{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":925,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":925,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"68502a2299b6","filters":{"topic":"Artificial Intelligence in Games"}},"results":[{"id":"W2574978968","doi":"10.1126/science.aam6960","title":"DeepStack: Expert-level artificial intelligence in heads-up no-limit poker","year":2017,"lang":"en","type":"article","venue":"Science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":812,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures","keywords":"Perfect information; Exploit; Computer science; Imperfect; Artificial intelligence; Intuition; Computation; Limit (mathematics); Machine learning; Cognitive science; Mathematical economics; Algorithm; Mathematics; Psychology","authors":[{"name":"Matej Moravčík","is_ca":true},{"name":"Martin Schmid","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Viliam Lisý","is_ca":true},{"name":"Dustin Morrill","is_ca":true},{"name":"Nolan Bard","is_ca":true},{"name":"Trevor Davis","is_ca":true},{"name":"Kevin Waugh","is_ca":true},{"name":"Michael Johanson","is_ca":true},{"name":"Michael Bowling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1440522495334658,"gpt":0.3769481268805651,"spread":0.2328958773470994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00307829,0.0007584951,0.0006767978,0.0006488551,0.0009963162,0.002337115,0.002115978,0.001725535,0.006699051],"category_scores_gemma":[0.01179067,0.0005157605,0.0005996628,0.0004209607,0.003187619,0.005432067,0.004316255,0.00320962,0.001220176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373889,"about_ca_system_score_gemma":0.001822575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002357399,"about_ca_topic_score_gemma":0.004449412,"domain_scores_codex":[0.9986489,0.0005607888,0.00006139871,0.0002478775,0.0003065234,0.0001744153],"domain_scores_gemma":[0.9974306,0.001551932,0.0001755688,0.0004582639,0.0002042149,0.0001794254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003966927,0.0004238496,0.006498119,0.0002586369,0.0001160564,0.0002470076,0.0009335325,0.2207913,0.003234457,0.4948561,0.01496822,0.257276],"study_design_scores_gemma":[0.00004445375,0.00006253626,0.0003605529,0.00004329274,0.00001366253,0.00005753094,0.0001022701,0.6561475,0.00164868,0.3350816,0.006420566,0.00001738667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07325454,0.0004564099,0.8913268,0.003334115,0.0001561836,0.0001715829,0.0001834281,0.002206089,0.02891096],"genre_scores_gemma":[0.7186656,0.0002487068,0.2665902,0.001094238,0.00006645808,0.0001944266,0.0002811382,0.0002776208,0.01258156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006699051,"threshold_uncertainty_score":0.02241057,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2914296650","doi":"10.1609/aiide.v1i1.18726","title":"Cooperative Pathfinding","year":2005,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":665,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Computer science; Heuristic; Path (computing); Series (stratigraphy); Computation; Mathematical optimization; Space (punctuation); Shortest path problem; Algorithm; Artificial intelligence; Theoretical computer science; Graph; Mathematics","authors":[{"name":"David Silver","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04673518047539069,"gpt":0.2919325657892966,"spread":0.2451973853139059,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004652419,0.001111955,0.0007068096,0.0009389437,0.001326917,0.001325829,0.001983644,0.001395813,0.007836998],"category_scores_gemma":[0.00223246,0.0003944258,0.000765533,0.001025085,0.001045991,0.001920339,0.002407619,0.0008442617,0.001902157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007096508,"about_ca_system_score_gemma":0.001700252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006671114,"about_ca_topic_score_gemma":0.007372889,"domain_scores_codex":[0.9994006,0.00008461894,0.00003265712,0.0002367469,0.0001711538,0.0000741943],"domain_scores_gemma":[0.9990408,0.0003160112,0.0001037901,0.0002385314,0.0002122166,0.0000886477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002450809,0.0002725095,0.002000317,0.0004540116,0.0001189058,0.000323323,0.000656854,0.2524854,0.01984926,0.1299282,0.01812473,0.5755414],"study_design_scores_gemma":[0.0001198366,0.0003957461,0.001067208,0.00007027898,0.00008943472,0.0008910276,0.0004463403,0.8083749,0.01295841,0.1009325,0.07457102,0.0000833245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01763605,0.000367044,0.9598514,0.0001348671,0.00008951875,0.0002023911,0.0001353636,0.001391176,0.02019227],"genre_scores_gemma":[0.3717415,0.0007368639,0.6060305,0.0001740378,0.00004256131,0.0003972953,0.0006452498,0.0002097812,0.02002232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007836998,"threshold_uncertainty_score":0.02621734,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2020135152","doi":"10.1145/1273496.1273531","title":"Combining online and offline knowledge in UCT","year":2007,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":458,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Function (biology); Value (mathematics); Bellman equation; Online and offline; Offline learning; Monte Carlo tree search; Online algorithm; Artificial intelligence; Value network; Monte Carlo method; Machine learning; Algorithm; Mathematical optimization; Online learning; Mathematics; Statistics","authors":[{"name":"Sylvain Gelly","is_ca":false},{"name":"David Silver","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04959359877610941,"gpt":0.3435625851067528,"spread":0.2939689863306434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002859684,0.001603946,0.002124268,0.001263865,0.0008888359,0.002059908,0.003195156,0.002137216,0.005965828],"category_scores_gemma":[0.01581622,0.0009743562,0.0007639196,0.001332186,0.001946379,0.005089648,0.003176061,0.00244247,0.001356884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001754077,"about_ca_system_score_gemma":0.002868933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007306256,"about_ca_topic_score_gemma":0.007442752,"domain_scores_codex":[0.9976652,0.0007214252,0.0001316066,0.0005013425,0.0006744815,0.000305926],"domain_scores_gemma":[0.9920639,0.005062255,0.0004697234,0.00138664,0.0007554385,0.0002619969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002813653,0.0002042024,0.001645222,0.00009650869,0.00006009886,0.0001245515,0.0001093853,0.7574291,0.001167372,0.02151551,0.004645112,0.2127215],"study_design_scores_gemma":[0.00002789167,0.00002640527,0.0000901515,0.000009728074,0.000009253434,0.00002946909,0.00001166285,0.9875811,0.0008388984,0.01046685,0.0009004113,0.000008180867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02322219,0.0001909804,0.9614258,0.0003505175,0.00005406962,0.0001163868,0.00008131594,0.004136241,0.01042242],"genre_scores_gemma":[0.5641307,0.0001467343,0.4290672,0.0003681773,0.00007466534,0.0003365333,0.0003678192,0.0008816542,0.004626662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007306256,"threshold_uncertainty_score":0.01995766,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995987707","doi":"10.1126/science.1144079","title":"Checkers Is Solved","year":2007,"lang":"en","type":"article","venue":"Science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":436,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Task (project management); Heuristic; Artificial intelligence; Process (computing); State (computer science); Video game; Perfection; Algorithm; Programming language; Multimedia; Engineering","authors":[{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Yngvi Björnsson","is_ca":true},{"name":"Akihiro Kishimoto","is_ca":true},{"name":"Martin Müller","is_ca":true},{"name":"Robert W. Lake","is_ca":true},{"name":"Paul Lu","is_ca":true},{"name":"Steve Sutphen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03679734623591062,"gpt":0.3270783915183024,"spread":0.2902810452823918,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001364737,0.001145141,0.0009746914,0.00111274,0.003537387,0.005825676,0.001856534,0.002548053,0.05234935],"category_scores_gemma":[0.009989784,0.0007564033,0.001492446,0.001289485,0.003929027,0.006121363,0.004221335,0.003588208,0.01238092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002050715,"about_ca_system_score_gemma":0.004630087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008549187,"about_ca_topic_score_gemma":0.008265529,"domain_scores_codex":[0.9971555,0.0006265218,0.0001578887,0.0008027145,0.0007976106,0.000459734],"domain_scores_gemma":[0.997036,0.001142342,0.0002717409,0.0006109705,0.000608563,0.0003304178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002025799,0.00008748371,0.001586376,0.0003648038,0.00008624034,0.0002712036,0.0006251321,0.00565718,0.001490558,0.7412267,0.1373236,0.1110781],"study_design_scores_gemma":[0.00009230716,0.00009457532,0.0005918654,0.0001877894,0.00006335332,0.0004498973,0.0004976785,0.01507665,0.003011761,0.3910987,0.5887861,0.00004933285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02829391,0.003329337,0.2393252,0.02895328,0.00395719,0.0003699057,0.002372793,0.009431732,0.6839665],"genre_scores_gemma":[0.399764,0.003870599,0.2518615,0.008210871,0.001131753,0.0003737746,0.006641711,0.003221805,0.3249241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05234935,"threshold_uncertainty_score":0.175126,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2006791053","doi":"10.1126/science.1259433","title":"Heads-up limit hold’em poker is solved","year":2015,"lang":"en","type":"article","venue":"Science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":350,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates - Technology Futures; Compute Canada","keywords":"Perfect information; Limit (mathematics); Imperfect; Computation; Computer science; Combinatorial game theory; Mathematical economics; Extensive-form game; Sequential game; Complete information; Game theory; Theoretical computer science; Mathematics; Algorithm","authors":[{"name":"Michael Bowling","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Michael Johanson","is_ca":true},{"name":"Oskari Tammelin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1142824154970506,"gpt":0.3346387999782774,"spread":0.2203563844812268,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009032011,0.0003966372,0.0006082666,0.0003175742,0.001175013,0.00170477,0.001054832,0.001302584,0.01191224],"category_scores_gemma":[0.00651786,0.0002045666,0.0006550483,0.0002961976,0.001750756,0.002894311,0.002053456,0.00163898,0.001028866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007684434,"about_ca_system_score_gemma":0.001190202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001432845,"about_ca_topic_score_gemma":0.00208479,"domain_scores_codex":[0.999326,0.0001715057,0.00003269058,0.0001872862,0.0001192774,0.0001633211],"domain_scores_gemma":[0.9979705,0.001358743,0.0001281265,0.0002289225,0.0001367948,0.0001769145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006213617,0.0002679213,0.004519114,0.0002405485,0.00006968101,0.0006615489,0.0007266602,0.06092261,0.00522364,0.862082,0.01132374,0.05334119],"study_design_scores_gemma":[0.00008502836,0.0001655462,0.000768556,0.00002876356,0.00002891311,0.0002072233,0.0003989715,0.2641059,0.007348161,0.7202212,0.006612288,0.00002941283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6037826,0.0001847823,0.2962915,0.003730383,0.0002026014,0.0001843862,0.0003812989,0.001351316,0.09389112],"genre_scores_gemma":[0.9502325,0.00005667844,0.0290222,0.0003222102,0.00003686505,0.00006646381,0.0002685259,0.00007332092,0.01992128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01191224,"threshold_uncertainty_score":0.03985035,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1561304027","doi":"10.1609/aimag.v34i1.2449","title":"Interactive Narrative: An Intelligent Systems Approach","year":2013,"lang":"en","type":"article","venue":"AI Magazine","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":291,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Narrative; Entertainment; Computer science; Multimedia; Human–computer interaction; Visual arts; Art; Literature","authors":[{"name":"Mark Riedl","is_ca":false},{"name":"Vadim Bulitko","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02883995263335977,"gpt":0.298659651766415,"spread":0.2698196991330552,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001770489,0.0008297264,0.0005829853,0.002328493,0.001595304,0.008801316,0.002346273,0.002429663,0.006782858],"category_scores_gemma":[0.002542284,0.0005214578,0.0009628159,0.001177667,0.00937796,0.008797278,0.003069214,0.002521164,0.001179273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003732126,"about_ca_system_score_gemma":0.002186967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003524483,"about_ca_topic_score_gemma":0.002820384,"domain_scores_codex":[0.9986756,0.0006513468,0.00007763804,0.0002192361,0.0002730488,0.0001030666],"domain_scores_gemma":[0.9987066,0.0008117939,0.0001005848,0.0001228365,0.0001463222,0.0001118645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001119993,0.00001978234,0.0002303357,0.0001547559,0.00002585046,0.0001340963,0.00130168,0.003853905,0.0002471162,0.9772207,0.00274692,0.01405368],"study_design_scores_gemma":[0.00001742518,0.00003538939,0.0002232793,0.0001922741,0.00003181077,0.0002623275,0.001334715,0.01547149,0.0005816987,0.811841,0.1699842,0.0000244478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00959935,0.02312186,0.5288864,0.02023756,0.0006462939,0.000269456,0.0001787657,0.0005427046,0.4165177],"genre_scores_gemma":[0.6169454,0.02524417,0.2885582,0.003191573,0.001072909,0.0006979617,0.0003341128,0.0002577245,0.06369799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008801316,"threshold_uncertainty_score":0.02707863,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2101861158","doi":"10.1016/s0004-3702(01)00130-8","title":"The