{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":82,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":82,"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":"f944c05b5d1a","filters":{"venue":"International Joint Conference on Artificial Intelligence"}},"results":[{"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":"W2573425638","doi":"","title":"Distraction-based neural networks for modeling documents","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Topic Modeling","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":"York University","funders":"","keywords":"Automatic summarization; Computer science; Distraction; GRASP; Artificial neural network; Representation (politics); Artificial intelligence; Natural language processing; Traverse; Information retrieval; Machine learning; Programming language","authors":[{"name":"Chen Qian","is_ca":false},{"name":"Xiaodan Zhu","is_ca":true},{"name":"Zhen-Hua Ling","is_ca":false},{"name":"Si Wei","is_ca":false},{"name":"Hui Jiang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1398677867114096,"gpt":0.3404630588579287,"spread":0.2005952721465191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001133525,0.00100516,0.0007083417,0.001234916,0.0003610871,0.0008548521,0.001705898,0.001367037,0.001706061],"category_scores_gemma":[0.004334306,0.0003835753,0.0007674712,0.001625191,0.0004856942,0.001800916,0.0006992488,0.002224764,0.0005663378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001507738,"about_ca_system_score_gemma":0.000487492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007560385,"about_ca_topic_score_gemma":0.009002744,"domain_scores_codex":[0.9996195,0.0001348726,0.00002425653,0.0001268029,0.00005370995,0.00004071113],"domain_scores_gemma":[0.9988685,0.0007273203,0.0001229742,0.0000871127,0.0001557915,0.00003836815],"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":[0.0001788505,0.00010644,0.001660289,0.0001047629,0.00006743332,0.0000801744,0.0001418137,0.9070443,0.002310797,0.01058839,0.002160335,0.07555647],"study_design_scores_gemma":[0.000004975131,0.00001118073,0.0001016277,0.000004022051,0.00000500141,0.00000647212,0.000004694368,0.995483,0.0002547498,0.003826982,0.0002940553,0.000003250125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1277213,0.003166227,0.8617611,0.00105975,0.0002323304,0.0001394108,0.0008907966,0.00189478,0.003134407],"genre_scores_gemma":[0.862803,0.001249006,0.1254451,0.0003461314,0.0002266591,0.0004108938,0.001842603,0.0001128783,0.007563742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007560385,"threshold_uncertainty_score":0.01503271,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W105409620","doi":"","title":"Planning with loops","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","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 Toronto","funders":"","keywords":"Planner; Computer science; Strengths and weaknesses; Iterative method; Work (physics); Automated theorem proving; Theoretical computer science; Algorithm; Artificial intelligence; Engineering","authors":[{"name":"Hector J. Levesque","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1145712606826355,"gpt":0.3172324644868931,"spread":0.2026612038042576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002134976,0.0009947839,0.0005261264,0.0005113715,0.001392003,0.002229576,0.001452289,0.001050283,0.01421437],"category_scores_gemma":[0.009068165,0.000609598,0.001284542,0.000654681,0.003049986,0.003901896,0.003108391,0.001822432,0.001844003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024739,"about_ca_system_score_gemma":0.002235686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002732476,"about_ca_topic_score_gemma":0.003510878,"domain_scores_codex":[0.9978952,0.0007729509,0.0001256067,0.000481724,0.0004971203,0.0002272775],"domain_scores_gemma":[0.9961128,0.002420777,0.000219096,0.000789393,0.0003163472,0.0001416217],"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":[0.0002405306,0.00009448762,0.000782412,0.0004284656,0.00009699067,0.000301987,0.001221392,0.07915681,0.003428642,0.7964796,0.006287631,0.111481],"study_design_scores_gemma":[0.00006477032,0.0001154744,0.0001462418,0.000101874,0.00006884476,0.0001861725,0.0002609868,0.1081585,0.007199164,0.8116021,0.07205733,0.00003855037],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008564669,0.0003200861,0.9572228,0.000504147,0.00008683288,0.0001910319,0.0001589848,0.001810757,0.03114074],"genre_scores_gemma":[0.3406239,0.0007292545,0.6372344,0.0003582545,0.00008235579,0.0004211823,0.0005525838,0.0005757468,0.0194223],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01421437,"threshold_uncertainty_score":0.04755181,"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":"W119782953","doi":"10.5591/978-1-57735-516-8/ijcai11-099","title":"A uniform approach for generating proofs and strategies for both true and false QBF formulas","year":2011,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":60,"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":"Correctness; Mathematical proof; Computer science; Conjunctive normal form; Falsity; True quantified Boolean formula; Simple (philosophy); Solver; Algorithm; Representation (politics); Computation; Theoretical computer science; Mathematics; Programming language","authors":[{"name":"Alexandra Goultiaeva","is_ca":true},{"name":"Allen Van Gelder","is_ca":false},{"name":"Fahiem Bacchus","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.266406923665083,"gpt":0.3559615866280027,"spread":0.0895546629629197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008239971,0.001756355,0.001436275,0.002282563,0.0011636,0.004641372,0.005461903,0.002355279,0.01217015],"category_scores_gemma":[0.02876075,0.001958324,0.003882357,0.001914491,0.003242761,0.007576753,0.005703295,0.005563302,0.004961602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002018836,"about_ca_system_score_gemma":0.00341686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001157264,"about_ca_topic_score_gemma":0.001336597,"domain_scores_codex":[0.9887707,0.003540702,0.001407497,0.002367571,0.003222571,0.0006909581],"domain_scores_gemma":[0.984362,0.006581139,0.0006768356,0.005922968,0.002141317,0.0003156772],"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":[0.0001810873,0.000176743,0.0003515302,0.0006318731,0.0001237784,0.0002547452,0.0004731421,0.01287131,0.01110811,0.8194651,0.004279118,0.1500834],"study_design_scores_gemma":[0.0003829196,0.0002901903,0.0002606268,0.000373018,0.0003023076,0.001051589,0.0001557761,0.1628986,0.04756993,0.7146046,0.07196257,0.0001479564],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007654755,0.00006331105,0.996552,0.0001225875,0.00002772526,0.0001845505,0.00007888743,0.0008720887,0.001333357],"genre_scores_gemma":[0.03347864,0.0002279925,0.9623275,0.0002038228,0.00004584446,0.0005616678,0.0003327531,0.000470235,0.002351508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01217015,"threshold_uncertainty_score":0.04357767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W131525165","doi":"","title":"Dual lookups in pattern databases","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":54,"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; Lookup table; Protein Data Bank (RCSB PDB); Heuristic; Table (database); Cube (algebra); Extension (predicate logic); Dual (grammatical number); Tree (set theory); Database; Combinatorics; Programming language; Mathematics; Artificial intelligence","authors":[{"name":"Ariel Felner","is_ca":false},{"name":"Uzi Zahavi","is_ca":false},{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Robert C. Holte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1547039293367191,"gpt":0.3363811980348475,"spread":0.1816772686981283,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003517209,0.0006234614,0.001284581,0.002246968,0.001600647,0.005067876,0.003886145,0.001980409,0.00907429],"category_scores_gemma":[0.0173671,0.00164839,0.001341638,0.0042668,0.002503813,0.01235741,0.006520682,0.002561436,0.003003015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001415504,"about_ca_system_score_gemma":0.001836397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00252023,"about_ca_topic_score_gemma":0.002547273,"domain_scores_codex":[0.9950247,0.001168862,0.0006757648,0.0009823294,0.001669505,0.0004787779],"domain_scores_gemma":[0.9890205,0.003616489,0.0006273764,0.005537575,0.0009206475,0.0002773522],"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":[0.0006752257,0.0002140125,0.003476319,0.0005488742,0.00008178623,0.0007046603,0.0006619213,0.03078311,0.007126013,0.4153305,0.01580832,0.5245894],"study_design_scores_gemma":[0.0001559992,0.0002148113,0.0004590205,0.0001877893,0.0001097598,0.00146982,0.0004843506,0.2479379,0.02604374,0.6537391,0.06908078,0.0001170464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02823076,0.001286151,0.9528341,0.0007298267,0.0002099331,0.0001821451,0.0007771069,0.004490729,0.01125908],"genre_scores_gemma":[0.2698196,0.0006833739,0.71876,0.0005069316,0.00007140375,0.0002940119,0.001500733,0.0007785303,0.007585418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00907429,"threshold_uncertainty_score":0.03035659,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2574227367","doi":"","title":"Monte Carlo tree search in continuous action spaces with execution uncertainty","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":49,"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; Set (abstract data type); Fidelity; Machine learning; Tree (set theory); Action (physics); Artificial intelligence; Monte Carlo method; Kernel (algebra); Domain (mathematical analysis); Mathematics","authors":[{"name":"Timothy J. Yee","is_ca":true},{"name":"Viliam Lisý","is_ca":true},{"name":"Michael Bowling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1274409482000282,"gpt":0.3388143823562566,"spread":0.2113734341562284,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002047504,0.0006157301,0.001212315,0.0006734459,0.0004630728,0.0007626329,0.001062305,0.001342961,0.001768956],"category_scores_gemma":[0.008587288,0.0005909461,0.0006600482,0.0007883928,0.001436723,0.001182651,0.0009241236,0.001259584,0.0002150587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001271818,"about_ca_system_score_gemma":0.00188491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426092,"about_ca_topic_score_gemma":0.01163916,"domain_scores_codex":[0.999284,0.0003506508,0.00003504619,0.0001063561,0.0001438719,0.0000800983],"domain_scores_gemma":[0.9935209,0.005526104,0.000327226,0.0001738406,0.0002714572,0.0001804704],"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":[0.00003303061,0.00001123025,0.0003225083,0.00001576001,0.00001027455,0.00002048849,0.00001592606,0.9880079,0.0001163809,0.006791281,0.0001355556,0.004519665],"study_design_scores_gemma":[0.000004039902,0.000004602573,0.00003460298,0.000001721847,0.000001261776,0.000002422461,0.000001679347,0.9972844,0.00003068319,0.00258071,0.00005271592,0.000001181228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06569245,0.0005481931,0.9305221,0.0003357963,0.00003200927,0.00005248046,0.00007413647,0.0003441707,0.002398659],"genre_scores_gemma":[0.8088353,0.0002670766,0.1883175,0.0001387605,0.00003413007,0.000172885,0.000194037,0.0001072763,0.001932993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01426092,"threshold_uncertainty_score":0.02835584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2912387951","doi":"","title":"Solving checkers","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":46,"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; Value (mathematics); Simple (philosophy); Space (punctuation); Artificial intelligence; Theoretical computer science; Machine learning","authors":[{"name":"Jonathan Schaeffer","is_ca":true},{"name":"Yngvi Björnsson","is_ca":true},{"name":"Neil Burch","is_ca":true},{"name":"Akihiro Kishimoto","is_ca":true},{"name":"Miroslav