_gemma":[0.9664536,0.02821014,0.001325503,0.002239132,0.001319829,0.0004518212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001598444,0.0001223162,0.0009682385,0.0002762343,0.0001735337,0.0001467396,0.0001243786,0.5520223,0.0003946869,0.3760311,0.006049947,0.06353062],"study_design_scores_gemma":[0.00002086434,0.00000860714,0.00007411089,0.00002115908,0.00002380248,0.00002578401,0.000006147053,0.6865842,0.0001313475,0.3120824,0.001010426,0.00001114253],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001796047,0.0002454215,0.9963275,0.0004115019,0.00002362926,0.00001981432,0.0001311924,0.0001049529,0.0009400097],"genre_scores_gemma":[0.323187,0.002385198,0.6604764,0.0007561602,0.0005813026,0.0006469527,0.001850681,0.0003463762,0.009769924],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007847304,"threshold_uncertainty_score":0.04150105,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of British Columbia","keywords":"Independence (probability theory); Computer science; Theoretical computer science; Mathematical economics; Econometrics; Mathematics; Statistics","authors":[{"name":"Petr Šimeček","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03499973794160964,"gpt":0.2755188765652267,"spread":0.2405191386236171,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01619425,0.001527408,0.001294305,0.001615744,0.001714348,0.005383051,0.00439953,0.004678053,0.01844727],"category_scores_gemma":[0.08319382,0.001299517,0.003260103,0.003115026,0.007440364,0.01313322,0.004072824,0.01443346,0.004598211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0022651,"about_ca_system_score_gemma":0.001329692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003857008,"about_ca_topic_score_gemma":0.002402801,"domain_scores_codex":[0.9876502,0.007796594,0.0007533883,0.001880485,0.001616783,0.0003025955],"domain_scores_gemma":[0.9168026,0.07251735,0.002057782,0.005970938,0.002068077,0.0005832547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003308764,0.00002418256,0.0004174055,0.0002054871,0.00004787403,0.0001881865,0.000347597,0.004965333,0.000157836,0.9554563,0.01710727,0.02104953],"study_design_scores_gemma":[0.000009529172,0.0000188907,0.0001628057,0.00008764919,0.00001831225,0.000223312,0.00004077268,0.01777986,0.00009297849,0.9490109,0.03253169,0.00002337944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001511693,0.005351618,0.9528553,0.01341472,0.002607439,0.00007454962,0.0006831288,0.0004678695,0.02303362],"genre_scores_gemma":[0.2240433,0.01454109,0.7007176,0.01452528,0.01518917,0.001330839,0.002174529,0.001236366,0.02624188],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01844727,"threshold_uncertainty_score":0.08564442,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Abdullah Rashwan","is_ca":true},{"name":"Pascal Poupart","is_ca":true},{"name":"Zhitang Chen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04058151097011865,"gpt":0.2622338983830841,"spread":0.2216523874129655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002264416,0.001413933,0.001698036,0.00114517,0.0006148398,0.001083948,0.00327652,0.002070782,0.005956697],"category_scores_gemma":[0.009591878,0.0014736,0.001299726,0.001702617,0.0009840801,0.002484913,0.001980807,0.003634386,0.00397098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001992,"about_ca_system_score_gemma":0.00146789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01104848,"about_ca_topic_score_gemma":0.02186186,"domain_scores_codex":[0.9984505,0.0007104263,0.0000678782,0.0004328959,0.0001945957,0.0001436349],"domain_scores_gemma":[0.996726,0.002142477,0.0001514553,0.0004275214,0.0004401618,0.0001123455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003783327,0.0002014392,0.0009337215,0.0001818828,0.0002417912,0.0000941776,0.00009094648,0.5862759,0.004100439,0.009990237,0.007973381,0.3895377],"study_design_scores_gemma":[0.000006464543,0.00001209564,0.00007206394,0.000005194401,0.000009061798,0.00001311056,0.000004157721,0.995496,0.0005607702,0.003510993,0.0003055166,0.000004579617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01074434,0.0004905701,0.9847903,0.0001996759,0.00009366994,0.00005167611,0.0001883764,0.002343856,0.001097523],"genre_scores_gemma":[0.485624,0.0004728148,0.4989783,0.0005263683,0.0001536894,0.0004013186,0.002739485,0.000972933,0.01013109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01104848,"threshold_uncertainty_score":0.02196836,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Regina","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Machine learning","authors":[{"name":"Cory J. Butz","is_ca":true},{"name":"Jhonatan de S. Oliveira","is_ca":true},{"name":"André E. dos Santos","is_ca":true},{"name":"André Teixeira","is_ca":true},{"name":"Pascal Poupart","is_ca":true},{"name":"Agastya