challenge of poker","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":271,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Exploit; Construct (python library); Deception; Artificial intelligence; Probabilistic logic; Adversary; Class (philosophy); Perfect information; Machine learning; Task (project management); Computer security; Engineering; Psychology","authors":[{"name":"Darse Billings","is_ca":true},{"name":"Aaron Davidson","is_ca":true},{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Duane Szafron","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.100451595296851,"gpt":0.3046174899691713,"spread":0.2041658946723203,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004556031,0.0004520594,0.0009113016,0.0009365638,0.007389592,0.01188542,0.001572324,0.008010879,0.02492087],"category_scores_gemma":[0.01977531,0.0003297023,0.0004274742,0.0009844224,0.01785367,0.02135807,0.00507875,0.01157186,0.007247059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502399,"about_ca_system_score_gemma":0.002428453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001223041,"about_ca_topic_score_gemma":0.001463162,"domain_scores_codex":[0.9967507,0.001637909,0.00007068626,0.0004621053,0.0007234629,0.0003551399],"domain_scores_gemma":[0.9944494,0.002874436,0.0001938337,0.0008368685,0.0007721338,0.0008734615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001836933,0.00001202723,0.0001104142,0.00002477016,0.000003257782,0.00003587501,0.0005772238,0.0001160036,0.00003177741,0.9508649,0.03736544,0.01084001],"study_design_scores_gemma":[0.000008318639,0.00000763461,0.00006896746,0.00004031855,0.00000178779,0.0000826309,0.0009040797,0.0003486133,0.00004801801,0.8322541,0.166228,0.000007504814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01028597,0.009142369,0.01837418,0.3882797,0.005587564,0.00002896634,0.0001022743,0.0002026815,0.5679963],"genre_scores_gemma":[0.6423124,0.009496029,0.01102561,0.06056077,0.006858836,0.0001671512,0.0001942918,0.0004724328,0.2689125],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02492087,"threshold_uncertainty_score":0.08336866,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119296376","doi":"10.1007/s10994-006-6205-6","title":"Adaptive game AI with dynamic scripting","year":2006,"lang":"en","type":"article","venue":"Machine Learning","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":260,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; University of Alberta","keywords":"Computer science; Scripting language; CLARITY; Human–computer interaction; Computer game; Game design; Sequential game; Scalability; Artificial intelligence; Game design document; Adaptation (eye); Multimedia; Game theory; Game Developer; Programming language; Database","authors":[{"name":"Pieter Spronck","is_ca":false},{"name":"Marc Ponsen","is_ca":false},{"name":"I.G. Sprinkhuizen-Kuyper","is_ca":false},{"name":"Eric Postma","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01036808213155851,"gpt":0.2475459678214451,"spread":0.2371778856898866,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000786034,0.0004432313,0.0003657373,0.0002997755,0.0003900206,0.0009441922,0.001157675,0.0005596645,0.00500068],"category_scores_gemma":[0.005126024,0.0003764075,0.0004007127,0.0002495949,0.001109497,0.001649566,0.001244379,0.0013821,0.0005015446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004396095,"about_ca_system_score_gemma":0.0005844488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001658333,"about_ca_topic_score_gemma":0.001752457,"domain_scores_codex":[0.9994215,0.0002441232,0.00003426349,0.0001191278,0.0001302201,0.00005076421],"domain_scores_gemma":[0.998445,0.0009021113,0.00008857007,0.000299924,0.000139808,0.0001245928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003639298,0.0003803184,0.002313068,0.0001993596,0.0001076966,0.0003641419,0.0008205376,0.3994777,0.01481976,0.4154472,0.005504163,0.1602022],"study_design_scores_gemma":[0.00002998577,0.00004349212,0.0001520803,0.000009050589,0.00001149807,0.00004919859,0.00002803521,0.8831071,0.001724012,0.1117929,0.00304081,0.00001174612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03875492,0.00008675335,0.9353548,0.0002641062,0.00008899769,0.0001489111,0.00006145167,0.00172786,0.02351225],"genre_scores_gemma":[0.7903158,0.00007467595,0.2001772,0.0001533568,0.00002599742,0.0002410123,0.0001047347,0.0001436872,0.008763399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00500068,"threshold_uncertainty_score":0.01672894,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994685255","doi":"10.1016/s0004-3702(01)00152-7","title":"Games solved: Now and in the future","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":253,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Exploit; State (computer science); Space (punctuation); State space; Domain (mathematical analysis); Combinatorial game theory; Brute force; Game complexity; Game theory; Sequential game; Computational complexity theory; Artificial intelligence; Theoretical computer science; Algorithm; Mathematics; Mathematical economics; Worst-case complexity; Computer security","authors":[{"name":"H.J. van den Herik","is_ca":false},{"name":"J.W.H.M. Uiterwijk","is_ca":false},{"name":"Jack van Rijswijck","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05601123108821538,"gpt":0.288640933092087,"spread":0.2326297020038717,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799021,0.0007905904,0.0008087206,0.0008386065,0.001538934,0.01004756,0.002044678,0.003015787,0.01065605],"category_scores_gemma":[0.0102205,0.0004034065,0.0005744146,0.000916391,0.003536086,0.01228835,0.002997884,0.003686845,0.002138931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002898492,"about_ca_system_score_gemma":0.003515816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00794435,"about_ca_topic_score_gemma":0.01376622,"domain_scores_codex":[0.9974456,0.001262138,0.0001399732,0.0002803382,0.0004891484,0.0003828873],"domain_scores_gemma":[0.9974186,0.0006963266,0.0002360859,0.0001997221,0.0006146977,0.000834438],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002822216,0.0001901279,0.003722716,0.0005723613,0.0001202359,0.0001645812,0.002887326,0.004562836,0.001069794,0.7449678,0.04569719,0.1957629],"study_design_scores_gemma":[0.00006667722,0.0001070887,0.00185834,0.0004010818,0.00004469714,0.0002134054,0.005105887,0.0140423,0.0008579351,0.6608968,0.3163498,0.00005603989],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.1371213,0.07937229,0.1511811,0.174187,0.007532553,0.0003290813,0.001079064,0.001179199,0.4480183],"genre_scores_gemma":[0.8222916,0.02258155,0.07505817,0.006164137,0.001107667,0.0002404104,0.001191873,0.000318082,0.07104649],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.01065605,"threshold_uncertainty_score":0.03564805,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2913781869","doi":"10.1016/j.artint.2019.103216","title":"The Hanabi challenge: A new frontier for AI research","year":2019,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Google (Canada)","funders":"","keywords":"Computer science; Domain (mathematical analysis); Imperfect; Artificial intelligence; Frontier; Perfect information; State (computer science); Cognitive science; Data science; Human–computer interaction; Psychology","authors":[{"name":"Nolan Bard","is_ca":false},{"name":"Jakob Foerster","is_ca":false},{"name":"Sarath Chandar","is_ca":true},{"name":"Neil Burch","is_ca":false},{"name":"Marc Lanctot","is_ca":false},{"name":"Hai-Jing Song","is_ca":false},{"name":"Emilio Parisotto","is_ca":false},{"name":"Vincent Dumoulin","is_ca":true},{"name":"Subhodeep Moitra","is_ca":true},{"name":"Edward Hughes","is_ca":false},{"name":"Iain Dunning","is_ca":false},{"name":"Shibl Mourad","is_ca":false},{"name":"Hugo Larochelle","is_ca":true},{"name":"Marc G. Bellemare","is_ca":true},{"name":"Michael Bowling","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1628522236355365,"gpt":0.4126734209769778,"spread":0.2498211973414413,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003772688,0.0009807901,0.001178184,0.0007808294,0.00287164,0.00466689,0.003438527,0.001961228,0.01425428],"category_scores_gemma":[0.01375959,0.0005203729,0.0006330229,0.0009469746,0.003893171,0.008987051,0.004964871,0.005909472,0.002783474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001957485,"about_ca_system_score_gemma":0.004760937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008076472,"about_ca_topic_score_gemma":0.01018004,"domain_scores_codex":[0.9975441,0.001340994,0.00006950364,0.0003777783,0.0005119785,0.0001556889],"domain_scores_gemma":[0.9937477,0.00413742,0.0001985124,0.0006278144,0.0005415455,0.0007469786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004423499,0.0002923063,0.002027617,0.0006542672,0.00007055992,0.0001113363,0.001481953,0.01693523,0.001465959,0.6769581,0.05038111,0.2491791],"study_design_scores_gemma":[0.00007940926,0.000133582,0.0007534909,0.0002309847,0.00001748429,0.0001068676,0.001154026,0.09532993,0.001659566,0.7796156,0.1208446,0.00007461296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05000562,0.01078606,0.7318398,0.06432147,0.002534381,0.0005885342,0.0007396562,0.001693113,0.1374914],"genre_scores_gemma":[0.426234,0.006526984,0.5133267,0.007562194,0.001438658,0.0009657363,0.001435594,0.0006643805,0.04184581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01425428,"threshold_uncertainty_score":0.04768527,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1664963465","doi":"","title":"Approximating game-theoretic optimal strategies for full-scale poker","year":2003,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":214,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Abstraction; Sequential game; Combinatorial game theory; Class (philosophy); Game theory; Computation; Scale (ratio); Key (lock); Mathematical economics; Theoretical computer science; Artificial intelligence; Mathematics; Algorithm","authors":[{"name":"Darse Billings","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Aaron Davidson","is_ca":true},{"name":"Robert C. Holte","is_ca":true},{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Terence Conrad Schauenberg","is_ca":true},{"name":"Duane Szafron","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0273082856829337,"gpt":0.2861029956219506,"spread":0.2587947099390169,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001384748,0.0008007704,0.001104633,0.0006305102,0.0004631946,0.001920182,0.001099494,0.001222858,0.00384353],"category_scores_gemma":[0.008025653,0.000663685,0.000927936,0.0004877455,0.001571901,0.002451701,0.001890099,0.002196422,0.0003580333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002450086,"about_ca_system_score_gemma":0.001481298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00484179,"about_ca_topic_score_gemma":0.005710643,"domain_scores_codex":[0.9994198,0.0002146802,0.00002506685,0.00008157694,0.000152475,0.0001064074],"domain_scores_gemma":[0.9977063,0.001662488,0.0001468607,0.0002333344,0.0001197421,0.0001312028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006881055,0.00002975526,0.0004041215,0.00003995413,0.00001448507,0.00003547188,0.0001062366,0.9055353,0.0003831165,0.08786852,0.0003964546,0.005117796],"study_design_scores_gemma":[0.000007044005,0.00001291246,0.00004379955,0.000004059887,0.000001726306,0.000006450357,0.00001777007,0.9589349,0.00009587884,0.04072763,0.000145259,0.000002595237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2069673,0.0001971693,0.7825389,0.0003674868,0.00002947512,0.00007261539,0.0001744643,0.0003393696,0.009313262],"genre_scores_gemma":[0.8738024,0.0001526986,0.1220328,0.00007277992,0.00001770324,0.0001232302,0.000305838,0.00009395912,0.003398515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00484179,"threshold_uncertainty_score":0.01777667,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156523042","doi":"10.1109/aamas.2004.238","title":"Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms","year":2004,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Gamut; Computer science; Suite; Benchmarking; Generator (circuit theory); Test suite; Nash equilibrium; Theoretical computer science; Variation (astronomy); Architecture; Algorithm; Artificial intelligence; Machine learning; Mathematical optimization; Test case; Mathematics","authors":[{"name":"Eugene Nudelman","is_ca":false},{"name":"Jennifer R. Wortman","is_ca":false},{"name":"Yoav Shoham","is_ca":false},{"name":"Kevin Leyton‐Brown","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08309830794725798,"gpt":0.3423465536720401,"spread":0.2592482457247821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01258691,0.002999775,0.001773911,0.004129222,0.0007762316,0.003045338,0.0035366,0.001896465,0.003718252],"category_scores_gemma":[0.05833088,0.0008758031,0.001248212,0.00268312,0.001996451,0.003725859,0.003392496,0.003674167,0.0009239057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001518047,"about_ca_system_score_gemma":0.001843558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002710493,"about_ca_topic_score_gemma":0.004876463,"domain_scores_codex":[0.9908516,0.005439449,0.0004961301,0.0007641133,0.002081631,0.0003670963],"domain_scores_gemma":[0.9663256,0.02558013,0.001027604,0.004656256,0.001664807,0.0007455472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008867511,0.001359514,0.01113212,0.001017062,0.0008020084,0.0002761289,0.0005487849,0.6378581,0.003903428,0.08540756,0.03288302,0.2239255],"study_design_scores_gemma":[0.0001338718,0.0003466276,0.000858437,0.00005993847,0.00004129797,0.00008919664,0.00007878155,0.9364335,0.003362005,0.05377599,0.004776058,0.00004426309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07477766,0.0007617187,0.897903,0.0006883649,0.000209114,0.001065833,0.001943215,0.01266093,0.009990157],"genre_scores_gemma":[0.3227871,0.0004348533,0.6691953,0.0003814499,0.00006665926,0.001903538,0.002161428,0.001613313,0.001456337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01258691,"threshold_uncertainty_score":0.06656671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2518462","doi":"10.1609/aiide.v3i1.18780","title":"Interactive