{"meta":{"query_hash":"1fb3a78d8e29","filters":{"venue":"Adaptive Agents and Multi-Agents Systems"},"cohort_total":63,"direct_labels_cover":0,"predictions_cover":63,"exported":63,"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/1fb3a78d8e29","api":"https://metacan.xera.ac/api/v1/cohort?venue=Adaptive+Agents+and+Multi-Agents+Systems"},"results":[{"id":"W108564685","doi":"10.5555/2484920.2484946","title":"The impact of culture on crowd dynamics: an empirical approach","year":2013,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Crowds; Pedestrian; Crowd simulation; Macro; Computer science; Crowd psychology; Set (abstract data type); Domain (mathematical analysis); Dynamics (music); Human–computer interaction; Data science; Simulation; Artificial intelligence; Computer security; Transport engineering; Sociology; Engineering; Mathematics","score_opus":0.04528727989469312,"score_gpt":0.3138364174435642,"score_spread":0.2685491375488711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W108564685","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960758,0.00009948357,0.0014020746,0.00007407346,0.0000039001948,0.000045269364,0.00020830819,0.000007289679,0.0020837525],"genre_scores_gemma":[0.999017,0.00007672536,0.00052496215,0.000017796181,0.0000041962853,0.000041583662,0.00012515382,0.00000467397,0.00018790357],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996572,0.0023298466,0.00015461651,0.00036463808,0.0004222216,0.00015678335],"domain_scores_gemma":[0.93946356,0.049013965,0.0046358844,0.0031361426,0.0030818966,0.00066858676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043208273,0.00034920248,0.00031172714,0.0015596865,0.00076583214,0.0010492956,0.00076914526,0.0006257771,0.0023957277],"category_scores_gemma":[0.039931193,0.0002705011,0.0004905833,0.0017991071,0.0013441113,0.0014951846,0.0014042986,0.0009241474,0.00039801517],"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":[0.00023679288,0.0006905707,0.96305734,0.00013405607,0.00024807992,0.00020433293,0.005787956,0.010400532,0.0005573351,0.002011828,0.0007554845,0.015915629],"study_design_scores_gemma":[0.000036656318,0.00084632414,0.90132815,0.00014511522,0.00020329723,0.0005567819,0.018096268,0.07050879,0.002103932,0.0026780232,0.0034114001,0.000085294196],"about_ca_topic_score_codex":0.0077778935,"about_ca_topic_score_gemma":0.006827585,"teacher_disagreement_score":0.0077778935,"about_ca_system_score_codex":0.0007679354,"about_ca_system_score_gemma":0.00056626654,"threshold_uncertainty_score":0.02285099},"labels":[],"label_agreement":null},{"id":"W136985510","doi":"10.5555/2034396.2034517","title":"Escaping local optima in POMDP planning as inference","year":2011,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":3,"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 Waterloo","funders":"","keywords":"Inference; Partially observable Markov decision process; Local optimum; Reinforcement learning; Computer science; Mathematical optimization; Observable; Greedy algorithm; Artificial intelligence; Machine learning; Mathematics; Markov chain; Markov model","score_opus":0.14580520123192156,"score_gpt":0.3207535313869368,"score_spread":0.17494833015501524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W136985510","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021550706,0.0002570921,0.97554374,0.00025900867,0.000018458872,0.000043187796,0.000020788595,0.00040059924,0.0019063638],"genre_scores_gemma":[0.7663519,0.00024520798,0.23132463,0.00017704384,0.000029458328,0.00020719023,0.000049113347,0.00011187333,0.0015036461],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989944,0.0004414437,0.00005151698,0.0001668119,0.00022754888,0.000118143376],"domain_scores_gemma":[0.9957508,0.003364935,0.0002818339,0.00028025205,0.00019360613,0.0001286334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029250209,0.00092575926,0.0016256355,0.0006072966,0.000615276,0.00094094564,0.001495914,0.0012747815,0.0013483847],"category_scores_gemma":[0.008670349,0.00079169637,0.0007210283,0.0006155971,0.0026204176,0.0016983374,0.0021339415,0.0024055156,0.00021627666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043562562,0.00001877312,0.00025748025,0.000038557653,0.000024732028,0.000037812348,0.000071515504,0.9690899,0.00043094176,0.017356018,0.00019774934,0.012433004],"study_design_scores_gemma":[0.000013223251,0.000014821702,0.000028608783,0.000006205497,0.0000058604446,0.0000052345003,0.000007509139,0.98516244,0.00025693135,0.014368229,0.00012674803,0.000004116733],"about_ca_topic_score_codex":0.0058028074,"about_ca_topic_score_gemma":0.0055414257,"teacher_disagreement_score":0.0058028074,"about_ca_system_score_codex":0.001348704,"about_ca_system_score_gemma":0.001464654,"threshold_uncertainty_score":0.015469193},"labels":[],"label_agreement":null},{"id":"W145585440","doi":"10.5555/2484920.2485185","title":"Model based approach to detect emergent behavior in multi-agent systems","year":2013,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Advanced Software Engineering Methodologies","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":"Carleton University; University of Calgary","funders":"","keywords":"Agent-oriented software engineering; Computer science; Unified Modeling Language; Sequence diagram; Software engineering; Software deployment; Multi-agent system; Software; Software development; Systems engineering; Artificial intelligence; Engineering","score_opus":0.18208004838312175,"score_gpt":0.3285164316386605,"score_spread":0.14643638325553876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W145585440","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002683385,0.00010758083,0.99412185,0.00011892712,0.000023423923,0.000093780895,0.000046331053,0.00087543007,0.0019292],"genre_scores_gemma":[0.20577036,0.00036396948,0.7899628,0.00014528361,0.000038903778,0.0005776528,0.0002443707,0.00015877593,0.0027378965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998487,0.0004090705,0.00010982454,0.00022850295,0.0006839501,0.00008154231],"domain_scores_gemma":[0.99784064,0.0011899726,0.000255969,0.0002965186,0.0003599741,0.00005695107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013544764,0.0010726884,0.0008430314,0.0017206316,0.0007115117,0.0018617591,0.0017905479,0.001650098,0.0025200094],"category_scores_gemma":[0.0043099034,0.0006585781,0.0015608707,0.00067682937,0.0009248154,0.0020727373,0.0015646799,0.0020514736,0.0005450984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012524452,0.00028885715,0.0035474359,0.00046002725,0.00020909125,0.000879897,0.00071693666,0.6911109,0.021653546,0.18380067,0.0027130262,0.094494335],"study_design_scores_gemma":[0.000016827298,0.000042211093,0.00017507379,0.000026582906,0.00003158695,0.00009533769,0.000047553127,0.9678403,0.0036732703,0.024194866,0.0038390486,0.000017231077],"about_ca_topic_score_codex":0.0039135315,"about_ca_topic_score_gemma":0.00437767,"teacher_disagreement_score":0.0039135315,"about_ca_system_score_codex":0.0012855993,"about_ca_system_score_gemma":0.0014480836,"threshold_uncertainty_score":0.00932771},"labels":[],"label_agreement":null},{"id":"W1514520309","doi":"10.5555/1838206.1838534","title":"On multi-robot area coverage","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"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 British Columbia","funders":"","keywords":"Robot; Task (project management); Computer science; Point (geometry); Path (computing); Mobile robot; Actuator; Motion planning; Robot kinematics; Human–computer interaction; Robot control; Task analysis; Artificial intelligence; Real-time computing; Engineering; Computer network; Systems engineering","score_opus":0.07079121496115787,"score_gpt":0.2913392058689059,"score_spread":0.22054799090774801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1514520309","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006306921,0.008904674,0.9708134,0.00068215077,0.00025971612,0.000098754776,0.00019571617,0.00034360398,0.012395191],"genre_scores_gemma":[0.6233835,0.019703468,0.33638906,0.0009326331,0.0014414953,0.00069421285,0.0010939087,0.0004637364,0.015897974],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974044,0.0008286016,0.0000897564,0.0005953413,0.00079106976,0.00029077166],"domain_scores_gemma":[0.99654824,0.0021259012,0.00029258014,0.000459004,0.00037335927,0.00020087596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014287668,0.0017126757,0.0019366717,0.0018199743,0.0010745975,0.0019247083,0.002754303,0.0017762876,0.004517135],"category_scores_gemma":[0.006691925,0.0006215981,0.00086971244,0.004365455,0.0020898678,0.0035332246,0.0039645536,0.0018560146,0.0011110721],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022508821,0.000045590885,0.0010120478,0.00049515086,0.00016090226,0.0003921058,0.0002204998,0.7175834,0.0027165753,0.13684475,0.012403506,0.1279004],"study_design_scores_gemma":[0.000048364906,0.00010947401,0.0005861102,0.000121623336,0.000051945106,0.0003859526,0.00008797012,0.80145097,0.0015151101,0.16484387,0.03076046,0.00003812884],"about_ca_topic_score_codex":0.0048917546,"about_ca_topic_score_gemma":0.0024518045,"teacher_disagreement_score":0.0048917546,"about_ca_system_score_codex":0.0014120733,"about_ca_system_score_gemma":0.0006710588,"threshold_uncertainty_score":0.015111327},"labels":[],"label_agreement":null},{"id":"W1530765895","doi":"10.5555/1402821.1402883","title":"A new approach to cooperative pathfinding","year":2008,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":27,"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 Alberta","funders":"","keywords":"Pathfinding; Computer science; Plan (archaeology); Fidelity; Path (computing); Human–computer interaction; Distributed computing; Operations research; Artificial intelligence; Shortest path problem; Theoretical computer science; Engineering; Computer network","score_opus":0.13236742723817527,"score_gpt":0.29515774679958934,"score_spread":0.16279031956141407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1530765895","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002962867,0.00029193948,0.9918932,0.0002148068,0.00010713022,0.00004028463,0.000023293618,0.00030714445,0.0041593756],"genre_scores_gemma":[0.12564273,0.0007574083,0.861864,0.0003214589,0.00021751809,0.00033958463,0.0001283807,0.00015086375,0.010578116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999015,0.00016227459,0.0000398472,0.00032141744,0.00040342048,0.000058012403],"domain_scores_gemma":[0.99923015,0.00026269173,0.00007986379,0.00019489837,0.0001622642,0.0000702723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005490114,0.00076520804,0.0007415045,0.0009782065,0.00079756096,0.0014346562,0.0021808106,0.0013149831,0.003170521],"category_scores_gemma":[0.0021813607,0.00039673626,0.0006717158,0.00096296874,0.001396234,0.0024746717,0.003167531,0.0017298893,0.00094143744],"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":[0.00015112243,0.0001863975,0.0008255196,0.00040911927,0.00013016751,0.00037518053,0.0009676498,0.246939,0.029397022,0.24614301,0.010022801,0.46445298],"study_design_scores_gemma":[0.000056519864,0.00017421601,0.00030046472,0.000041225285,0.000049812435,0.00051994185,0.00012621377,0.8097226,0.0050566476,0.119681634,0.06422662,0.000044123124],"about_ca_topic_score_codex":0.0018577796,"about_ca_topic_score_gemma":0.0018382384,"teacher_disagreement_score":0.003170521,"about_ca_system_score_codex":0.00062258466,"about_ca_system_score_gemma":0.0008234449,"threshold_uncertainty_score":0.010606468},"labels":[],"label_agreement":null},{"id":"W1540112079","doi":"10.5555/1402821.1402866","title":"Using adaptive consultation of experts to improve convergence rates in multiagent learning","year":2008,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Regret; Computer science; Outcome (game theory); Convergence (economics); Advice (programming); Multi-agent system; Set (abstract data type); Class (philosophy); Nash equilibrium; Process (computing); Frame (networking); Order (exchange); Artificial intelligence; Machine learning; Mathematical optimization; Mathematics","score_opus":0.367641049994208,"score_gpt":0.4650858587974098,"score_spread":0.09744480880320183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1540112079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031096574,0.00039992697,0.96445125,0.0004497471,0.00004596952,0.000084128216,0.000013344177,0.00043656072,0.003022467],"genre_scores_gemma":[0.8285431,0.00023609813,0.16747111,0.00036189958,0.00010738085,0.00023762672,0.00004522069,0.000109443834,0.0028881645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583745,0.0024896264,0.00013140577,0.00046597788,0.0007129547,0.00036260023],"domain_scores_gemma":[0.9789094,0.016339006,0.0013206587,0.001009814,0.0018103648,0.00061079947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007607407,0.0014976591,0.0016205142,0.000985935,0.0008360433,0.0009937638,0.002524609,0.002917725,0.0020892604],"category_scores_gemma":[0.03539832,0.0005424902,0.0005965087,0.00061028573,0.0016332499,0.002151028,0.0022077342,0.0020484505,0.0006305589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043567095,0.00019793774,0.0014854402,0.0001096203,0.000082783,0.00019662283,0.0003064837,0.91146815,0.002233562,0.025052205,0.0017885693,0.056642827],"study_design_scores_gemma":[0.000035571607,0.000069496935,0.00007946176,0.000009472387,0.00000817959,0.000023446732,0.000011080289,0.9928461,0.00055864896,0.00606815,0.00028282765,0.000007720306],"about_ca_topic_score_codex":0.0030718162,"about_ca_topic_score_gemma":0.001947578,"teacher_disagreement_score":0.007607407,"about_ca_system_score_codex":0.0012997815,"about_ca_system_score_gemma":0.0012953621,"threshold_uncertainty_score":0.04023224},"labels":[],"label_agreement":null},{"id":"W1546624303","doi":"10.5555/1838206.1838442","title":"An investigation of representations of combinatorial auctions","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Treewidth; Combinatorial auction; Chordal graph; Partial k-tree; Combinatorics; Computer science; Graph; Mathematics; Equivalence (formal languages); Discrete mathematics; Theoretical computer science; Pathwidth; Line graph; Common value auction; 1-planar graph","score_opus":0.14548509585147926,"score_gpt":0.4075015803021488,"score_spread":0.2620164844506695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1546624303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069565706,0.0015325648,0.8157385,0.0024594725,0.00028663236,0.00021707553,0.00021395339,0.0002580704,0.10972797],"genre_scores_gemma":[0.71786976,0.0021702927,0.26140362,0.00064273365,0.00047983608,0.00035151938,0.000573329,0.00026701362,0.016241912],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997619,0.0010571483,0.00014808403,0.00034605782,0.0006492016,0.00018056808],"domain_scores_gemma":[0.9940758,0.0030228873,0.0005337652,0.0014946014,0.00064207724,0.00023092503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020836766,0.0007288074,0.0007252006,0.0012114969,0.0010269015,0.003992525,0.0020823653,0.0016557052,0.0071280873],"category_scores_gemma":[0.014581221,0.0006134944,0.0016095418,0.0021800117,0.0027688951,0.010815898,0.002095819,0.003964115,0.00081880967],"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":[0.000009146464,0.000021967191,0.000064670974,0.000023732993,0.0000036056986,0.000019038034,0.000101691665,0.0054756617,0.00019330603,0.9874106,0.00044154943,0.0062349476],"study_design_scores_gemma":[0.000011242247,0.000027941456,0.000077197416,0.000026212523,0.0000068994013,0.0000736514,0.00008772776,0.04872976,0.0002259147,0.9439467,0.0067785303,0.000008125451],"about_ca_topic_score_codex":0.0012377176,"about_ca_topic_score_gemma":0.00056183606,"teacher_disagreement_score":0.0071280873,"about_ca_system_score_codex":0.0020406719,"about_ca_system_score_gemma":0.0007979083,"threshold_uncertainty_score":0.023845851},"labels":[],"label_agreement":null},{"id":"W1555338578","doi":"10.5555/1838206.1838401","title":"Using bisimulation for policy transfer in MDPs","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":12,"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":"Markov decision process; Computer science; Bisimulation; Artificial intelligence; Markov process; Work (physics); Theoretical computer science; Mathematics","score_opus":0.12857996272755987,"score_gpt":0.35545820968810005,"score_spread":0.22687824696054018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1555338578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070414506,0.00021767002,0.98847896,0.00025097094,0.000044099623,0.00007835201,0.000055729073,0.00029013355,0.0035426056],"genre_scores_gemma":[0.65099955,0.0008191057,0.3404536,0.00042765285,0.00010781856,0.0013405356,0.00033171423,0.00044518377,0.0050749425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774855,0.0012324632,0.00014335536,0.00035822438,0.00034837294,0.00016908409],"domain_scores_gemma":[0.99052167,0.0075329286,0.00061610574,0.00053942314,0.00050716265,0.00028270014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004637215,0.002022827,0.0022145156,0.0013044296,0.00089016306,0.00172718,0.0020620665,0.0022836775,0.0074841646],"category_scores_gemma":[0.018574342,0.0011034906,0.0015032444,0.0010302804,0.0026378501,0.003376481,0.004161487,0.0034203758,0.0010740934],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045220415,0.000033197015,0.00015392744,0.00007328436,0.000032157674,0.000039419312,0.000058558253,0.9152627,0.00028387073,0.07266043,0.0003094513,0.011047772],"study_design_scores_gemma":[0.000016420345,0.000021776379,0.00001312821,0.000013279085,0.000005537146,0.0000063818766,0.0000058465685,0.9485861,0.00017486628,0.05065836,0.0004915995,0.0000066071043],"about_ca_topic_score_codex":0.0045451494,"about_ca_topic_score_gemma":0.0033332317,"teacher_disagreement_score":0.0074841646,"about_ca_system_score_codex":0.0024286497,"about_ca_system_score_gemma":0.0025147903,"threshold_uncertainty_score":0.02503705},"labels":[],"label_agreement":null},{"id":"W1584723732","doi":"10.5555/1838206.1838443","title":"Parameterizing