Uller","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":0.1273764187861364,"gpt":0.3387486621145042,"spread":0.2113722433283677,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002039626,0.001262956,0.001033346,0.0008678359,0.00217069,0.003145921,0.001878646,0.002410403,0.01469696],"category_scores_gemma":[0.01311406,0.0007517028,0.00163306,0.0005925719,0.003774994,0.004153038,0.004323009,0.002632515,0.001392854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001752618,"about_ca_system_score_gemma":0.003029354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004931485,"about_ca_topic_score_gemma":0.006482091,"domain_scores_codex":[0.9972319,0.0008606128,0.0001544012,0.0007271834,0.0005512605,0.0004746935],"domain_scores_gemma":[0.9945059,0.003629749,0.0004284079,0.000576747,0.0004613694,0.000397684],"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":[0.0005353207,0.0004099188,0.006337451,0.0007264125,0.0002184608,0.000568452,0.001403053,0.1095028,0.007732676,0.7197859,0.02252119,0.1302584],"study_design_scores_gemma":[0.0002802203,0.000339688,0.001570516,0.0002050552,0.0001244388,0.0005982683,0.001234908,0.3179394,0.008702919,0.6124572,0.05645482,0.00009262468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2450215,0.0008656668,0.6165301,0.005433858,0.0006741743,0.0007432124,0.0007524021,0.002480557,0.1274986],"genre_scores_gemma":[0.6376697,0.0004846628,0.3320861,0.0007236329,0.0001201753,0.0002520659,0.0008992964,0.0003922255,0.02737206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01469696,"threshold_uncertainty_score":0.0491662,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2574075402","doi":"","title":"Predicting confusion in information visualization from eye tracking and interaction data","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":43,"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":"Confusion; Visualization; Computer science; Focus (optics); Human–computer interaction; Information visualization; Data visualization; Eye tracking; Random forest; User satisfaction; Tracking (education); Data science; Artificial intelligence; Psychology","authors":[{"name":"Sébastien Lallé","is_ca":true},{"name":"Cristina Conati","is_ca":true},{"name":"Giuseppe Carenini","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1565846372061156,"gpt":0.3893210314244875,"spread":0.232736394218372,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003180642,0.00133012,0.001015359,0.004063077,0.000429434,0.001690119,0.0003534896,0.001038063,0.0008499969],"category_scores_gemma":[0.03403416,0.0003136936,0.000788651,0.00153187,0.0003262059,0.001827335,0.0009810546,0.0008341653,0.0004475709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005108629,"about_ca_system_score_gemma":0.0004410767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0040184,"about_ca_topic_score_gemma":0.005431735,"domain_scores_codex":[0.9974842,0.0008195839,0.000302357,0.0004526885,0.0007104694,0.0002305818],"domain_scores_gemma":[0.9620067,0.02918736,0.003913956,0.001136758,0.002918068,0.000837199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004460009,0.0009411027,0.5924256,0.001069548,0.0005287295,0.0006128494,0.005152845,0.01227579,0.1009189,0.0003102757,0.002406462,0.2788979],"study_design_scores_gemma":[0.00005981354,0.002429267,0.7795416,0.0001591603,0.0002321918,0.00103543,0.0014939,0.1854356,0.02661286,0.001387843,0.001366265,0.0002460275],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9691671,0.0005995333,0.0276417,0.00008125525,0.00003725849,0.0001262981,0.0008240449,0.000777533,0.0007452929],"genre_scores_gemma":[0.9810939,0.0001906456,0.0174657,0.00002727872,0.0000193848,0.00007867237,0.0007912319,0.00004685482,0.0002862281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004063077,"threshold_uncertainty_score":0.01682103,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2248999929","doi":"","title":"A reputation management approach for resource constrained trustee agents","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Access Control and Trust","field":"Social Sciences","cited_by":40,"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":"Reputation; Reputation management; Computer science; Constraint (computer-aided design); Control (management); Work (physics); Trust management (information system); Resource (disambiguation); Computer security; Resource management (computing); Risk analysis (engineering); Multi-agent system; Business; Distributed computing; Law; Engineering; Computer network","authors":[{"name":"Han Yu","is_ca":false},{"name":"Chunyan Miao","is_ca":false},{"name":"Bo An","is_ca":false},{"name":"Cyril Leung","is_ca":true},{"name":"Victor Lesser","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1450471926422882,"gpt":0.3607568540229891,"spread":0.2157096613807009,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024081,0.0006701496,0.0008853877,0.0007062161,0.001354829,0.002334939,0.002363881,0.00114175,0.002647547],"category_scores_gemma":[0.007161432,0.0004221142,0.0007149465,0.0007582714,0.001098555,0.002743194,0.001972554,0.00148538,0.0005286945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820311,"about_ca_system_score_gemma":0.00194728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005013965,"about_ca_topic_score_gemma":0.005643073,"domain_scores_codex":[0.9981932,0.0006799329,0.00009992655,0.0003328129,0.0004813773,0.0002128083],"domain_scores_gemma":[0.9967592,0.001194899,0.0005138684,0.0004081615,0.0007725382,0.0003511795],"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":[0.0001914302,0.0001783855,0.001696279,0.0001870517,0.0001483202,0.0006047316,0.0007319839,0.6210493,0.00693277,0.2589914,0.006422624,0.1028657],"study_design_scores_gemma":[0.00001785055,0.00004737682,0.0001445666,0.000009309639,0.00003069233,0.00009201367,0.00005725842,0.9747422,0.0005960834,0.0214307,0.002808407,0.00002351262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01339878,0.000380829,0.9775622,0.000699768,0.00007299069,0.0000936858,0.00003076247,0.0002182739,0.007542691],"genre_scores_gemma":[0.8254225,0.0003764073,0.1667655,0.0001294263,0.0001281243,0.0001721687,0.00005105173,0.00005504343,0.006899725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005013965,"threshold_uncertainty_score":0.01320732,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W25763866","doi":"10.1371/journal.pone.0115657","title":"Regret-based utility elicitation in constraint-based decision problems","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; University of Toronto","funders":"","keywords":"Regret; Preference elicitation; Mathematical optimization; Computer science; Preference; Heuristic; Constraint (computer-aided design); Minimax; Binary decision diagram; Optimization problem; Product (mathematics); Artificial intelligence; Machine learning; Mathematics; Theoretical computer science","authors":[{"name":"Craig Boutilier","is_ca":true},{"name":"Relu Patrascu","is_ca":true},{"name":"Pascal Poupart","is_ca":false},{"name":"Dale Schuurmans","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1005012886310663,"gpt":0.3222350740173838,"spread":0.2217337853863176,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01712432,0.001383428,0.001701139,0.001358456,0.0005632221,0.001943141,0.001596002,0.001652415,0.01199842],"category_scores_gemma":[0.06537531,0.0004751235,0.0008974022,0.001730492,0.0009777038,0.001379267,0.001871984,0.003232054,0.0009483041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002289126,"about_ca_system_score_gemma":0.003805584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003224276,"about_ca_topic_score_gemma":0.006984306,"domain_scores_codex":[0.9710868,0.02517833,0.0005386157,0.0007490534,0.002024106,0.0004230483],"domain_scores_gemma":[0.9340677,0.05977726,0.001790434,0.001529049,0.002051914,0.0007836926],"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":[0.004495551,0.001169145,0.00261804,0.002058274,0.0005890458,0.0003279661,0.0006468215,0.462113,0.001534367,0.08143871,0.04766097,0.3953483],"study_design_scores_gemma":[0.001041906,0.0005854418,0.0009738561,0.0002940492,0.0001024151,0.00009555953,0.0001619065,0.8637533,0.001063857,0.1249009,0.006971897,0.00005486329],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03715936,0.002360598,0.9099602,0.006935734,0.0002443855,0.00352884,0.002265281,0.0006631856,0.03688249],"genre_scores_gemma":[0.6162268,0.001398932,0.371178,0.001230795,0.0001655484,0.003163553,0.002048569,0.0001497977,0.004438032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01712432,"threshold_uncertainty_score":0.09056318,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2294581381","doi":"10.5591/978-1-57735-516-8/ijcai11-027","title":"Social distance games","year":2011,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Closeness; Stability (learning theory); Computer science; Context (archaeology); Mathematical economics; Transferable utility; Perspective (graphical); Social Welfare; Measure (data warehouse); Microeconomics; Game theory; Economics; Mathematics; Artificial intelligence; Machine learning; Data mining","authors":[{"name":"Simina Brânzei","is_ca":true},{"name":"Kate Larson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2965024916787837,"gpt":0.3082783851999703,"spread":0.01177589352118652,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002474846,0.00125302,0.001256375,0.001126865,0.001834616,0.00369363,0.002153578,0.002275083,0.007725157],"category_scores_gemma":[0.008940456,0.0003810944,0.0008845949,0.001336274,0.003839736,0.005852429,0.003539875,0.002479,0.001149786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002605861,"about_ca_system_score_gemma":0.001245786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001812772,"about_ca_topic_score_gemma":0.00129206,"domain_scores_codex":[0.9953451,0.002192555,0.0002681681,0.0007619738,0.001093816,0.0003384119],"domain_scores_gemma":[0.9955929,0.002708562,0.000351044,0.0004125751,0.0005165776,0.0004182067],"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":[0.000009404615,0.00001594071,0.00009420633,0.00003256575,0.00001202311,0.00003761111,0.000121211,0.004987763,0.0001454725,0.9888194,0.0008226816,0.004901705],"study_design_scores_gemma":[0.00001890666,0.00002361767,0.00006551992,0.00001907146,0.000009250842,0.0000719855,0.0001128429,0.02570625,0.0001386899,0.9593342,0.01449,0.000009722612],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03780582,0.00137783,0.8485374,0.004301146,0.0003148768,0.0004858626,0.0004954447,0.0001370095,0.1065445],"genre_scores_gemma":[0.7830514,0.001874018,0.1733514,0.001281489,0.0003403535,0.0009327003,0.000604367,0.00009862106,0.03846566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007725157,"threshold_uncertainty_score":0.0258432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W26274488","doi":"10.1111/vcp.12275","title":"Lambda depth-first proof number search and its application to go","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":34,"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":"Depth-first search; Computer science; Search algorithm; Proof of concept; Search tree; Iterative deepening depth-first search; Theoretical computer science; Space (punctuation); Beam stack search; Combinatorial search; Breadth-first search; Algorithm; Search problem; Mathematics; Beam search; Best-first search","authors":[{"name":"Kazuki Yoshizoe","is_ca":false},{"name":"Akihiro Kishimoto","is_ca":false},{"name":"Martin