Storytelling: A Player Modelling Approach","year":2007,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":200,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Storytelling; Interactive storytelling; Entertainment; Computer science; Multimedia; Human–computer interaction; Style (visual arts); Entertainment industry; Narrative; Visual arts; Art","authors":[{"name":"David Thue","is_ca":true},{"name":"Vadim Bulitko","is_ca":true},{"name":"Marcia L. Spetch","is_ca":true},{"name":"Eric Wasylishen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06494872172888295,"gpt":0.2916616737695535,"spread":0.2267129520406705,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001681455,0.001504967,0.000762591,0.00117085,0.0006755656,0.003182986,0.003693527,0.001975821,0.005305527],"category_scores_gemma":[0.004286175,0.001106592,0.001687666,0.0006606936,0.00132588,0.003565032,0.002020139,0.002103194,0.001602097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009547758,"about_ca_system_score_gemma":0.0007804311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003139627,"about_ca_topic_score_gemma":0.004417965,"domain_scores_codex":[0.9984397,0.0008125477,0.00007731709,0.0003106153,0.0002836407,0.00007613387],"domain_scores_gemma":[0.9979681,0.001427677,0.0001216644,0.0002316549,0.0001548138,0.00009603077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007547757,0.0005989685,0.005933704,0.001505655,0.0005265889,0.001851663,0.01429979,0.3519247,0.03996475,0.3282551,0.007983641,0.2464006],"study_design_scores_gemma":[0.00005624402,0.0001326993,0.0005772127,0.0001172282,0.0001433026,0.0006499625,0.0006315545,0.8900341,0.00964172,0.04789126,0.05005032,0.00007437164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00529862,0.0001956415,0.9881224,0.0002366407,0.00001763524,0.0001126631,0.00012338,0.0007435184,0.005149534],"genre_scores_gemma":[0.2557084,0.0009258842,0.7296715,0.0001627849,0.0000564974,0.0004793288,0.0007106397,0.0004845139,0.01180044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005305527,"threshold_uncertainty_score":0.01774877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2913764096","doi":"10.1016/s0004-3702(01)00121-7","title":"Computer Go","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Domain (mathematical analysis); Representation (politics); Artificial intelligence; Data science; Human–computer interaction; Management science; Mathematics; Engineering","authors":[{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0945553412646733,"gpt":0.3002023316696072,"spread":0.2056469904049339,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004095456,0.0009992488,0.0005172375,0.001500216,0.0009726157,0.00296863,0.001118243,0.001229787,0.5186321],"category_scores_gemma":[0.001962892,0.0003190445,0.0004575411,0.001132779,0.000654559,0.002950041,0.002597839,0.001270197,0.4046291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005422578,"about_ca_system_score_gemma":0.0008102356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002470325,"about_ca_topic_score_gemma":0.002946013,"domain_scores_codex":[0.9995572,0.00007616996,0.00001455407,0.00009901868,0.0001968486,0.00005627084],"domain_scores_gemma":[0.9993068,0.0001139544,0.00001962287,0.0003090694,0.0001479524,0.0001025809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007865615,0.00009576046,0.0003673721,0.0001409892,0.00001403387,0.0001114324,0.0001436543,0.0005407488,0.002824745,0.05510069,0.6009902,0.3395917],"study_design_scores_gemma":[0.00001188738,0.00001043772,0.0002195726,0.00003497249,0.000004769606,0.00006285815,0.00003565998,0.0007749159,0.0005538418,0.009115872,0.9891695,0.000005698321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001314481,0.000624793,0.02381379,0.001362446,0.0006425429,0.0001314856,0.00219919,0.00723487,0.9626764],"genre_scores_gemma":[0.0115386,0.0008247797,0.007244145,0.0007678962,0.0002198359,0.00015211,0.004699624,0.0009721516,0.973581],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5186321,"threshold_uncertainty_score":0.686613,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2168356773","doi":"10.48550/arxiv.1207.1411","title":"Bayes' Bluff: Opponent Modelling in Poker","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Prior probability; Computer science; Adversary; Artificial intelligence; Dirichlet distribution; Fictitious play; Bluff; Probabilistic logic; Domain (mathematical analysis); Task (project management); Class (philosophy); Observability; Machine learning; Bayesian probability; Game theory; Mathematical economics; Mathematics; Applied mathematics","authors":[{"name":"Finnegan Southey","is_ca":true},{"name":"Michael Bowling","is_ca":true},{"name":"Bryce Larson","is_ca":true},{"name":"Carmelo Piccione","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Darse Billings","is_ca":true},{"name":"Chris Rayner","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1747774249227732,"gpt":0.2159533918694924,"spread":0.04117596694671916,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006038558,0.0008945987,0.001264672,0.0007897117,0.001101574,0.002301875,0.00233551,0.002806106,0.005697268],"category_scores_gemma":[0.02222631,0.0008589231,0.001098733,0.0006474782,0.004174096,0.004347029,0.002822907,0.004192932,0.0008151547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001791723,"about_ca_system_score_gemma":0.001410068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00491031,"about_ca_topic_score_gemma":0.004899348,"domain_scores_codex":[0.9972231,0.00159951,0.0000732969,0.0003907299,0.0004888682,0.0002244261],"domain_scores_gemma":[0.991744,0.007087934,0.0003478821,0.0003922355,0.0002217363,0.000206088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009400884,0.00003294279,0.0009878427,0.00006801232,0.00004131726,0.0001695834,0.0005629575,0.2460406,0.0004718978,0.7343738,0.001344832,0.01581234],"study_design_scores_gemma":[0.00001434052,0.00001283282,0.0001164704,0.00001719399,0.000007914968,0.00004066231,0.00004313634,0.5579724,0.0001858188,0.4404287,0.001146557,0.00001391488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01634452,0.000226044,0.9723462,0.0015599,0.0000519807,0.00004008343,0.00006339021,0.0001246017,0.009243328],"genre_scores_gemma":[0.7908402,0.0005282209,0.1895617,0.000790116,0.0001695033,0.0002518967,0.0001551174,0.0001809569,0.01752232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006038558,"threshold_uncertainty_score":0.03193533,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056740053","doi":"","title":"The Grand Challenge of Computer Go: Monte Carlo Tree Search and Extensions","year":2012,"lang":"en","type":"preprint","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Athabasca University; University of Alberta","funders":"Sixth Framework Programme; Seventh Framework Programme; Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo tree search; Computer science; Monte Carlo method; Tree (set theory); Game tree; Artificial intelligence; Theoretical computer science; Machine learning; Game theory; Mathematics; Mathematical economics; Sequential game; Statistics","authors":[{"name":"Sylvain Gelly","is_ca":false},{"name":"Levente Kocsis","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09049253033088579,"gpt":0.3200413653549884,"spread":0.2295488350241027,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006154395,0.0007820641,0.002538713,0.001206273,0.001433222,0.003097025,0.003776414,0.003695377,0.0107485],"category_scores_gemma":[0.0364786,0.0007528605,0.0013778,0.002093132,0.003119911,0.00776285,0.004151344,0.004752321,0.001490906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00208699,"about_ca_system_score_gemma":0.003706304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006391182,"about_ca_topic_score_gemma":0.01110919,"domain_scores_codex":[0.9972167,0.001756138,0.00008728322,0.0002817137,0.0004268098,0.0002314116],"domain_scores_gemma":[0.9784406,0.01828867,0.0004368512,0.001469683,0.0006837918,0.0006804095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002509535,0.000146772,0.0007284053,0.0002618553,0.00008578396,0.00007903716,0.0002928137,0.1973463,0.0001573715,0.7275484,0.01972028,0.05338202],"study_design_scores_gemma":[0.0000443117,0.00002523059,0.00007662978,0.00004129942,0.00001340134,0.00002751171,0.00003483914,0.4894187,0.00005683157,0.5065916,0.00365857,0.00001108594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0419489,0.006924662,0.8814975,0.01450673,0.0006869172,0.0002024531,0.0004827898,0.0009929669,0.0527571],"genre_scores_gemma":[0.5296608,0.004632204,0.4344445,0.003177642,0.0009884919,0.000755595,0.0008266101,0.001046285,0.02446781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0107485,"threshold_uncertainty_score":0.03595734,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102256448","doi":"10.1109/tciaig.2010.2067212","title":"Monte Carlo Tree Search in Hex","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Intelligence and AI in Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo tree search; Monte Carlo method; Game tree; Computer science; Tree (set theory); Olympiad; Theoretical computer science; Algorithm; Artificial intelligence; Game theory; Mathematics; Mathematical economics; Combinatorics; Statistics; Sequential game","authors":[{"name":"Broderick Arneson","is_ca":true},{"name":"Ryan Hayward","is_ca":true},{"name":"Philip Henderson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03421570580501265,"gpt":0.3124778418136939,"spread":0.2782621360086812,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064535,0.0003090924,0.0007248779,0.0004393772,0.0005222878,0.0009714183,0.0007508916,0.0008548586,0.00574016],"category_scores_gemma":[0.005088984,0.0002550575,0.0004349117,0.0005517122,0.001105136,0.001116502,0.0009834025,0.0008447092,0.0004882598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008843139,"about_ca_system_score_gemma":0.0008594496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006007894,"about_ca_topic_score_gemma":0.005689013,"domain_scores_codex":[0.9994751,0.000244678,0.00002032152,0.00006159054,0.0001182578,0.00008001535],"domain_scores_gemma":[0.998401,0.001182685,0.00008020845,0.000114655,0.0001484498,0.00007299386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001197164,0.00003357996,0.0009405952,0.00003539121,0.00001735915,0.00008344939,0.00009351835,0.8294067,0.0004814708,0.1556171,0.001311986,0.01185927],"study_design_scores_gemma":[0.00001570879,0.00001408202,0.00008532569,0.000005059038,0.000003034536,0.00001189657,0.00001901963,0.9668642,0.000172934,0.0319174,0.0008875647,0.000003741795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.195923,0.0005310818,0.7499408,0.000643244,0.0001380443,0.0001318218,0.0002998404,0.0006373869,0.05175473],"genre_scores_gemma":[0.8351283,0.0001927411,0.156383,0.0003159095,0.00002482691,0.0001939122,0.000323406,0.0001417068,0.007296271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006007894,"threshold_uncertainty_score":0.01920277,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4321428174","doi":"10.1145/2093548.2093574","title":"The grand challenge of computer Go","year":2012,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Sixth Framework Programme; Seventh Framework Programme","keywords":"Monte Carlo tree search; Computer science; Monte Carlo method; Tree (set theory); Artificial intelligence; Game tree; Theoretical computer science; Machine learning; Game theory; Mathematical economics; Sequential game; Mathematics","authors":[{"name":"Sylvain Gelly","is_ca":false},{"name":"Levente Kocsis","is_ca":false},{"name":"Marc Schoenauer","is_ca":false},{"name":"Michèle Sébag","is_ca":false},{"name":"David Silver","is_ca":false},{"name":"Csaba Szepesvári","is_ca":true},{"name":"Olivier Teytaud","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1080938249366108,"gpt":0.3387268185554478,"spread":0.230632993618837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002238341,0.0006001252,0.0005309742,0.0008786664,0.002338044,0.00520029,0.001477428,0.00274154,0.01475915],"category_scores_gemma":[0.01084428,0.0002450868,0.0004326897,0.0008941354,0.006414582,0.008954646,0.00411369,0.004552156,0.003436193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002292123,"about_ca_system_score_gemma":0.002440575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00499732,"about_ca_topic_score_gemma":0.004714589,"domain_scores_codex":[0.9980136,0.0007870733,0.00005472502,0.0003046271,0.0005694234,0.0002705092],"domain_scores_gemma":[0.9969277,0.001568458,0.00008617606,0.0003970624,0.0005593401,0.0004612772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006847148,0.00002841342,0.0002859284,0.0001076421,0.00001258078,0.0000453618,0.000423668,0.00284534,0.0003641457,0.8788971,0.04588757,0.07103378],"study_design_scores_gemma":[0.00002121327,0.00004383238,0.0003659351,0.0002186287,0.000009040286,0.0001738511,0.0006147128,0.00969094,0.0006510264,0.5960301,0.392148,0.00003265473],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.04608418,0.03684025,0.1834471,0.1884936,0.004700725,0.0001411947,0.000434045,0.001549721,0.538309],"genre_scores_gemma":[0.6803277,0.02533627,0.1333265,0.02025596,0.003223656,0.0002977232,0.0007015534,0.001502387,0.1350284],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.01475915,"threshold_uncertainty_score":0.04937428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1558653343","doi":"","title":"Real-time