the winner determination problem for combinatorial auctions","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Combinatorial auction; Parameterized complexity; Common value auction; Computer science; Bidding; Mathematical optimization; Leverage (statistics); Computational complexity theory; Theoretical computer science; Mathematics; Artificial intelligence; Algorithm; Economics","score_opus":0.172506683914167,"score_gpt":0.4008345885727295,"score_spread":0.2283279046585625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1584723732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08465507,0.00042034523,0.90532684,0.0007814484,0.00005878098,0.00028063115,0.00016453303,0.00030379684,0.0080085015],"genre_scores_gemma":[0.83959997,0.00059158634,0.15627445,0.00020930737,0.000077478675,0.0004906147,0.00039900996,0.00018740159,0.002170166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9908092,0.0055604847,0.00050199573,0.00123666,0.0011514656,0.00074019766],"domain_scores_gemma":[0.97064173,0.022127593,0.001979241,0.0038437173,0.00088913686,0.00051863334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008738198,0.0018436455,0.002114998,0.0009060709,0.00084951275,0.0048095197,0.0034302315,0.0018984582,0.00312787],"category_scores_gemma":[0.04743868,0.0009441854,0.0015919617,0.0017178245,0.0035969522,0.01189715,0.0034187457,0.0051661306,0.00043712292],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029413472,0.00024672275,0.0013616615,0.00024214645,0.00015356884,0.00013128237,0.00024843903,0.6803473,0.0021110652,0.28612292,0.0016432112,0.027097557],"study_design_scores_gemma":[0.00009003507,0.000075389224,0.00018543651,0.000030499094,0.000038401995,0.000086692555,0.00012726572,0.61508137,0.0011791115,0.38135436,0.0017184285,0.00003290729],"about_ca_topic_score_codex":0.000951643,"about_ca_topic_score_gemma":0.0010105107,"teacher_disagreement_score":0.008738198,"about_ca_system_score_codex":0.0026278032,"about_ca_system_score_gemma":0.002406211,"threshold_uncertainty_score":0.046212614},"labels":[],"label_agreement":null},{"id":"W1722021969","doi":"10.5555/2484920.2485197","title":"Rating players in games with real-valued outcomes","year":2013,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Order (exchange); Value (mathematics); Population; Focus (optics); Game theory; Rational agent; Artificial intelligence; Machine learning; Mathematical economics; Mathematics; Economics","score_opus":0.07056963481023519,"score_gpt":0.2546227073203595,"score_spread":0.18405307251012432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1722021969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084322125,0.0002583465,0.90374935,0.0005869202,0.00012283663,0.00061958085,0.00023207335,0.0005635568,0.009545158],"genre_scores_gemma":[0.7820044,0.00028026666,0.21239704,0.00013462572,0.00012017078,0.000392513,0.00027573304,0.000058970258,0.0043363674],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9922782,0.004446168,0.000570119,0.001309413,0.0011623569,0.00023377151],"domain_scores_gemma":[0.9861901,0.008101972,0.0022639146,0.0012415253,0.0013812806,0.0008212632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006313853,0.0012969064,0.0012286736,0.001310574,0.00072336977,0.0025715001,0.0016104232,0.0011689194,0.0046675126],"category_scores_gemma":[0.03073525,0.00042734254,0.0005712533,0.0009104637,0.001537543,0.004260753,0.002158652,0.0015314433,0.0010881604],"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":[0.0019545262,0.00064741774,0.02743452,0.00068400573,0.00032516368,0.00067576935,0.0023088036,0.22249304,0.011758296,0.3612431,0.009673454,0.36080196],"study_design_scores_gemma":[0.00012284146,0.00024535737,0.003089641,0.00006428571,0.000064962966,0.00015426856,0.0003764755,0.8442112,0.0027484987,0.14394847,0.0048997984,0.00007419873],"about_ca_topic_score_codex":0.002111817,"about_ca_topic_score_gemma":0.0026712103,"teacher_disagreement_score":0.006313853,"about_ca_system_score_codex":0.0010289507,"about_ca_system_score_gemma":0.00061474345,"threshold_uncertainty_score":0.033391178},"labels":[],"label_agreement":null},{"id":"W181767045","doi":"10.5555/2484920.2485213","title":"On the analysis of joining communities of agent-basedweb services","year":2013,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Concordia University","funders":"","keywords":"Web service; World Wide Web; Computer science; WS-Policy; Key (lock); Incentive; Mechanism (biology); Services computing; Business; Web development; Knowledge management; Computer security; Web application security","score_opus":0.04024602118495016,"score_gpt":0.2550220696143017,"score_spread":0.21477604842935152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W181767045","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16152684,0.0032761318,0.7903671,0.0037929674,0.00014763352,0.00047603334,0.00023755564,0.00021596422,0.039959744],"genre_scores_gemma":[0.8741419,0.0022890142,0.109439194,0.00038650943,0.0004185146,0.00045111316,0.0002642707,0.00018921478,0.01242025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99506325,0.0022292333,0.00017124973,0.00062798406,0.0012421154,0.00066613406],"domain_scores_gemma":[0.9638101,0.025511961,0.0036810169,0.0015027558,0.0026437421,0.0028503756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0056717903,0.00091578346,0.001485057,0.0041745636,0.0034464316,0.0040523494,0.002700181,0.0030228128,0.008258477],"category_scores_gemma":[0.036802836,0.000774991,0.0017347124,0.003434172,0.0054985313,0.008209678,0.004627989,0.0024977017,0.0007635095],"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":[0.0001387167,0.0001726306,0.0031237002,0.000211223,0.000088786306,0.00036210477,0.00096664,0.12818015,0.0017393696,0.8426925,0.0024890313,0.019835135],"study_design_scores_gemma":[0.00003234203,0.00006452243,0.0013522367,0.000067612804,0.00003755242,0.00019135787,0.0003289481,0.5542009,0.00044130013,0.43790606,0.0053328164,0.00004436808],"about_ca_topic_score_codex":0.006464797,"about_ca_topic_score_gemma":0.0036892302,"teacher_disagreement_score":0.008258477,"about_ca_system_score_codex":0.0038268114,"about_ca_system_score_gemma":0.001956664,"threshold_uncertainty_score":0.02999562},"labels":[],"label_agreement":null},{"id":"W189510620","doi":"10.5555/1838206.1838300","title":"Optimal policy switching algorithms for reinforcement learning","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":29,"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":"Reinforcement learning; Computer science; Task (project management); Function approximation; Term (time); Function (biology); Artificial intelligence; Mathematical optimization; Q-learning; Machine learning; Algorithm; Mathematics; Artificial neural network; Engineering","score_opus":0.05548713756434804,"score_gpt":0.3163627497273219,"score_spread":0.2608756121629739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W189510620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005134111,0.0004003887,0.991503,0.00016927069,0.00004741728,0.0000595657,0.000026472608,0.0003059002,0.0023538356],"genre_scores_gemma":[0.59698373,0.0008321248,0.3954748,0.0003412707,0.00013064551,0.0008130967,0.00021436438,0.00019879348,0.005011176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990702,0.0004071347,0.000053261072,0.00015116773,0.00022086462,0.00009743308],"domain_scores_gemma":[0.99728847,0.002113807,0.00016549052,0.00010947858,0.00022644803,0.00009628199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020821374,0.001189238,0.0014650548,0.00079528,0.00044194478,0.0010300056,0.0015716777,0.0014595087,0.0046946364],"category_scores_gemma":[0.007191912,0.0005578952,0.0005727783,0.0007114283,0.0014098543,0.0013264138,0.0013958771,0.0025074554,0.0006836968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008640675,0.0000929384,0.0003283193,0.000088297624,0.000041411033,0.000031133077,0.00006943924,0.86359227,0.0004243212,0.06594282,0.0016092189,0.067693375],"study_design_scores_gemma":[0.0000204624,0.00001671238,0.000021932168,0.0000069014277,0.000003713704,0.000004511725,0.0000035021596,0.9754724,0.00011347684,0.023947462,0.00038518323,0.0000036394513],"about_ca_topic_score_codex":0.003093485,"about_ca_topic_score_gemma":0.00223926,"teacher_disagreement_score":0.0046946364,"about_ca_system_score_codex":0.001451451,"about_ca_system_score_gemma":0.0014149252,"threshold_uncertainty_score":0.015705109},"labels":[],"label_agreement":null},{"id":"W1900134344","doi":"10.5555/1838206.1838478","title":"Symbolic model checking for agent interactions","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Model checking; Computer science; Symbolic trajectory evaluation; Semantics (computer science); Computation tree logic; Protocol (science); Programming language; Theoretical computer science; Formal verification; Temporal logic; Modalities; Cryptographic protocol; Symbolic execution; Computer security; Software; Cryptography","score_opus":0.1435389464135784,"score_gpt":0.3691226349973471,"score_spread":0.2255836885837687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1900134344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019838473,0.00010129394,0.97358096,0.00026662266,0.000037918413,0.00007441688,0.00013853665,0.0025089185,0.0034527497],"genre_scores_gemma":[0.6758862,0.00024132799,0.31848824,0.00018359211,0.000044542907,0.00041275268,0.0005352084,0.00047652744,0.003731494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99332315,0.0025702696,0.0003307567,0.0006797485,0.0025325108,0.0005635651],"domain_scores_gemma":[0.9848498,0.0110915685,0.000979955,0.0020468812,0.0008674752,0.00016431772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035595652,0.00094270264,0.0013438194,0.0013211664,0.0011619964,0.0030552142,0.0025806513,0.0013785331,0.004745407],"category_scores_gemma":[0.018251603,0.0007740816,0.0018124614,0.0011915234,0.0034907963,0.004437075,0.0037387153,0.002587615,0.0006455816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026129044,0.000101699006,0.0014117584,0.00034891005,0.00014996849,0.0004542736,0.00056140043,0.49392578,0.007848351,0.45599627,0.0016307463,0.037309628],"study_design_scores_gemma":[0.000062899235,0.000027373935,0.00006420351,0.000033005068,0.000032980464,0.000043699936,0.00004313397,0.84793603,0.0060461108,0.14347789,0.002217239,0.000015368483],"about_ca_topic_score_codex":0.0071879495,"about_ca_topic_score_gemma":0.0084061185,"teacher_disagreement_score":0.0071879495,"about_ca_system_score_codex":0.0026931302,"about_ca_system_score_gemma":0.0041489657,"threshold_uncertainty_score":0.019540131},"labels":[],"label_agreement":null},{"id":"W1965168983","doi":"10.1109/aamas.2004.79","title":"Building a Multi-Agent System for Automatic Negotiation in Web Service Applications","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Interoperability; The Internet; Automation; Web service; Computer science; Negotiation; Context (archaeology); World Wide Web; Service (business); Service-oriented architecture; Software engineering; Engineering; Business","score_opus":0.059328003844414404,"score_gpt":0.30034803407894534,"score_spread":0.24102003023453095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965168983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013087398,0.00019950388,0.9793474,0.0002977309,0.000090250694,0.00031727127,0.000015458047,0.0023889665,0.0042561134],"genre_scores_gemma":[0.21301226,0.00021785141,0.78182554,0.00017728178,0.000072135386,0.00044637691,0.00008744805,0.0002171704,0.00394395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981431,0.00077168946,0.00019071567,0.00021754923,0.00056145136,0.000115489944],"domain_scores_gemma":[0.9988342,0.00050538225,0.00008136121,0.00019806037,0.00024541817,0.00013567711],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003249166,0.00045412043,0.0008054237,0.0005168671,0.0019535183,0.002843529,0.0018857878,0.002185568,0.0028692791],"category_scores_gemma":[0.0047004437,0.00060739357,0.00078576175,0.00044666926,0.0010476949,0.0034770377,0.0031456205,0.002526548,0.001423864],"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":[0.0006194962,0.00088009384,0.0036465414,0.0004941999,0.00027839578,0.0023599118,0.0032903128,0.26606402,0.0860257,0.2827151,0.012475048,0.3411512],"study_design_scores_gemma":[0.000085488886,0.00009951445,0.0001986143,0.00003927603,0.000051675193,0.00021901168,0.0001012563,0.92360467,0.0143604735,0.030868242,0.030326039,0.0000457418],"about_ca_topic_score_codex":0.0019081825,"about_ca_topic_score_gemma":0.0019529057,"teacher_disagreement_score":0.003249166,"about_ca_system_score_codex":0.00056326314,"about_ca_system_score_gemma":0.0014097992,"threshold_uncertainty_score":0.017183423},"labels":[],"label_agreement":null},{"id":"W2058345792","doi":"10.1109/aamas.2004.192","title":"Negotiating Gestalt: Artistic Expression and Coalition Formation in Multiagent Systems","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Slime Mold and Myxomycetes Research","field":"Engineering","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 Calgary","funders":"","keywords":"Negotiation; Gestalt psychology; Expression (computer science); Painting; Representation (politics); Computer science; Control (management); Space (punctuation); Human–computer interaction; Autonomous agent; Artificial intelligence; Art; Visual arts; Sociology; Epistemology; Political science; Politics; Programming language","score_opus":0.05349610606819741,"score_gpt":0.2763378046049061,"score_spread":0.22284169853670868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058345792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05492754,0.0007584911,0.92335325,0.0011286455,0.00007646089,0.00012135429,0.000048323956,0.00052851124,0.019057369],"genre_scores_gemma":[0.77229196,0.00056768704,0.21849647,0.00012814021,0.000062708365,0.00027795293,0.000099777506,0.000092808834,0.007982536],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838674,0.0008442679,0.000104674524,0.00023160245,0.00032308817,0.00010963667],"domain_scores_gemma":[0.996135,0.002554998,0.00035380735,0.00044154155,0.00022533676,0.00028929205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002314406,0.00052384357,0.0005176421,0.0006669416,0.0014951954,0.0030765235,0.0012248701,0.0015823668,0.0040144683],"category_scores_gemma":[0.009335274,0.00043625705,0.0006035687,0.0006691693,0.003818252,0.0042107147,0.003976041,0.0012115891,0.0005335652],"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":[0.00026184574,0.00017977609,0.002691489,0.00038438718,0.000113370166,0.0011807564,0.006233362,0.26945293,0.014765748,0.5715941,0.0027480703,0.13039418],"study_design_scores_gemma":[0.000085761596,0.00010724966,0.00066902995,0.00005683073,0.000034487926,0.00037496086,0.0008498736,0.61193824,0.005522022,0.35536948,0.024924098,0.00006792361],"about_ca_topic_score_codex":0.0022089425,"about_ca_topic_score_gemma":0.0017704371,"teacher_disagreement_score":0.0040144683,"about_ca_system_score_codex":0.00073010597,"about_ca_system_score_gemma":0.0007922782,"threshold_uncertainty_score":0.013429701},"labels":[],"label_agreement":null},{"id":"W2067164575","doi":"10.1109/aamas.2004.225","title":"RedAgent-2003: An Autonomous Market-Based Supply-Chain Management Agent","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":29,"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; Supply chain; Competition (biology); Key (lock); Autonomous agent; Supply chain management; Order (exchange); Heuristic; Domain (mathematical analysis); Industrial organization; Risk analysis (engineering); Operations research; Business; Artificial intelligence; Computer security; Marketing; Engineering","score_opus":0.12065370169704831,"score_gpt":0.36219677373254566,"score_spread":0.24154307203549735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067164575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055489175,0.00040050436,0.9025338,0.00067077484,0.00031653506,0.0007079121,0.00041908497,0.013252356,0.026209868],"genre_scores_gemma":[0.35623592,0.00022986082,0.6250346,0.00034535016,0.00006532874,0.0005791138,0.0007510352,0.00049296056,0.016265808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947387,0.0001760056,0.00003591879,0.00008974017,0.0001726774,0.00005187961],"domain_scores_gemma":[0.9986106,0.0004756019,0.00015243351,0.00025222028,0.00028052696,0.00022855564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019068841,0.00048065104,0.00047810352,0.00042099415,0.00060200546,0.001308493,0.0021162943,0.0010901864,0.005308905],"category_scores_gemma":[0.0026739952,0.0003741864,0.00044205558,0.00034084063,0.00058883487,0.0015229152,0.0014471628,0.0011600046,0.0013280362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001184396,0.0014875203,0.0051919515,0.00048432374,0.0003229085,0.0006965006,0.0005455927,0.4340484,0.023267591,0.14065383,0.053366374,0.3387506],"study_design_scores_gemma":[0.00016448203,0.0001504434,0.0003021513,0.000015269083,0.000030147876,0.000102873004,0.000028068047,0.9403625,0.0056493967,0.011507954,0.04166303,0.00002367386],"about_ca_topic_score_codex":0.0017607923,"about_ca_topic_score_gemma":0.0020355617,"teacher_disagreement_score":0.005308905,"about_ca_system_score_codex":0.00060431677,"about_ca_system_score_gemma":0.0013128171,"threshold_uncertainty_score":0.017760038},"labels":[],"label_agreement":null},{"id":"W2080977664","doi":"10.1109/aamas.2004.31","title":"A