{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":6,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":6,"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":"1b1b5196a5ce","filters":{"venue":"Advances in Computer Games"}},"results":[{"id":"W1580378151","doi":"10.1007/978-0-387-35706-5_13","title":"Building the Checkers 10-Piece Endgame Databases","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":40,"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":"Chess endgame; Database; nobody; Computer science; Artificial intelligence","authors":[{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Yngvi Björnsson","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Robert W. Lake","is_ca":true},{"name":"P. Lu","is_ca":true},{"name":"Steve Sutphen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03809776430838342,"gpt":0.3123459949166396,"spread":0.2742482306082561,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008065354,0.001028854,0.001540008,0.002890054,0.001133638,0.004998484,0.005502841,0.00106704,0.0355519],"category_scores_gemma":[0.004562177,0.00151269,0.001736865,0.002717621,0.0006887892,0.006941629,0.003309268,0.001389037,0.01600378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001220099,"about_ca_system_score_gemma":0.001763986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01335632,"about_ca_topic_score_gemma":0.02243936,"domain_scores_codex":[0.99871,0.0001100244,0.0001268648,0.0003872267,0.0005364418,0.0001293655],"domain_scores_gemma":[0.9986644,0.0002248915,0.00006408082,0.000638449,0.0003102216,0.00009799779],"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":[0.0009077816,0.0004458415,0.005024471,0.0005913629,0.0002581765,0.0004826832,0.0006755525,0.03244142,0.006186883,0.1107436,0.1723937,0.6698484],"study_design_scores_gemma":[0.0002658469,0.0003499294,0.003057209,0.0003106726,0.0003103578,0.0008234819,0.001145563,0.3670679,0.03597001,0.1529022,0.4376324,0.0001644529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05286399,0.001906337,0.7546715,0.0009475278,0.0004924531,0.0008192493,0.0354119,0.05915716,0.09372976],"genre_scores_gemma":[0.2514788,0.001194956,0.6138372,0.0004741405,0.00009142362,0.0004947421,0.07377873,0.004030494,0.05461942],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0355519,"threshold_uncertainty_score":0.118933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1516840567","doi":"10.1007/978-0-387-35706-5_23","title":"Solving the Oshi-Zumo Game","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":20,"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":"Nash equilibrium; Mathematical economics; Computer science; Game theory; Strategy; Mathematics","authors":[{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05315334453245216,"gpt":0.3524715400949214,"spread":0.2993181955624692,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005267833,0.0008497876,0.0008591171,0.0002998178,0.0009768691,0.001423722,0.001042575,0.001200693,0.009859203],"category_scores_gemma":[0.001763861,0.0002604438,0.0006375447,0.0004321684,0.0009702746,0.002728687,0.001573125,0.001597257,0.0006703345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006020729,"about_ca_system_score_gemma":0.001216025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002030113,"about_ca_topic_score_gemma":0.004391118,"domain_scores_codex":[0.9997533,0.00008836414,0.00001341924,0.00003710962,0.00005039497,0.00005740064],"domain_scores_gemma":[0.9997037,0.0002026732,0.00001616731,0.00002456914,0.0000177506,0.00003507362],"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":[0.0002369277,0.0001254593,0.0004117235,0.0002184794,0.00004391059,0.0001266634,0.0005939268,0.04736306,0.002808198,0.8737898,0.006573274,0.06770874],"study_design_scores_gemma":[0.00008991526,0.0001093637,0.0002544609,0.00006525243,0.00003678271,0.00006605153,0.0004800223,0.1993517,0.002081406,0.7783293,0.01910674,0.00002906643],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1526174,0.0007288242,0.6028398,0.00213795,0.0004466914,0.0002968461,0.0001802323,0.000284825,0.2404674],"genre_scores_gemma":[0.6781561,0.001279379,0.2167871,0.0003663268,0.0001181445,0.0004378598,0.0002934972,0.0001120522,0.1024496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009859203,"threshold_uncertainty_score":0.03298235,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1579046794","doi":"10.1007/978-0-387-35706-5_9","title":"DF-PN in Go: An Application to the One-Eye