strategy gaines: a new AI research challenge","year":2003,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Domain (mathematical analysis); Operations research; Artificial intelligence; Human–computer interaction; Data science; Engineering","authors":[{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2825745515600075,"gpt":0.4172919766152771,"spread":0.1347174250552695,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008167002,0.0006516423,0.001224763,0.001074536,0.00137174,0.00921444,0.002081156,0.003764237,0.0111391],"category_scores_gemma":[0.01682326,0.0003093469,0.000651997,0.001069099,0.007227493,0.02645279,0.002704119,0.008311839,0.002233054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001823539,"about_ca_system_score_gemma":0.002128081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009483669,"about_ca_topic_score_gemma":0.001239648,"domain_scores_codex":[0.9968365,0.001163995,0.00009960191,0.0004450284,0.001216764,0.0002380692],"domain_scores_gemma":[0.9841283,0.01094415,0.0004524158,0.0009304196,0.001794361,0.001750424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001224814,0.0001146859,0.0004728251,0.0002256705,0.00002601811,0.0000663296,0.0009012863,0.00182964,0.0007104311,0.8726541,0.01762964,0.1052469],"study_design_scores_gemma":[0.00003440714,0.000104973,0.0002781301,0.0001125557,0.00001195115,0.0001231704,0.001387137,0.00906658,0.0004432493,0.8953742,0.09304126,0.00002249214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.04062356,0.0601929,0.2308291,0.4358726,0.004561645,0.00009717541,0.0002820414,0.0007968363,0.2267441],"genre_scores_gemma":[0.7511286,0.04336426,0.1184347,0.0159822,0.008176663,0.0003166059,0.0003052684,0.000660578,0.06163109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0111391,"threshold_uncertainty_score":0.04319173,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2022504108","doi":"10.1109/cig.2013.6633643","title":"Portfolio greedy search and simulation for large-scale combat in starcraft","year":2013,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":125,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Portfolio; Greedy algorithm; Artificial intelligence; State (computer science); Machine learning; Mathematical optimization; Algorithm; Mathematics","authors":[{"name":"David G. Churchill","is_ca":true},{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03788244393986911,"gpt":0.3196235751881126,"spread":0.2817411312482435,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009742095,0.0006273899,0.0008649168,0.0005426366,0.0003975391,0.0007765967,0.0007828227,0.001008163,0.001990214],"category_scores_gemma":[0.003849497,0.0004221196,0.0003749287,0.00057732,0.0008382581,0.0007717913,0.0008238504,0.0006826249,0.0002334713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009581261,"about_ca_system_score_gemma":0.001123227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01093944,"about_ca_topic_score_gemma":0.008639117,"domain_scores_codex":[0.9997316,0.0001374582,0.00001147768,0.00003138459,0.00005163801,0.00003649767],"domain_scores_gemma":[0.9984872,0.001211783,0.000086466,0.00004643741,0.00009108477,0.00007700236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003055921,0.00001083255,0.0002422267,0.000009712195,0.000006387175,0.0000158661,0.00001310411,0.9951569,0.00009251674,0.001608318,0.0001253006,0.002688179],"study_design_scores_gemma":[0.000005106611,0.000006854044,0.00002529276,0.000001145327,9.0767e-7,0.000001665368,0.000004107588,0.9991794,0.00004571789,0.0006625431,0.00006637118,8.81416e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2768984,0.0006772822,0.7070209,0.0005990517,0.00007965921,0.0001749426,0.000164458,0.0009687677,0.01341657],"genre_scores_gemma":[0.9077123,0.00018291,0.0896577,0.00008797445,0.0000128308,0.0001823083,0.0001111441,0.00006826998,0.001984548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01093944,"threshold_uncertainty_score":0.02175152,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1863869622","doi":"10.1016/s0004-3702(01)00166-7","title":"World-championship-caliber Scrabble☆☆SCRABBLE® is a registered trademark. All intellectual property rights in and to the game are owned in the USA by Hasbro Inc., in Canada by Hasbro Canada Corporation, and throughout the rest of the world by J.W. Spear &amp; Sons Limited of Maidenhead, Berkshire, England, a subsidiary of Mattel Inc.","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Championship; Corporation; Intellectual property; Computer science; Operations research; Advertising; Engineering; Business; Political science; Law","authors":[{"name":"B. L. Sheppard","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08528845174103022,"gpt":0.2779775682452863,"spread":0.1926891165042561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004656614,0.00068438,0.0005396766,0.0008114127,0.00122688,0.002863996,0.000695195,0.001114637,0.4593337],"category_scores_gemma":[0.002196396,0.0002648732,0.0002166771,0.001396118,0.0006809287,0.002660246,0.001010652,0.001343902,0.2010943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007673261,"about_ca_system_score_gemma":0.001459093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004477948,"about_ca_topic_score_gemma":0.01551292,"domain_scores_codex":[0.999492,0.00003174127,0.00001621026,0.000105041,0.0003064719,0.00004845743],"domain_scores_gemma":[0.9985978,0.0002192188,0.0001156537,0.0001331125,0.000427489,0.0005066859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000102012,0.00006846418,0.0003609662,0.0001505689,0.000005753966,0.0000553368,0.0001618969,0.00009398907,0.001351913,0.02692446,0.7315146,0.23921],"study_design_scores_gemma":[0.000005999322,0.00002867001,0.0002516165,0.00002845336,0.000001631591,0.00003372394,0.00002598931,0.00009045382,0.0002146401,0.0009081967,0.9984066,0.000004050555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001126964,0.001402367,0.001794142,0.001178722,0.001233979,0.00004710306,0.0007416241,0.0009870364,0.9914881],"genre_scores_gemma":[0.008014997,0.0008388282,0.001039851,0.0002186296,0.00009083382,0.00003174325,0.0009075658,0.0003046316,0.9885529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4593337,"threshold_uncertainty_score":0.7711948,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1944385714","doi":"10.1007/3-540-47987-2_94","title":"Modeling Students’ Emotions from Cognitive Appraisal in Educational Games","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Probabilistic logic; Cognitive appraisal; Educational game; Cognition; Personality; Game theory; Appraisal theory; Cognitive psychology; Artificial intelligence; Human–computer interaction; Multimedia; Psychology; Social psychology; Mathematical economics","authors":[{"name":"Cristina Conati","is_ca":true},{"name":"Xiaoming Zhou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0481980177279456,"gpt":0.3263712115191295,"spread":0.2781731937911839,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007365106,0.0005622493,0.00025822,0.0003151465,0.0001701499,0.00142021,0.0005461546,0.0004749821,0.001521132],"category_scores_gemma":[0.00634598,0.0002165835,0.0003461732,0.0002592225,0.000266642,0.0006904675,0.000439025,0.0008795232,0.000195247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007102177,"about_ca_system_score_gemma":0.0002525397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005492209,"about_ca_topic_score_gemma":0.006518552,"domain_scores_codex":[0.9997029,0.0001595354,0.00001088184,0.00003736649,0.00004300643,0.00004627366],"domain_scores_gemma":[0.9978562,0.00169976,0.0001508392,0.00005519894,0.0001318814,0.0001060439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002125891,0.003337163,0.2994872,0.0004078,0.0005865828,0.0005184089,0.004528637,0.4576078,0.01398754,0.02445312,0.003888162,0.1890717],"study_design_scores_gemma":[0.00004346773,0.00026623,0.08572017,0.00002423331,0.00007992201,0.00004013748,0.0004994826,0.9008167,0.001646916,0.01031061,0.0005183268,0.00003373375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9703371,0.0001193063,0.02389221,0.0001175676,0.00002314126,0.00005426619,0.00008704881,0.00006271308,0.005306668],"genre_scores_gemma":[0.9970291,0.00002720557,0.002269425,0.000009745715,0.000002402156,0.00002560968,0.00004786033,0.000005733747,0.0005828775],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005492209,"threshold_uncertainty_score":0.01092046,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1750339092","doi":"10.1609/aiide.v8i1.12527","title":"Fast Heuristic Search for RTS Game Combat Scenarios","year":2012,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":122,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Scripting language; Computer science; Adversary; Heuristic; Incremental heuristic search; State (computer science); Sorting; Iterative deepening depth-first search; Beam search; Search algorithm; Artificial intelligence; Computer security; Algorithm; Programming language","authors":[{"name":"David G. Churchill","is_ca":true},{"name":"Abdallah Saffidine","is_ca":false},{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0665393189233454,"gpt":0.3124185084308653,"spread":0.2458791895075199,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008359968,0.0008202938,0.0006929051,0.0007128114,0.0004481868,0.0009345676,0.00103816,0.001018174,0.004870855],"category_scores_gemma":[0.004398862,0.0006108001,0.0005566038,0.0004681281,0.0007407901,0.001315494,0.001044936,0.001061115,0.000785258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008737672,"about_ca_system_score_gemma":0.001069338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003843639,"about_ca_topic_score_gemma":0.005345522,"domain_scores_codex":[0.9995669,0.0002091098,0.0000174996,0.00005707952,0.00009116602,0.0000582723],"domain_scores_gemma":[0.998449,0.001138662,0.00008906281,0.00014182,0.000107234,0.00007428748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002120865,0.00008237414,0.0008402683,0.0001043896,0.00004051836,0.0001287774,0.0001323525,0.9207643,0.0017908,0.02717527,0.00209248,0.04663625],"study_design_scores_gemma":[0.00003163834,0.0000280793,0.00006878467,0.000007223729,0.000003982286,0.00001405258,0.00002620577,0.9896667,0.0002873483,0.009174627,0.0006877077,0.000003587766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.159561,0.0005635124,0.8153501,0.0003473179,0.00004700585,0.0001856444,0.0002597672,0.002246134,0.02143951],"genre_scores_gemma":[0.7022989,0.0002524032,0.2930793,0.00009870582,0.00001935937,0.0002604557,0.0004419545,0.0002811507,0.003267828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004870855,"threshold_uncertainty_score":0.01629466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153678894","doi":"","title":"Achieving master level play in 9×9 computer go","year":2008,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Heuristic; Artificial intelligence; Monte Carlo tree search; Function (biology); Value (mathematics); Tree (set theory); State (computer science); Bellman equation; Monte Carlo method; Domain (mathematical analysis); Machine learning; Algorithm; Theoretical computer science; Mathematical optimization; Mathematics; Statistics","authors":[{"name":"Sylvain Gelly","is_ca":false},{"name":"David Silver","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04533729866204827,"gpt":0.2195444041823804,"spread":0.1742071055203321,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183581,0.0008349498,0.0008556959,0.0005485456,0.0007374124,0.001041676,0.001333107,0.001567798,0.009666081],"category_scores_gemma":[0.003870582,0.0002935567,0.000656047,0.0003820194,0.001218584,0.001487296,0.002254543,0.001115887,0.000893329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234489,"about_ca_system_score_gemma":0.001331904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01000525,"about_ca_topic_score_gemma":0.01284306,"domain_scores_codex":[0.9993622,0.0001376207,0.00002618723,0.0001298766,0.0001254247,0.0002187198],"domain_scores_gemma":[0.9988399,0.0006800066,0.0001050818,0.0001270869,0.00007173818,0.0001761545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008456086,0.0004578462,0.004059731,0.0001441411,0.00007019745,0.00038711,0.0008202732,0.8059977,0.003198024,0.07112946,0.004613389,0.1082765],"study_design_scores_gemma":[0.00009252477,0.0002296468,0.0008310394,0.00001966576,0.000009697598,0.00007715362,0.0001977011,0.9545045,0.001667669,0.03896645,0.00338842,0.00001538761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6143522,0.0001159774,0.3148961,0.000545228,0.0000479003,0.0003965911,0.0002517808,0.002912136,0.06648202],"genre_scores_gemma":[0.9135765,0.00003205499,0.07740148,0.00008599772,0.000008080999,0.0001545956,0.0002464,0.0001405563,0.008354369],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01000525,"threshold_uncertainty_score":0.03233629,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1778554682","doi":"","title":"Reinforcement learning of local shape in the game of go","year":2007,"lang":"en","type":"article","venue":"UCL Discovery (University College London)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Template; Function (biology); Artificial intelligence; Reinforcement; Computer game; Temporal difference learning; Variety (cybernetics); Machine learning; Multimedia","authors":[{"name":"David Silver","is_ca":true},{"name":"Richard