Müller","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1110297032690316,"gpt":0.3652190956948882,"spread":0.2541893924258566,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003898789,0.001667874,0.001736039,0.003909977,0.001247967,0.002470294,0.002721777,0.001753546,0.02403836],"category_scores_gemma":[0.01833704,0.0008306915,0.00185762,0.003438349,0.002104335,0.003614897,0.003498893,0.002289125,0.007867255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001244562,"about_ca_system_score_gemma":0.003125782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003752767,"about_ca_topic_score_gemma":0.004291233,"domain_scores_codex":[0.9975846,0.0006870075,0.0002095008,0.000404768,0.0007941336,0.0003199801],"domain_scores_gemma":[0.992184,0.005673436,0.0003416766,0.0009183016,0.0007238265,0.0001587587],"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":[0.0007845287,0.0002451304,0.0007295567,0.001445145,0.0001446429,0.0005154859,0.0003025046,0.03860772,0.006769,0.200953,0.041249,0.7082543],"study_design_scores_gemma":[0.0003703645,0.0003549411,0.0003711651,0.0002410325,0.0001318673,0.0007189234,0.00009809407,0.315585,0.01063176,0.6144727,0.05688394,0.0001401424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004541024,0.0008334263,0.9707227,0.0008129264,0.0003035741,0.0003472091,0.0003299446,0.009265513,0.01284364],"genre_scores_gemma":[0.07973493,0.0007445386,0.9060145,0.0006168999,0.0002103968,0.0005547965,0.0005151885,0.001266401,0.01034236],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02403836,"threshold_uncertainty_score":0.08041638,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2914998942","doi":"","title":"Goal change","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; York University","funders":"","keywords":"Situation calculus; Action (physics); Belief revision; Computer science; Calculus (dental); Contraction (grammar); Mathematical economics; Artificial intelligence; Epistemology; Mathematics; Philosophy; Linguistics; Physics","authors":[{"name":"Steven Shapiro","is_ca":false},{"name":"Yves Lespérance","is_ca":true},{"name":"Hector J. Levesque","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1718701374104723,"gpt":0.3329877338590056,"spread":0.1611175964485333,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005791866,0.001101715,0.000576684,0.001088412,0.001707515,0.003066975,0.002439798,0.002362263,0.01438629],"category_scores_gemma":[0.02112345,0.0004638599,0.001966874,0.0008034904,0.003624359,0.006284754,0.005918403,0.003411023,0.003378595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00203285,"about_ca_system_score_gemma":0.002023701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001805978,"about_ca_topic_score_gemma":0.001578922,"domain_scores_codex":[0.9936935,0.002185727,0.0003179836,0.001722883,0.001594638,0.000485234],"domain_scores_gemma":[0.9904656,0.00323426,0.0007361978,0.003108155,0.001947623,0.000508156],"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":[0.00009459555,0.0001057224,0.001138458,0.0002359814,0.00005661809,0.0001276379,0.002753722,0.001879479,0.001967993,0.892814,0.006284891,0.09254097],"study_design_scores_gemma":[0.00007847019,0.0001684437,0.001191967,0.0001808297,0.00009502485,0.000344182,0.001313643,0.01131277,0.00512962,0.8294526,0.1506826,0.00004970299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01471058,0.0006537697,0.7868192,0.003994279,0.0004673866,0.000638132,0.000263287,0.001647925,0.1908055],"genre_scores_gemma":[0.5748085,0.0006311265,0.355837,0.002449536,0.0001858669,0.001195208,0.0006227058,0.0009046563,0.06336544],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01438629,"threshold_uncertainty_score":0.04812694,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2397939602","doi":"","title":"Controlling the hypothesis space in probabilistic plan recognition","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; Université de Sherbrooke","funders":"","keywords":"Plan (archaeology); Computer science; Probabilistic logic; Heuristic; Sequence (biology); Artificial intelligence; Machine learning; Space (punctuation); Execution time; Combinatorial explosion; Theoretical computer science; Distributed computing; Mathematics","authors":[{"name":"Froduald Kabanza","is_ca":true},{"name":"Julien Filion","is_ca":true},{"name":"Abder Rezak Benaskeur","is_ca":true},{"name":"Hengameh Irandoust","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1571855741428569,"gpt":0.2803320152353006,"spread":0.1231464410924437,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006929553,0.001001499,0.001852153,0.001633996,0.0008926459,0.002538139,0.003318579,0.001639238,0.003028707],"category_scores_gemma":[0.03405099,0.00129153,0.001165158,0.001392332,0.003543834,0.005941638,0.00279287,0.002838169,0.000471173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002094868,"about_ca_system_score_gemma":0.002683755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007419684,"about_ca_topic_score_gemma":0.007155793,"domain_scores_codex":[0.99571,0.001965496,0.0002448209,0.0008355378,0.0008636143,0.0003803976],"domain_scores_gemma":[0.9626853,0.03238745,0.001576538,0.001871707,0.0009534811,0.0005254301],"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":[0.0004789125,0.0001699568,0.002642768,0.0001433501,0.00009830185,0.0001137153,0.0002711652,0.8203815,0.002287917,0.04043168,0.001077116,0.1319035],"study_design_scores_gemma":[0.0000162873,0.00003226772,0.0001217566,0.000008280209,0.000008990465,0.00001608523,0.00001794389,0.9725024,0.0007738268,0.02632942,0.0001630621,0.000009786477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03721057,0.0002096328,0.9600124,0.0002925852,0.00001754588,0.00007115493,0.00006885019,0.001097471,0.001019625],"genre_scores_gemma":[0.62157,0.0001979971,0.3757593,0.0002953423,0.00004904317,0.0003231063,0.0003209907,0.0002490458,0.001235119],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007419684,"threshold_uncertainty_score":0.03664738,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2889093876","doi":"","title":"Predicting Glycemia in Type 1 Diabetes Patients: Experiments with XG-Boost.","year":2018,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Diabetes Management and Research","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Diabetes mellitus; Type 2 diabetes; Medicine; Computer science; Intensive care medicine; Endocrinology","authors":[{"name":"Cooper Midroni","is_ca":false},{"name":"Peter Leimbigler","is_ca":false},{"name":"Gaurav Baruah","is_ca":true},{"name":"Maheedhar Kolla","is_ca":true},{"name":"Alfred J. Whitehead","is_ca":false},{"name":"Yan Fossat","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09958495237028905,"gpt":0.3561212640888013,"spread":0.2565363117185122,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001889183,0.0009408708,0.001028489,0.0002411121,0.0002559418,0.0003869413,0.000813002,0.0009232488,0.001473841],"category_scores_gemma":[0.003148639,0.0001783276,0.0005301966,0.0003830522,0.0002073279,0.0004226038,0.0003741768,0.001398291,0.0004417296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003159555,"about_ca_system_score_gemma":0.0006627928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00971648,"about_ca_topic_score_gemma":0.006382842,"domain_scores_codex":[0.9997318,0.0001225638,0.00001607021,0.00005063669,0.00003375158,0.00004513541],"domain_scores_gemma":[0.9984964,0.001005079,0.00005277178,0.0001404065,0.0001226054,0.0001828114],"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":[0.03770965,0.01528094,0.1095829,0.000921687,0.001460119,0.0008583848,0.0004004894,0.3278529,0.008626021,0.0008825583,0.05176399,0.4446603],"study_design_scores_gemma":[0.001838131,0.005409297,0.04175583,0.00009675125,0.0004612791,0.0002290072,0.0002408874,0.9378715,0.006474538,0.002195881,0.003369325,0.00005753599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802581,0.003125754,0.00770075,0.00170768,0.0006734096,0.0001010062,0.00195765,0.001596886,0.002878756],"genre_scores_gemma":[0.9854902,0.0005616361,0.009666434,0.0003453698,0.0000896854,0.0000390518,0.002331587,0.00003126973,0.001444798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00971648,"threshold_uncertainty_score":0.01931983,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W70833783","doi":"","title":"Central clustering of categorical data with automated feature weighting","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Categorical variable; Cluster analysis; Computer science; Weighting; Artificial intelligence; Data mining; Kernel (algebra); Pattern recognition (psychology); Feature (linguistics); Clustering high-dimensional data; Machine learning; Mathematics","authors":[{"name":"Lifei Chen","is_ca":false},{"name":"Shengrui Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1255337164547048,"gpt":0.3539444780504479,"spread":0.2284107615957432,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005235359,0.0006904207,0.001746107,0.002987151,0.001239921,0.001645401,0.002603458,0.001139299,0.0007269349],"category_scores_gemma":[0.01480134,0.0006166576,0.001298856,0.0027858,0.001401549,0.002561636,0.002883365,0.001545617,0.0007105593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009766532,"about_ca_system_score_gemma":0.001710219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002543875,"about_ca_topic_score_gemma":0.002387199,"domain_scores_codex":[0.9958813,0.001312742,0.0003728524,0.001075521,0.001102859,0.0002546083],"domain_scores_gemma":[0.9928244,0.002048777,0.0007751234,0.001552217,0.002585944,0.0002136019],"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":[0.0004938879,0.000246025,0.007271534,0.0002258632,0.0002268867,0.0001314828,0.0008277399,0.1188065,0.02328213,0.03235281,0.003906422,0.8122287],"study_design_scores_gemma":[0.00003351564,0.00008842564,0.001820094,0.00002206607,0.00003572747,0.0001751307,0.0001389956,0.9422653,0.01262578,0.04020299,0.002518752,0.00007325795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008407294,0.00007873472,0.9909549,0.00002809848,0.00001438019,0.00003105683,0.00002185071,0.0003554534,0.0001082553],"genre_scores_gemma":[0.1754855,0.00009942499,0.8231546,0.00004742665,0.00003783325,0.0001473232,0.0003146783,0.0001507962,0.0005623089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005235359,"threshold_uncertainty_score":0.02768755,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2403069539","doi":"","title":"Learning finite Beta-Liouville mixture models via variational bayes for proportional data clustering","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Cluster analysis; Mixture model; Expectation–maximization algorithm; Computer science; Inference; Bayesian inference; Artificial intelligence; Data point; Algorithm; Pattern recognition (psychology); Mathematics; Bayesian probability; Statistics","authors":[{"name":"Wentao Fan","is_ca":true},{"name":"Nizar Bouguila","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1644953914173749,"gpt":0.3381483742376632,"spread":0.1736529828202883,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008426469,0.001289853,0.002489285,0.002238336,0.001171606,0.002724419,0.004237605,0.00257254,0.002509531],"category_scores_gemma":[0.02213115,0.001705209,0.002444451,0.001959756,0.002513548,0.003417994,0.003878665,0.004011756,0.000858694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002498228,"about_ca_system_score_gemma":0.002234473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006857791,"about_ca_topic_score_gemma":0.007109369,"domain_scores_codex":[0.9954412,0.002466454,0.0001846173,0.0008200782,0.0008915119,0.000196032],"domain_scores_gemma":[0.9926052,0.005776136,0.0003444117,0.0004889509,0.0006018275,0.0001833686],"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":[0.0001032541,0.00008228776,0.001028686,0.0001732899,0.000143952,0.00008624653,0.0003694107,0.7033736,0.001971447,0.2033792,0.00194681,0.08734181],"study_design_scores_gemma":[0.000004866474,0.000006417916,0.00005144731,0.000008580246,0.000005611061,0.00001591915,0.000008669192,0.9581406,0.0002183315,0.04102728,0.0005023747,0.000009922031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009377686,0.00008728806,0.9986205,0.00005458207,0.000007035012,0.00001534346,0.00001441532,0.0000702455,0.0001928332],"genre_scores_gemma":[0.1189434,0.0005271435,0.8761837,0.0002381021,0.00009145034,0.0004541685,0.0004489845,0.0002851615,0.002827874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008426469,"threshold_uncertainty_score":0.04456395,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W49807964","doi":"","title":"A