Kalra","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1101625039061681,"gpt":0.4259945006744092,"spread":0.3158319967682411,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07888181,0.00238004,0.002398009,0.004009867,0.001796341,0.003603094,0.006262287,0.004395455,0.008453873],"category_scores_gemma":[0.3629445,0.001570235,0.002789994,0.003796657,0.005811171,0.01400488,0.005187638,0.007791521,0.0008106391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003292119,"about_ca_system_score_gemma":0.002596385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008232944,"about_ca_topic_score_gemma":0.008297862,"domain_scores_codex":[0.9650451,0.02793948,0.001002439,0.002747256,0.002757485,0.0005082591],"domain_scores_gemma":[0.3567132,0.6140142,0.004688465,0.01736353,0.005879018,0.001341549],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00115141,0.000491281,0.03068307,0.001372268,0.001351196,0.000291244,0.001076333,0.3150942,0.0009753709,0.3813008,0.01037856,0.2558343],"study_design_scores_gemma":[0.0001011846,0.0001610482,0.00296986,0.0002873354,0.0001473612,0.0003561477,0.0002026401,0.7640078,0.0006032425,0.2280183,0.003092149,0.00005289724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04670438,0.005988003,0.9394851,0.002474841,0.0001683268,0.0001542351,0.0004349507,0.0005317101,0.004058418],"genre_scores_gemma":[0.5648026,0.005401975,0.4170009,0.001093047,0.0009021986,0.0006470347,0.002709146,0.001129532,0.006313423],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07888181,"threshold_uncertainty_score":0.4171718,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Cory J. Butz","is_ca":true},{"name":"André E. dos Santos","is_ca":true},{"name":"Jhonatan de S. Oliveira","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05278634295251541,"gpt":0.3002076185333202,"spread":0.2474212755808048,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005754736,0.002012172,0.001698802,0.003500689,0.0010855,0.001949406,0.002555759,0.002079271,0.007294945],"category_scores_gemma":[0.02574718,0.001008038,0.002186722,0.002403511,0.00145848,0.004680894,0.00413442,0.004526392,0.001958689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001135519,"about_ca_system_score_gemma":0.00305217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004757195,"about_ca_topic_score_gemma":0.005108402,"domain_scores_codex":[0.9942978,0.002373842,0.0003119494,0.001152703,0.00161249,0.0002512302],"domain_scores_gemma":[0.9796529,0.01534869,0.0009830599,0.002133774,0.001356422,0.0005250996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001034371,0.0003467131,0.005080906,0.0005117949,0.0005476298,0.0003286476,0.0005465399,0.1299875,0.01213036,0.0746894,0.006170662,0.7686254],"study_design_scores_gemma":[0.0001444773,0.000137815,0.0009190344,0.00005194013,0.00009363805,0.0002509701,0.00006906913,0.8266652,0.005424417,0.1616128,0.004566046,0.00006456556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003802404,0.0001443284,0.9944299,0.00009203173,0.00001846342,0.00006987551,0.0001356127,0.000874447,0.0004329452],"genre_scores_gemma":[0.08141287,0.0001973114,0.9157029,0.0001424707,0.00005294104,0.0002487415,0.0008485003,0.0003990485,0.0009951787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007294945,"threshold_uncertainty_score":0.03043431,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Multi-agent system; Tree (set theory); Graphical model; Computer security; Artificial intelligence","authors":[{"name":"Yang Xiang","is_ca":true},{"name":"Abdulrahman Alshememry","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03175791186320538,"gpt":0.2517132354035583,"spread":0.2199553235403529,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001996503,0.0004280719,0.0008169677,0.001096715,0.001206158,0.001933693,0.002025689,0.001330617,0.002709438],"category_scores_gemma":[0.01377105,0.0006619797,0.001475073,0.001252589,0.000923819,0.003231572,0.003440438,0.002287145,0.0009405652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070672,"about_ca_system_score_gemma":0.002023382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002471671,"about_ca_topic_score_gemma":0.003993754,"domain_scores_codex":[0.997838,0.0009105736,0.00009624376,0.0004260128,0.0005501339,0.0001791187],"domain_scores_gemma":[0.9943793,0.002632866,0.000445967,0.001487673,0.0006559492,0.0003981649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002233599,0.0003076518,0.005034729,0.0002279094,0.000154295,0.0005166122,0.0009942574,0.4081882,0.005799859,0.4242386,0.01656816,0.1377464],"study_design_scores_gemma":[0.00001071601,0.00002472374,0.0002260427,0.00001823402,0.00002686353,0.00008699445,0.00009991051,0.8255509,0.001615545,0.1687063,0.003624199,0.000009511945],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01264604,0.00009071027,0.9845345,0.0002154834,0.00002282496,0.00006078661,0.0002677988,0.000401853,0.00175996],"genre_scores_gemma":[0.4427946,0.0002653867,0.550397,0.0001871213,0.00004801041,0.0002727456,0.002191158,0.000278236,0.003565851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002709438,"threshold_uncertainty_score":0.01055866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}