S. Sutton","is_ca":true},{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01524687706555709,"gpt":0.2284758731527574,"spread":0.2132289960872003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00153583,0.0006589373,0.0008369324,0.0004203618,0.0003395461,0.0006900017,0.001343168,0.0008408048,0.002182015],"category_scores_gemma":[0.008115722,0.0003053352,0.0003596545,0.0002072786,0.001606239,0.001405033,0.001023228,0.0009686932,0.0003066431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013086,"about_ca_system_score_gemma":0.0006730106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003314106,"about_ca_topic_score_gemma":0.003677709,"domain_scores_codex":[0.9995844,0.0001706893,0.00001364646,0.00008659652,0.00007875948,0.00006592473],"domain_scores_gemma":[0.9966698,0.002361147,0.0002503413,0.0001946574,0.00021706,0.0003070348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006312781,0.0003626472,0.006371999,0.00008253162,0.00006166707,0.0001362837,0.0002285852,0.872176,0.009486323,0.01229239,0.0009100192,0.09726028],"study_design_scores_gemma":[0.00002624896,0.0001245938,0.0004094234,0.000003308866,0.000006348005,0.00001608707,0.00001821217,0.9936886,0.001238785,0.004274057,0.0001875016,0.00000670953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5728041,0.0001205965,0.4195522,0.0004294258,0.00002800172,0.0001480086,0.00004592788,0.0009601718,0.005911623],"genre_scores_gemma":[0.9603713,0.00002466089,0.03763619,0.0000715088,0.00000680305,0.00007391796,0.00003414776,0.00004527802,0.001736253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003314106,"threshold_uncertainty_score":0.008122385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1605527696","doi":"10.1007/978-3-642-12239-2_14","title":"Towards a Generic Framework for Automated Video Game Level Creation","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Encoding (memory); Video game; Genetic algorithm; Generative grammar; Game design; Human–computer interaction; Artificial intelligence; Multimedia; Machine learning","authors":[{"name":"Nathan Sorenson","is_ca":true},{"name":"Philippe Pasquier","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05360482209948655,"gpt":0.3185226923221993,"spread":0.2649178702227128,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001627308,0.001213478,0.001193473,0.001839613,0.001000523,0.004617696,0.003993661,0.002013001,0.008266776],"category_scores_gemma":[0.003206664,0.001194552,0.002448861,0.001072951,0.00141061,0.003775372,0.004332726,0.002594027,0.004694868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001136376,"about_ca_system_score_gemma":0.00189925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006038303,"about_ca_topic_score_gemma":0.009443504,"domain_scores_codex":[0.9986695,0.0001804845,0.0001401379,0.0003356395,0.0004873296,0.0001870456],"domain_scores_gemma":[0.9991278,0.0002074911,0.00005852689,0.0002996778,0.0002264155,0.00008011291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000287654,0.0005024525,0.00265998,0.0008762963,0.0002251412,0.0006717854,0.001117684,0.07825328,0.02470833,0.4174266,0.03429064,0.4389801],"study_design_scores_gemma":[0.00004563172,0.00006065612,0.0005618522,0.0001590594,0.0001061494,0.0003977908,0.0002003645,0.7702995,0.01948084,0.1237438,0.08487236,0.00007195533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008466088,0.00004773244,0.9892082,0.00004291896,0.00001841991,0.000147119,0.0001341553,0.008147494,0.001407389],"genre_scores_gemma":[0.04034992,0.0001725391,0.9533014,0.00007902495,0.00002296571,0.0002799352,0.0009274835,0.001203386,0.003663374],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008266776,"threshold_uncertainty_score":0.02765512,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097778153","doi":"10.1145/1390156.1390278","title":"Sample-based learning and search with permanent and transient memories","year":2008,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Sample (material); Function (biology); Artificial intelligence; Transient (computer programming); Q-learning; Matching (statistics); Bellman equation; Machine learning; Mathematical optimization; Mathematics; Operating system","authors":[{"name":"David Silver","is_ca":true},{"name":"Richard S. Sutton","is_ca":true},{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04074702411668039,"gpt":0.275774518047986,"spread":0.2350274939313056,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204866,0.0005874276,0.0007607301,0.0004194225,0.0002844838,0.0007869015,0.001960535,0.000711228,0.002917675],"category_scores_gemma":[0.004448511,0.0003956018,0.0004687948,0.0004258422,0.001024328,0.001915465,0.001283106,0.001186237,0.0005015337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007521538,"about_ca_system_score_gemma":0.001007665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003849109,"about_ca_topic_score_gemma":0.004749704,"domain_scores_codex":[0.9995329,0.0001279574,0.0000370693,0.000132224,0.0001164789,0.00005334574],"domain_scores_gemma":[0.9979644,0.001010495,0.0002055708,0.00047861,0.0002329398,0.0001080089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004229484,0.0002161825,0.002509956,0.0001269073,0.0001057868,0.00009994276,0.0001589295,0.6869864,0.00578124,0.02715348,0.001504625,0.2749337],"study_design_scores_gemma":[0.00002431831,0.00008631983,0.0001552076,0.000004473942,0.000009295642,0.00002339569,0.000005850757,0.9909275,0.001769372,0.006348409,0.0006387564,0.000007153487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04444223,0.0001567302,0.9504001,0.0001496179,0.00003144868,0.00006081085,0.0000410386,0.002460308,0.002257852],"genre_scores_gemma":[0.709849,0.0001020728,0.2857295,0.0001515217,0.00002442573,0.0002132224,0.0001065583,0.0001516276,0.003672077],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003849109,"threshold_uncertainty_score":0.009760618,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098487995","doi":"10.1609/aiide.v7i1.12435","title":"Build Order Optimization in StarCraft","year":2011,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":101,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Computer science; Action (physics); Order (exchange); Unit (ring theory); Resource (disambiguation); Resource allocation; Pathfinding; Mathematical optimization; Artificial intelligence; Theoretical computer science; Shortest path problem; Mathematics","authors":[{"name":"David G. Churchill","is_ca":true},{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0564658959255539,"gpt":0.2790584538778487,"spread":0.2225925579522948,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006044167,0.0008193463,0.0009912541,0.0005686149,0.0005088794,0.0009687504,0.0007471734,0.0008637102,0.006465379],"category_scores_gemma":[0.002274499,0.000571164,0.0006783634,0.0006192845,0.001658241,0.001424802,0.001401283,0.001546541,0.0004677793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001160671,"about_ca_system_score_gemma":0.001121769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006292224,"about_ca_topic_score_gemma":0.006944578,"domain_scores_codex":[0.9995325,0.0001527966,0.00002044054,0.0001060619,0.0001177795,0.00007042292],"domain_scores_gemma":[0.9991807,0.0005658638,0.00006782564,0.00005082129,0.00006115293,0.00007366673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000133518,0.00007913408,0.0006950606,0.0001576193,0.00002975069,0.0001124445,0.0001790559,0.8285033,0.001182346,0.1387358,0.00211325,0.02807868],"study_design_scores_gemma":[0.000052558,0.00008300984,0.000189131,0.0000241257,0.00001166843,0.00003611849,0.00008074214,0.8523071,0.0006705655,0.1411347,0.00539843,0.00001198882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09696374,0.00123657,0.8749358,0.0005334149,0.000101981,0.0001242834,0.0001967479,0.0005430928,0.02536432],"genre_scores_gemma":[0.6833302,0.0008783545,0.299269,0.0001881506,0.00005236071,0.0002121659,0.0003615935,0.0002678856,0.01544022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006465379,"threshold_uncertainty_score":0.02162886,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153039919","doi":"10.1007/s10994-012-5280-0","title":"Temporal-difference search in computer Go","year":2012,"lang":"en","type":"article","venue":"Machine Learning","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo tree search; Temporal difference learning; Computer science; Search algorithm; Monte Carlo method; Bootstrapping (finance); Tree (set theory); Artificial intelligence; Reinforcement learning; Markov decision process; Beam search; Markov chain Monte Carlo; Search problem; Machine learning; Algorithm; Markov process; Mathematics; Statistics; Bayesian probability","authors":[{"name":"David Silver","is_ca":false},{"name":"Richard S. Sutton","is_ca":true},{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04416456190730939,"gpt":0.309821783679691,"spread":0.2656572217723816,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001060499,0.0002462101,0.0007783281,0.001077393,0.0009870261,0.001546345,0.001368791,0.001683669,0.01201134],"category_scores_gemma":[0.01125833,0.0002498654,0.0004447931,0.001031712,0.001411138,0.004730643,0.001395672,0.001338555,0.0003903428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001226753,"about_ca_system_score_gemma":0.001126137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005371386,"about_ca_topic_score_gemma":0.006255462,"domain_scores_codex":[0.999464,0.000207928,0.00002534056,0.00008577417,0.0001221228,0.00009486244],"domain_scores_gemma":[0.996052,0.003027573,0.0001866116,0.0002584764,0.0002366418,0.0002386655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000339973,0.0001018786,0.0009772377,0.00009445382,0.00002903674,0.0001026901,0.0002397569,0.05810903,0.0009238943,0.9115672,0.0021495,0.02536524],"study_design_scores_gemma":[0.00004559043,0.00004448233,0.0003454382,0.0000148428,0.00001297472,0.0000516067,0.00009475691,0.3168863,0.0003460063,0.6809716,0.001173447,0.00001299414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5414586,0.002452018,0.3709971,0.003603374,0.0003286379,0.00009553944,0.00027514,0.0005811909,0.08020837],"genre_scores_gemma":[0.9730266,0.0001862922,0.01811767,0.0001170724,0.00003444725,0.00002561343,0.0000744608,0.00004687405,0.008371031],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01201134,"threshold_uncertainty_score":0.04018199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1947291763","doi":"10.48550/arxiv.1412.6564","title":"Move Evaluation in Go Using Deep Convolutional Neural Networks","year":2014,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Monte Carlo tree search; Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Machine learning; Monte Carlo method","authors":[{"name":"Chris J. Maddison","is_ca":true},{"name":"Aja Huang","is_ca":false},{"name":"Ilya Sutskever","is_ca":false},{"name":"David Silver","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1116652315484495,"gpt":0.227260463415021,"spread":0.1155952318665715,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000950059,0.001295962,0.0008494803,0.000867118,0.0004775526,0.001069458,0.001542206,0.001325308,0.003208261],"category_scores_gemma":[0.004178477,0.0003640311,0.0004906448,0.0004536841,0.0007068012,0.00190095,0.001091831,0.001463105,0.0007068433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001936008,"about_ca_system_score_gemma":0.001097381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03758287,"about_ca_topic_score_gemma":0.06316879,"domain_scores_codex":[0.9994316,0.0001201366,0.00002353027,0.0001707867,0.000120692,0.0001333733],"domain_scores_gemma":[0.9989748,0.0005549147,0.0001198225,0.00008702573,0.0001374291,0.0001260344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00114066,0.0004773985,0.01797825,0.0001795877,0.0001835196,0.0002061333,0.0001321581,0.7351831,0.004690301,0.0068114,0.006989859,0.2260276],"study_design_scores_gemma":[0.00002226111,0.00009041029,0.00143688,0.00001620542,0.00001063098,0.00001586206,0.00002607417,0.9942147,0.001143748,0.002532569,0.0004824771,0.000008187921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7719266,0.001706428,0.1963063,0.0009407909,0.0002072606,0.0002649304,0.0009953735,0.005389265,0.02226303],"genre_scores_gemma":[0.9744539,0.0001092083,0.01981727,0.0001447054,0.00001528263,0.00003802832,0.000646906,0.00006147315,0.004713184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03758287,"threshold_uncertainty_score":0.07472825,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012220350","doi":"10.1016/j.disc.2005.12.038","title":"A note on <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.gif\" overflow=\"scroll\"><mml:mi>k</mml:mi></mml:math>-cop, <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si2.gif\" overflow=\"scroll\"><mml:mi>l</mml:mi></mml:math>-robber games on graphs","year":2006,"lang":"lv","type":"article","venue":"Discrete Mathematics","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":88,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria; Université de Montréal; Université du Québec à Montréal","funders":"","keywords":"Algorithm; Mathematics; Computer science; Combinatorics","authors":[{"name":"Geňa Hahn","is_ca":true},{"name":"Gary MacGillivray","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02307160409932155,"gpt":0.261477874880912,"spread":0.2384062707815904,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001643611,0.001466121,0.001076147,0.001876311,0.001734692,0.003707797,0.002229773,0.002120708,0.3468299],"category_scores_gemma":[0.007070913,0.0008986583,0.001390522,0.002705608,0.001051892,0.0066438,0.001711902,0.004604167,0.2025376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002774052,"about_ca_system_score_gemma":0.001768568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01638447,"about_ca_topic_score_gemma":0.03618428,"domain_scores_codex":[0.9989519,0.0002716326,0.0001064529,0.0001282347,0.0004449592,0.0000967178],"domain_scores_gemma":[0.9953586,0.002200774,0.0001345875,0.0004014431,0.001556885,0.0003477454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002492234,0.00002244392,0.00002916283,0.0000550974,0.000002747448,0.00003267858,0.0000392495,0.000106587,0.00022659,0.01158035,0.9781737,0.009706474],"study_design_scores_gemma":[0.00001423796,0.00001546116,0.0002793926,0.00006105578,0.000003000991,0.00005460731,0.0000394758,0.0003507468,0.0003789566,0.01171623,0.9870613,0.00002556493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001426071,0.004451835,0.091182,0.1240924,0.06770604,0.0003957105,0.01918564,0.01525244,0.6763079],"genre_scores_gemma":[0.01408483,0.006663219,0.06869866,0.02662498,0.01764171,0.0004689215,0.01454383,0.01278688,0.8384869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3468299,"threshold_uncertainty_score":0.9316679,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W193176855","doi":"10.5591/978-1-57735-516-8/ijcai11-054","title":"Accelerating