simple feature selection method for text classification","year":2001,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Feature selection; Computer science; Information gain; Feature (linguistics); Simple (philosophy); Set (abstract data type); Artificial intelligence; Selection (genetic algorithm); Pattern recognition (psychology); Data mining; Feature extraction; Machine learning","authors":[{"name":"Pascal Soucy","is_ca":true},{"name":"Guy W. Mineau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1510900227271396,"gpt":0.3835078255874474,"spread":0.2324178028603078,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002278403,0.001651373,0.002019338,0.003022626,0.0009101505,0.00107601,0.001194247,0.001332577,0.004820641],"category_scores_gemma":[0.006156411,0.0003193792,0.001582974,0.003925041,0.0005095998,0.001469815,0.0007955912,0.0009734111,0.003160294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004149337,"about_ca_system_score_gemma":0.0007573023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001470541,"about_ca_topic_score_gemma":0.001394637,"domain_scores_codex":[0.9971007,0.000722519,0.0002365316,0.0005252296,0.001292128,0.0001229094],"domain_scores_gemma":[0.9979283,0.001051132,0.0001157213,0.0002287362,0.0006250978,0.00005101961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002489852,0.0001660993,0.001129406,0.0002850081,0.000159847,0.0001447241,0.00007858877,0.009971893,0.0203154,0.002003688,0.0086727,0.9568236],"study_design_scores_gemma":[0.0005533823,0.001512734,0.01445585,0.0001822656,0.0004831048,0.002662949,0.0001805051,0.7893302,0.08213478,0.02697795,0.08112256,0.0004037596],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008176571,0.0006047228,0.9860548,0.0001626072,0.0002029307,0.000689494,0.0006146905,0.002649612,0.0008446968],"genre_scores_gemma":[0.07247215,0.0004311839,0.9202034,0.0001619129,0.0002463771,0.00141054,0.001618347,0.0001887184,0.00326735],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004820641,"threshold_uncertainty_score":0.01612669,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1552684655","doi":"","title":"Using linear programming for Bayesian exploration in Markov decision processes","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Markov decision process; Computer science; Bellman equation; Reinforcement learning; Mathematical optimization; Markov process; Linear programming; Machine learning; Representation (politics); Artificial intelligence; Markov chain; Key (lock); Bayesian probability; Partially observable Markov decision process; Markov model; Algorithm; Mathematics","authors":[{"name":"Pablo Samuel Castro","is_ca":true},{"name":"Doina Precup","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2143689443380605,"gpt":0.3942220206217933,"spread":0.1798530762837328,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005429449,0.001746528,0.002431212,0.001169428,0.0007312061,0.002001735,0.001928613,0.002100803,0.00510543],"category_scores_gemma":[0.01922418,0.001230014,0.001271504,0.001840999,0.002730521,0.002883603,0.002424606,0.00387709,0.0007216192],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002958881,"about_ca_system_score_gemma":0.002516607,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008297607,"about_ca_topic_score_gemma":0.008343421,"domain_scores_codex":[0.997115,0.001929027,0.00007797917,0.0002839121,0.0003771482,0.0002169226],"domain_scores_gemma":[0.9859586,0.01283591,0.0005173181,0.0001920376,0.0003100446,0.0001860604],"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":[0.00005443228,0.00006057691,0.0003629464,0.0001034416,0.00004078874,0.00004671535,0.0001003188,0.8628524,0.000159665,0.1183311,0.0007718895,0.01711575],"study_design_scores_gemma":[0.00001130209,0.00001104683,0.00002480928,0.00001110201,0.000003652657,0.000004173749,0.000005993588,0.9287184,0.0000465999,0.07089499,0.0002624415,0.00000545611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003499975,0.0004855718,0.9937435,0.0004606768,0.00001677441,0.00003040711,0.00003503135,0.0001307525,0.001597274],"genre_scores_gemma":[0.457115,0.002044464,0.5301758,0.0006198586,0.0002652164,0.001176781,0.0003608175,0.00032834,0.007913793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008297607,"threshold_uncertainty_score":0.028714,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W143659154","doi":"","title":"Evaluating a decision-theoretic approach to Tailored example selection","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":24,"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":"Computer science; Selection (genetic algorithm); Probabilistic logic; Mechanism (biology); Process (computing); Artificial intelligence; Machine learning; Management science; Engineering","authors":[{"name":"Kasia Müldner","is_ca":true},{"name":"Cristina Conati","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2154223549669306,"gpt":0.3802068011370155,"spread":0.1647844461700849,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02637238,0.001444019,0.001011414,0.001849827,0.0008377495,0.003542714,0.003119801,0.002468579,0.006932339],"category_scores_gemma":[0.1011688,0.0005372032,0.001049676,0.0009757144,0.004299127,0.003811809,0.002949759,0.002174809,0.0004681056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00503259,"about_ca_system_score_gemma":0.003895387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003134411,"about_ca_topic_score_gemma":0.002452805,"domain_scores_codex":[0.9647982,0.02436958,0.0008386073,0.001850826,0.007308459,0.0008343104],"domain_scores_gemma":[0.8963098,0.08754286,0.003576754,0.005027901,0.005849039,0.001693769],"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":[0.001328942,0.001208213,0.004584776,0.0005090533,0.0002208663,0.0001084392,0.000720532,0.4551905,0.003285738,0.4268052,0.001542603,0.1044951],"study_design_scores_gemma":[0.0001821876,0.0006653498,0.0007418917,0.00006006238,0.0000679916,0.00004675549,0.00009579643,0.8951968,0.002149918,0.09894744,0.001800295,0.00004560494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1155594,0.0005562619,0.8606196,0.001562238,0.00007251608,0.000727744,0.0001237544,0.0003979809,0.02038047],"genre_scores_gemma":[0.7266972,0.0001713225,0.2708266,0.0002180518,0.00004554797,0.0004581967,0.0001108262,0.00005393185,0.001418332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02637238,"threshold_uncertainty_score":0.1394721,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W126088545","doi":"","title":"Iterated belief change: a transition system approach","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":24,"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":"Belief revision; Iterated function; Interpretation (philosophy); Computer science; Transition (genetics); Set (abstract data type); Operator (biology); Transition system; Artificial intelligence; Theoretical computer science; Mathematics; Programming language","authors":[{"name":"Aaron Hunter","is_ca":true},{"name":"James P. Delgrande","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1373754609918432,"gpt":0.3023690247565112,"spread":0.1649935637646681,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002839827,0.000705613,0.0007525801,0.001445869,0.0009541116,0.00262227,0.002333848,0.001873218,0.006562362],"category_scores_gemma":[0.007862786,0.0005633112,0.002144105,0.0008783173,0.003425438,0.005321089,0.001992958,0.002873291,0.0006778377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001983847,"about_ca_system_score_gemma":0.001288953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004539431,"about_ca_topic_score_gemma":0.003065516,"domain_scores_codex":[0.998231,0.0006730027,0.0001150788,0.0003753552,0.0004331596,0.0001724339],"domain_scores_gemma":[0.9959313,0.002835429,0.0003033778,0.0003216745,0.000424378,0.0001838836],"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":[0.00005145916,0.00003678813,0.0006552326,0.00008256988,0.00008749735,0.0003513875,0.0006735473,0.06116478,0.00109534,0.9188983,0.0006962269,0.01620678],"study_design_scores_gemma":[0.00004055063,0.00005406181,0.000177959,0.00002865784,0.00006548069,0.000124393,0.00007758588,0.2632168,0.0009214421,0.7312317,0.004026596,0.00003475545],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006406759,0.0002484895,0.9851896,0.0006264342,0.00008022116,0.00006455191,0.00006043745,0.0003080619,0.007015359],"genre_scores_gemma":[0.61073,0.0005607928,0.3806014,0.000306573,0.000211586,0.0003256358,0.0001150847,0.0001255334,0.007023296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006562362,"threshold_uncertainty_score":0.02195328,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2464078358","doi":"","title":"Misrepresentation in district voting","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":24,"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":"Misrepresentation; Probabilistic logic; Voting; Representation (politics); Computer science; Accountability; Political science; Law; Artificial intelligence; Politics","authors":[{"name":"Yoram Bachrach","is_ca":false},{"name":"Omer Lev","is_ca":true},{"name":"Yoad Lewenberg","is_ca":false},{"name":"Yair Zick","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1865305051284422,"gpt":0.3116819358125312,"spread":0.125151430684089,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01512259,0.0003995651,0.001367821,0.001466889,0.002031037,0.004173529,0.002212024,0.001656356,0.007052637],"category_scores_gemma":[0.1088243,0.0005162253,0.0007266215,0.002208209,0.002779229,0.004774125,0.003948854,0.002258289,0.001044106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002656178,"about_ca_system_score_gemma":0.001363722,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001286941,"about_ca_topic_score_gemma":0.001504579,"domain_scores_codex":[0.969592,0.01984085,0.001198129,0.003045466,0.004116432,0.002207084],"domain_scores_gemma":[0.9183812,0.04614361,0.01157284,0.0174146,0.004757769,0.001729943],"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":[0.000979237,0.0001379089,0.03829186,0.0002234402,0.0001813401,0.0004226991,0.001688602,0.1172642,0.00172182,0.7091271,0.006339055,0.1236228],"study_design_scores_gemma":[0.0001268783,0.0003065026,0.0133092,0.00009631233,0.0001329251,0.0007499299,0.001121761,0.3069368,0.004707103,0.6594908,0.01292021,0.000101488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4107651,0.00100915,0.5383188,0.002543241,0.0002174773,0.0002680862,0.0004826256,0.0005000907,0.04589551],"genre_scores_gemma":[0.9791244,0.0001247696,0.01677104,0.0001412072,0.0000586663,0.00007631989,0.0001286563,0.00005106567,0.003523958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01512259,"threshold_uncertainty_score":0.07997686,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W96574868","doi":"","title":"Inferring complex agent motions from partial trajectory observations","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Computer science; Probabilistic logic; Trajectory; Path (computing); Inference; Heuristic; Graph; Artificial intelligence; Motion planning; Markov chain; Motion (physics); Machine learning; Algorithm; Theoretical computer science; Robot","authors":[{"name":"Finnegan Southey","is_ca":false},{"name":"Wesley Loh","is_ca":true},{"name":"Dana