Study of Limited-Precision, Incremental Elicitation in Auctions","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":14,"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 Toronto","funders":"","keywords":"Computer science; Common value auction; Mathematics; Statistics","score_opus":0.23534547740435424,"score_gpt":0.41986437181124664,"score_spread":0.1845188944068924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080977664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16451283,0.0027273542,0.8137277,0.0017426491,0.00008437953,0.00016786749,0.00010341493,0.000102956175,0.016830873],"genre_scores_gemma":[0.9070652,0.000969835,0.087262526,0.00016246278,0.00019847372,0.0001070276,0.000067916335,0.000036303383,0.004130301],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.989277,0.006856459,0.00050569786,0.0008787259,0.0020038064,0.00047839008],"domain_scores_gemma":[0.7282855,0.25422156,0.0063365465,0.0067902403,0.0035036767,0.0008625486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012200021,0.0008985068,0.0016933711,0.00096895307,0.00088278594,0.0043567526,0.0035933533,0.0034764146,0.003670475],"category_scores_gemma":[0.12555939,0.0011799632,0.0011566597,0.0024616541,0.0038667177,0.010332082,0.0022919571,0.003269486,0.00020321993],"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":[0.0007428083,0.00032280016,0.002232757,0.00088660436,0.00019901615,0.00045064706,0.0015088057,0.30738813,0.004160646,0.6218527,0.0012695492,0.058985695],"study_design_scores_gemma":[0.00012357309,0.00024444965,0.0010526957,0.0000917756,0.00007966032,0.00021755593,0.00026139262,0.58564174,0.001353498,0.40937248,0.0015003182,0.000060889313],"about_ca_topic_score_codex":0.0020886261,"about_ca_topic_score_gemma":0.0014101631,"teacher_disagreement_score":0.012200021,"about_ca_system_score_codex":0.0022260267,"about_ca_system_score_gemma":0.0018425937,"threshold_uncertainty_score":0.06452066},"labels":[],"label_agreement":null},{"id":"W2102177865","doi":"10.5555/1838206.1838528","title":"Coalition detection and identification","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Identification (biology); Computer science; Population; Internet privacy; Public relations; Data science; Computer security; Knowledge management; Political science; Sociology","score_opus":0.07904217333949029,"score_gpt":0.3523602975403669,"score_spread":0.27331812420087664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102177865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01604386,0.0011300883,0.9715646,0.0015269694,0.00021909371,0.00022731206,0.00021953126,0.00043831137,0.008630357],"genre_scores_gemma":[0.3737678,0.0013207262,0.6075023,0.0004491814,0.00030452554,0.00039562015,0.0009453128,0.0002102552,0.015104265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934002,0.0025018733,0.00030050767,0.0016967622,0.0016103588,0.0004902868],"domain_scores_gemma":[0.9748437,0.015137023,0.0019573702,0.0046111937,0.0027317319,0.00071903627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007742876,0.0009770688,0.0020930206,0.0044830805,0.0022673286,0.004161078,0.0033713565,0.0026841736,0.006105537],"category_scores_gemma":[0.045490462,0.00090845075,0.0017680578,0.002906123,0.0034017814,0.0058661783,0.0061635445,0.0030964923,0.0021948018],"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":[0.00023519639,0.00025867164,0.012797338,0.00041387798,0.00022762582,0.00026151518,0.0010377045,0.068102114,0.0043866215,0.3343645,0.01581169,0.562103],"study_design_scores_gemma":[0.000037830305,0.00009432791,0.0031288993,0.000114196235,0.00004875059,0.00060426455,0.00063890597,0.62644064,0.005764567,0.34243828,0.020614127,0.00007516469],"about_ca_topic_score_codex":0.0036513016,"about_ca_topic_score_gemma":0.0025751696,"teacher_disagreement_score":0.007742876,"about_ca_system_score_codex":0.0022797242,"about_ca_system_score_gemma":0.0023734088,"threshold_uncertainty_score":0.04094875},"labels":[],"label_agreement":null},{"id":"W2105888928","doi":"10.5555/2343896.2344007","title":"Detecting and identifying coalitions","year":2012,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Applications","field":"Decision Sciences","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 Waterloo","funders":"","keywords":"Computer science; Computer security; Population; Risk analysis (engineering); Business","score_opus":0.39964016734424845,"score_gpt":0.4423090878843384,"score_spread":0.04266892054008997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105888928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08242783,0.00019181996,0.91217554,0.0003018508,0.000050806146,0.0002984053,0.00014387147,0.00059456803,0.0038153133],"genre_scores_gemma":[0.48147878,0.0001625901,0.5152762,0.00012019575,0.000029672898,0.00019144986,0.00029058786,0.000048265898,0.002402273],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99715817,0.0007998178,0.00017697692,0.0006995128,0.0009384426,0.00022706456],"domain_scores_gemma":[0.9909019,0.004686658,0.00130646,0.0015624398,0.0011800338,0.0003624317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023260477,0.000802066,0.0010396444,0.0027182566,0.0014396666,0.0014818811,0.0022319378,0.0018844383,0.0013422488],"category_scores_gemma":[0.014709548,0.00047368472,0.00088790857,0.001269067,0.0012726851,0.0027240587,0.0034030234,0.0013915495,0.0005772567],"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":[0.0006291617,0.0006274734,0.05095558,0.00072601356,0.00043337184,0.0014043859,0.0025962638,0.15468268,0.055742037,0.10943656,0.0059646885,0.61680186],"study_design_scores_gemma":[0.000058701684,0.0003428223,0.006831598,0.00009103669,0.00012781606,0.0015554889,0.0012794522,0.848587,0.04388838,0.086848415,0.010291342,0.00009807534],"about_ca_topic_score_codex":0.0017096318,"about_ca_topic_score_gemma":0.0018499265,"teacher_disagreement_score":0.0027182566,"about_ca_system_score_codex":0.00053471787,"about_ca_system_score_gemma":0.0012550373,"threshold_uncertainty_score":0.012301445},"labels":[],"label_agreement":null},{"id":"W2107952055","doi":"10.5555/2343896.2343940","title":"Analysis of methods for solving MDPs","year":2012,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Convergence (economics); Mathematical proof; Simple (philosophy); Computer science; Mathematical optimization; Value (mathematics); Function (biology); Bellman equation; Power iteration; Markov decision process; Applied mathematics; Mathematics; Algorithm; Iterative method; Markov process; Statistics","score_opus":0.18693710335017455,"score_gpt":0.4286122566916662,"score_spread":0.24167515334149164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107952055","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033785596,0.00027096068,0.9933536,0.00016540432,0.000024373232,0.00007319724,0.000040514788,0.00017885052,0.0025146096],"genre_scores_gemma":[0.24459451,0.0006273009,0.7490056,0.00015403252,0.000078743724,0.0007512002,0.00020890865,0.0002435816,0.004336073],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99583983,0.0016983908,0.00026453478,0.00051366864,0.0014093259,0.0002742813],"domain_scores_gemma":[0.9833655,0.014173641,0.0005304795,0.0010139283,0.0007621142,0.00015436992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058353255,0.0011663747,0.0008938765,0.0010259765,0.0006505648,0.0017894275,0.0019366835,0.0013214112,0.0060425713],"category_scores_gemma":[0.022436043,0.0008546554,0.0016692509,0.0008814869,0.0029512935,0.0027600704,0.0031074495,0.002591587,0.0005352688],"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":[0.00009347699,0.00004999752,0.00054383837,0.0005695281,0.00011513646,0.00008493565,0.00028659336,0.28933996,0.0015614061,0.6318665,0.0013836174,0.074104935],"study_design_scores_gemma":[0.00005162791,0.000034394357,0.0000708965,0.00008107406,0.000021545804,0.000035500132,0.000026295404,0.6306882,0.0015653777,0.36228377,0.0051295515,0.000011810564],"about_ca_topic_score_codex":0.0019054778,"about_ca_topic_score_gemma":0.0018502981,"teacher_disagreement_score":0.0060425713,"about_ca_system_score_codex":0.00185699,"about_ca_system_score_gemma":0.0019849988,"threshold_uncertainty_score":0.030860543},"labels":[],"label_agreement":null},{"id":"W2111063504","doi":"10.1109/aamas.2004.142","title":"Improving Modeling of Other Agents Using Stereotypes and Compactification of Observations","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","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 Calgary","funders":"","keywords":"Stereotype (UML); Compactification (mathematics); Computer science; k-nearest neighbors algorithm; Artificial intelligence; Econometrics; Mathematics","score_opus":0.19384959096504983,"score_gpt":0.31117455534595767,"score_spread":0.11732496438090784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111063504","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03425732,0.00009042827,0.96336645,0.00017453494,0.000029652623,0.000050935858,0.00008375652,0.00028527624,0.0016616405],"genre_scores_gemma":[0.6105336,0.0002633163,0.38650596,0.000099985635,0.00004975327,0.00019910553,0.00031725818,0.00011412676,0.0019168182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980508,0.00072728685,0.0001865095,0.00041526055,0.00047623462,0.00014385134],"domain_scores_gemma":[0.98831844,0.005659228,0.0019522001,0.0028765406,0.00087419426,0.00031934594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003239895,0.0011476988,0.0011658947,0.0007395786,0.00058133376,0.0021827423,0.0030218684,0.0013163942,0.0023168381],"category_scores_gemma":[0.016353462,0.0008005312,0.001795562,0.00070239225,0.0013334875,0.007947474,0.002539585,0.0019765696,0.00048424886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016218475,0.000102395505,0.003684137,0.00007858052,0.00007086786,0.00016216186,0.00067982014,0.89767045,0.0021686237,0.05905817,0.00032986034,0.03583284],"study_design_scores_gemma":[0.000010707996,0.00003270484,0.00017025328,0.000011485351,0.000010425945,0.000021228034,0.000043589676,0.97806484,0.00071970414,0.020356892,0.00054628385,0.000011912459],"about_ca_topic_score_codex":0.009808807,"about_ca_topic_score_gemma":0.007078697,"teacher_disagreement_score":0.009808807,"about_ca_system_score_codex":0.001144831,"about_ca_system_score_gemma":0.0012381851,"threshold_uncertainty_score":0.019503415},"labels":[],"label_agreement":null},{"id":"W2123268776","doi":"10.1109/aamas.2004.145","title":"Improving User Satisfaction in Agent-Based Electronic Marketplaces by Reputation Modelling and Adjustable Product Quality","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":79,"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 Waterloo","funders":"","keywords":"Reputation; Quality (philosophy); Product (mathematics); Reinforcement learning; Purchasing; Exploit; Computer science; Value (mathematics); Business; Marketing; Computer security; Artificial intelligence; Machine learning","score_opus":0.11356323354125197,"score_gpt":0.3651636397994741,"score_spread":0.2516004062582221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123268776","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6776165,0.000173507,0.31680402,0.0004113466,0.00003131066,0.00014898868,0.000045366236,0.000909606,0.0038594564],"genre_scores_gemma":[0.97661805,0.0000392886,0.022581007,0.000035396468,0.000009598171,0.000036603546,0.00002990356,0.000017973982,0.0006323274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984536,0.00092376507,0.00006901405,0.00014928362,0.00029081557,0.00011345163],"domain_scores_gemma":[0.9955428,0.0025126794,0.0006951884,0.00041609252,0.0005572406,0.00027606558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002559496,0.0005519363,0.0008890137,0.0005172355,0.00046453308,0.0013217843,0.0010672448,0.000893495,0.0017783128],"category_scores_gemma":[0.008349744,0.00034860178,0.00045541837,0.0005312863,0.00051809405,0.0020479583,0.00067643245,0.0006831181,0.00042652548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027666544,0.0037486968,0.040232375,0.00032280904,0.00037368303,0.000487534,0.00097446336,0.617995,0.02352795,0.01655749,0.0022507661,0.2907625],"study_design_scores_gemma":[0.00012040614,0.0004018582,0.0029781829,0.0000053213244,0.000036996327,0.000046922767,0.0000890436,0.9899997,0.0018339156,0.0039639333,0.0005044971,0.000019211755],"about_ca_topic_score_codex":0.0023429294,"about_ca_topic_score_gemma":0.0020970819,"teacher_disagreement_score":0.002559496,"about_ca_system_score_codex":0.0006198,"about_ca_system_score_gemma":0.00048666808,"threshold_uncertainty_score":0.013536096},"labels":[],"label_agreement":null},{"id":"W2143499977","doi":"10.1109/aamas.2004.171","title":"Model Sharing in Multiagent Systems","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","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":"University of Saskatchewan","funders":"","keywords":"Computer science; Multi-agent system; Agent-based model; Agent-based social simulation; Distributed computing; Artificial intelligence","score_opus":0.1133346570670415,"score_gpt":0.2978018557228957,"score_spread":0.1844671986558542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143499977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03620896,0.0018667555,0.9434161,0.001512079,0.00015595413,0.00017648036,0.000034570563,0.0004331262,0.016196068],"genre_scores_gemma":[0.7946293,0.0013557615,0.19793344,0.0003007718,0.00019049531,0.00039992965,0.00012318988,0.0001039382,0.0049631298],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9890071,0.0060768174,0.00068121706,0.001030189,0.0026718637,0.00053282065],"domain_scores_gemma":[0.989636,0.005396724,0.0007956199,0.002823153,0.00075905764,0.00058951025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008365642,0.0009622506,0.0015063924,0.0010244683,0.002598893,0.0046453085,0.002632673,0.0024721539,0.0031212869],"category_scores_gemma":[0.016621461,0.00092948636,0.0012404752,0.0012058906,0.0036513729,0.008033157,0.0074170534,0.0027189108,0.0006147959],"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":[0.00015527823,0.0001819673,0.0015595729,0.00026082946,0.00023798308,0.0005702249,0.002350186,0.2957355,0.0034184128,0.62428576,0.0015117867,0.06973255],"study_design_scores_gemma":[0.000057435947,0.000094412004,0.00019916715,0.000037163376,0.000051212584,0.00016584081,0.00029774327,0.44711444,0.0017892985,0.53897566,0.011175102,0.000042539363],"about_ca_topic_score_codex":0.0019360773,"about_ca_topic_score_gemma":0.0014653717,"teacher_disagreement_score":0.008365642,"about_ca_system_score_codex":0.0016169917,"about_ca_system_score_gemma":0.0016502937,"threshold_uncertainty_score":0.044242322},"labels":[],"label_agreement":null},{"id":"W2157501503","doi":"10.5555/1402795.1402817","title":"Cooperative search for optimizing pipeline operations","year":2008,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada); University of Calgary","funders":"","keywords":"Computer science; Pipeline (software); State (computer science); Beam search; Search and rescue; Search algorithm; Energy (signal processing); Mathematical optimization; Distributed computing; Artificial intelligence; Algorithm","score_opus":0.34903643798788536,"score_gpt":0.42891797323251013,"score_spread":0.07988153524462477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157501503","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06406089,0.00032085553,0.9304616,0.00016451484,0.000023676776,0.00008451047,0.000021890173,0.0002544486,0.004607649],"genre_scores_gemma":[0.8499935,0.00018576028,0.1471477,0.000053207,0.000018491442,0.00020302285,0.00004192939,0.00003366741,0.0023228154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994814,0.00020170292,0.000022788428,0.0000896177,0.00015210662,0.000052449614],"domain_scores_gemma":[0.9988808,0.00068206503,0.00011664362,0.00009171149,0.00016790789,0.00006081661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014101872,0.0006936186,0.0007207645,0.00064744946,0.0005033999,0.00077436387,0.0012296506,0.0009019979,0.0015236934],"category_scores_gemma":[0.0032164138,0.0003392722,0.0004785905,0.00071263715,0.0007885357,0.0009698998,0.0013053612,0.0006777669,0.00019264329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006676248,0.0000712435,0.00040091574,0.00006041066,0.000037892314,0.00007241954,0.00008250358,0.9535618,0.002836538,0.010949437,0.000427557,0.031432617],"study_design_scores_gemma":[0.000015969561,0.00004245638,0.00004844414,0.000002166896,0.0000068790164,0.000012472812,0.000011212611,0.99585116,0.0006011878,0.003013004,0.00039183535,0.0000032328496],"about_ca_topic_score_codex":0.0033392962,"about_ca_topic_score_gemma":0.0023092742,"teacher_disagreement_score":0.0033392962,"about_ca_system_score_codex":0.0006357142,"about_ca_system_score_gemma":0.00095576607,"threshold_uncertainty_score":0.0074578524},"labels":[],"label_agreement":null},{"id":"W2160070657","doi":"10.5555/2772879.2773478","title":"Exploiting