Problem","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":20,"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":"Problem solver; Solver; Order (exchange); Computer science; Mathematics; Theoretical computer science; Algorithm; Mathematical optimization","authors":[{"name":"Akihiro Kishimoto","is_ca":true},{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02263971972645847,"gpt":0.299328276221947,"spread":0.2766885564954885,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005157131,0.0005691191,0.0007830544,0.0005616056,0.001417394,0.001531574,0.001231639,0.0025955,0.02102217],"category_scores_gemma":[0.003527497,0.000182347,0.0009848399,0.0009282111,0.001333635,0.00286401,0.002551847,0.001835628,0.001900353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007564833,"about_ca_system_score_gemma":0.0007840893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005476586,"about_ca_topic_score_gemma":0.005007245,"domain_scores_codex":[0.9997191,0.00007542461,0.00001279125,0.00006857789,0.00006761682,0.00005662531],"domain_scores_gemma":[0.9995081,0.0003109418,0.00002053326,0.00006131858,0.00005313148,0.00004595297],"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":[0.0001023418,0.00006325296,0.0005344137,0.0001551221,0.00001576693,0.0002458314,0.0002741683,0.01718242,0.001214281,0.8569462,0.02277003,0.1004962],"study_design_scores_gemma":[0.00003470321,0.00001763429,0.0002018171,0.00002792777,0.00001127336,0.0001789349,0.0001533032,0.06323119,0.0007799002,0.914247,0.0211024,0.0000138427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04672114,0.0007966431,0.6585521,0.00335787,0.0006902912,0.000211758,0.0005690461,0.001482382,0.2876188],"genre_scores_gemma":[0.4892103,0.00107535,0.4241073,0.0006015394,0.0002791894,0.0002368777,0.0007100847,0.0006024723,0.08317692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02102217,"threshold_uncertainty_score":0.07032615,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1489504788","doi":"10.1007/978-0-387-35706-5_17","title":"Solving 7×7 Hex: Virtual Connections and Game-State Reduction","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":16,"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; University of Alberta","keywords":"Outcome (game theory); Reduction (mathematics); Sequential game; Game tree; Computer science; State (computer science); Connection (principal bundle); Tree (set theory); Combinatorial game theory; Extensive-form game; Monte Carlo tree search; Game theory; Theoretical computer science; Mathematical optimization; Mathematical economics; Algorithm; Mathematics; Combinatorics","authors":[{"name":"Ryan Hayward","is_ca":true},{"name":"Yngvi Björnsson","is_ca":true},{"name":"Michael Johanson","is_ca":true},{"name":"Morgan Kan","is_ca":true},{"name":"Nathan Po","is_ca":true},{"name":"Jack van Rijswijck","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01846941880043569,"gpt":0.2704984088857533,"spread":0.2520289900853177,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003955744,0.0009579544,0.0007485625,0.0003496327,0.001143514,0.001749858,0.001578091,0.001213626,0.02566446],"category_scores_gemma":[0.001706578,0.0005002426,0.0009564314,0.000525753,0.001370822,0.00302808,0.002395806,0.002338947,0.002642805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006101237,"about_ca_system_score_gemma":0.0007236831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002216138,"about_ca_topic_score_gemma":0.003115177,"domain_scores_codex":[0.9994941,0.0001672514,0.00002526528,0.0001097992,0.000117651,0.00008598609],"domain_scores_gemma":[0.9995418,0.000257616,0.00002432681,0.00009575021,0.00004846197,0.00003205591],"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":[0.0001601004,0.0001304928,0.0002532853,0.0002273611,0.00004981522,0.0001253292,0.0005951425,0.1114,0.003875956,0.7274818,0.01315136,0.1425493],"study_design_scores_gemma":[0.00003185766,0.00003643273,0.00008574844,0.00003008644,0.00002012613,0.00005324349,0.0001870901,0.2616567,0.00294386,0.7207349,0.01420036,0.00001956717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05331626,0.0001843042,0.8305373,0.0005380967,0.0002515157,0.000163222,0.0002206196,0.001239506,0.1135493],"genre_scores_gemma":[0.5529777,0.0003046443,0.3918341,0.000226409,0.00007692365,0.0004092491,0.0007038929,0.0007554914,0.05271161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02566446,"threshold_uncertainty_score":0.08585614,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1526187220","doi":"10.1007/978-0-387-35706-5_15","title":"Search