Wilkinson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3282678335686683,"gpt":0.355972819707533,"spread":0.02770498613886468,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000763177,0.0006888118,0.0006430096,0.001097258,0.0003961665,0.0008368296,0.0007518905,0.0007451323,0.0009066944],"category_scores_gemma":[0.005654177,0.0008034644,0.0006741327,0.0008339607,0.0007553168,0.001858598,0.001004729,0.001162785,0.0002128589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006855011,"about_ca_system_score_gemma":0.0009998388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01149577,"about_ca_topic_score_gemma":0.01587589,"domain_scores_codex":[0.9997166,0.00008384364,0.00001909605,0.00009433475,0.00006245919,0.00002371335],"domain_scores_gemma":[0.9974875,0.001680329,0.0003148043,0.0003295878,0.0001153594,0.00007246287],"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":[0.00007781372,0.00003530535,0.006976824,0.00006936073,0.00007283771,0.0001635125,0.0001472272,0.9153374,0.002389879,0.01834947,0.0006598805,0.05572039],"study_design_scores_gemma":[0.000004472556,0.000006047871,0.0008138437,0.000005404747,0.000008055078,0.00001621553,0.00001602847,0.9816428,0.0004662799,0.01670531,0.0003099958,0.000005603329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06319579,0.00009492964,0.9354039,0.0001332952,0.00001058299,0.00001987806,0.0002373183,0.0004591993,0.0004450479],"genre_scores_gemma":[0.7915857,0.000374783,0.2062794,0.00005010899,0.00002653797,0.00005193672,0.0006869886,0.00007799578,0.0008665322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01149577,"threshold_uncertainty_score":0.02285773,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2270172784","doi":"","title":"Reasoning about continuous uncertainty in the situation calculus","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":22,"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":"Formalism (music); Situation calculus; Computer science; Countable set; Action (physics); Theoretical computer science; Artificial intelligence; Calculus (dental); Algorithm; Mathematics; Discrete mathematics","authors":[{"name":"Vaishak Belle","is_ca":true},{"name":"Hector J. Levesque","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06629843824728475,"gpt":0.3025074020353032,"spread":0.2362089637880185,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009367282,0.0010118,0.001274001,0.002177464,0.002826064,0.007291828,0.002760335,0.002207631,0.00301453],"category_scores_gemma":[0.01574592,0.001095375,0.002583179,0.002382808,0.008093439,0.01295815,0.006083439,0.004715714,0.0004479489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004379177,"about_ca_system_score_gemma":0.002449073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007645269,"about_ca_topic_score_gemma":0.00573117,"domain_scores_codex":[0.9921296,0.003015321,0.0005626122,0.000974296,0.002547598,0.0007705207],"domain_scores_gemma":[0.9924775,0.005239763,0.0004761447,0.0007820656,0.0006428423,0.0003818673],"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":[0.00002540116,0.0000133466,0.0001918018,0.00003645262,0.00002565827,0.0001799373,0.0003947166,0.01192938,0.0002350599,0.9828076,0.0004730112,0.003687504],"study_design_scores_gemma":[0.00001410011,0.000007030227,0.0000466541,0.00001547952,0.00001830569,0.00005159078,0.00008638281,0.03443318,0.0001855711,0.9631776,0.001950088,0.00001413682],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02492563,0.001136762,0.9505125,0.003663056,0.0001280165,0.00007162202,0.0002919135,0.0004598471,0.01881077],"genre_scores_gemma":[0.7429689,0.001154382,0.2498262,0.0006362957,0.0003445935,0.0002303508,0.0003547462,0.00009994725,0.004384612],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009367282,"threshold_uncertainty_score":0.04953957,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W29265412","doi":"10.1111/1346-8138.14192","title":"Multiple agents moving target search","year":2003,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":22,"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); Search problem; Search algorithm; Bidirectional search; State (computer science); Multi-agent system; Incremental heuristic search; Artificial intelligence; Mathematical optimization; Beam search; Algorithm; Mathematics","authors":[{"name":"Mark Goldenberg","is_ca":true},{"name":"Alexander Kovarsky","is_ca":true},{"name":"Xiao‐Meng Wu","is_ca":true},{"name":"Jonathan Schaeffer","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1572151320510519,"gpt":0.3316735627996419,"spread":0.17445843074859,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001198233,0.0008865927,0.002193488,0.001744878,0.0008249599,0.001300549,0.001653608,0.002130336,0.007304829],"category_scores_gemma":[0.005664897,0.0003691143,0.000893235,0.001733996,0.0006485283,0.001730652,0.001230406,0.0009975143,0.001493111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008692515,"about_ca_system_score_gemma":0.001287844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004070888,"about_ca_topic_score_gemma":0.003716053,"domain_scores_codex":[0.9990126,0.0002954248,0.00005742609,0.0002112837,0.0002704303,0.0001528227],"domain_scores_gemma":[0.9982038,0.001133034,0.0001701918,0.0001363061,0.0002700594,0.00008673022],"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":[0.0006788651,0.0003391431,0.003269139,0.000439676,0.0002957641,0.0004996532,0.0001338238,0.4532461,0.00293186,0.02690717,0.01296193,0.4982969],"study_design_scores_gemma":[0.0001523797,0.0003072087,0.0006690947,0.00002906097,0.00009660264,0.0002585549,0.00006506794,0.9642668,0.00158936,0.02491831,0.007626558,0.0000210949],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1499636,0.0134718,0.7747725,0.005704804,0.0009491903,0.0006046825,0.0008445668,0.002417933,0.05127097],"genre_scores_gemma":[0.8311945,0.001993149,0.1411273,0.00101022,0.0003772586,0.0004901753,0.0008735921,0.0001861266,0.02274765],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007304829,"threshold_uncertainty_score":0.02443707,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W175472853","doi":"","title":"Coalitional bargaining with agent type uncertainty","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":21,"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":"Negotiation; Heuristic; Mathematical economics; Bayesian probability; Computer science; Bayesian game; Mathematical optimization; Type (biology); Game theory; Rational agent; Bargaining problem; Economics; Artificial intelligence; Mathematics; Repeated game","authors":[{"name":"Georgios Chalkiadakis","is_ca":true},{"name":"Craig Boutilier","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1938340754753523,"gpt":0.3125534067218377,"spread":0.1187193312464854,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007348159,0.0008421459,0.00206228,0.0009180449,0.001409038,0.003539399,0.003183145,0.00251501,0.003846141],"category_scores_gemma":[0.02539112,0.0007054748,0.001322989,0.002153773,0.002527279,0.004380543,0.002633717,0.002573163,0.0005849896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002917117,"about_ca_system_score_gemma":0.00197447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00378087,"about_ca_topic_score_gemma":0.003590034,"domain_scores_codex":[0.9951806,0.002437187,0.0001885246,0.0007051794,0.0009678765,0.0005206685],"domain_scores_gemma":[0.9857365,0.0110821,0.0009000037,0.001236209,0.0006129849,0.0004322857],"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":[0.0001440436,0.00005273694,0.0005819963,0.00003838557,0.00006565016,0.0001480985,0.0002661203,0.5981445,0.0004854291,0.3823184,0.0007851273,0.01696952],"study_design_scores_gemma":[0.00003324441,0.00001632558,0.00007559492,0.000007852158,0.00001254137,0.0000315795,0.00005377205,0.8045505,0.0002969462,0.1936976,0.001211362,0.00001256831],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03452216,0.0001616459,0.95676,0.0003213437,0.00004053827,0.00007901573,0.00007294906,0.0000829733,0.00795932],"genre_scores_gemma":[0.7326419,0.0003487906,0.2588271,0.0001230378,0.00007339494,0.0002256381,0.0001700677,0.00005826358,0.00753193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007348159,"threshold_uncertainty_score":0.03886127,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W184141742","doi":"","title":"Adaptive error-correcting output codes","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Multiclass classification; Class (philosophy); Generalization; Subspace topology; Binary number; Set (abstract data type); External Data Representation; Pattern recognition (psychology); Support vector machine; Mathematics","authors":[{"name":"Guoqiang Zhong","is_ca":true},{"name":"Mohamed Cheriet","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1593352712391407,"gpt":0.3339572235064923,"spread":0.1746219522673516,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003936147,0.001279384,0.001707203,0.001903631,0.001077261,0.001916237,0.003511959,0.001969363,0.003814512],"category_scores_gemma":[0.02805234,0.0003695042,0.0009713794,0.002766284,0.001723561,0.002730749,0.00259077,0.002811359,0.002791244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001230972,"about_ca_system_score_gemma":0.002855802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004006315,"about_ca_topic_score_gemma":0.003924671,"domain_scores_codex":[0.9935889,0.001337414,0.0004511235,0.001271687,0.002724233,0.0006266201],"domain_scores_gemma":[0.9828057,0.004318601,0.001309013,0.004646892,0.006623614,0.0002961703],"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":[0.0008686339,0.0002394596,0.004196491,0.0003083268,0.0001828776,0.0003729165,0.0002627953,0.2162832,0.0130897,0.09890281,0.01633731,0.6489555],"study_design_scores_gemma":[0.00005475741,0.0001203132,0.0007594611,0.00009177454,0.00005552246,0.0003537777,0.00004557133,0.9054305,0.0259646,0.05482499,0.01222896,0.00006981839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01002419,0.0005550517,0.9836116,0.0003207118,0.0002452858,0.0001107542,0.0002885744,0.001821413,0.003022398],"genre_scores_gemma":[0.4559054,0.0007347678,0.5282142,0.0006182811,0.0003504029,0.0005094293,0.001237965,0.0005170725,0.01191251],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004006315,"threshold_uncertainty_score":0.02081656,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2571941834","doi":"","title":"Generalized dictionary for multitask learning with boosting","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Multi-task learning; Artificial intelligence; Computer science; Boosting (machine learning); Machine learning; Gradient boosting; Feature learning; Task (project management)","authors":[{"name":"Boyu Wang","is_ca":true},{"name":"Joëlle Pineau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09497000881405422,"gpt":0.2954169608255133,"spread":0.2004469520114591,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002607003,0.00119137,0.002220941,0.0008245616,0.0005513844,0.001005096,0.002003214,0.001428578,0.002842654],"category_scores_gemma":[0.007019958,0.000554653,0.001160529,0.001283953,0.000901041,0.001616659,0.002372209,0.002045176,0.001684839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006810484,"about_ca_system_score_gemma":0.001197662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001683655,"about_ca_topic_score_gemma":0.001913794,"domain_scores_codex":[0.9984428,0.0007186025,0.00007923998,0.000265647,0.0003629606,0.0001307189],"domain_scores_gemma":[0.9979523,0.0008001297,0.0001521275,0.0004957275,0.0004670611,0.0001325152],"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":[0.0002987861,0.0002102345,0.001413983,0.0003868926,0.0002766381,0.0001752482,0.0002105578,0.5050573,0.006961646,0.08692726,0.01600488,0.3820766],"study_design_scores_gemma":[0.00001683834,0.00003753727,0.00007938368,0.00000795959,0.000009261772,0.00002731556,0.000008207118,0.9660088,0.00058,0.03147845,0.001738763,0.00000749798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00210219,0.0001753236,0.9968458,0.00008602989,0.00004344215,0.00003323733,0.00004036711,0.0002399667,0.0004336921],"genre_scores_gemma":[0.3192414,0.000712058,0.6738858,0.0005289235,0.0003476518,0.0006738243,0.0008205143,0.0002638554,0.003526034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002842654,"threshold_uncertainty_score":0.01378733,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W182621935","doi":"","title":"Flexible execution of partial order plans with temporal