Objects as Artifacts in Multi-Agent Based Social Simulations: Extended Abstract","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Language and cultural evolution","field":"Social Sciences","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":"University of Windsor","funders":"","keywords":"Artifact (error); Social learning; Computer science; Margin (machine learning); Population; Artificial intelligence; Social network (sociolinguistics); Machine learning; Knowledge management; World Wide Web; Sociology; Social media","score_opus":0.21739492152212178,"score_gpt":0.38830544025226765,"score_spread":0.17091051873014587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160070657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46270874,0.00040630382,0.5119362,0.00090487394,0.00008128099,0.00015302724,0.00011192797,0.0002256731,0.023472024],"genre_scores_gemma":[0.9421797,0.00028047923,0.05287435,0.000056431738,0.00001974776,0.00015030736,0.000043256532,0.000029543296,0.0043661753],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964607,0.00021473595,0.000015417067,0.000041568277,0.00005701511,0.0000252826],"domain_scores_gemma":[0.99886864,0.0007584235,0.00011179735,0.00013414287,0.000063870655,0.00006316105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053042965,0.00038803613,0.00040739367,0.00039140918,0.00047078848,0.0018222227,0.0009498829,0.00094070495,0.0019532952],"category_scores_gemma":[0.0024726912,0.00017529938,0.000549024,0.0003890082,0.0013086774,0.0014731194,0.0016397724,0.00065706193,0.00020041129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032575248,0.00007074175,0.0028158722,0.00007109026,0.000038264316,0.00025790645,0.00048494487,0.92796326,0.0021624784,0.05693122,0.00016054083,0.009011087],"study_design_scores_gemma":[0.000009114691,0.00004740214,0.00028484414,0.000009904779,0.000009439001,0.000035968667,0.00009670128,0.98151463,0.00042861016,0.016003275,0.0015514907,0.000008617847],"about_ca_topic_score_codex":0.0035333384,"about_ca_topic_score_gemma":0.0023337866,"teacher_disagreement_score":0.0035333384,"about_ca_system_score_codex":0.00050003076,"about_ca_system_score_gemma":0.0004163869,"threshold_uncertainty_score":0.00702554},"labels":[],"label_agreement":null},{"id":"W2164846267","doi":"10.5555/1838206.1838479","title":"An agent communication protocol for resolving conflicts","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","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":"Concordia University","funders":"","keywords":"Protocol (science); Argumentation theory; Computer science; Persuasion; Set (abstract data type); Communications protocol; Theoretical computer science; Artificial intelligence; Epistemology; Programming language; Computer network; Social psychology","score_opus":0.1142804543862047,"score_gpt":0.37020684032688433,"score_spread":0.2559263859406796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164846267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017650517,0.00012077368,0.9865652,0.00063971954,0.00020721526,0.00050061115,0.000079009165,0.0007643257,0.009358019],"genre_scores_gemma":[0.06890847,0.00030288828,0.91524523,0.00034671082,0.00012480364,0.0021320113,0.00038224785,0.00030803122,0.012249675],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9885018,0.0052258233,0.0012702608,0.0010158917,0.003549282,0.00043691794],"domain_scores_gemma":[0.9881799,0.00588369,0.00063859695,0.0023036718,0.002398654,0.000595514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009684579,0.0014097523,0.0010447477,0.0019889465,0.0036001445,0.0045026247,0.0040666657,0.00462936,0.0094096465],"category_scores_gemma":[0.02146778,0.00090795656,0.0012975829,0.0014730694,0.0028175428,0.008228004,0.0051627355,0.004236867,0.0030458868],"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":[0.00014892529,0.00013482654,0.0002532814,0.0003132805,0.00006661539,0.00044264068,0.0013979501,0.014803816,0.0072354786,0.8901859,0.010282614,0.074734636],"study_design_scores_gemma":[0.00032728884,0.0002663983,0.00018728594,0.0003323383,0.00016643535,0.0011779646,0.00046857115,0.27814344,0.025870837,0.37112433,0.3216698,0.00026526584],"about_ca_topic_score_codex":0.0012516457,"about_ca_topic_score_gemma":0.00090580113,"teacher_disagreement_score":0.009684579,"about_ca_system_score_codex":0.0013876258,"about_ca_system_score_gemma":0.0038117862,"threshold_uncertainty_score":0.051217556},"labels":[],"label_agreement":null},{"id":"W2165274745","doi":"10.1109/aamas.2004.188","title":"Multiagent Planning as Control Synthesis","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Petri Nets in System Modeling","field":"Computer Science","cited_by":13,"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 Toronto","funders":"","keywords":"Computer science; Distributed computing; Multi-agent system; Event (particle physics); Control (management); Relation (database); Constraint (computer-aided design); Simple (philosophy); Software; Supervisory control; Artificial intelligence; Engineering; Data mining; Programming language","score_opus":0.07099639763208417,"score_gpt":0.2985471640996341,"score_spread":0.22755076646754996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165274745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010666662,0.0011415666,0.98104525,0.00043321832,0.00014268092,0.000048355847,0.000019840867,0.00018179996,0.015920652],"genre_scores_gemma":[0.2512694,0.005185116,0.7209209,0.00053628726,0.00038917677,0.00056500843,0.00014871289,0.0001635048,0.020821882],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99929595,0.00022679224,0.00004693021,0.00014557593,0.00024088574,0.000043875625],"domain_scores_gemma":[0.9994398,0.00030686476,0.00005614834,0.00008501975,0.00007721575,0.00003502152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095271313,0.0007809537,0.00047823705,0.0005889413,0.0004401785,0.0014889253,0.0010564392,0.0009028083,0.003761541],"category_scores_gemma":[0.0012301896,0.00034480175,0.0006482849,0.00045196095,0.0019026868,0.00165464,0.0012291376,0.0012378992,0.00066248176],"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":[0.000018636372,0.000019680665,0.00007420318,0.0001543246,0.000020081352,0.000098098244,0.00014339066,0.12303358,0.0018905983,0.82822686,0.0011085002,0.04521207],"study_design_scores_gemma":[0.00004410941,0.00006272214,0.000081634884,0.00011020073,0.00003281859,0.000081895785,0.00006494016,0.31659335,0.0029593362,0.6037605,0.07617909,0.000029397328],"about_ca_topic_score_codex":0.0020964954,"about_ca_topic_score_gemma":0.002265585,"teacher_disagreement_score":0.003761541,"about_ca_system_score_codex":0.0013103456,"about_ca_system_score_gemma":0.0013568507,"threshold_uncertainty_score":0.012583554},"labels":[],"label_agreement":null},{"id":"W2169619645","doi":"10.5555/2484920.2485084","title":"Smart exploration in reinforcement learning using absolute temporal difference errors","year":2013,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":51,"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":"Reinforcement learning; Temporal difference learning; Computer science; State (computer science); Function (biology); Artificial intelligence; Function approximation; Control (management); Machine learning; Algorithm; Artificial neural network","score_opus":0.10786902931368043,"score_gpt":0.29261669850862043,"score_spread":0.18474766919494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169619645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02723811,0.00022446939,0.971386,0.000106082705,0.000027116035,0.000022733411,0.0000103711955,0.00015445604,0.00083053164],"genre_scores_gemma":[0.88897383,0.00016865991,0.10920404,0.000059496804,0.00003397859,0.00010964135,0.00003137017,0.00006312833,0.0013558455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903464,0.00041345137,0.00005918626,0.00015251481,0.00026848225,0.00007181858],"domain_scores_gemma":[0.9947207,0.0040114485,0.00043171042,0.00028622895,0.00034554885,0.00020434178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025821356,0.0008085276,0.0010390744,0.00048601048,0.0002595262,0.00085009774,0.0011260418,0.0008664833,0.0010680992],"category_scores_gemma":[0.010510602,0.0004126291,0.00039861017,0.00041096,0.0016786524,0.002003563,0.0015359757,0.0014193375,0.00014367164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015743973,0.00004524558,0.0007917265,0.00007360104,0.00003533576,0.000052790252,0.00007621475,0.923762,0.0018596351,0.036724716,0.00023850943,0.03618286],"study_design_scores_gemma":[0.00001076811,0.00002396163,0.000041817333,0.0000034761258,0.0000022146337,0.0000058332084,0.0000018289896,0.99226475,0.0003455589,0.0072167446,0.00007999109,0.0000030912083],"about_ca_topic_score_codex":0.0019112634,"about_ca_topic_score_gemma":0.001289838,"teacher_disagreement_score":0.0025821356,"about_ca_system_score_codex":0.00084907736,"about_ca_system_score_gemma":0.00081730296,"threshold_uncertainty_score":0.013655782},"labels":[],"label_agreement":null},{"id":"W2181417941","doi":"","title":"One-Sided Matching with Dynamic Preferences (Doctoral Consortium)","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Matching (statistics); Computer science; Set (abstract data type); Resource allocation; Optimal matching; Function (biology); Scheduling (production processes); Dynamic decision-making; Mechanism design; Mathematical optimization; Artificial intelligence; Mathematical economics; Economics; Mathematics","score_opus":0.19683862078797457,"score_gpt":0.28583406160738745,"score_spread":0.08899544081941288,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181417941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06958704,0.021295555,0.5610715,0.059039183,0.004836203,0.00027202175,0.0007329445,0.0004427805,0.28272283],"genre_scores_gemma":[0.6424249,0.021716604,0.169462,0.004584513,0.003909395,0.00057983445,0.00074607297,0.0002786808,0.15629801],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984688,0.00068855775,0.000074375734,0.00037235153,0.0002681391,0.00012767455],"domain_scores_gemma":[0.9961098,0.0025339883,0.00022969773,0.0003324158,0.00047417067,0.00031999592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041576177,0.0005515061,0.0006895994,0.00085020874,0.0012379645,0.002908701,0.0007263643,0.0016175992,0.018886365],"category_scores_gemma":[0.011024447,0.0004269093,0.0010298608,0.0012018535,0.0022317541,0.0033609984,0.0013698438,0.0027386423,0.0032666929],"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":[0.000056867997,0.000111371875,0.00064698176,0.0001108941,0.00003654577,0.00006074143,0.0002051723,0.00671594,0.00038326552,0.9027587,0.023592727,0.065320805],"study_design_scores_gemma":[0.000037868547,0.000051300973,0.00065001,0.00010291795,0.000021655926,0.00010224956,0.00009329073,0.02311858,0.00048484013,0.9261897,0.04912045,0.000027036807],"about_ca_topic_score_codex":0.0020411147,"about_ca_topic_score_gemma":0.0012445974,"teacher_disagreement_score":0.018886365,"about_ca_system_score_codex":0.002367404,"about_ca_system_score_gemma":0.0020607323,"threshold_uncertainty_score":0.06318116},"labels":[],"label_agreement":null},{"id":"W2182474583","doi":"10.5555/2772879.2772886","title":"Decision-theoretic Clustering of Strategies","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Gambling Behavior and Treatments","field":"Psychology","cited_by":9,"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 Waterloo; University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Constrained clustering; Greedy algorithm; Metric (unit); Correlation clustering; Mathematical optimization; Artificial intelligence; Machine learning; Data mining; Mathematics; CURE data clustering algorithm; Algorithm","score_opus":0.24976908939266285,"score_gpt":0.4187938455534094,"score_spread":0.16902475616074658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182474583","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049187813,0.00020234648,0.9407358,0.00043137223,0.000022361612,0.00023441752,0.00017404275,0.00018898577,0.008822905],"genre_scores_gemma":[0.7118716,0.00044565543,0.2766245,0.0002262368,0.000048760754,0.00058911234,0.000600035,0.000115303665,0.009478743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99751854,0.0012753302,0.0001333201,0.00054813566,0.00033376552,0.00019097007],"domain_scores_gemma":[0.99438405,0.0034047356,0.0005522498,0.0007792725,0.0004972545,0.00038238452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029524642,0.0012734048,0.0016438821,0.0015884662,0.00081285904,0.0020998658,0.0023329607,0.0016387329,0.006819587],"category_scores_gemma":[0.014012281,0.0006007737,0.0012925171,0.0012741485,0.0022253077,0.0031798193,0.0019682364,0.0020314152,0.0011239686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000121463425,0.000100790494,0.0013942228,0.00012281201,0.00010550772,0.00009136358,0.00033364273,0.5122244,0.00093792955,0.45288864,0.0019707915,0.029708508],"study_design_scores_gemma":[0.000028184215,0.00003491839,0.00026711816,0.000017514085,0.000013778004,0.00003532767,0.00006872322,0.75353855,0.0002686241,0.24460825,0.0011040125,0.000015102824],"about_ca_topic_score_codex":0.0023340676,"about_ca_topic_score_gemma":0.002478269,"teacher_disagreement_score":0.006819587,"about_ca_system_score_codex":0.0030519771,"about_ca_system_score_gemma":0.0014157237,"threshold_uncertainty_score":0.022813737},"labels":[],"label_agreement":null},{"id":"W2182604253","doi":"","title":"On Ability to Autonomously Execute Agent Programs with Sensing — Extended Abstract","year":2004,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Deliberation; Set (abstract data type); Programming language; Semantics (computer science); Answer set programming; State (computer science); Situation calculus; Artificial intelligence; Theoretical computer science","score_opus":0.04886104234643238,"score_gpt":0.27673081927630583,"score_spread":0.22786977692987345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182604253","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.087821625,0.00017259919,0.8875805,0.0014804578,0.000067938956,0.000093144736,0.00014254765,0.0034076406,0.019233625],"genre_scores_gemma":[0.8499109,0.00020688523,0.14300507,0.00050191453,0.00007318651,0.0001746953,0.00021846851,0.00043914292,0.0054698237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99638426,0.001247673,0.00033870677,0.00082110614,0.00074394105,0.00046424693],"domain_scores_gemma":[0.97703004,0.014805014,0.0014725786,0.004164772,0.0018543465,0.0006733185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003755139,0.00049172586,0.00041321368,0.0008734713,0.00096432434,0.0029796232,0.0016388241,0.001355177,0.0055943164],"category_scores_gemma":[0.019934604,0.0005544463,0.0011140094,0.0005820567,0.0063682157,0.0074132285,0.0052777203,0.0022386815,0.00067777594],"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":[0.00027983732,0.00012075479,0.0026875914,0.00018241136,0.000049821898,0.00048670446,0.0015453127,0.045860045,0.008366016,0.9066329,0.0020344208,0.03175424],"study_design_scores_gemma":[0.00004950049,0.0001580049,0.00071028137,0.000066852524,0.000055857916,0.00022634573,0.00025788796,0.31876615,0.012203167,0.6599006,0.007534249,0.00007109062],"about_ca_topic_score_codex":0.0035977452,"about_ca_topic_score_gemma":0.0015044715,"teacher_disagreement_score":0.0055943164,"about_ca_system_score_codex":0.0008377419,"about_ca_system_score_gemma":0.001085283,"threshold_uncertainty_score":0.019859314},"labels":[],"label_agreement":null},{"id":"W2185548553","doi":"10.5555/2615731.2615791","title":"Reputation-aware task allocation for human trustees","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":39,"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 British Columbia","funders":"","keywords":"Reputation; Task (project management); Computer science; Workload; Delegation; Crowdsourcing; Resource allocation; Operations research; Economics; Computer network; World Wide Web; Mathematics","score_opus":0.04947797062188094,"score_gpt":0.2924733497182121,"score_spread":0.24299537909633115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185548553","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11757844,0.0005069748,0.8740752,0.0004180897,0.000086016764,0.00018407204,0.000051474955,0.00097011606,0.006129574],"genre_scores_gemma":[0.9492871,0.0000763526,0.048712377,0.00003258143,0.000029216999,0.000078588324,0.000026764941,0.000034724348,0.0017223145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997531,0.0011414448,0.00012590335,0.0004599222,0.00042855667,0.00031316117],"domain_scores_gemma":[0.99244106,0.0030465038,0.0009863224,0.0016299088,0.0011042538,0.00079191057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003324628,0.0008009181,0.0010710557,0.00061023136,0.0011459352,0.0014194289,0.001937338,0.0008300276,0.0022037788],"category_scores_gemma":[0.010790567,0.00040254777,0.00052692235,0.0005169149,0.001065809,0.0019642725,0.0024163553,0.00095609255,0.0005958061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000907689,0.00046042536,0.0056713256,0.00037100096,0.00014103702,0.0003102294,0.0010138743,0.7384146,0.025453784,0.027614461,0.004468575,0.19517303],"study_design_scores_gemma":[0.000042518088,0.00012971547,0.0006253752,0.000007775799,0.000027292715,0.00007436121,0.00010587966,0.98718864,0.0021907582,0.007973101,0.001609941,0.00002469678],"about_ca_topic_score_codex":0.0037135351,"about_ca_topic_score_gemma":0.004019673,"teacher_disagreement_score":0.0037135351,"about_ca_system_score_codex":0.0011811345,"about_ca_system_score_gemma":0.0020720481,"threshold_uncertainty_score":0.017582536},"labels":[],"label_agreement":null},{"id":"W2190606234","doi":"10.5555/2936924.2936996","title":"State