best response calculation in large extensive games","year":2011,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Tree traversal; Computer science; Computation; Tree (set theory); Abstraction; Limit (mathematics); Perfect information; Game tree; Imperfect; Theoretical computer science; Sequential game; Game theory; Algorithm; Mathematics","authors":[{"name":"Michael Johanson","is_ca":true},{"name":"Kevin Waugh","is_ca":false},{"name":"Michael Bowling","is_ca":true},{"name":"Martin Zinkevich","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1055475456976191,"gpt":0.3231958775465029,"spread":0.2176483318488838,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002052425,0.0008671424,0.001030469,0.0006482988,0.0007930347,0.001701446,0.001506298,0.0008773927,0.005262255],"category_scores_gemma":[0.0130723,0.0004231845,0.0005773247,0.000707839,0.001300357,0.002637804,0.00202837,0.001689165,0.0007946274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001568184,"about_ca_system_score_gemma":0.001848938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00648538,"about_ca_topic_score_gemma":0.009275328,"domain_scores_codex":[0.9987323,0.0004323566,0.00005577164,0.0001615876,0.0003263776,0.0002916721],"domain_scores_gemma":[0.9941474,0.004278391,0.0003159921,0.0006434346,0.0003571557,0.0002576205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003230803,0.0001491364,0.002074068,0.0001133227,0.00005441167,0.0001482019,0.0002857939,0.8698677,0.003419083,0.07658207,0.002439912,0.04454319],"study_design_scores_gemma":[0.00001562469,0.00002184562,0.0001241157,0.000005507558,0.000005034628,0.00001245973,0.00003038592,0.9702119,0.0009576512,0.02825157,0.0003590215,0.000004931668],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1827033,0.0002195298,0.8008972,0.0005569009,0.00007659761,0.0001097624,0.0001187729,0.002137806,0.01318013],"genre_scores_gemma":[0.7888539,0.00008949375,0.2079143,0.0001203931,0.00002307603,0.0001207558,0.0001358247,0.0002109392,0.002531465],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00648538,"threshold_uncertainty_score":0.01760399,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1646839507","doi":"10.1609/aaai.v24i1.7562","title":"Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search","year":2010,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Monte Carlo tree search; Perfect information; Computer science; Set (abstract data type); A priori and a posteriori; Monte Carlo method; Variety (cybernetics); Search algorithm; Imperfect; Algorithm; Mathematical economics; Artificial intelligence; Mathematics; Statistics","authors":[{"name":"Jeffrey R. Long","is_ca":true},{"name":"Nathan Sturtevant","is_ca":true},{"name":"Michael Buro","is_ca":true},{"name":"Timothy Furtak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1901535286000289,"gpt":0.3337788850530906,"spread":0.1436253564530617,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01335407,0.0009028104,0.001453088,0.001860626,0.001054484,0.003080361,0.002140054,0.002660055,0.002107579],"category_scores_gemma":[0.1386604,0.0007884886,0.0005330947,0.001126813,0.004370203,0.007522608,0.002323887,0.002484789,0.0002794389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002395117,"about_ca_system_score_gemma":0.002287556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004703288,"about_ca_topic_score_gemma":0.003576616,"domain_scores_codex":[0.9945933,0.003648605,0.0001889495,0.0005260712,0.0006495069,0.0003935538],"domain_scores_gemma":[0.8715855,0.1167767,0.004496633,0.003393633,0.002099293,0.001648271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001905277,0.00009558174,0.006742303,0.0001681126,0.00007499042,0.00008690542,0.0004789819,0.6262693,0.0006873903,0.351595,0.0008938385,0.01271721],"study_design_scores_gemma":[0.00001865138,0.00004997565,0.0005643851,0.00002537836,0.00000895293,0.00002536387,0.00005182836,0.8919989,0.0002444809,0.1067375,0.0002584875,0.00001607001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3035552,0.001205324,0.6736985,0.002465825,0.00006520126,0.000174063,0.0001887535,0.0002758139,0.01837132],"genre_scores_gemma":[0.9624686,0.0003417337,0.03620337,0.000159794,0.00004089012,0.0001028748,0.00008022483,0.00004485316,0.0005576226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01335407,"threshold_uncertainty_score":0.07062393,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3080963199","doi":"10.22266/ijies2020.1031.45","title":"Football Game Based Optimization: an Application to Solve Energy Commitment Problem","year":2020,"lang":"en","type":"article","venue":"International journal of intelligent engineering and systems","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":79,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Villum Fonden","keywords":"Computer science; Football; Mathematical optimization; Operations research; Mathematics; Political science","authors":[{"name":"Mohammad Dehghani","is_ca":false},{"name":"Mohammad Mardaneh","is_ca":false},{"name":"Josep M. Guerrero","is_ca":false},{"name":"O.P. Malik","is_ca":true},{"name":"Vijay Kumar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02494442093663609,"gpt":0.2566309651829805,"spread":0.2316865442463444,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003530082,0.0008462177,0.0007378243,0.0005117791,0.0004542583,0.0006580713,0.0005464007,0.00081792,0.003354709],"category_scores_gemma":[0.0007541159,0.0002445038,0.0005177497,0.0006838673,0.0002748042,0.0004384517,0.0005780939,0.0006780874,0.000254925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004109357,"about_ca_system_score_gemma":0.0005644512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009874688,"about_ca_topic_score_gemma":0.006080302,"domain_scores_codex":[0.9998183,0.00006689174,0.00000911977,0.00003398658,0.00004521225,0.00002656703],"domain_scores_gemma":[0.999838,0.00009338855,0.00001632499,0.000007644766,0.00003213699,0.00001242334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005711335,0.0000655502,0.001006061,0.0001570091,0.00004073957,0.0001159317,0.00003159261,0.9325256,0.001884703,0.006844529,0.001376479,0.05589461],"study_design_scores_gemma":[0.000008198607,0.00003311117,0.0002307729,0.00000900732,0.000006814103,0.00002654832,0.0000115548,0.9959691,0.0004439683,0.001860898,0.001396183,0.000003912379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03620866,0.001367369,0.9435249,0.0004106607,0.0001493072,0.0001729389,0.0001337671,0.0004089286,0.01762359],"genre_scores_gemma":[0.7294863,0.001380161,0.2596697,0.0002244187,0.00008588827,0.0002662602,0.0002271681,0.0001114146,0.008548586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009874688,"threshold_uncertainty_score":0.01963443,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W84074851","doi":"","title":"Monte-Carlo exploration for deterministic planning","year":2009,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo method; Monte Carlo tree search; Random walk; Computer science; Mathematical optimization; Hybrid Monte Carlo; Markov chain Monte Carlo; Mathematics; Statistics","authors":[{"name":"Hootan Nakhost","is_ca":true},{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1131353051604986,"gpt":0.3519815830720948,"spread":0.2388462779115963,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002630964,0.001138593,0.001412768,0.001189862,0.0007783644,0.001534371,0.001522689,0.001518671,0.0120325],"category_scores_gemma":[0.01289662,0.0008831501,0.001198819,0.001627739,0.002135638,0.001861927,0.002016446,0.002651136,0.001912986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00189042,"about_ca_system_score_gemma":0.002635598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006337426,"about_ca_topic_score_gemma":0.006370147,"domain_scores_codex":[0.9980776,0.0009772148,0.00008072185,0.0001967997,0.000576504,0.00009118347],"domain_scores_gemma":[0.9937825,0.005166289,0.0002378983,0.0004039276,0.0003072389,0.000102245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004555793,0.00002234749,0.0002574406,0.0001717046,0.00003927894,0.00004464031,0.00004706833,0.7040585,0.0002636778,0.2644817,0.001783071,0.02878512],"study_design_scores_gemma":[0.00001485621,0.00001249456,0.00004241906,0.00002972357,0.000007320014,0.00001883186,0.000004653376,0.8894231,0.000147471,0.1055865,0.004703089,0.000009628769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001824953,0.001491968,0.9842279,0.0003481868,0.0000796684,0.00007886537,0.0001094365,0.0004202069,0.01141883],"genre_scores_gemma":[0.233145,0.003194568,0.7511009,0.000351292,0.0002789023,0.001339634,0.0004765271,0.0004373528,0.009675837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0120325,"threshold_uncertainty_score":0.04025275,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2230033167","doi":"","title":"Adversarial hierarchical-task network planning for complex real-time games","year":2015,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Monte Carlo tree search; Computer science; Adversarial system; Task (project management); Tree (set theory); Theoretical computer science; Artificial intelligence; Combinatorial game theory; Point (geometry); Machine learning; Game theory; Sequential game; Monte Carlo method; Mathematics; Mathematical economics","authors":[{"name":"Santiago Ontañón","is_ca":false},{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2196141500131067,"gpt":0.3844458776644416,"spread":0.1648317276513349,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001673571,0.0009428684,0.0008605099,0.0005359966,0.0006067909,0.0007429241,0.001320283,0.0007561723,0.003800185],"category_scores_gemma":[0.00686293,0.0003755555,0.0006135935,0.0006007868,0.001317901,0.002233011,0.001561657,0.001663691,0.0003176051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00179096,"about_ca_system_score_gemma":0.001865946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.011899,"about_ca_topic_score_gemma":0.01345698,"domain_scores_codex":[0.9992285,0.0003424017,0.00003494546,0.0001372136,0.0001564209,0.0001005917],"domain_scores_gemma":[0.9955583,0.003667763,0.0002428907,0.0001827218,0.0001400335,0.0002082907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005689155,0.0000340409,0.0003110099,0.00004306756,0.00001698665,0.00003356813,0.00006294162,0.9558778,0.0002818358,0.03057133,0.0007596906,0.01195076],"study_design_scores_gemma":[0.000006158169,0.00001221078,0.00003210011,0.000002913566,0.000002651383,0.000005643222,0.00000684285,0.9837036,0.00007848453,0.0159035,0.0002433628,0.000002483794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03679334,0.0002591226,0.9568267,0.0002871021,0.00003182156,0.0001031354,0.0001010358,0.0003246814,0.005272948],"genre_scores_gemma":[0.8025454,0.0003503318,0.1925594,0.0001465667,0.00003212614,0.0002459135,0.0002559138,0.0001169837,0.003747279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.011899,"threshold_uncertainty_score":0.02365947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102669142","doi":"10.1109/tciaig.2011.2161310","title":"A Generic Approach to Challenge Modeling for the Procedural Creation of Video Game Levels","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Computational Intelligence and AI in Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Generative grammar; Entertainment; Fitness function; Generative Design; Video game; Generative model; Constraint (computer-aided design); Multimedia; Human–computer interaction; Artificial intelligence; Function (biology); Genetic algorithm; Game design; Machine learning","authors":[{"name":"Nathan Sorenson","is_ca":true},{"name":"Philippe Pasquier","is_ca":true},{"name":"Steve DiPaola","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1476889815550019,"gpt":0.3228816404081994,"spread":0.1751926588531975,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008524907,0.0009840089,0.0006156528,0.0008011425,0.0008539439,0.002076782,0.002895019,0.001682348,0.005646148],"category_scores_gemma":[0.002450769,0.0006212419,0.002102282,0.0006734392,0.001672895,0.001569684,0.001752749,0.002576854,0.001101006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282218,"about_ca_system_score_gemma":0.001529388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002962871,"about_ca_topic_score_gemma":0.004434555,"domain_scores_codex":[0.999254,0.0001920675,0.00004163135,0.0001926013,0.0002626957,0.00005703587],"domain_scores_gemma":[0.9994369,0.0001973225,0.00005496999,0.0001513619,0.0001090781,0.00005037273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000338678,0.00009250111,0.0006696114,0.0002190907,0.00006331394,0.0001837772,0.0005378444,0.3642056,0.01027018,0.5695048,0.002397253,0.05182211],"study_design_scores_gemma":[0.00001028339,0.00003108412,0.0001350991,0.00003832725,0.00002375073,0.00009660709,0.00005053435,0.8858254,0.002256927,0.09374075,0.01776658,0.00002468562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009031962,0.00002387068,0.9963872,0.00007294535,0.00001427664,0.00005407177,0.00003199564,0.0001134887,0.002398896],"genre_scores_gemma":[0.08606253,0.0001578699,0.9083112,0.000138519,0.00003457721,0.0004436317,0.0001548133,0.0002288831,0.004468012],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005646148,"threshold_uncertainty_score":0.01888824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W24557711","doi":"10.1007/s12185-014-1536-8","title":"Towards