constraints","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":17,"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":"Computer science; Plan (archaeology); Schedule; Executor; Generalization; Variety (cybernetics); State (computer science); Order (exchange); Distributed computing; Operations research; Artificial intelligence; Programming language; Operating system; Engineering","authors":[{"name":"Christian Muise","is_ca":true},{"name":"J. Christopher Beck","is_ca":true},{"name":"Sheila A. McIlraith","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08728091937032868,"gpt":0.2954974309334702,"spread":0.2082165115631415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001857362,0.00067609,0.0005346544,0.000492431,0.0005037709,0.0009376429,0.00112962,0.0005085114,0.00133385],"category_scores_gemma":[0.003616079,0.0004727927,0.0008879136,0.000632892,0.000977606,0.001490553,0.001223145,0.001261345,0.0002170133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006817413,"about_ca_system_score_gemma":0.001842531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005875618,"about_ca_topic_score_gemma":0.008688425,"domain_scores_codex":[0.9988171,0.0003821236,0.00009012369,0.0001694569,0.000417895,0.0001232916],"domain_scores_gemma":[0.9984022,0.0008615833,0.0001780633,0.0003440752,0.0001347482,0.00007933293],"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":[0.0002294405,0.0001333526,0.0009497963,0.0001751961,0.00008041725,0.0005008556,0.000474672,0.785517,0.01550067,0.06455894,0.002199921,0.1296798],"study_design_scores_gemma":[0.00004479033,0.00007541366,0.0002054957,0.00002216117,0.00002645567,0.00005630227,0.00006196299,0.9524493,0.007086478,0.03425656,0.00569358,0.00002159068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03203443,0.0001418498,0.9626186,0.0001980087,0.00003414707,0.0001628024,0.0001477859,0.00195696,0.002705423],"genre_scores_gemma":[0.3686346,0.0002403638,0.6284729,0.0000896488,0.00002798756,0.0002985173,0.0003249065,0.0002664974,0.001644629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005875618,"threshold_uncertainty_score":0.01168281,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W140552972","doi":"","title":"Solving 8x8 Hex.","year":2009,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Mathematics, Computing, and Information Processing","field":"Computer Science","cited_by":17,"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; Programming language","authors":[{"name":"Philip Henderson","is_ca":true},{"name":"Broderick Arneson","is_ca":false},{"name":"Ryan Hayward","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1120449328600894,"gpt":0.3244736612575101,"spread":0.2124287283974208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002366596,0.0008183394,0.0006967769,0.0002555667,0.0006545949,0.000855156,0.001020086,0.0009931684,0.0758862],"category_scores_gemma":[0.0008069713,0.0003120477,0.0005086823,0.0005244471,0.0004376086,0.0008755703,0.001278527,0.000801655,0.006762849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003727075,"about_ca_system_score_gemma":0.0006475822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002373273,"about_ca_topic_score_gemma":0.005496016,"domain_scores_codex":[0.9997672,0.00004086704,0.0000117063,0.00007086003,0.00005799102,0.00005128163],"domain_scores_gemma":[0.9997525,0.0001149136,0.00001568169,0.00003870382,0.00005804709,0.00002014977],"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":[0.001357386,0.0004044134,0.004669159,0.001235691,0.0001765146,0.001435961,0.0006900587,0.3106918,0.01382981,0.07446554,0.08480679,0.5062369],"study_design_scores_gemma":[0.0004789042,0.000315664,0.001462074,0.0001242825,0.00006142837,0.0005930506,0.001451184,0.7228637,0.01784012,0.1278887,0.1268786,0.00004238551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2272914,0.0005376583,0.574976,0.001619596,0.0006755732,0.0003842569,0.004052113,0.005724563,0.1847389],"genre_scores_gemma":[0.4715906,0.0001701818,0.4546833,0.0004323169,0.00004402579,0.0003287073,0.003923275,0.001018699,0.06780899],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0758862,"threshold_uncertainty_score":0.2538646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W110110163","doi":"","title":"An action description language for iterated belief change","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":12,"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":"Ontic; Action (physics); Iterated function; Epistemology; Computer science; Semantics (computer science); Projection (relational algebra); Sequence (biology); Philosophy of language; Cognitive science; Mathematics; Algorithm; Philosophy; Programming language; Psychology; Metaphysics","authors":[{"name":"Aaron Hunter","is_ca":true},{"name":"James P. Delgrande","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2624631747405078,"gpt":0.3886631209659038,"spread":0.126199946225396,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005160301,0.001219502,0.00100963,0.001452953,0.001452304,0.004065495,0.00338786,0.002465294,0.00892779],"category_scores_gemma":[0.005856892,0.001169934,0.003070813,0.001300534,0.003766093,0.008060764,0.003363681,0.004871239,0.001813556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002679336,"about_ca_system_score_gemma":0.003602388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006022897,"about_ca_topic_score_gemma":0.006066325,"domain_scores_codex":[0.996219,0.001267878,0.0006686029,0.0006121979,0.0009339507,0.0002984545],"domain_scores_gemma":[0.9958987,0.002073881,0.0003719763,0.0006615243,0.0007735246,0.0002203416],"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":[0.00006129149,0.00004058138,0.0001369861,0.0001728546,0.00002315137,0.0002911943,0.0008544966,0.009980111,0.001600392,0.9675216,0.003602131,0.01571515],"study_design_scores_gemma":[0.0002141247,0.0001095099,0.0001203335,0.0002033197,0.0001029348,0.0005639497,0.0003141758,0.1785918,0.005750131,0.6826602,0.131229,0.0001405208],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001797887,0.0001721266,0.989623,0.0007956519,0.0001442496,0.0001457497,0.0004938829,0.001973994,0.004853577],"genre_scores_gemma":[0.1274727,0.0004716426,0.8587589,0.0009458691,0.0001882572,0.0009472169,0.001360771,0.0006930658,0.009161743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00892779,"threshold_uncertainty_score":0.02986646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W167512791","doi":"10.1519/jsc.0b013e31822600c9","title":"A Description Logic of Change.","year":2006,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Undecidable problem; Computer science; Description logic; Dynamic logic (digital electronics); Modal logic; Decidability; Satisfiability; Linear temporal logic; Temporal logic; Multimodal logic; EXPTIME; Boolean satisfiability problem; Modal; Theoretical computer science; Artificial intelligence; Algorithm; Computational complexity theory","authors":[{"name":"Alessandro Artale","is_ca":false},{"name":"Carsten Lutz","is_ca":false},{"name":"David Toman","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3215270559872621,"gpt":0.339487671271858,"spread":0.01796061528459586,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006039622,0.0008631221,0.0005151235,0.002419819,0.002962178,0.009886458,0.002363011,0.002864033,0.09759644],"category_scores_gemma":[0.01534255,0.0003787854,0.0007054819,0.002288003,0.007260935,0.01094588,0.006043007,0.004902723,0.0474318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00423905,"about_ca_system_score_gemma":0.005175137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004043044,"about_ca_topic_score_gemma":0.004470028,"domain_scores_codex":[0.9940064,0.00293856,0.0004608881,0.0008673705,0.001302555,0.0004240614],"domain_scores_gemma":[0.9933386,0.001740922,0.0004179238,0.001563309,0.002147743,0.0007914972],"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":[0.00008085326,0.000038045,0.0005974959,0.0002994301,0.00001366832,0.0002370931,0.003190571,0.0001259624,0.0002764431,0.5133665,0.3362491,0.1455249],"study_design_scores_gemma":[0.000004625002,0.00001269631,0.00014549,0.000204167,0.000003263686,0.0001372439,0.0008170857,0.00009611266,0.00006579703,0.03059604,0.9679078,0.000009700306],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.002807497,0.01501529,0.03226451,0.11289,0.03346496,0.0004711127,0.00176393,0.001734004,0.7995887],"genre_scores_gemma":[0.126315,0.01839337,0.0597296,0.05326255,0.01311722,0.001481459,0.003222551,0.002055661,0.7224227],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.09759644,"threshold_uncertainty_score":0.3264926,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W15018536","doi":"10.1021/ic035133a","title":"Compiling control knowledge into preconditions for planning in the situation calculus","year":2003,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":12,"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":"Situation calculus; Computer science; Calculus (dental); Domain (mathematical analysis); Control (management); Action (physics); Plan (archaeology); Transformation (genetics); Domain knowledge; Mathematics; Artificial intelligence","authors":[{"name":"Alfredo Gabaldon","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1388539723988244,"gpt":0.3607942404534089,"spread":0.2219402680545845,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002754968,0.001076898,0.0008232222,0.001531453,0.001081056,0.002759211,0.00170109,0.001112497,0.01083401],"category_scores_gemma":[0.00726318,0.0009643091,0.002526835,0.00109415,0.003387093,0.003851575,0.003220795,0.002160398,0.001478071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002218741,"about_ca_system_score_gemma":0.003303695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01570964,"about_ca_topic_score_gemma":0.0256648,"domain_scores_codex":[0.998553,0.0003395793,0.0001655645,0.0003206618,0.0003518746,0.000269304],"domain_scores_gemma":[0.9976304,0.001547734,0.0001493515,0.0003101828,0.0002621681,0.0001002544],"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":[0.0002966146,0.0001389676,0.001144904,0.0006629316,0.0001218046,0.0008071329,0.0009797481,0.3681937,0.002503723,0.4938719,0.005200615,0.126078],"study_design_scores_gemma":[0.0001017792,0.00005532544,0.0002078464,0.0001661593,0.00009817903,0.00006171245,0.000221955,0.4195599,0.003912498,0.563113,0.01246076,0.0000408847],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009008159,0.0001515985,0.9803911,0.0005585103,0.00007423961,0.000246048,0.0005568398,0.002087493,0.006925877],"genre_scores_gemma":[0.2871853,0.0005178599,0.7050756,0.0002449732,0.0001217986,0.0006841738,0.00154136,0.0003374502,0.004291513],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01570964,"threshold_uncertainty_score":0.03624338,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W23186342","doi":"10.1021/ac3032146","title":"Exchanging reputation information between communities: a payment-function approach","year":2009,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Reputation; Payment; Function (biology); Honesty; Information exchange; Business; Computer science; Multi-agent system; Focus (optics); Internet privacy; Finance; Telecommunications","authors":[{"name":"Georgia Kastidou","is_ca":true},{"name":"Kate Larson","is_ca":true},{"name":"Robin