of the Art Control of Atari Games Using Shallow Reinforcement Learning","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":56,"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 Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Benchmark (surveying); Representation (politics); Artificial intelligence; Set (abstract data type); Strengths and weaknesses; Simple (philosophy); Key (lock); Artificial neural network; Feature learning; Machine learning","score_opus":0.04777979186199204,"score_gpt":0.2658761195988801,"score_spread":0.21809632773688803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2190606234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0728725,0.00096091017,0.90982074,0.00041075543,0.00010755236,0.00011392329,0.00007527644,0.00073953747,0.014898789],"genre_scores_gemma":[0.9447827,0.00024121953,0.052100804,0.00010378098,0.000030788564,0.00009298831,0.00007215355,0.00004738665,0.0025282798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959856,0.00010514635,0.000026071157,0.00008634796,0.00011326415,0.000070497066],"domain_scores_gemma":[0.9990103,0.00056357746,0.00009765029,0.00010948545,0.00014263153,0.00007621785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010111791,0.0009839527,0.00085793866,0.00029504037,0.00034110053,0.0009490675,0.0015941338,0.0008724889,0.0030845879],"category_scores_gemma":[0.002531181,0.00034086744,0.00048682466,0.0001886713,0.0010537781,0.00092467625,0.0013389668,0.0014388298,0.00035725883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000112405716,0.00007172674,0.0004920569,0.00010272667,0.00004070289,0.000036393292,0.000046541205,0.9276539,0.0014079239,0.013765042,0.00074277364,0.055527862],"study_design_scores_gemma":[0.000008784979,0.000027235303,0.000037569447,0.0000045203005,0.0000027426552,0.000003028125,0.000002231188,0.9974535,0.0002005523,0.0020579374,0.00019943806,0.000002355564],"about_ca_topic_score_codex":0.007398173,"about_ca_topic_score_gemma":0.0071498505,"teacher_disagreement_score":0.007398173,"about_ca_system_score_codex":0.0008763081,"about_ca_system_score_gemma":0.0009967914,"threshold_uncertainty_score":0.0147102475},"labels":[],"label_agreement":null},{"id":"W2207815291","doi":"10.5555/2772879.2773387","title":"Quality and Budget Aware Task Allocation for Spatial Crowdsourcing","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":40,"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 British Columbia","funders":"","keywords":"Crowdsourcing; Reputation; Computer science; Task (project management); Quality (philosophy); Field (mathematics); Budget constraint; Trustworthiness; Computer security; World Wide Web; Engineering; Economics; Microeconomics","score_opus":0.11764615710736107,"score_gpt":0.32829033621983045,"score_spread":0.21064417911246938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2207815291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025075758,0.00069874007,0.9695058,0.00055199134,0.00008395174,0.00019549357,0.00006243858,0.0002501288,0.003575666],"genre_scores_gemma":[0.8544038,0.0005324752,0.14066799,0.00013162752,0.00012817557,0.00029942932,0.00007389433,0.00014028884,0.0036223815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967102,0.0014960171,0.00018070338,0.00055171864,0.0007003491,0.00036108342],"domain_scores_gemma":[0.9943416,0.0029091926,0.0008205727,0.0006649682,0.00069280807,0.0005707064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0044033877,0.0013663305,0.001530374,0.000989691,0.0010881501,0.001823835,0.0026592088,0.0014902544,0.0023460211],"category_scores_gemma":[0.015571882,0.00076498464,0.0005947038,0.0012431353,0.0012562075,0.002525923,0.0027086071,0.001253591,0.0005014572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004229372,0.00018804541,0.0015949357,0.00033668595,0.000104749306,0.00020650444,0.0004337767,0.86007726,0.008625615,0.04131631,0.0030894212,0.08360375],"study_design_scores_gemma":[0.000040364925,0.000084623236,0.0005202192,0.000024755167,0.000024094401,0.00007415203,0.00010190956,0.96898425,0.0012334145,0.027029235,0.0018577216,0.000025312293],"about_ca_topic_score_codex":0.0047452,"about_ca_topic_score_gemma":0.0037537122,"teacher_disagreement_score":0.0047452,"about_ca_system_score_codex":0.002145292,"about_ca_system_score_gemma":0.0022806842,"threshold_uncertainty_score":0.023287594},"labels":[],"label_agreement":null},{"id":"W2220059168","doi":"10.5555/2772879.2773464","title":"Voting with Social Influence: Using Arguments to Uncover Ground Truth","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Ground truth; Voting; Estimator; Computer science; Social network (sociolinguistics); Class (philosophy); Social choice theory; Artificial intelligence; Mathematical economics; Mathematics; Social media; Political science; Statistics; Law","score_opus":0.08594709231979451,"score_gpt":0.32997753807344793,"score_spread":0.2440304457536534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2220059168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13823488,0.0009338257,0.85249865,0.0026858768,0.000095671625,0.000118145785,0.00024442212,0.0002504514,0.004938082],"genre_scores_gemma":[0.9265347,0.00032520609,0.071144044,0.00019894459,0.00021607654,0.00016608024,0.00036575287,0.000078776495,0.00097052025],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98856723,0.007872353,0.00035929674,0.001422202,0.0014115367,0.00036730664],"domain_scores_gemma":[0.8398546,0.14133835,0.008401352,0.0068192487,0.002614151,0.00097229396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017516613,0.0013375347,0.0024736244,0.0036289592,0.0015083309,0.0057082097,0.0033124401,0.0049938685,0.003195331],"category_scores_gemma":[0.15848188,0.0010115756,0.001389927,0.0025563098,0.0053477385,0.0127371745,0.004579554,0.003615242,0.0004247716],"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":[0.0008251036,0.00023801789,0.018213883,0.00048257818,0.0004961892,0.00031905994,0.0011610382,0.3961308,0.0018760377,0.48429197,0.0025524285,0.09341295],"study_design_scores_gemma":[0.00006520901,0.00004889624,0.0007539132,0.000055720026,0.000033309127,0.000035855057,0.0000674496,0.7277826,0.0005507334,0.2697916,0.0007938657,0.000020830665],"about_ca_topic_score_codex":0.0013464323,"about_ca_topic_score_gemma":0.0010577046,"teacher_disagreement_score":0.017516613,"about_ca_system_score_codex":0.0019222118,"about_ca_system_score_gemma":0.0013099569,"threshold_uncertainty_score":0.09263784},"labels":[],"label_agreement":null},{"id":"W2257265444","doi":"10.5555/2034396.2034511","title":"SR-APL: a model for a programming language for rational BDI agents with prioritized goals","year":2011,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Deliberation; Computer science; Rationality; Declarative programming; Plan (archaeology); Programming language; Artificial intelligence; Knowledge management; Programming paradigm; Inductive programming; Epistemology","score_opus":0.1285531246108941,"score_gpt":0.310251174366983,"score_spread":0.18169804975608891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257265444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015058939,0.00017971732,0.98759425,0.0004802681,0.00009537063,0.00015451405,0.00039856674,0.00285222,0.0067392658],"genre_scores_gemma":[0.052952223,0.00040612352,0.9317737,0.0005612174,0.00012241205,0.0009184981,0.0008491604,0.00069385883,0.011722696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989003,0.00031385853,0.00015842507,0.00021997327,0.00029747369,0.000110022986],"domain_scores_gemma":[0.99885654,0.00047120242,0.00016204754,0.00019806027,0.00022703217,0.00008511255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018391667,0.0010901147,0.00076392357,0.00081470975,0.0008760276,0.0033395377,0.0031175665,0.0016908603,0.013747913],"category_scores_gemma":[0.0026428895,0.0010040753,0.0021766734,0.00083518977,0.002212125,0.0048920726,0.0024154957,0.0036903992,0.0050916956],"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":[0.00011598005,0.000049226972,0.00020256912,0.00034907967,0.000030501427,0.00024733492,0.00051660027,0.026650745,0.0019399059,0.9325262,0.0075997114,0.029772155],"study_design_scores_gemma":[0.00015410037,0.00012503171,0.00011356063,0.00017329027,0.00007121973,0.00046730696,0.00014049678,0.30358672,0.0034746735,0.5103148,0.18131252,0.00006629526],"about_ca_topic_score_codex":0.0023568359,"about_ca_topic_score_gemma":0.0032984554,"teacher_disagreement_score":0.013747913,"about_ca_system_score_codex":0.0011162492,"about_ca_system_score_gemma":0.0015286105,"threshold_uncertainty_score":0.0459913},"labels":[],"label_agreement":null},{"id":"W2273410534","doi":"10.5555/2615731.2617504","title":"Modeling agent trustworthiness with credibility for message recommendation in social networks","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Access Control and Trust","field":"Social Sciences","cited_by":3,"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 Waterloo","funders":"","keywords":"Credibility; Computer science; Trustworthiness; Recommender system; Similarity (geometry); Source credibility; Multi-agent system; World Wide Web; Internet privacy; Artificial intelligence","score_opus":0.09187409978039231,"score_gpt":0.3414822652082259,"score_spread":0.2496081654278336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2273410534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04894862,0.00034936555,0.9455781,0.0008768437,0.00007148987,0.00007915713,0.00006881267,0.0001624868,0.00386517],"genre_scores_gemma":[0.93385196,0.00029264568,0.0622633,0.000075145945,0.00013736528,0.00013030224,0.00006770877,0.000051233113,0.003130414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99631435,0.001892117,0.00021322389,0.0005521658,0.0007861945,0.00024207859],"domain_scores_gemma":[0.9777439,0.017108735,0.0024399296,0.00090446393,0.0012643337,0.0005385472],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005363431,0.0010222095,0.0011616738,0.0014982817,0.0010319813,0.0022062883,0.0021982747,0.0024041154,0.0019574324],"category_scores_gemma":[0.039810438,0.0008163541,0.0011067295,0.0010615648,0.0018050041,0.0052259085,0.00169632,0.002202455,0.00036755067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008371412,0.000049448594,0.0017931869,0.000049493177,0.000064536194,0.0001432216,0.00027644544,0.9272056,0.00050727895,0.06054783,0.0004448778,0.008834412],"study_design_scores_gemma":[0.000007078954,0.000013444746,0.00010504891,0.0000043156883,0.000009921763,0.0000133188905,0.000012479668,0.9853801,0.000074595904,0.014195954,0.0001770331,0.0000066859393],"about_ca_topic_score_codex":0.013047021,"about_ca_topic_score_gemma":0.008598179,"teacher_disagreement_score":0.013047021,"about_ca_system_score_codex":0.002636191,"about_ca_system_score_gemma":0.0012406311,"threshold_uncertainty_score":0.028364897},"labels":[],"label_agreement":null},{"id":"W2286609364","doi":"10.5555/2034396.2034427","title":"Basis function discovery using spectral clustering and bisimulation metrics","year":2011,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Markov decision process; Cluster analysis; Set (abstract data type); Bellman equation; State space; Feature (linguistics); Function (biology); Artificial intelligence; State (computer science); Markov process; Feature vector; Basis (linear algebra); Markov chain; Focus (optics); Machine learning; Quality (philosophy); Mathematical optimization; Algorithm; Mathematics","score_opus":0.14918220564433027,"score_gpt":0.28652763876973,"score_spread":0.1373454331253997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2286609364","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017226772,0.00042515542,0.97969466,0.0002559248,0.000024246408,0.00009220596,0.00008836987,0.00030352373,0.0018891067],"genre_scores_gemma":[0.46357176,0.00075595133,0.5314435,0.00015537888,0.000065869026,0.0005133514,0.00093957986,0.00032754987,0.002227048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801683,0.00092191354,0.0001177302,0.00030253222,0.00051317574,0.00012785941],"domain_scores_gemma":[0.9938029,0.0035754812,0.0006128433,0.00062136434,0.0011144534,0.00027291692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033366613,0.0013584102,0.0020306103,0.0046078092,0.0012519897,0.0020466996,0.0019488661,0.0019986834,0.00260717],"category_scores_gemma":[0.018847529,0.000780482,0.0014547172,0.0026875495,0.0013375627,0.0029587438,0.0029086454,0.0017422837,0.0008688978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013645238,0.00019694598,0.0021177363,0.00021749284,0.00013735573,0.000080252175,0.00019300393,0.68938357,0.0014942057,0.1422291,0.0039264713,0.15988746],"study_design_scores_gemma":[0.0000068944323,0.00001253597,0.000086451444,0.000012840072,0.0000046019504,0.000012737525,0.0000145039485,0.9622932,0.00025820613,0.036820225,0.00046998757,0.000007756958],"about_ca_topic_score_codex":0.005548796,"about_ca_topic_score_gemma":0.0036723327,"teacher_disagreement_score":0.005548796,"about_ca_system_score_codex":0.002113881,"about_ca_system_score_gemma":0.002238985,"threshold_uncertainty_score":0.017646194},"labels":[],"label_agreement":null},{"id":"W2288689989","doi":"10.5555/2615731.2616084","title":"Correlated multi-dimensional qos metrics for trust evaluation within web services","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Access Control and Trust","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Quality of service; Computer science; Reputation; Mobile QoS; Web service; Service (business); Latent Dirichlet allocation; Selection (genetic algorithm); Service provider; Dirichlet distribution; Task (project management); Computer network; Data mining; World Wide Web; Information retrieval; Topic model; Mathematics; Machine learning","score_opus":0.09265999085829293,"score_gpt":0.3506057261559008,"score_spread":0.2579457352976079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2288689989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09273164,0.0004689211,0.90545803,0.00026282552,0.00003203623,0.00007385989,0.00008829828,0.00021362264,0.00067066314],"genre_scores_gemma":[0.93094,0.0001385182,0.06850173,0.000033132135,0.000031106898,0.00006411406,0.000101751146,0.000018221484,0.00017140967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9933343,0.004047102,0.00045281605,0.00057768717,0.0013576938,0.00023036338],"domain_scores_gemma":[0.98073053,0.011430057,0.0032749411,0.0017997642,0.0021077967,0.0006568438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058787493,0.0009217159,0.0010320112,0.0024934618,0.00065492594,0.0020454717,0.0009930846,0.0010074932,0.0004931702],"category_scores_gemma":[0.032257818,0.0004379888,0.00084168976,0.0022583178,0.001338553,0.0033464152,0.0014077452,0.0014879325,0.00016190881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026408574,0.00013497884,0.016690122,0.00010551242,0.00021921913,0.00016597456,0.00038329532,0.869456,0.003830713,0.045156747,0.00067727274,0.06291607],"study_design_scores_gemma":[0.0000032191808,0.000025251995,0.0007401165,0.000005419827,0.000008253554,0.000020723643,0.000017439279,0.9899436,0.00028911314,0.008837919,0.00009618323,0.000012661828],"about_ca_topic_score_codex":0.003874202,"about_ca_topic_score_gemma":0.0037239352,"teacher_disagreement_score":0.0058787493,"about_ca_system_score_codex":0.0024610548,"about_ca_system_score_gemma":0.0009899527,"threshold_uncertainty_score":0.03109014},"labels":[],"label_agreement":null},{"id":"W2290471876","doi":"10.5555/2615731.2615789","title":"A logical theory of robot localization","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Logic, Reasoning, and Knowledge","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":"University of Toronto","funders":"","keywords":"Rotation formalisms in three dimensions; Probabilistic logic; Computer science; Robot; Logical framework; Artificial intelligence; Categorical variable; Representation (politics); Robotics; Task (project management); Domain (mathematical analysis); Extension (predicate logic); Action (physics); Logical conjunction; Theoretical computer science; Mathematics; Programming language; Engineering; Machine learning; Systems