Fuzzy Agents with Dynamic Personality for Human Behavior Simulation","year":2003,"lang":"en","type":"article","venue":"International Journal of Hematology","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Personality; Fuzzy logic; Psychology; Representation (politics); Cognitive psychology; Artificial intelligence; Social psychology; Computer science","authors":[{"name":"Nasser Ghasem-Aghaee","is_ca":false},{"name":"Tuncer Ören","is_ca":true},{"name":"Yuan Yuan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05617438159950045,"gpt":0.3913061455232036,"spread":0.3351317639237032,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008969512,0.0006212658,0.0004461303,0.0006442174,0.0005079412,0.0009604622,0.0006410085,0.0008427616,0.002404723],"category_scores_gemma":[0.004205174,0.0003619328,0.0008162911,0.0002651013,0.000775282,0.0008215494,0.0009892847,0.000818314,0.0003195117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008534904,"about_ca_system_score_gemma":0.0006671221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01066195,"about_ca_topic_score_gemma":0.007323472,"domain_scores_codex":[0.9997061,0.0001709924,0.00001347176,0.00004618677,0.0000354256,0.00002780529],"domain_scores_gemma":[0.9989232,0.0007157961,0.0001119916,0.00007354605,0.0001060626,0.00006934413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006968914,0.00003669384,0.002849831,0.00004488022,0.00005729706,0.00008298076,0.0003096529,0.9543036,0.0008096898,0.01853791,0.0004494051,0.02244835],"study_design_scores_gemma":[0.000004532558,0.00001468214,0.0002060382,0.000005091331,0.000004149784,0.000009101321,0.00002728932,0.9927972,0.00005820478,0.00665125,0.0002190868,0.000003413584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1487856,0.0002911808,0.843187,0.0005744833,0.00006379633,0.0001123264,0.0001133738,0.0003276596,0.006544495],"genre_scores_gemma":[0.8771023,0.0001888045,0.1202076,0.00006738379,0.00002126469,0.0001809368,0.0001035087,0.00002696932,0.002101274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01066195,"threshold_uncertainty_score":0.02119982,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012061263","doi":"10.1016/s0004-3702(01)00165-5","title":"Games, computers, and artificial intelligence","year":2002,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Vedecká Grantová Agentúra MŠVVaŠ SR a SAV","keywords":"Artificial intelligence; Computer science; Artificial Sweetener; Biology","authors":[{"name":"Jonathan Schaeffer","is_ca":true},{"name":"H.J. van den Herik","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08793558553946206,"gpt":0.2975744220529197,"spread":0.2096388365134576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001023688,0.0009123668,0.0009804533,0.0009716043,0.001162926,0.007659192,0.0007767669,0.002134504,0.01008779],"category_scores_gemma":[0.003939959,0.0002911987,0.0003892176,0.001556992,0.007687367,0.004784109,0.00172123,0.00247457,0.0008399917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002641331,"about_ca_system_score_gemma":0.00193233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007212706,"about_ca_topic_score_gemma":0.008182417,"domain_scores_codex":[0.9986424,0.0007703096,0.00005851241,0.0001293727,0.000287585,0.0001116915],"domain_scores_gemma":[0.9986309,0.0009378557,0.00009695718,0.00009295833,0.0001106565,0.0001306588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002231284,0.00002725341,0.0002618155,0.0001633535,0.00001999338,0.00002358194,0.0003946345,0.001548457,0.0001024495,0.9771892,0.006247763,0.01399919],"study_design_scores_gemma":[0.00002512606,0.00001785646,0.0003811607,0.00009234757,0.00001293849,0.00003734646,0.0005290722,0.001943631,0.00006638923,0.9202108,0.07667476,0.000008554674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02613024,0.1083473,0.07299844,0.031586,0.002263195,0.0002033473,0.0002349407,0.0001699471,0.7580665],"genre_scores_gemma":[0.8547713,0.04363299,0.02973979,0.004616434,0.001324368,0.000456452,0.0002834761,0.00008456124,0.06509059],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01008779,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W182596629","doi":"","title":"Temporal difference learning applied to a high-performance game-playing program","year":2001,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Task (project management); Temporal difference learning; Artificial intelligence; Class (philosophy); Function (biology); Sequential game; Machine learning; Game design; Reinforcement learning; Game theory; Engineering; Mathematics","authors":[{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Markian Hlynka","is_ca":true},{"name":"Vili Jussila","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09513926961760284,"gpt":0.3262099561051989,"spread":0.231070686487596,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068193,0.0003176638,0.0002938543,0.0002910141,0.000313136,0.0005104452,0.0005826792,0.0005154941,0.002437052],"category_scores_gemma":[0.004540688,0.000137694,0.0002518251,0.0003196397,0.0003291003,0.0004769826,0.0004966406,0.0007153176,0.0001802415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006750594,"about_ca_system_score_gemma":0.0006071572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009023246,"about_ca_topic_score_gemma":0.006922875,"domain_scores_codex":[0.9997354,0.00008619406,0.00001630182,0.00006571497,0.00006170361,0.00003462384],"domain_scores_gemma":[0.9988356,0.0007981456,0.00004607752,0.00005202717,0.0002056406,0.00006247238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001373851,0.0007404227,0.004463547,0.0001764844,0.00006653828,0.0002850482,0.0003987989,0.3427238,0.0287567,0.009289369,0.00141972,0.6103056],"study_design_scores_gemma":[0.000015857,0.0001360749,0.000449255,0.000002202086,0.000005746404,0.00001817048,0.0000154419,0.9941221,0.003673372,0.001235813,0.0003212932,0.000004685099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3958881,0.0001418289,0.5965433,0.0002692558,0.0001177537,0.000190434,0.00004752661,0.001317633,0.005484221],"genre_scores_gemma":[0.9020336,0.00004033743,0.09561543,0.00005378752,0.00001047137,0.00005700742,0.00004522207,0.00003087385,0.002113164],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009023246,"threshold_uncertainty_score":0.01794147,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123018439","doi":"10.65109/umek3334","title":"Abstraction pathologies in extensive games","year":2009,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Abstraction; Computer science; Solver; Combinatorial game theory; Sequential game; Extensive-form game; Game theory; Repeated game; Theoretical computer science; Mathematical economics; Mathematics; Programming language","authors":[{"name":"Kevin Waugh","is_ca":true},{"name":"David Schnizlein","is_ca":true},{"name":"Michael Bowling","is_ca":true},{"name":"Duane Szafron","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03659273412157045,"gpt":0.3077348041073538,"spread":0.2711420699857833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002838293,0.001199736,0.001118586,0.001479168,0.001565121,0.003007614,0.001297344,0.001568403,0.004298681],"category_scores_gemma":[0.0128468,0.0006617082,0.001881873,0.001016361,0.006189824,0.0078116,0.004165556,0.004860534,0.0005739007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002089571,"about_ca_system_score_gemma":0.001201487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700548,"about_ca_topic_score_gemma":0.001260773,"domain_scores_codex":[0.997245,0.001187245,0.0001520193,0.0003140307,0.0008470641,0.0002547557],"domain_scores_gemma":[0.9939711,0.004294812,0.0004587054,0.0005334235,0.0003759423,0.0003660666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001133292,0.000008880265,0.0002036892,0.00003847294,0.00001093921,0.0001014338,0.0002295963,0.01076318,0.0003284762,0.984774,0.000466663,0.003063271],"study_design_scores_gemma":[0.000006507476,0.000007135704,0.00005131738,0.00001004765,0.000004480087,0.00004893027,0.00003498403,0.0231049,0.0001088405,0.9752985,0.001318318,0.000006065756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06230725,0.0009904052,0.899461,0.001699641,0.00009177996,0.0001497559,0.0001540321,0.0002554932,0.0348906],"genre_scores_gemma":[0.8633416,0.001256283,0.1236724,0.0006510839,0.0002276034,0.0003558115,0.0001940015,0.0001096207,0.01019158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004298681,"threshold_uncertainty_score":0.01516098,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114793692","doi":"10.1609/aaai.v28i1.8810","title":"Solving Imperfect Information Games Using Decomposition","year":2014,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures; Compute Canada","keywords":"Perfect information; Computer science; Decomposition; Extensive-form game; Imperfect; Sequential game; Subgame perfect equilibrium; Mathematical optimization; Nash equilibrium; Normal-form game; Construct (python library); Combinatorial game theory; Subgame; Game theory; Best response; Mathematical economics; Mathematics; Epsilon-equilibrium","authors":[{"name":"Neil Burch","is_ca":true},{"name":"Michael Johanson","is_ca":true},{"name":"Michael Bowling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05366474124327942,"gpt":0.3096907656912596,"spread":0.2560260244479802,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002808391,0.001774588,0.001874145,0.001131626,0.0009243403,0.002456419,0.001468107,0.001205802,0.004678993],"category_scores_gemma":[0.009018881,0.0008958012,0.002327934,0.0009798416,0.002391928,0.003287496,0.003126654,0.003163195,0.0006968786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00242999,"about_ca_system_score_gemma":0.003684436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004505374,"about_ca_topic_score_gemma":0.004004858,"domain_scores_codex":[0.9969159,0.001345632,0.0001833395,0.0005353591,0.0005526241,0.000467075],"domain_scores_gemma":[0.9945446,0.004069821,0.0003110117,0.0005133069,0.0002786347,0.000282701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002064002,0.0001820215,0.0006845596,0.0003110037,0.0001269915,0.000152503,0.0003980477,0.6641023,0.003235012,0.2713245,0.003296891,0.05597986],"study_design_scores_gemma":[0.00004862609,0.0000516753,0.00009544243,0.00003396251,0.00002510104,0.0000407622,0.00006563793,0.7832403,0.001235259,0.2132825,0.001861686,0.00001901955],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01629989,0.0001068629,0.9752703,0.0002842603,0.00003446273,0.0002090457,0.0001025519,0.0002926183,0.00740002],"genre_scores_gemma":[0.3226332,0.0003963872,0.6691357,0.0002731482,0.00007004761,0.0007396023,0.0004119641,0.0001997293,0.006140179],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004678993,"threshold_uncertainty_score":0.01763093,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2112320870","doi":"10.1145/2702123.2702242","title":"Now You Can Compete With Anyone","year":2015,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Software deployment; Load balancing (electrical power); Multimedia; Human–computer interaction; Software engineering","authors":[{"name":"Rodrigo Vicencio-Moreira","is_ca":true},{"name":"Regan L. Mandryk","is_ca":true},{"name":"Carl Gutwin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0528076355882725,"gpt":0.2736525821452432,"spread":0.2208449465569707,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004375648,0.001214731,0.0008085334,0.0008612095,0.002150688,0.003879196,0.001144671,0.001104991,0.5566701],"category_scores_gemma":[0.002536446,0.0003265653,0.0005871925,0.0004552105,0.0005202655,0.004520827,0.006458727,0.002486335,0.3910998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002903075,"about_ca_system_score_gemma":0.0005539144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001918165,"about_ca_topic_score_gemma":0.006123913,"domain_scores_codex":[0.9994218,0.00006080221,0.00001827136,0.0001009867,0.000215385,0.0001827876],"domain_scores_gemma":[0.9980031,0.00007531711,0.00003859683,0.0001310193,0.0003828335,0.001369162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001616574,0.0002388317,0.001332137,0.0001373894,0.00001771145,0.0003398147,0.0008200487,0.0000801544,0.00182954,0.00489444,0.8280478,0.1621005],"study_design_scores_gemma":[0.00001288525,0.00007276246,0.0009212879,0.00007338799,0.00000554893,0.0002597138,0.0007829919,0.0001485756,0.0002519156,0.001230049,0.9962197,0.0000212234],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0114927,0.001347896,0.01335258,0.0099893,0.008369775,0.0003372887,0.001421761,0.006673325,0.9470153],"genre_scores_gemma":[0.0239515,0.0006319226,0.004276608,0.002197746,0.0005786463,0.0001170727,0.001106907,0.0009671348,0.9661726],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5566701,"threshold_uncertainty_score":0.6323562,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1511096005","doi":"10.1609/aimag.v33i3.2419","title":"Real‐Time Strategy Game Competitions","year":2012,"lang":"en","type":"article","venue":"AI Magazine","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Multitude; Order (exchange); Computer science; Operations research; Management science; Data science; Engineering; Business; Political science","authors":[{"name":"Michael Buro","is_ca":true},{"name":"David