Cohen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3099019691943408,"gpt":0.4082486194065966,"spread":0.09834665021225586,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01747533,0.001108272,0.002247113,0.003038417,0.003430266,0.006669411,0.006113349,0.00569322,0.01456993],"category_scores_gemma":[0.0543532,0.0008797362,0.001497663,0.004176594,0.00279274,0.01398104,0.007289791,0.002202884,0.003292588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003649183,"about_ca_system_score_gemma":0.004217144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00440611,"about_ca_topic_score_gemma":0.002680557,"domain_scores_codex":[0.9848622,0.008058155,0.000615496,0.002191633,0.003038631,0.00123385],"domain_scores_gemma":[0.9693563,0.01403542,0.002310918,0.006061761,0.006054491,0.002181102],"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":[0.0006563947,0.0007097688,0.0111369,0.0003539325,0.0002214841,0.001227225,0.001348689,0.08526178,0.003290574,0.5661098,0.01948593,0.3101976],"study_design_scores_gemma":[0.0001220806,0.0003232067,0.001564489,0.00007958653,0.000124653,0.0009654464,0.0007295571,0.6232021,0.001690942,0.3489376,0.0221495,0.0001109178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04419516,0.000655706,0.9126444,0.006192841,0.0002697258,0.0008380839,0.0003655423,0.001177114,0.03366137],"genre_scores_gemma":[0.7539219,0.0006107462,0.2214834,0.0005482201,0.0002738239,0.0005400343,0.0003249516,0.0001686566,0.02212825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01747533,"threshold_uncertainty_score":0.09241951,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1563879233","doi":"","title":"Topological mapping through distributed, passive sensors","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Probabilistic logic; Computer science; Wireless sensor network; Navigability; Maximization; Topology (electrical circuits); Graph; occam; Network topology; Theoretical computer science; Distributed computing; Artificial intelligence; Mathematical optimization; Mathematics; Computer network","authors":[{"name":"Dimitri Marinakis","is_ca":true},{"name":"Gregory Dudek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1108811833227111,"gpt":0.3200244180757382,"spread":0.2091432347530271,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015008,0.0005320424,0.0004578935,0.001052347,0.0004319489,0.001069832,0.001369928,0.0007457763,0.00111175],"category_scores_gemma":[0.00707094,0.0004431065,0.0005156956,0.0008925415,0.001228308,0.00366468,0.001628078,0.0007074198,0.0002139859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004799663,"about_ca_system_score_gemma":0.0004244529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009839291,"about_ca_topic_score_gemma":0.0009653019,"domain_scores_codex":[0.999376,0.0001913636,0.00002300764,0.0001688981,0.0002098022,0.00003101424],"domain_scores_gemma":[0.9985046,0.0008682296,0.0001861187,0.0002525545,0.0001415953,0.00004679066],"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":[0.0001770404,0.0000665822,0.001980204,0.0001940405,0.00006693759,0.0002202978,0.0003383649,0.6653271,0.01330004,0.159698,0.0008868874,0.1577446],"study_design_scores_gemma":[0.00001675845,0.00006073332,0.0004587635,0.00001461544,0.00001660908,0.0001137438,0.00005126236,0.9034753,0.00291487,0.09075034,0.002110892,0.00001602201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01418688,0.0001290464,0.9842209,0.0001059828,0.00001898142,0.00001786895,0.00004044884,0.0001961606,0.001083825],"genre_scores_gemma":[0.6934903,0.0004795804,0.303074,0.00008356787,0.00006121109,0.0001158039,0.0001633151,0.00006756846,0.002464807],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001369928,"threshold_uncertainty_score":0.005367935,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2576517076","doi":"","title":"Strong-Cyclic Planning when Fairness is Not a Valid Assumption.","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Fairness measure; Max-min fairness; Mathematical optimization; Microeconomics; Economics; Mathematics; Computer network; Resource allocation","authors":[{"name":"Alberto Rivas","is_ca":true},{"name":"Sheila A. McIlraith","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1960748712451184,"gpt":0.3507000163299686,"spread":0.1546251450848502,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01075762,0.0006904438,0.0008168046,0.0008973838,0.00243459,0.003137097,0.002699056,0.002128745,0.01173046],"category_scores_gemma":[0.0554904,0.0009325938,0.001314871,0.001017403,0.00344602,0.008368867,0.004964831,0.003889443,0.001163446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002468464,"about_ca_system_score_gemma":0.008238727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021045,"about_ca_topic_score_gemma":0.01467587,"domain_scores_codex":[0.9920203,0.003177346,0.0005395261,0.001646536,0.001304144,0.001312145],"domain_scores_gemma":[0.9580818,0.0254817,0.002038549,0.009069076,0.00349983,0.001829079],"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":[0.0007198939,0.0001488397,0.002607645,0.0002955216,0.0001459138,0.0006656759,0.0005664562,0.02825738,0.001488415,0.9248283,0.01047884,0.02979717],"study_design_scores_gemma":[0.00005220013,0.00003989519,0.0001908364,0.00003936052,0.00003889282,0.0001188515,0.0001206408,0.05803498,0.0009534355,0.936326,0.004062372,0.00002252164],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06673674,0.0004697987,0.8373501,0.00670827,0.0008799432,0.0006713519,0.001370616,0.0016589,0.08415429],"genre_scores_gemma":[0.8082754,0.000217616,0.1775427,0.001387391,0.0002844082,0.0004301489,0.0009106206,0.0003360325,0.01061566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01173046,"threshold_uncertainty_score":0.05689245,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2579792020","doi":"","title":"Dynamic task allocation algorithm for hiring workers that learn","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Crowdsourcing; Computer science; Task (project management); Automation; Competence (human resources); Machine learning; Work (physics); Artificial intelligence; Engineering","authors":[{"name":"Shengying Pan","is_ca":true},{"name":"Kate Larson","is_ca":true},{"name":"Josh Bradshaw","is_ca":true},{"name":"Edith Law","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07856348913146355,"gpt":0.3072318620184597,"spread":0.2286683728869962,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002866636,0.001316997,0.002240982,0.001025719,0.001108583,0.001107535,0.002943302,0.002622724,0.006308378],"category_scores_gemma":[0.007710317,0.0007498211,0.00089543,0.001004171,0.001188618,0.001600405,0.002015881,0.002041121,0.002547893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436475,"about_ca_system_score_gemma":0.002100783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00363957,"about_ca_topic_score_gemma":0.003607979,"domain_scores_codex":[0.9988812,0.0003287722,0.00007175635,0.0003413489,0.0001685931,0.0002083326],"domain_scores_gemma":[0.9969335,0.001752922,0.0002758944,0.0003663304,0.0003638842,0.0003075126],"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":[0.0006818686,0.0005136046,0.002215357,0.000239564,0.0001087479,0.0001363434,0.0004134623,0.5406399,0.005168017,0.01120504,0.009439889,0.4292382],"study_design_scores_gemma":[0.00007564852,0.00009096828,0.0002770987,0.00001377378,0.00001392173,0.00003521061,0.0000491871,0.9867907,0.0008020247,0.0105211,0.001315932,0.00001440261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02798075,0.0005129835,0.9650284,0.0006635285,0.0001774722,0.0003064736,0.00008966052,0.001501529,0.003739236],"genre_scores_gemma":[0.5722953,0.0002980181,0.4145455,0.0005721489,0.0002572156,0.0009556172,0.0003137763,0.0002428288,0.01051964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006308378,"threshold_uncertainty_score":0.02110362,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2575405755","doi":"","title":"The complexity of learning acyclic CP-nets","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Dimension (graph theory); Concept class; Theoretical computer science; Tree (set theory); Computational complexity theory; Core (optical fiber); Mistake; Upper and lower bounds; VC dimension; Artificial intelligence; Algorithm; Mathematics; Machine learning; Combinatorics","authors":[{"name":"Eisa Alanazi","is_ca":true},{"name":"Malek Mouhoub","is_ca":true},{"name":"Sandra Zilles","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1408938287173567,"gpt":0.3353116497484666,"spread":0.1944178210311099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00507049,0.000658229,0.001318776,0.0009779712,0.0009604823,0.003176235,0.002784504,0.002155857,0.003575026],"category_scores_gemma":[0.04892993,0.0006942999,0.001279543,0.001443139,0.001994687,0.009250753,0.002860219,0.003815074,0.0003338531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003588175,"about_ca_system_score_gemma":0.002520236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004855966,"about_ca_topic_score_gemma":0.004680701,"domain_scores_codex":[0.9949018,0.001795835,0.0004677452,0.001134274,0.00128156,0.0004187146],"domain_scores_gemma":[0.9333236,0.05835725,0.00219624,0.00333321,0.001906334,0.000883259],"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":[0.0006580078,0.0002733514,0.00813208,0.0004511622,0.0001563728,0.0002746424,0.0004650616,0.6731289,0.002435139,0.1689668,0.003564672,0.1414938],"study_design_scores_gemma":[0.00001944087,0.00003226809,0.0003758771,0.00001244376,0.00001629271,0.00005528072,0.00003926197,0.8380538,0.0007239682,0.1603008,0.0003592614,0.0000112249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.196543,0.0004343576,0.7947289,0.002393845,0.00004422892,0.0002484353,0.0007850475,0.0008098633,0.004012394],"genre_scores_gemma":[0.8115057,0.0005221021,0.1816221,0.0005538567,0.000103778,0.0003592522,0.001686295,0.0001945457,0.003452359],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00507049,"threshold_uncertainty_score":0.02681565,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W17380902","doi":"","title":"Symmetric component caching","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":10,"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":"Decomposition; Overhead (engineering); Homogeneous space; Pruning; Computer science; Constraint (computer-aided design); Mathematical optimization; Component (thermodynamics); Decomposition method (queueing theory); Space (punctuation); Mathematics; Algorithm; Discrete mathematics","authors":[{"name":"Matthew Kitching","is_ca":true},{"name":"Fahiem Bacchus","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09724514953681351,"gpt":0.3221370999008255,"spread":0.2248919503640119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009974331,0.001448353,0.001916868,0.001841301,0.001836725,0.00421763,0.004385925,0.001545274,0.05657241],"category_scores_gemma":[0.005597848,0.0006064178,0.001273447,0.005051785,0.0009020108,0.006325726,0.004819924,0.001328499,0.01848311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001891029,"about_ca_system_score_gemma":0.003278983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007311525,"about_ca_topic_score_gemma":0.01181022,"domain_scores_codex":[0.99855,0.0002334696,0.0001532066,0.0002525614,0.0004637463,0.0003469334],"domain_scores_gemma":[0.9964054,0.0004993333,0.0001018572,0.002190407,0.0006465711,0.0001564621],"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":[0.002125404,0.0004512981,0.002026739,0.0009503055,0.0002532427,0.0005602602,0.0002909179,0.03981495,0.01077171,0.267924,0.1922814,0.4825498],"study_design_scores_gemma":[0.0004195886,0.0001915613,0.0006841999,0.0001580555,0.0001780082,0.0007407563,0.0002405687,0.3778116,0.02289058,0.4557777,0.1407909,0.0001165143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03946857,0.002487466,0.8300396,0.001269408,0.0007135493,0.0008370632,0.009652562,0.0343314,0.08120041],"genre_scores_gemma":[0.3601006,0.0016252,0.5356037,0.0008639358,0.0002789601,0.0009980904,0.02482543,0.004700308,0.07100376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05657241,"threshold_uncertainty_score":0.1892536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2252001768","doi":"","title":"Decidable reasoning in a logic of limited belief with introspection and unknown individuals","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":9,"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":"Decidability; Introspection; Semantics (computer science); Computer science; Identity (music); Artificial intelligence; Epistemology; Theoretical computer science; Programming