engineering","score_opus":0.060617050818150615,"score_gpt":0.27013361654936147,"score_spread":0.20951656573121086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2290471876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053369543,0.002800622,0.88337374,0.012891298,0.000468956,0.00006653247,0.0003323259,0.00044108107,0.094288476],"genre_scores_gemma":[0.53015286,0.004008887,0.42880413,0.0054258914,0.0013308028,0.00062329177,0.0007459099,0.000226094,0.028682182],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979767,0.000809311,0.00013950106,0.00038763825,0.0005053914,0.00018142024],"domain_scores_gemma":[0.9975662,0.0012550171,0.00018971051,0.00037593895,0.00043778153,0.00017540484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032643944,0.0007125522,0.0006661869,0.0019360587,0.002617381,0.0053851143,0.0022415067,0.0022790248,0.0071932157],"category_scores_gemma":[0.004425287,0.00057259406,0.0019755212,0.0014831822,0.012381539,0.011172235,0.0031857267,0.0042279684,0.0017437297],"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":[0.0000024559477,0.000003219391,0.000022266058,0.00001572647,0.000002933677,0.000019006811,0.00008962189,0.0005504655,0.000072758,0.99682754,0.0006583812,0.0017355614],"study_design_scores_gemma":[0.000008134713,0.0000077112145,0.00002950704,0.000021128011,0.0000048713114,0.00003274262,0.00006329557,0.002891289,0.000107548905,0.98385435,0.01297295,0.000006498263],"about_ca_topic_score_codex":0.0032442329,"about_ca_topic_score_gemma":0.001678479,"teacher_disagreement_score":0.0071932157,"about_ca_system_score_codex":0.0034137669,"about_ca_system_score_gemma":0.0019841725,"threshold_uncertainty_score":0.02476871},"labels":[],"label_agreement":null},{"id":"W2293023860","doi":"10.5555/2772879.2773541","title":"Emotional Interactions between Artificial Companion Agents and the Elderly","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Economic shortage; Bridge (graph theory); Population; Psychology; Computer science; Applied psychology; Artificial intelligence; Medicine; Environmental health","score_opus":0.16526824575205465,"score_gpt":0.3399170186184293,"score_spread":0.17464877286637467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293023860","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9006586,0.0022660557,0.049338356,0.0012850445,0.00016971494,0.00007892177,0.000034488265,0.00018872102,0.045980047],"genre_scores_gemma":[0.98773265,0.00034338943,0.006964282,0.00019167918,0.00002239571,0.000026009575,0.000016609449,0.000011412024,0.004691597],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99952936,0.00034546017,0.000015745802,0.00003251898,0.000050157872,0.000026767708],"domain_scores_gemma":[0.99919933,0.00042648334,0.00011879506,0.000046539273,0.00010543928,0.000103445265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006438307,0.0003249008,0.00014731442,0.00017420681,0.00054774154,0.0012364208,0.00025330146,0.0004834775,0.002441384],"category_scores_gemma":[0.002652464,0.000118651216,0.00016783428,0.000089141606,0.0007200138,0.00079400564,0.0011065044,0.00032732706,0.00035624296],"study_design_candidate":"observational","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":[0.0028675722,0.0012699752,0.06604342,0.0018040702,0.0004982658,0.008674522,0.16997571,0.028657692,0.17236964,0.09397936,0.020421652,0.43343818],"study_design_scores_gemma":[0.00048550335,0.004813132,0.118441254,0.00090448454,0.0007604834,0.011686217,0.110064164,0.18735611,0.03824127,0.15242893,0.37438202,0.0004363725],"about_ca_topic_score_codex":0.0005289652,"about_ca_topic_score_gemma":0.0005238271,"teacher_disagreement_score":0.002441384,"about_ca_system_score_codex":0.00023197489,"about_ca_system_score_gemma":0.00016329272,"threshold_uncertainty_score":0.008167207},"labels":[],"label_agreement":null},{"id":"W2293507961","doi":"10.5555/2772879.2773548","title":"One-Sided Matching with Dynamic Preferences","year":2015,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","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":"University of Waterloo","funders":"","keywords":"Matching (statistics); Computer science; Set (abstract data type); Function (biology); Resource allocation; Scheduling (production processes); Sequential game; Mechanism design; Optimal matching; Contrast (vision); Game theory; Mathematical optimization; Mathematical economics; Artificial intelligence; Economics; Mathematics","score_opus":0.16363275091144538,"score_gpt":0.2740471760258787,"score_spread":0.1104144251144333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293507961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07535247,0.00022470251,0.8956926,0.0015752623,0.00013210802,0.00024116444,0.00023543055,0.0001232938,0.026422935],"genre_scores_gemma":[0.80863845,0.00039250587,0.17458683,0.00054903486,0.0001729084,0.00038749634,0.00018901956,0.00006090862,0.015022852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9899511,0.005428608,0.00057082105,0.0020708903,0.0011941292,0.00078441616],"domain_scores_gemma":[0.98352,0.009517509,0.002276371,0.003246758,0.00073593133,0.00070344744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008095077,0.0007981654,0.0014990567,0.00077314954,0.0012536114,0.0037788728,0.0023964185,0.0029421006,0.013267256],"category_scores_gemma":[0.02778926,0.00068406377,0.0016964491,0.0014532047,0.0030851525,0.007732907,0.0028397334,0.0027383845,0.0015137993],"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":[0.00008663487,0.000093115996,0.0005274276,0.000055063803,0.000036554055,0.000084998836,0.00015567649,0.017133107,0.0006874343,0.96680933,0.0007740026,0.013556687],"study_design_scores_gemma":[0.000056371082,0.000054758373,0.00016684785,0.000012528441,0.0000151154145,0.00006720041,0.000053606946,0.08778247,0.0003194285,0.90928155,0.002173305,0.000016793792],"about_ca_topic_score_codex":0.0008054766,"about_ca_topic_score_gemma":0.0006482398,"teacher_disagreement_score":0.013267256,"about_ca_system_score_codex":0.0018187991,"about_ca_system_score_gemma":0.0013662212,"threshold_uncertainty_score":0.044383347},"labels":[],"label_agreement":null},{"id":"W2295665692","doi":"10.5555/2034396.2034406","title":"Efficient planning in R-max","year":2011,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":9,"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 Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Mathematical optimization; Markov decision process; Value (mathematics); Artificial intelligence; Algorithm; Machine learning; Mathematics; Markov process","score_opus":0.12420306409074311,"score_gpt":0.2922210493630056,"score_spread":0.16801798527226247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295665692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012274354,0.0002920233,0.9777425,0.00026771636,0.000027019936,0.00009720672,0.00012481495,0.0009348306,0.008239472],"genre_scores_gemma":[0.39411822,0.00040032595,0.597923,0.0002227628,0.000036721067,0.0004050702,0.00037051187,0.00039161448,0.006131679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984754,0.0006714217,0.00007578097,0.000387853,0.00021778981,0.00017165074],"domain_scores_gemma":[0.9972608,0.0019393293,0.00020961241,0.0003195007,0.00017789117,0.00009295864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023183401,0.0009939711,0.001227201,0.00055135397,0.0006931092,0.0012954823,0.0016023392,0.0011522098,0.0064850673],"category_scores_gemma":[0.006433127,0.00067765807,0.00095378654,0.00081136206,0.0018835327,0.002297632,0.0019986674,0.0016081678,0.0012144956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000194701,0.00007243508,0.00042442253,0.00021750918,0.000039474322,0.00011330791,0.00013761608,0.8238164,0.0013342566,0.10131669,0.003045019,0.069288164],"study_design_scores_gemma":[0.000032859938,0.000047731657,0.00008069953,0.000019185009,0.000011038015,0.00003192512,0.000028188468,0.90666336,0.0015490793,0.08909764,0.0024270418,0.000011249045],"about_ca_topic_score_codex":0.0032531188,"about_ca_topic_score_gemma":0.004750637,"teacher_disagreement_score":0.0064850673,"about_ca_system_score_codex":0.0013074073,"about_ca_system_score_gemma":0.0025886944,"threshold_uncertainty_score":0.02169472},"labels":[],"label_agreement":null},{"id":"W2399400183","doi":"10.5555/2615731.2616114","title":"An agent-based game for the predictive diagnosis of parkinson's disease","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Parkinson's Disease Mechanisms and Treatments","field":"Medicine","cited_by":30,"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 British Columbia","funders":"","keywords":"Disease; Computer science; Dimension (graph theory); Parkinson's disease; Artificial intelligence; Psychology; Medicine; Pathology","score_opus":0.049102521491156646,"score_gpt":0.3015570224745988,"score_spread":0.2524545009834422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399400183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19000807,0.0007005768,0.7724027,0.002025979,0.00036613602,0.0019872054,0.0007183737,0.006162484,0.025628455],"genre_scores_gemma":[0.7085753,0.00034492908,0.2780421,0.00046503678,0.000044385968,0.0011547332,0.00043957282,0.00010616835,0.010827819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996082,0.00018547608,0.000028258553,0.00006301395,0.000077448705,0.000037668207],"domain_scores_gemma":[0.9991749,0.0005066211,0.0000423598,0.000041451367,0.00007191982,0.00016269204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006594726,0.00092341466,0.0004356177,0.0002855498,0.00043439193,0.00089245394,0.0011001629,0.0009973604,0.0047910092],"category_scores_gemma":[0.0023926187,0.00018433073,0.0004065731,0.00011545271,0.0003793661,0.000779274,0.0010703105,0.00081083,0.0005211567],"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":[0.0062407088,0.0061224196,0.015496785,0.0013767703,0.0005129736,0.0035980626,0.0028268099,0.34709254,0.07016783,0.09354176,0.038257673,0.41476575],"study_design_scores_gemma":[0.00033529708,0.0010118859,0.0017800388,0.00006404562,0.000076994205,0.00035954977,0.00015605899,0.94811416,0.005333843,0.013195775,0.029508516,0.00006377987],"about_ca_topic_score_codex":0.0024439842,"about_ca_topic_score_gemma":0.0022321937,"teacher_disagreement_score":0.0047910092,"about_ca_system_score_codex":0.00043995492,"about_ca_system_score_gemma":0.0006325443,"threshold_uncertainty_score":0.01602757},"labels":[],"label_agreement":null},{"id":"W2404625657","doi":"10.5555/2615731.2616149","title":"Imputation, social choice, and partial preferences","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Missing data; Conceptualization; Computer science; Artificial intelligence; Machine learning; Preference; Social choice theory; Statistics; Mathematics","score_opus":0.09478026045088839,"score_gpt":0.2753525177555754,"score_spread":0.180572257304687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404625657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00726703,0.0002486011,0.98744327,0.0016221153,0.0000958228,0.000052504554,0.00019160475,0.00008695302,0.0029920249],"genre_scores_gemma":[0.42849937,0.000898422,0.5580521,0.0010015747,0.00062205497,0.0006113431,0.00085185614,0.00012021068,0.009343095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9902175,0.0069654947,0.00031212353,0.0011532163,0.000902376,0.00044929722],"domain_scores_gemma":[0.97356963,0.019335028,0.002161208,0.0033327844,0.0010385938,0.00056271633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011067029,0.00093471847,0.0020816221,0.0015338558,0.0014538441,0.002214836,0.0039187004,0.0032842082,0.0083785355],"category_scores_gemma":[0.04615683,0.0008108002,0.0023232675,0.003014879,0.0027991603,0.0051220814,0.0033702399,0.004627565,0.0009229937],"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":[0.00014514875,0.00014203417,0.003066938,0.00021451572,0.00022003365,0.00019288447,0.00029890888,0.09562384,0.00022684038,0.8176393,0.0057503525,0.0764792],"study_design_scores_gemma":[0.000038447186,0.000028964778,0.0002986778,0.0000354529,0.000022414008,0.00007113565,0.000048769318,0.20756781,0.00014760313,0.78881377,0.0029075039,0.00001941666],"about_ca_topic_score_codex":0.0022495184,"about_ca_topic_score_gemma":0.002557199,"teacher_disagreement_score":0.011067029,"about_ca_system_score_codex":0.0013317658,"about_ca_system_score_gemma":0.0021744997,"threshold_uncertainty_score":0.05852878},"labels":[],"label_agreement":null},{"id":"W2406539874","doi":"10.5555/2034396.2034454","title":"Smart walkers!: enhancing the mobility of the elderly","year":2011,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"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 Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Activity recognition; Discriminant; Sigmoid function; Intelligent sensor; Linear discriminant analysis; Artificial intelligence; Machine learning; Human–computer interaction; Random forest; Data mining; Wireless sensor network; Artificial neural network; Computer network","score_opus":0.1021268820539132,"score_gpt":0.26767368446236034,"score_spread":0.16554680240844716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406539874","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39702633,0.0054901415,0.582451,0.001324041,0.00031700433,0.00025571344,0.0006290637,0.0043486133,0.008158118],"genre_scores_gemma":[0.84710425,0.0020779194,0.14324735,0.00017616464,0.000089022375,0.00011044917,0.00041074277,0.000080598154,0.006703491],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999056,0.000027015925,0.0000056750314,0.000020805968,0.00003037421,0.000010459683],"domain_scores_gemma":[0.9998481,0.000049465296,0.000023608014,0.000014744439,0.000041789477,0.000022153577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003153921,0.0003729789,0.00033495872,0.0003491883,0.00015273658,0.0003419531,0.00033880596,0.0003486262,0.0013440344],"category_scores_gemma":[0.0008958196,0.00011408952,0.00015522329,0.00024156489,0.00017118965,0.00078806723,0.00055504387,0.00019549474,0.0005472054],"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":[0.00066810875,0.00032098056,0.008545034,0.00039355838,0.0000625227,0.00025222517,0.0004981002,0.022289049,0.04758439,0.0046489253,0.009871918,0.9048651],"study_design_scores_gemma":[0.00031048438,0.0027744651,0.031902507,0.00021818485,0.00023348915,0.0022038675,0.00070733705,0.81716955,0.05502054,0.02039692,0.068941355,0.000121401],"about_ca_topic_score_codex":0.0012834766,"about_ca_topic_score_gemma":0.0018142177,"teacher_disagreement_score":0.0013440344,"about_ca_system_score_codex":0.00011391331,"about_ca_system_score_gemma":0.00025426547,"threshold_uncertainty_score":0.004496217},"labels":[],"label_agreement":null},{"id":"W2406623448","doi":"10.5555/2615731.2616087","title":"Policy optimization by marginal-map probabilistic inference in generative models","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Machine Learning and Algorithms","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 Waterloo","funders":"","keywords":"Partially observable Markov decision process; Inference; Computer science; Mathematical optimization; Generative model; Bounded function; Benchmark (surveying); Probabilistic logic; Machine learning; Artificial intelligence; Algorithm; Mathematics; Generative grammar; Markov model; Markov chain","score_opus":0.03596804359116691,"score_gpt":0.2878306369712302,"score_spread":0.2518625933800633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406623448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045503653,0.000087848544,0.9943606,0.00011041996,0.000009717322,0.000016988657,0.000030991374,0.0001736226,0.0006594795],"genre_scores_gemma":[0.6322411,0.00031498115,0.36442208,0.00019413626,0.000060457343,0.00023150595,0.00023574277,0.00021979687,0.0020801944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990145,0.00039433144,0.000039898594,0.00020657854,0.00023464671,0.00011004438],"domain_scores_gemma":[0.9966587,0.0026943432,0.00018807997,0.00019398567,0.00017139579,0.000093478935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023076038,0.0010272239,0.0015497399,0.00072295003,0.0005871528,0.0014445026,0.0021311804,0.0014888452,0.002456649],"category_scores_gemma":[0.008791993,0.0009983853,0.0012655533,0.00086736423,0.002164701,0.001734786,0.0022484697,0.0025546544,0.00030079353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022327506,0.000012979899,0.00019363698,0.00003077038,0.000016912325,0.000020062062,0.0000339364,0.9727623,0.0002128758,0.019485295,0.0002251341,0.006983835],"study_design_scores_gemma":[0.0000036452955,0.000003937781,0.000019713047,0.0000025416514,0.000002473116,0.0000036330653,0.0000036185904,0.99024385,0.00011175863,0.009500965,0.000101656995,0.0000023368611],"about_ca_topic_score_codex":0.012252528,"about_ca_topic_score_gemma":0.0107825585,"teacher_disagreement_score":0.012252528,"about_ca_system_score_codex":0.001961825,"about_ca_system_score_gemma":0.002474535,"threshold_uncertainty_score":0.024362445},"labels":[],"label_agreement":null},{"id":"W2407630320","doi":"10.5555/2615731.2617513","title":"Distributed multiagent resource allocation with adaptive preemption for dynamic tasks","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Multi-Agent Systems and Negotiation","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 Waterloo","funders":"","keywords":"Preemption; Computer science; Distributed computing; Resource allocation; Task (project management); Multi-agent system; Resource (disambiguation); Resource management (computing); Computer network; Artificial intelligence; Engineering; Operating