and Knowledge in Lines of Action","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":8,"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":"Olympiad; Action (physics); Domain (mathematical analysis); Gold medal; Computer science; Class (philosophy); Position (finance); Artificial intelligence; Mathematics education; Psychology; Mathematics; Art; Business; Art history","authors":[{"name":"Darse Billings","is_ca":true},{"name":"Yngvi Björnsson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0518174757458234,"gpt":0.3466774546907443,"spread":0.2948599789449209,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003787067,0.0005013303,0.0004427884,0.0009269305,0.000788119,0.00378606,0.0007710158,0.001404743,0.01787649],"category_scores_gemma":[0.002414794,0.0003262812,0.000380546,0.001852408,0.002945033,0.008491211,0.0007767722,0.001439314,0.00227497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001681515,"about_ca_system_score_gemma":0.0008833367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002352194,"about_ca_topic_score_gemma":0.002434184,"domain_scores_codex":[0.9996545,0.0001251487,0.00002230173,0.00006460596,0.0001014328,0.00003198215],"domain_scores_gemma":[0.9991822,0.0005266289,0.00005598092,0.00009675762,0.0001011632,0.00003738513],"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":[0.000008043086,0.000007380518,0.00007123894,0.00005798178,0.000004216328,0.00002647071,0.0003677905,0.001140626,0.0001081656,0.9637269,0.005491341,0.0289899],"study_design_scores_gemma":[0.000005149617,0.000006780467,0.00008573901,0.00007193968,0.000004523938,0.00005825186,0.0001653978,0.002716432,0.0001786023,0.9349425,0.06175931,0.000005311042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.01268222,0.01982213,0.1946806,0.005171708,0.0004365958,0.00004470855,0.0002442386,0.0002820488,0.7666359],"genre_scores_gemma":[0.5084698,0.01795718,0.07982905,0.0007192451,0.0005820337,0.0002497518,0.0005757281,0.000246439,0.3913708],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01787649,"threshold_uncertainty_score":0.05980289,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1548895693","doi":"10.1007/978-0-387-35706-5_1","title":"Evaluation Function Tuning via Ordinal Correlation","year":2004,"lang":"en","type":"book-chapter","venue":"Advances in Computer Games","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":7,"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":"Heuristic; Metric (unit); Correlation; Computer science; Function (biology); Feature (linguistics); Quality (philosophy); Evaluation function; Space (punctuation); Artificial intelligence; Ordinal optimization; Mathematics; Data mining; Machine learning; Ordinal data; Engineering","authors":[{"name":"Dave Gomboc","is_ca":true},{"name":"T.A. Marsland","is_ca":true},{"name":"Michael Buro","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03255499166891886,"gpt":0.2965026661006124,"spread":0.2639476744316935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006208108,0.0007797338,0.00192749,0.001195706,0.0005907416,0.001973567,0.001628933,0.001082829,0.005944444],"category_scores_gemma":[0.02002043,0.0005069968,0.0006577799,0.001249157,0.001111801,0.002877781,0.002191294,0.002627597,0.001294226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009834071,"about_ca_system_score_gemma":0.0009013879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006688247,"about_ca_topic_score_gemma":0.0008589796,"domain_scores_codex":[0.994591,0.002657079,0.0003051827,0.0006534001,0.001497893,0.0002954123],"domain_scores_gemma":[0.9910101,0.005557403,0.0004320939,0.001461934,0.001310142,0.0002282526],"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":[0.0005482199,0.0002555983,0.001617235,0.0002670575,0.0001447564,0.00004964773,0.0001139533,0.1674225,0.008956575,0.1208049,0.00817877,0.6916407],"study_design_scores_gemma":[0.00003003219,0.00009254484,0.0005301626,0.00002815726,0.00002257252,0.00003900155,0.00001272402,0.9472793,0.00325607,0.04681971,0.001868287,0.00002136134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008159295,0.0005479281,0.9863963,0.00014622,0.00008033995,0.00003905466,0.00002489114,0.0008980824,0.003707911],"genre_scores_gemma":[0.4762866,0.0003975271,0.5095438,0.0002551053,0.0002401117,0.0002513287,0.0001954651,0.0006863787,0.01214364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006208108,"threshold_uncertainty_score":0.03283203,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}