G. Churchill","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02933503070841547,"gpt":0.2981434790828846,"spread":0.2688084483744691,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002257744,0.0007489413,0.0007039285,0.0008591536,0.0011035,0.001933531,0.001596246,0.001047324,0.01256961],"category_scores_gemma":[0.006851769,0.0002115343,0.0005065567,0.001163703,0.0009545202,0.001861078,0.001681687,0.001471462,0.001964006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001505174,"about_ca_system_score_gemma":0.00118593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002830109,"about_ca_topic_score_gemma":0.006825839,"domain_scores_codex":[0.9976585,0.0009404902,0.0001051178,0.0002435766,0.0007363289,0.0003160016],"domain_scores_gemma":[0.9959611,0.001858167,0.0003143585,0.0002160272,0.0005676647,0.001082666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002505891,0.002884681,0.005901182,0.001656231,0.0003609424,0.001074867,0.002776923,0.04316758,0.01320588,0.4206896,0.0921047,0.4136715],"study_design_scores_gemma":[0.0008085783,0.00276308,0.01214553,0.0003599723,0.0001052255,0.001112431,0.002179989,0.1257764,0.007271534,0.1630814,0.684171,0.0002249102],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.404845,0.008390944,0.1604058,0.004733493,0.002650213,0.001624612,0.001049548,0.001316636,0.4149838],"genre_scores_gemma":[0.8835459,0.002963606,0.04410088,0.001071661,0.0004684109,0.00077481,0.001963365,0.0002002941,0.06491094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01256961,"threshold_uncertainty_score":0.04204953,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153816030","doi":"10.7939/r35n5v","title":"Computing Robust Counter-Strategies","year":2007,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Bounding overwatch; Adaptation (eye); Variety (cybernetics); Bayesian probability; Simple (philosophy); Artificial intelligence; Machine learning","authors":[{"name":"Michael Johanson","is_ca":true},{"name":"Michael Bowling","is_ca":true},{"name":"Martin Zinkevich","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0451494206295277,"gpt":0.3070415953745863,"spread":0.2618921747450585,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002485748,0.001429076,0.001415133,0.001230121,0.0006121201,0.001809924,0.002075653,0.002072102,0.002487041],"category_scores_gemma":[0.01700268,0.0006560667,0.0008542626,0.0005176846,0.001506035,0.00248778,0.002021481,0.001546019,0.0005588559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001300637,"about_ca_system_score_gemma":0.001424419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002024415,"about_ca_topic_score_gemma":0.002048624,"domain_scores_codex":[0.9983387,0.0005007054,0.0001240669,0.0004724426,0.0003683368,0.0001957894],"domain_scores_gemma":[0.9936869,0.003940296,0.0005948441,0.0008989269,0.0006314923,0.0002475744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002069428,0.00007616581,0.002337922,0.0000553847,0.00009864271,0.000107086,0.0001073561,0.9323457,0.003562943,0.01685559,0.0005879997,0.04365818],"study_design_scores_gemma":[0.000009505648,0.00002635213,0.0001049941,0.00000365618,0.000006345044,0.00001679062,0.00001572975,0.990604,0.001284977,0.007766082,0.000155432,0.000006170842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0992016,0.0001061465,0.8964137,0.0001873884,0.00004341392,0.00009641246,0.00005737965,0.001226556,0.002667453],"genre_scores_gemma":[0.7798814,0.00004912564,0.2179708,0.0001207077,0.00002387512,0.000169009,0.0001682609,0.0001659938,0.001450872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002487041,"threshold_uncertainty_score":0.01314604,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156078757","doi":"10.1109/cig.2007.368082","title":"Adversarial Planning Through Strategy Simulation","year":2007,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Adversarial system; Software deployment; Nash equilibrium; Video game; Quality (philosophy); Automated planning and scheduling; Game theory; Artificial intelligence; Operations research; Software engineering; Multimedia; Mathematical optimization; Engineering","authors":[{"name":"Frantisek Sailer","is_ca":true},{"name":"Michael Buro","is_ca":true},{"name":"Marc Lanctot","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09375477713319037,"gpt":0.3799972725835327,"spread":0.2862424954503423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002048166,0.0009574796,0.0006872781,0.0006957299,0.0004873809,0.001012945,0.001278062,0.0008491199,0.00275218],"category_scores_gemma":[0.006306889,0.0005247156,0.0006049316,0.0005523465,0.001706825,0.001654716,0.001737885,0.001277735,0.0003195739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004658,"about_ca_system_score_gemma":0.001528069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00413004,"about_ca_topic_score_gemma":0.004291039,"domain_scores_codex":[0.9988369,0.0006143083,0.00004924102,0.0001636471,0.0002471035,0.00008879738],"domain_scores_gemma":[0.9965514,0.002636425,0.0002224592,0.0003134351,0.0001533658,0.0001228712],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004257232,0.00002333618,0.00026409,0.00002492379,0.00002096847,0.00003924756,0.00005427595,0.9520869,0.0005732768,0.03295282,0.0002629187,0.01365473],"study_design_scores_gemma":[0.000007743109,0.00001345889,0.00002263178,0.000004070937,0.000003559197,0.000009128738,0.000006267004,0.9775928,0.0003361758,0.02162619,0.0003747386,0.000003300445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01184256,0.00008147467,0.9841388,0.0001397135,0.00001301972,0.00007291865,0.00003143936,0.0004049988,0.00327514],"genre_scores_gemma":[0.6976312,0.0002295753,0.2994791,0.00008640131,0.00002489378,0.0002687009,0.000120806,0.0001008917,0.002058483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00413004,"threshold_uncertainty_score":0.01083183,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W190498140","doi":"10.1609/aaai.v26i1.8269","title":"Finding Optimal Abstract Strategies in Extensive-Form Games","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates; Western Canada Research Grid; Compute Canada","keywords":"Nash equilibrium; Extensive-form game; Computer science; Sequential game; Mathematical economics; Limit (mathematics); Normal-form game; Solution concept; Repeated game; Best response; State space; Epsilon-equilibrium; Symmetric game; Computation; Mathematical optimization; Combinatorial game theory; Strategy; Equilibrium selection; Game theory; Mathematics; Algorithm","authors":[{"name":"Michael Johanson","is_ca":true},{"name":"Nolan Bard","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Michael Bowling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09497848147463588,"gpt":0.3277930247737362,"spread":0.2328145432991003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001125283,0.0009665176,0.001084784,0.0008258644,0.0005352553,0.001704254,0.0008500852,0.0009705783,0.002548283],"category_scores_gemma":[0.004879525,0.000622267,0.0007350329,0.0006530141,0.001355089,0.002637473,0.001364824,0.001090687,0.0003886168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00121614,"about_ca_system_score_gemma":0.00152028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001946025,"about_ca_topic_score_gemma":0.002518107,"domain_scores_codex":[0.9993153,0.0002601332,0.0000516795,0.0001276496,0.000147427,0.00009773499],"domain_scores_gemma":[0.9983692,0.001269118,0.0001183859,0.0001036253,0.0000747763,0.00006500111],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001294307,0.0001115895,0.001307889,0.0001756174,0.00006608012,0.0001272436,0.0003663556,0.7411317,0.002970261,0.1736315,0.00117728,0.07880505],"study_design_scores_gemma":[0.00003320107,0.00005256061,0.0001331966,0.00002184989,0.00001454835,0.00002933823,0.00008350642,0.8310333,0.001586052,0.1660808,0.0009194019,0.00001225475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06805541,0.0001209399,0.9260751,0.0001915643,0.00000869225,0.0001160347,0.00007968432,0.000313627,0.005038849],"genre_scores_gemma":[0.5787667,0.0002597227,0.4175436,0.0001017286,0.00001128414,0.0002843527,0.0002418573,0.0000928472,0.002697891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002548283,"threshold_uncertainty_score":0.008823752,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W196854640","doi":"10.1609/aiide.v3i1.18778","title":"Memory-Efficient Abstractions for Pathfinding","year":2007,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Pathfinding; Abstraction; Computer science; Perspective (graphical); Path (computing); Work (physics); State (computer science); Human–computer interaction; Theoretical computer science; Programming language; Shortest path problem; Artificial intelligence","authors":[{"name":"Nathan Sturtevant","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05392906527082882,"gpt":0.3095958223270369,"spread":0.2556667570562081,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005304097,0.0009038747,0.0007351324,0.0008831338,0.0008940742,0.001786031,0.002074626,0.0007315037,0.00573892],"category_scores_gemma":[0.00346218,0.0007357972,0.001496033,0.001280507,0.0012861,0.004651881,0.003522762,0.002319295,0.00122139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000956694,"about_ca_system_score_gemma":0.00111062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005745726,"about_ca_topic_score_gemma":0.006610409,"domain_scores_codex":[0.9994192,0.0001125156,0.00005749666,0.0001267961,0.0001907198,0.00009321492],"domain_scores_gemma":[0.9983461,0.0007298157,0.0001288761,0.0005935479,0.0001429099,0.00005873295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000469973,0.00009355244,0.0012186,0.0005213241,0.000103101,0.0002018556,0.001374255,0.251724,0.01683395,0.3622882,0.009115264,0.3560559],"study_design_scores_gemma":[0.00006473731,0.00008947979,0.0003824767,0.00008032461,0.00007389793,0.0001341383,0.0002503221,0.5670536,0.01347386,0.3915871,0.02676109,0.00004898368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01257095,0.000247381,0.9815131,0.0001228665,0.00002844798,0.00003519762,0.0001675965,0.002662339,0.002652165],"genre_scores_gemma":[0.2778586,0.0005907738,0.715669,0.0000929723,0.0000290659,0.0001983182,0.0006548126,0.000540707,0.004365723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005745726,"threshold_uncertainty_score":0.0191986,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152070548","doi":"10.1109/re.2006.19","title":"Emotional Requirements in Video Games","year":2006,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Human–computer interaction; Video game; Emotional expression; Multimedia; Cognitive psychology; Psychology","authors":[{"name":"David Callele","is_ca":true},{"name":"Eric Neufeld","is_ca":true},{"name":"Kevin A. Schneider","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0389813789453596,"gpt":0.2983603652367821,"spread":0.2593789862914225,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009868391,0.000535165,0.0001773982,0.0004630152,0.0006600142,0.002378644,0.0005809127,0.0009753624,0.002343287],"category_scores_gemma":[0.005509467,0.0004217946,0.0003126996,0.0002545444,0.001866396,0.002195668,0.001491323,0.001107094,0.0004060858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055683,"about_ca_system_score_gemma":0.0003768045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002130205,"about_ca_topic_score_gemma":0.001409517,"domain_scores_codex":[0.9983119,0.0008632638,0.00009807492,0.0001321529,0.000447799,0.000146864],"domain_scores_gemma":[0.9986485,0.0007090705,0.0001395714,0.000112802,0.0002981501,0.00009201049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001278416,0.00005616091,0.0008531437,0.0002548503,0.00001592975,0.0007543348,0.007404297,0.02397754,0.02083903,0.8846231,0.00442534,0.05666848],"study_design_scores_gemma":[0.00007263361,0.0001516897,0.003257039,0.0002539012,0.00004352353,0.001143891,0.005826207,0.1449479,0.0140802,0.7034644,0.1266576,0.0001008535],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1343444,0.000766005,0.7018002,0.003071423,0.0001403785,0.0002512882,0.0001413851,0.0008179525,0.1586671],"genre_scores_gemma":[0.902688,0.0003677992,0.08500724,0.0003515073,0.00003768658,0.0002547239,0.0001249277,0.0001755959,0.01099255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002378644,"threshold_uncertainty_score":0.007839084,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1589331835","doi":"10.1007/11424918_9","title":"Heuristic Search Applied to Abstract Combat Games","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Heuristic; Adversarial system; Tournament; Artificial intelligence; Mathematical optimization; Theoretical computer science; Operations research; Mathematics","authors":[{"name":"Alexander Kovarsky","is_ca":true},{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03450216205097408,"gpt":0.2919005191359224,"spread":0.2573983570849483,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007590805,0.0008415679,0.001248263,0.0009730551,0.0007211682,0.00129016,0.001325956,0.001063018,0.007682535],"category_scores_gemma":[0.00412795,0.000458818,0.0006725438,0.001556529,0.0009983925,0.001037551,0.0010203,0.001329476,0.0006067161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147184,"about_ca_system_score_gemma":0.001433098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008775077,"about_ca_topic_score_gemma":0.007777463,"domain_scores_codex":[0.9995352,0.0002233347,0.00002306993,0.00004671574,0.0001138739,0.00005778821],"domain_scores_gemma":[0.9989756,0.0007732747,0.00003746519,0.00006740011,0.000102917,0.00004331221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001346287,0.00008338514,0.0002328702,0.0001921369,0.00004742505,0.0000777918,0.0001515739,0.6765925,0.0006898544,0.1846623,0.006019976,0.1311155],"study_design_scores_gemma":[0.00003845485,0.00003130394,0.00007324739,0.00002190958,0.00001221381,0.00001575673,0.00002319159,0.918103,0.0002365662,0.07917517,0.002260992,0.000008236741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0298555,0.001263786,0.9155656,0.0003168949,0.0002597306,0.0001825663,0.0001262937,0.0006835953,0.05174613],"genre_scores_gemma":[0.5191609,0.001213326,0.4503447,0.0001426052,0.0001585929,0.0004299353,0.0003064631,0.0003279442,0.02791553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008775077,"threshold_uncertainty_score":0.02570063,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}