system","score_opus":0.038351474185839625,"score_gpt":0.26888200538747953,"score_spread":0.2305305312016399,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2407630320","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007384953,0.00025929915,0.990284,0.00008274998,0.000055341552,0.000045004595,0.000004879813,0.00017125557,0.0017124977],"genre_scores_gemma":[0.5910669,0.00029558415,0.4026068,0.000153966,0.00011670822,0.00026918927,0.00003238881,0.00008893329,0.0053695487],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886024,0.00047094654,0.00006987039,0.00020635035,0.00028295003,0.00010961964],"domain_scores_gemma":[0.99891925,0.0005076012,0.00011832268,0.0002238543,0.0001352071,0.00009581075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015580537,0.00052290346,0.0007052809,0.0003465046,0.0007484027,0.00097973,0.0017076535,0.0007189791,0.0016032859],"category_scores_gemma":[0.0031918518,0.0003119965,0.000489757,0.00042885102,0.00069723796,0.0013149099,0.0015292284,0.0012950148,0.00037522014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043656066,0.0003153251,0.00076561474,0.00029472032,0.00016169345,0.0005591534,0.0005194743,0.65218765,0.028751628,0.12343927,0.0029868549,0.18958196],"study_design_scores_gemma":[0.00005960205,0.00007396615,0.0001261281,0.000011690227,0.000024922174,0.00011447528,0.000031868924,0.9652556,0.0030532463,0.024274798,0.006959204,0.000014489142],"about_ca_topic_score_codex":0.00080504775,"about_ca_topic_score_gemma":0.0010020469,"teacher_disagreement_score":0.0017076535,"about_ca_system_score_codex":0.0005193638,"about_ca_system_score_gemma":0.00080430007,"threshold_uncertainty_score":0.008239865},"labels":[],"label_agreement":null},{"id":"W2465754415","doi":"10.5555/2936924.2936979","title":"The Echo Chamber: Strategic Voting and Homophily in Social Networks","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":26,"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 Waterloo","funders":"","keywords":"Homophily; Voting; Politics; Population; Social network (sociolinguistics); Voter model; Echo (communications protocol); Ideology; Phenomenon; Political science; Microeconomics; Computer science; Economics; Sociology; Computer security; Social psychology; Law; Social media; Psychology; Mathematics","score_opus":0.04929803298486006,"score_gpt":0.29339422801641696,"score_spread":0.24409619503155688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465754415","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7412129,0.0008656871,0.22981393,0.0035624,0.00009398723,0.00010461262,0.00023118399,0.000112687805,0.024002563],"genre_scores_gemma":[0.99325764,0.00018085225,0.004462499,0.000111331465,0.000043232514,0.000040190822,0.000034421257,0.000006861965,0.0018629743],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879766,0.000723196,0.000029779862,0.0002342609,0.000099549194,0.000115519404],"domain_scores_gemma":[0.99462825,0.0035410533,0.0009503564,0.00041645672,0.0001655965,0.00029830518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001581433,0.00032236244,0.00077060267,0.0007386087,0.0010449134,0.0018279074,0.0009646455,0.0014701968,0.0043910337],"category_scores_gemma":[0.008184093,0.00032037066,0.00057076383,0.00070044806,0.0017479998,0.0036842015,0.0012350249,0.00094352214,0.0003834672],"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":[0.00036463724,0.00018710537,0.022319311,0.00016814409,0.0001735984,0.00052923296,0.0016079478,0.12530781,0.0034798225,0.81047285,0.0042806854,0.031108731],"study_design_scores_gemma":[0.00010032715,0.00013545452,0.006181791,0.000040575942,0.00008772666,0.0002592357,0.00047694836,0.61944443,0.00057655334,0.3677379,0.0049089547,0.000050000966],"about_ca_topic_score_codex":0.0018619656,"about_ca_topic_score_gemma":0.0019868228,"teacher_disagreement_score":0.0043910337,"about_ca_system_score_codex":0.0006340472,"about_ca_system_score_gemma":0.00032618752,"threshold_uncertainty_score":0.014689505},"labels":[],"label_agreement":null},{"id":"W2467276603","doi":"10.5555/2936924.2937201","title":"Investigating the Characteristics of One-Sided Matching Mechanisms: (Extended Abstract)","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","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":"University of Waterloo","funders":"","keywords":"Comparability; Matching (statistics); Probabilistic logic; Computer science; Dictatorship; Space (punctuation); Mechanism (biology); Task (project management); Theoretical computer science; Artificial intelligence; Mathematics; Statistics; Economics; Epistemology; Political science","score_opus":0.10171893914074176,"score_gpt":0.2588623865931234,"score_spread":0.15714344745238162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2467276603","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48021567,0.0017390896,0.47479108,0.0027590469,0.0002939233,0.00043957276,0.00095624896,0.00034779418,0.038457572],"genre_scores_gemma":[0.9563569,0.00085316563,0.03399533,0.00041134254,0.00020042436,0.00031107827,0.00031618474,0.00007276022,0.0074827997],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968401,0.0014091609,0.0002542635,0.00068186416,0.00037879878,0.0004356655],"domain_scores_gemma":[0.9432678,0.032684658,0.012820558,0.006872216,0.002616535,0.0017382146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00764063,0.0005534822,0.0015131609,0.0014444133,0.0010158026,0.0024420053,0.0024994453,0.0022686147,0.020703966],"category_scores_gemma":[0.048298225,0.0004778074,0.0014291417,0.0026551876,0.0021379478,0.0048403954,0.0016792023,0.0019715063,0.0014986135],"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":[0.0002710108,0.00029487972,0.0038604091,0.00047111127,0.00015049554,0.00018899028,0.00028952942,0.052055992,0.0022794341,0.90539426,0.0054430813,0.029300911],"study_design_scores_gemma":[0.00016765644,0.000285314,0.001937562,0.000091801056,0.000060949722,0.000280971,0.00011613599,0.16757369,0.0010292862,0.825958,0.0024514438,0.000047034064],"about_ca_topic_score_codex":0.0007110887,"about_ca_topic_score_gemma":0.00042868202,"teacher_disagreement_score":0.020703966,"about_ca_system_score_codex":0.0010868675,"about_ca_system_score_gemma":0.0012214391,"threshold_uncertainty_score":0.06926167},"labels":[],"label_agreement":null},{"id":"W2479077205","doi":"10.5555/2936924.2937144","title":"Convergence and Quality of Iterative Voting under Non-Scoring Rules: (Extended Abstract)","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Voting; Convergence (economics); Computer science; Quality (philosophy); Iterative method; Social choice theory; Anti-plurality voting; Cardinal voting systems; Mathematical optimization; Algorithm; Mathematical economics; Mathematics; Economics; Political science","score_opus":0.1481050922965997,"score_gpt":0.30445444729394866,"score_spread":0.15634935499734895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2479077205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7743272,0.00065497204,0.20582846,0.0007697625,0.00005605947,0.00017196834,0.0002947935,0.00017342855,0.017723441],"genre_scores_gemma":[0.986466,0.00013020652,0.011602742,0.000044115626,0.00002452912,0.00006126134,0.00014398934,0.000036309295,0.0014908115],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946267,0.0028504357,0.00033615643,0.0007874183,0.0008688605,0.00053048035],"domain_scores_gemma":[0.8138257,0.14102553,0.018055262,0.0117087,0.012838248,0.0025465323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012535053,0.00035429242,0.001103846,0.0014935383,0.00073229504,0.002726555,0.0015008878,0.0012655635,0.005713043],"category_scores_gemma":[0.122450694,0.00030824696,0.00093250856,0.001650483,0.0030442888,0.0032438843,0.0014113756,0.0019006586,0.00065998937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014327107,0.0005791562,0.055906065,0.0004470328,0.00046609485,0.00031894312,0.002062413,0.42635396,0.0032400575,0.42343462,0.003965718,0.08179332],"study_design_scores_gemma":[0.00011805896,0.00025216275,0.013239295,0.00007566503,0.000059958624,0.000166483,0.00027727854,0.6080292,0.001511368,0.37529713,0.00092265324,0.000050737748],"about_ca_topic_score_codex":0.003043516,"about_ca_topic_score_gemma":0.0014245652,"teacher_disagreement_score":0.012535053,"about_ca_system_score_codex":0.0016715459,"about_ca_system_score_gemma":0.0009793441,"threshold_uncertainty_score":0.066292465},"labels":[],"label_agreement":null},{"id":"W2494900181","doi":"10.5555/2936924.2936947","title":"Strategy-Proofness in the Stable Matching Problem with Couples","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":9,"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 Toronto","funders":"","keywords":"Matching (statistics); Complementarity (molecular biology); Stable marriage problem; Preference; Set (abstract data type); Computer science; Mathematical economics; Pareto principle; Stability (learning theory); Economics; Microeconomics; Mathematical optimization; Mathematics; Statistics","score_opus":0.08909092805595974,"score_gpt":0.25137584936900165,"score_spread":0.1622849213130419,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2494900181","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23418829,0.00041183547,0.7507626,0.002397568,0.000052176518,0.00045071467,0.0005252182,0.00030834318,0.0109034],"genre_scores_gemma":[0.8219425,0.00048268653,0.17123552,0.00053230155,0.00011975031,0.0005181155,0.00058031833,0.00011102927,0.0044777608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99206865,0.0040263757,0.0006474351,0.0016989004,0.0008050171,0.00075364916],"domain_scores_gemma":[0.9588304,0.03253428,0.0033730038,0.002820947,0.0009972069,0.0014441877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008285511,0.0009868479,0.0022062832,0.0010022618,0.0022845913,0.0038344776,0.0024629862,0.003214592,0.008157583],"category_scores_gemma":[0.045248564,0.00081068586,0.0020463823,0.0017519663,0.0038212095,0.007815083,0.0036126217,0.0030336962,0.000824853],"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":[0.00053614436,0.00044193328,0.0047869356,0.00048782388,0.00035090875,0.0006145879,0.0015084432,0.12220937,0.0032140329,0.8024462,0.0041782213,0.059225332],"study_design_scores_gemma":[0.00020051718,0.0001294762,0.0003126884,0.000038667782,0.00003764603,0.00020959019,0.00020764445,0.17716551,0.0008735325,0.81907195,0.0017221555,0.000030591356],"about_ca_topic_score_codex":0.0012347514,"about_ca_topic_score_gemma":0.00071802596,"teacher_disagreement_score":0.008285511,"about_ca_system_score_codex":0.0011220531,"about_ca_system_score_gemma":0.0024060614,"threshold_uncertainty_score":0.043818474},"labels":[],"label_agreement":null},{"id":"W2523006798","doi":"10.5555/2936924.2937242","title":"Strategic Voting and Social Networks: (Doctoral Consortium)","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Opinion Dynamics and Social Influence","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Affect (linguistics); Voting; Everyday life; Outcome (game theory); Population; Computer science; Politics; Social choice theory; Sociology; Political science; Public relations; Microeconomics; Economics; Communication","score_opus":0.08410439711991427,"score_gpt":0.306347443168548,"score_spread":0.22224304604863374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523006798","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027155705,0.24065419,0.016667986,0.3151424,0.015905589,0.0001050958,0.0012838853,0.00021281121,0.38287234],"genre_scores_gemma":[0.39594296,0.30155137,0.012064744,0.013448027,0.013574684,0.00031195904,0.0011023001,0.00015246707,0.26185146],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963367,0.00013366142,0.000016726228,0.000083318206,0.000096813164,0.000035844896],"domain_scores_gemma":[0.9987884,0.00059645943,0.00008836663,0.000044373657,0.00029386466,0.00018859118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013006359,0.00037437087,0.0003743621,0.0011478493,0.00096942123,0.0032825728,0.00025994668,0.0013276411,0.017021349],"category_scores_gemma":[0.0025389232,0.0001469159,0.00030369117,0.0014020762,0.0011818464,0.002050401,0.0008329347,0.0013214219,0.0029271983],"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":[0.00006417299,0.00010257671,0.0026376762,0.00046450668,0.000046790567,0.00011494743,0.0011705811,0.0015285242,0.00039528369,0.43373784,0.338034,0.22170301],"study_design_scores_gemma":[0.00003461394,0.00004952273,0.008442369,0.0009922907,0.000043835895,0.00029063408,0.0009136516,0.0027713068,0.00038862927,0.25283188,0.7332079,0.00003336411],"about_ca_topic_score_codex":0.0033917993,"about_ca_topic_score_gemma":0.0030864934,"teacher_disagreement_score":0.017021349,"about_ca_system_score_codex":0.0016389021,"about_ca_system_score_gemma":0.0013737738,"threshold_uncertainty_score":0.056942105},"labels":[],"label_agreement":null},{"id":"W2531256015","doi":"10.5555/2936924.2937219","title":"A Kinect-based Interactive Game to Improve the Cognitive Inhibition of the Elderly: (Demonstration)","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Mind wandering and attention","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Cognition; Personalization; Computer science; Theme (computing); Game play; Process (computing); Human–computer interaction; Psychology; Multimedia; World Wide Web","score_opus":0.05845525293081405,"score_gpt":0.29000513439508596,"score_spread":0.2315498814642719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2531256015","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8373993,0.00081799814,0.13617674,0.0009724544,0.00045654978,0.0021246918,0.0026017923,0.00561854,0.013832072],"genre_scores_gemma":[0.79143274,0.0007730914,0.1876097,0.00048838207,0.00008277982,0.0022102583,0.0013435357,0.00018842495,0.015871078],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99988604,0.000027909557,0.000009927156,0.00002383223,0.000026768093,0.00002549598],"domain_scores_gemma":[0.99980706,0.00007980648,0.00001288327,0.000011982993,0.000026033757,0.000062311214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003929952,0.0009975134,0.00035660094,0.00021123805,0.00014373467,0.00024059217,0.0007953626,0.0007068214,0.0066279834],"category_scores_gemma":[0.0006912602,0.00015259495,0.00036365612,0.0000703802,0.00016059818,0.000405985,0.00080720626,0.00042318788,0.0009770711],"study_design_candidate":"observational","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":[0.00898589,0.011998641,0.012717651,0.0035210433,0.0003481059,0.003211329,0.002599795,0.013440706,0.49806252,0.0021853545,0.03541561,0.4075134],"study_design_scores_gemma":[0.006280869,0.046206977,0.2105569,0.00087018753,0.0006710169,0.0109367175,0.0014225206,0.26721877,0.322584,0.0033077975,0.12947594,0.0004684176],"about_ca_topic_score_codex":0.0010594024,"about_ca_topic_score_gemma":0.0015915554,"teacher_disagreement_score":0.0066279834,"about_ca_system_score_codex":0.00008327848,"about_ca_system_score_gemma":0.0001937415,"threshold_uncertainty_score":0.022172809},"labels":[],"label_agreement":null},{"id":"W2620615090","doi":"10.5555/3091125.3091250","title":"Distant Truth: Bias Under Vote Distortion Costs","year":2017,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":3,"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 Toronto","funders":"","keywords":"Nash equilibrium; Voting; Computer science; Distortion (music); Outcome (game theory); Surprise; Set (abstract data type); Social choice theory; Econometrics; Mathematical economics; Mathematics; Social psychology; Bandwidth (computing); Psychology","score_opus":0.20115803981468233,"score_gpt":0.29770024886114627,"score_spread":0.09654220904646393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620615090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42565438,0.0023589372,0.5363439,0.0066000046,0.00017187832,0.00022380696,0.00064358953,0.0002951489,0.027708333],"genre_scores_gemma":[0.9809672,0.00037668485,0.014967108,0.00024239306,0.000117444455,0.0000776921,0.00012909506,0.000056525012,0.0030658056],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99398816,0.0029601185,0.00026786586,0.0010572423,0.0010065122,0.0007200672],"domain_scores_gemma":[0.9462562,0.040374797,0.005994681,0.004640732,0.0017011319,0.00103239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007862802,0.00080986007,0.0021789053,0.0012818151,0.0012030574,0.0035718696,0.0020803527,0.0028994817,0.0075167255],"category_scores_gemma":[0.063143276,0.0006115043,0.0012306595,0.0012759871,0.0036828911,0.0053481515,0.0033538595,0.0034956213,0.0005289317],"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":[0.0007146848,0.00010396613,0.01012852,0.00033626868,0.00019874584,0.00039741115,0.0005192458,0.15307482,0.0022866456,0.7969796,0.0019634753,0.033296645],"study_design_scores_gemma":[0.00016535724,0.000170901,0.0037956324,0.00008021045,0.00009153893,0.00039046348,0.00023795235,0.22405916,0.0019435437,0.76606953,0.0029330475,0.00006260166],"about_ca_topic_score_codex":0.0020182407,"about_ca_topic_score_gemma":0.0015775868,"teacher_disagreement_score":0.007862802,"about_ca_system_score_codex":0.00317673,"about_ca_system_score_gemma":0.0010553333,"threshold_uncertainty_score":0.041583},"labels":[],"label_agreement":null},{"id":"W2620853660","doi":"10.5555/3091125.3091207","title":"Forward Actor-Critic for Nonlinear Function Approximation in Reinforcement Learning","year":2017,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Reinforcement Learning in Robotics","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 Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Bellman equation; Function approximation; Nonlinear system; Function (biology); Class (philosophy); Artificial intelligence; Temporal difference learning; Mathematical optimization; Machine learning; Artificial neural network; Mathematics","score_opus":0.07223075096191683,"score_gpt":0.309418597286914,"score_spread":0.23718784632499718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620853660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027293153,0.00037397165,0.99448746,0.00013420555,0.000050423303,0.000030148007,0.000015477037,0.00026100915,0.0019180207],"genre_scores_gemma":[0.65965873,0.0008231437,0.32714632,0.00029092986,0.000105466235,0.00038019838,0.00010920644,0.00019535665,0.011290703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994042,0.00023461516,0.000031898213,0.000115263385,0.00016559586,0.000048427395],"domain_scores_gemma":[0.9981445,0.0012830356,0.00011430953,0.00013936851,0.00026129058,0.000057427158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020957452,0.0012729142,0.0011402239,0.0004254496,0.0003907899,0.0008706378,0.0013603838,0.0014612275,0.0030324159],"category_scores_gemma":[0.005353895,0.00061772886,0.0006460789,0.00045605176,0.0013481419,0.0008851603,0.0010133693,0.0026996315,0.00068500516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005019353,0.000031403088,0.00032816493,0.00009744124,0.000041205578,0.00006907418,0.000054380373,0.932126,0.0011050161,0.029808369,0.0010651746,0.03522356],"study_design_scores_gemma":[0.000005012286,0.000008953005,0.000018249872,0.0000043135774,0.0000030375027,0.000005740419,0.00000127468,0.9954477,0.00020348458,0.003933962,0.00036545927,0.0000027739932],"about_ca_topic_score_codex":0.0052340776,"about_ca_topic_score_gemma":0.005072001,"teacher_disagreement_score":0.0052340776,"about_ca_system_score_codex":0.0011307881,"about_ca_system_score_gemma":0.0012556661,"threshold_uncertainty_score":0.011083484},"labels":[],"label_agreement":null},{"id":"W2621346283","doi":"10.5555/3091125.3091215","title":"Divide and Conquer: Using Geographic Manipulation to Win District-Based Elections","year":2017,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":34,"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 Toronto","funders":"","keywords":"Gerrymandering; Ballot; Divide and conquer algorithms; Computer science; Voting; Redistricting; Secret ballot; Political science; Computer security; Law; Algorithm; Politics; Democracy","score_opus":0.16505426797796252,"score_gpt":0.302302601841162,"score_spread":0.13724833386319946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621346283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6542864,0.0010628378,0.2628419,0.002756021,0.0001596576,0.00046025746,0.00032636937,0.0004452587,0.07766139],"genre_scores_gemma":[0.9825477,0.00006892704,0.012857136,0.00012277698,0.000027102771,0.0000696989,0.00007441675,0.00003652172,0.0041956664],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957703,0.0026584135,0.00009548613,0.00053260324,0.00037842835,0.00056475523],"domain_scores_gemma":[0.9933698,0.003226345,0.001309915,0.0013754406,0.00025123975,0.0004670924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033707155,0.0003995259,0.0009988039,0.00066865585,0.0018207724,0.0023990339,0.0015600192,0.0013717356,0.012681256],"category_scores_gemma":[0.015865108,0.00027328279,0.0005284766,0.0011470759,0.002155621,0.0032069846,0.0026079635,0.001512764,0.0012765098],"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":[0.0019954045,0.0006327385,0.033592466,0.00032855337,0.00035286119,0.0006891656,0.0032100019,0.19649887,0.006179898,0.51830804,0.014967211,0.2232448],"study_design_scores_gemma":[0.0009434771,0.0007024786,0.010996848,0.000073988114,0.00015000516,0.00042439406,0.0024195616,0.5029799,0.0038828626,0.4341561,0.043179963,0.00009044531],"about_ca_topic_score_codex":0.0029315783,"about_ca_topic_score_gemma":0.0051206043,"teacher_disagreement_score":0.012681256,"about_ca_system_score_codex":0.0014099248,"about_ca_system_score_gemma":0.0010487649,"threshold_uncertainty_score":0.04242301},"labels":[],"label_agreement":null},{"id":"W2621370674","doi":"10.5555/3091125.3091251","title":"A Restricted Markov Tree Model for Inference and Generation in Social Choice with Incomplete Preferences","year":2017,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inference; Graphical model; Consistency (knowledge bases); Machine learning; Markov chain; Intuition; Markov model; Ranking (information retrieval); Artificial intelligence; Tree (set theory); Probabilistic logic; Social choice theory; Data mining; Mathematics; Mathematical economics","score_opus":0.24764105549389467,"score_gpt":0.3200695065619617,"score_spread":0.07242845106806703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621370674","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076497844,0.0001359808,0.9900394,0.00031562676,0.000024132356,0.00004760214,0.00035757388,0.00023970571,0.0011900911],"genre_scores_gemma":[0.505409,0.0005567088,0.48367232,0.00035009353,0.00018481589,0.0006822321,0.0017625883,0.00021494909,0.0071672145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996725,0.0019802903,0.00015304473,0.00060453755,0.00034766627,0.00018951095],"domain_scores_gemma":[0.99026626,0.007907938,0.0005670968,0.00061847555,0.00040370636,0.00023651985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050503667,0.00068980316,0.0016814083,0.001395523,0.00085558224,0.0019221781,0.003407812,0.001856102,0.00772624],"category_scores_gemma":[0.015965587,0.0007921333,0.0019223574,0.002145149,0.0016315745,0.0041590296,0.001506064,0.002667595,0.0014538815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012671603,0.000055362674,0.0013399847,0.0000960633,0.000084068364,0.00016237273,0.0002377984,0.593853,0.0005865384,0.377059,0.0019279784,0.024471186],"study_design_scores_gemma":[0.00001553744,0.00001345441,0.00009892124,0.0000064869464,0.000008689023,0.000021885846,0.000008257289,0.88187784,0.00006484438,0.1171436,0.0007298647,0.000010699165],"about_ca_topic_score_codex":0.008866191,"about_ca_topic_score_gemma":0.012429795,"teacher_disagreement_score":0.008866191,"about_ca_system_score_codex":0.001902408,"about_ca_system_score_gemma":0.0016722749,"threshold_uncertainty_score":0.026709199},"labels":[],"label_agreement":null},{"id":"W29505111","doi":"10.1111/mmi.13947","title":"Koko: engineering affective applications","year":2009,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Allergy and Infectious Diseases; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Affect (linguistics); Entertainment; Computer science; Empathy; Multimedia; Human–computer interaction; Psychology; Social psychology; Visual arts; Communication","score_opus":0.04968349176655432,"score_gpt":0.295271183228059,"score_spread":0.2455876914615047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W29505111","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014020234,0.00353002,0.17926176,0.003326877,0.0029431873,0.0005311804,0.0018634764,0.0066720163,0.7878513],"genre_scores_gemma":[0.120591976,0.004509192,0.08572887,0.001066622,0.00037603072,0.00096042454,0.0023646753,0.002147349,0.7822549],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953747,0.00009188659,0.000037784266,0.000082241415,0.00018232784,0.000068366164],"domain_scores_gemma":[0.99951184,0.00008090232,0.000028423068,0.00014377094,0.00014423237,0.00009078188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005529823,0.0007648202,0.00040189977,0.00077390095,0.0011511561,0.0017997896,0.0010073718,0.0010027636,0.1548589],"category_scores_gemma":[0.0017220106,0.00027433355,0.00038193984,0.00074016344,0.0005882476,0.0022595602,0.0036243189,0.0009207333,0.07807535],"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":[0.00023352623,0.00015601484,0.0007945231,0.0008555568,0.000025592775,0.0005802833,0.00072741194,0.0018931652,0.025239937,0.17969099,0.16286139,0.6269416],"study_design_scores_gemma":[0.000016032911,0.0000436566,0.0005633495,0.00013046611,0.000011165526,0.0004687879,0.00020995195,0.0037397728,0.0046395957,0.022914845,0.9672387,0.000023629937],"about_ca_topic_score_codex":0.0005043898,"about_ca_topic_score_gemma":0.0009964795,"teacher_disagreement_score":0.1548589,"about_ca_system_score_codex":0.00059211144,"about_ca_system_score_gemma":0.00071979495,"threshold_uncertainty_score":0.5180546},"labels":[],"label_agreement":null},{"id":"W2963606302","doi":"10.5555/2936924.2936943","title":"Budgetary Effects on Pricing Equilibrium in Online Markets","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":3,"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 Toronto","funders":"","keywords":"Submodular set function; Budget constraint; Market clearing; Valuation (finance); Nash equilibrium; Microeconomics; Clearing; Economics; Mathematical economics; Uniqueness; Monotone polygon; Mathematical optimization; Mathematics","score_opus":0.1186784481878477,"score_gpt":0.37016680573510363,"score_spread":0.25148835754725596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963606302","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35614663,0.0013364018,0.56239736,0.002235667,0.00010213333,0.00016812768,0.00028392894,0.00020845456,0.07712132],"genre_scores_gemma":[0.9641623,0.0005984921,0.030363096,0.00017361737,0.00006409377,0.00014844717,0.00006753182,0.00005220068,0.004370266],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99651366,0.002198026,0.00012287451,0.00030313744,0.00034678433,0.0005156404],"domain_scores_gemma":[0.98372114,0.011941927,0.0020925202,0.0010813375,0.0006757699,0.000487284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004551166,0.0009871287,0.0014455381,0.0010917411,0.0011222963,0.0031971964,0.0016228751,0.0016633121,0.0092652375],"category_scores_gemma":[0.027758993,0.0008164552,0.0009857212,0.0011477339,0.0026071067,0.008706766,0.0027569237,0.0021349294,0.00040108626],"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":[0.00026727372,0.00017194818,0.0012426674,0.00014909712,0.00007255445,0.00019214816,0.00027093873,0.10336885,0.0015963503,0.87631226,0.0011583542,0.015197561],"study_design_scores_gemma":[0.00012983693,0.00007989034,0.00078166765,0.0000600886,0.000039019007,0.00007895403,0.00013108383,0.30974537,0.0008799642,0.6858482,0.0021874995,0.000038419275],"about_ca_topic_score_codex":0.0023791352,"about_ca_topic_score_gemma":0.0024452785,"teacher_disagreement_score":0.0092652375,"about_ca_system_score_codex":0.002008835,"about_ca_system_score_gemma":0.0011307525,"threshold_uncertainty_score":0.03099531},"labels":[],"label_agreement":null},{"id":"W3022125085","doi":"10.5555/2615731.2615757","title":"Progression and verification of situation calculus agents with bounded beliefs","year":2014,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Logic, Reasoning, and Knowledge","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":"York University","funders":"","keywords":"Situation calculus; Decidability; Bounded function; Action (physics); Computer science; Domain (mathematical analysis); Object (grammar); Temporal logic; Belief revision; Calculus (dental); Order (exchange); Mathematics; Theoretical computer science; Discrete mathematics; Artificial intelligence","score_opus":0.03857713149392297,"score_gpt":0.2800804080179356,"score_spread":0.24150327652401263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022125085","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.103170216,0.00025047816,0.88801867,0.00085357117,0.00005283298,0.00020202449,0.0003303022,0.0016495457,0.0054723374],"genre_scores_gemma":[0.7789321,0.0002899343,0.21597572,0.00021904286,0.000073878444,0.0002933613,0.00071577315,0.00015685895,0.0033432413],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.991975,0.0020228818,0.00054909807,0.0015028623,0.002855953,0.0010943175],"domain_scores_gemma":[0.97866565,0.015013075,0.0017083447,0.0018872089,0.0020235793,0.0007021933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007826946,0.00101814,0.0009589631,0.0018146529,0.0016677378,0.0035641596,0.0026015579,0.0014697837,0.0035387354],"category_scores_gemma":[0.031043561,0.00082663365,0.0029750834,0.0010792809,0.005532158,0.009025323,0.0042213746,0.004063621,0.00047436435],"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":[0.0003105234,0.00013173102,0.002549875,0.00024386817,0.00013407385,0.0011366548,0.00198181,0.09036722,0.009570187,0.87133366,0.0007111956,0.02152931],"study_design_scores_gemma":[0.000111019894,0.00009444897,0.0003538705,0.00006819559,0.00008584068,0.000147772,0.00020602667,0.39185312,0.009081303,0.59452116,0.0034203662,0.000056965317],"about_ca_topic_score_codex":0.011236188,"about_ca_topic_score_gemma":0.0059694406,"teacher_disagreement_score":0.011236188,"about_ca_system_score_codex":0.0032009876,"about_ca_system_score_gemma":0.004659688,"threshold_uncertainty_score":0.04139328},"labels":[],"label_agreement":null},{"id":"W3140464205","doi":"","title":"Investigating the characteristics of one-sided matching mechanisms","year":2016,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","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 Waterloo","funders":"","keywords":"Computer science; Matching (statistics); Mathematics; Statistics","score_opus":0.10926594919314152,"score_gpt":0.2541527099557087,"score_spread":0.14488676076256718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3140464205","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.76034546,0.0005483311,0.22159988,0.0015859905,0.00006501581,0.00025713726,0.00019075525,0.00017909051,0.015228359],"genre_scores_gemma":[0.9861568,0.00017527492,0.011171087,0.00008140794,0.000037403628,0.00009696592,0.00006526258,0.000021304933,0.002194589],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99536705,0.002538321,0.0002729297,0.0006754495,0.00048802013,0.0006582981],"domain_scores_gemma":[0.88761455,0.0786587,0.017394599,0.0094011305,0.0035899547,0.0033410464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010613529,0.00044121192,0.0017255988,0.0017072546,0.0012355385,0.0037051737,0.0034286329,0.003988132,0.009508922],"category_scores_gemma":[0.08469413,0.00084858725,0.0010217057,0.0017181868,0.002672091,0.007267947,0.002302611,0.0019547783,0.0006413602],"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":[0.00036659307,0.00030959194,0.007827513,0.0001932792,0.00014233314,0.00020573933,0.0005060828,0.046131004,0.0029601315,0.9201287,0.0013101718,0.019918922],"study_design_scores_gemma":[0.00021458001,0.00022051345,0.0023876808,0.000042986165,0.00006305086,0.00026837163,0.0003459632,0.34297886,0.00095086585,0.6515141,0.0009681472,0.000044869248],"about_ca_topic_score_codex":0.0005810695,"about_ca_topic_score_gemma":0.0005120868,"teacher_disagreement_score":0.010613529,"about_ca_system_score_codex":0.0013191861,"about_ca_system_score_gemma":0.0016364689,"threshold_uncertainty_score":0.05613041},"labels":[],"label_agreement":null},{"id":"W79747542","doi":"10.5555/1838206.1838398","title":"Quasi deterministic POMDPs and DecPOMDPs","year":2010,"lang":"en","type":"article","venue":"Adaptive Agents and Multi-Agents Systems","topic":"Bayesian Modeling and Causal Inference","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":"Université Laval","funders":"","keywords":"Observability; Observable; Computer science; Extension (predicate logic); Markov decision process; Mathematical optimization; Hierarchy; Class (philosophy); Polynomial hierarchy; Theoretical computer science; Mathematics; Time complexity; Markov process; Applied mathematics; Algorithm; Artificial intelligence","score_opus":0.06089110723984551,"score_gpt":0.29445388733306554,"score_spread":0.23356278009322004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W79747542","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020636592,0.00044828877,0.9706454,0.0005546106,0.000061896215,0.00006984485,0.00069912226,0.00020910404,0.006675182],"genre_scores_gemma":[0.741669,0.0012079225,0.24428345,0.0003844278,0.00012602047,0.00036731325,0.0011892628,0.00010793761,0.010664734],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885404,0.00031566306,0.000073100084,0.00031844722,0.00029231052,0.00014642475],"domain_scores_gemma":[0.9962365,0.0024281389,0.0005464214,0.00034414543,0.0002897987,0.0001550509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013082462,0.0006932767,0.00079375406,0.0004764137,0.00058291585,0.001455381,0.00095410424,0.0009496902,0.005057963],"category_scores_gemma":[0.004877493,0.0005541146,0.000871871,0.00072589895,0.0012793964,0.0025587403,0.0012922672,0.0019429141,0.00033102575],"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":[0.00007007398,0.000046238132,0.0010965676,0.0001639958,0.00005074044,0.00020546628,0.00010486907,0.36917612,0.0008328681,0.61055773,0.0015261619,0.01616919],"study_design_scores_gemma":[0.000019131669,0.000027791742,0.00031104565,0.000017149123,0.000013176719,0.000068282876,0.000037692847,0.6951871,0.00041753106,0.2994321,0.004457289,0.000011666325],"about_ca_topic_score_codex":0.0042624283,"about_ca_topic_score_gemma":0.004924057,"teacher_disagreement_score":0.005057963,"about_ca_system_score_codex":0.0014049481,"about_ca_system_score_gemma":0.0014704949,"threshold_uncertainty_score":0.016920567},"labels":[],"label_agreement":null}]}