language; Philosophy","authors":[{"name":"Gerhard Lakemeyer","is_ca":false},{"name":"Hector J. Levesque","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0531541692446631,"gpt":0.2781331706778161,"spread":0.224979001433153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007911269,0.0007884454,0.0009400786,0.001605183,0.002128587,0.005558042,0.003002075,0.001853261,0.002359674],"category_scores_gemma":[0.01343219,0.001024966,0.003973198,0.001705354,0.006324818,0.0109867,0.00521786,0.004797416,0.0003242098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003495278,"about_ca_system_score_gemma":0.002471381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008338917,"about_ca_topic_score_gemma":0.008104579,"domain_scores_codex":[0.9947377,0.002064028,0.0004038985,0.0009750561,0.00113811,0.0006811519],"domain_scores_gemma":[0.986021,0.01111609,0.0007571394,0.0009916275,0.0007340574,0.0003801876],"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":[0.00008115809,0.00005224163,0.0004127342,0.0001186865,0.00005968515,0.000535457,0.00127191,0.0124419,0.001108365,0.9733884,0.001315623,0.00921372],"study_design_scores_gemma":[0.00005092817,0.00002166981,0.0001372516,0.00003557163,0.00005773053,0.000175778,0.0001810908,0.04801512,0.001390546,0.9465005,0.003406597,0.00002725819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05526377,0.0009040458,0.918856,0.00515631,0.0001025164,0.0001377032,0.0005607486,0.0008907754,0.01812828],"genre_scores_gemma":[0.6424168,0.000679557,0.3468894,0.001418326,0.0003819061,0.0002720839,0.0007905754,0.0001148019,0.007036494],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008338917,"threshold_uncertainty_score":0.04183924,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W301099071","doi":"","title":"Phase transitions of dominating clique problem and their implications to heuristics in satisfiability search","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":9,"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; Clique problem; Satisfiability; Monotone polygon; Clique; Computer science; Time complexity; Random graph; Local search (optimization); Mathematics; Boolean satisfiability problem; Graph; Theoretical computer science; Mathematical optimization; Algorithm; Combinatorics; Line graph; Pathwidth","authors":[{"name":"Joseph Culberson","is_ca":true},{"name":"Yong Gao","is_ca":true},{"name":"Cǎlin Anton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09141986618334437,"gpt":0.3564744036721659,"spread":0.2650545374888216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004310263,0.0005448826,0.0008590982,0.001714507,0.001200134,0.002457466,0.001444533,0.001590331,0.004065123],"category_scores_gemma":[0.04500169,0.0008154429,0.001063188,0.001616134,0.002615179,0.004864305,0.001519423,0.002774193,0.0003132866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001740416,"about_ca_system_score_gemma":0.001235371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002007084,"about_ca_topic_score_gemma":0.001835918,"domain_scores_codex":[0.9981276,0.0009455458,0.00005956795,0.000291249,0.0003226881,0.0002532554],"domain_scores_gemma":[0.9614007,0.0339998,0.00199193,0.001250532,0.0007166909,0.0006404183],"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":[0.0003820412,0.0002646956,0.006034265,0.0002323082,0.00008938875,0.0002904721,0.000572378,0.3381151,0.003032946,0.6103789,0.004867972,0.03573962],"study_design_scores_gemma":[0.00007539867,0.00006448999,0.0008084265,0.00003055658,0.00001861101,0.00009937932,0.00009562678,0.5840801,0.001134197,0.412316,0.001256015,0.00002117172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2414897,0.001359011,0.7398196,0.002895364,0.0000993203,0.0003031958,0.0003505366,0.000678,0.01300517],"genre_scores_gemma":[0.8751991,0.0007387729,0.1202711,0.0004634304,0.0001432622,0.0003510046,0.0003402329,0.0002049296,0.002288258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004310263,"threshold_uncertainty_score":0.02279514,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1581425933","doi":"","title":"A general framework for scheduling in a stochastic environment","year":2007,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":9,"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":"Computer science; Scheduling (production processes); Schedule; Machine learning; Theoretical computer science; Artificial intelligence; Distributed computing; Industrial engineering; Mathematical optimization; Engineering","authors":[{"name":"Julien Bidot","is_ca":false},{"name":"Thierry Vidal","is_ca":false},{"name":"Philippe Laborie","is_ca":false},{"name":"J. Christopher Beck","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0940925670022517,"gpt":0.3325678346717465,"spread":0.2384752676694948,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003491248,0.001449694,0.001000305,0.001908154,0.00153382,0.004160497,0.004119766,0.002341699,0.009343317],"category_scores_gemma":[0.00460335,0.0008535864,0.002831127,0.003356516,0.003567026,0.004646922,0.002539316,0.003683591,0.002750746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003080502,"about_ca_system_score_gemma":0.004571791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01042177,"about_ca_topic_score_gemma":0.009238135,"domain_scores_codex":[0.9974597,0.0008622489,0.0002235598,0.0004335133,0.000760993,0.0002599728],"domain_scores_gemma":[0.9984316,0.0006507344,0.000173037,0.0003348981,0.000274911,0.0001347],"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":[0.000007565165,0.0000101082,0.00006307826,0.00005941225,0.00001145144,0.00004145884,0.00010693,0.0356819,0.000354039,0.9563344,0.001227466,0.006102075],"study_design_scores_gemma":[0.00002251725,0.00004113361,0.000109547,0.00006412373,0.00001498203,0.000116776,0.00007323585,0.164663,0.0002890699,0.7832105,0.05136928,0.0000258614],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005499438,0.0002846175,0.9930184,0.0004774148,0.00005502282,0.00006057615,0.00012007,0.0001893166,0.005244717],"genre_scores_gemma":[0.06786254,0.001512533,0.9207106,0.0004381063,0.0003493738,0.0008561279,0.0004285786,0.0002793266,0.007562872],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01042177,"threshold_uncertainty_score":0.03125656,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2576239431","doi":"","title":"Action selection for hammer shots in curling","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":9,"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":"Delaunay triangulation; Curling; Hammer; Computer science; Action (physics); Selection (genetic algorithm); Artificial intelligence; State space; Triangulation; Computer vision; Mathematical optimization; Algorithm; Mathematics; Engineering; Geometry; Statistics; Structural engineering; Mechanical engineering; Physics","authors":[{"name":"Zaheen Farraz Ahmad","is_ca":true},{"name":"Robert C. Holte","is_ca":true},{"name":"Michael Bowling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1744397893897128,"gpt":0.359484580898046,"spread":0.1850447915083333,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160276,0.00103209,0.001476309,0.0006889015,0.0007629247,0.0008147392,0.002033127,0.001745711,0.00336624],"category_scores_gemma":[0.005781261,0.0005986571,0.000677051,0.0003730672,0.001674221,0.00131784,0.002292288,0.001480225,0.0004276113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00107854,"about_ca_system_score_gemma":0.001577485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006227775,"about_ca_topic_score_gemma":0.008105392,"domain_scores_codex":[0.9987739,0.00037637,0.00004689174,0.0003333271,0.000303644,0.0001658592],"domain_scores_gemma":[0.9979112,0.001412882,0.0001629647,0.0001261617,0.0001443592,0.0002423364],"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":[0.0003617356,0.0001250814,0.001528238,0.0001360417,0.0000649666,0.0003191432,0.0003041298,0.9171799,0.005247197,0.02007957,0.001468027,0.05318604],"study_design_scores_gemma":[0.00002742395,0.0001052957,0.0002434808,0.00001270515,0.000007820088,0.00005298949,0.0000584292,0.9826385,0.001217928,0.01501018,0.0006097716,0.00001557362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07726198,0.0002695941,0.9168755,0.0002443439,0.00005860508,0.0001698194,0.0000958216,0.0007034491,0.004320743],"genre_scores_gemma":[0.8525579,0.0001115992,0.1431257,0.0001707547,0.0000219036,0.0001649327,0.0002065149,0.0001320961,0.003508483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006227775,"threshold_uncertainty_score":0.01238304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2578463151","doi":"","title":"Linear arithmetic satisfiability via strategy improvement","year":2016,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Logic, programming, and type systems","field":"Computer Science","cited_by":8,"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":"Satisfiability modulo theories; Quantifier elimination; Satisfiability; Fragment (logic); Computer science; Modulo; Intuition; Automated theorem proving; Computability; Algorithm; Theoretical computer science; Arithmetic; Mathematics; Programming language; Discrete mathematics","authors":[{"name":"Azadeh Farzan","is_ca":true},{"name":"Zachary Kincaid","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1021667144782425,"gpt":0.3182398034035272,"spread":0.2160730889252847,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00202073,0.0009439389,0.0007961027,0.0009648201,0.0005610668,0.001368445,0.001980743,0.0009021337,0.004420451],"category_scores_gemma":[0.007233284,0.0004775916,0.001266727,0.0008661902,0.001265876,0.001601934,0.001794291,0.001611232,0.001001124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346968,"about_ca_system_score_gemma":0.002294414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002323991,"about_ca_topic_score_gemma":0.003448818,"domain_scores_codex":[0.9978185,0.0006140978,0.0001344044,0.0004440345,0.000702171,0.0002867941],"domain_scores_gemma":[0.9969416,0.001982758,0.0002391472,0.0004112615,0.0003488456,0.00007640047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003586741,0.0005578322,0.002638136,0.0004747241,0.0001536839,0.0003637079,0.0004466569,0.2998773,0.02080338,0.2080304,0.007327915,0.4589676],"study_design_scores_gemma":[0.0001107728,0.0001106813,0.0002253958,0.00003795399,0.00006638467,0.00008418725,0.00004699739,0.9043654,0.008017227,0.08294477,0.003970527,0.00001956517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05832394,0.0003969181,0.9170583,0.0007336283,0.00007219274,0.0003001463,0.0001213774,0.00343766,0.01955581],"genre_scores_gemma":[0.5300645,0.0002611244,0.4643637,0.0002690136,0.00005850158,0.000208837,0.000303041,0.0002860914,0.00418529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004420451,"threshold_uncertainty_score":0.01478791,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1540266240","doi":"","title":"Accelerated robust point cloud registration in natural environments through positive and unlabeled learning","year":2013,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Point cloud; Pairwise comparison; Computer vision; Image registration; Robot; Machine learning; Mobile robot; Pattern recognition (psychology); Image (mathematics)","authors":[{"name":"Maxime Latulippe","is_ca":true},{"name":"Alexandre Drouin","is_ca":true},{"name":"Philippe Giguère","is_ca":true},{"name":"François Laviolette","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06487840364842372,"gpt":0.2634298915574278,"spread":0.1985514879090041,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001607425,0.0007565543,0.001245228,0.0009620805,0.0008437338,0.0007825606,0.00187574,0.001167791,0.001019639],"category_scores_gemma":[0.006080992,0.0005516373,0.0007220564,0.001024197,0.001394902,0.002276909,0.002367963,0.001256096,0.0007385679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005665227,"about_ca_system_score_gemma":0.001131946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005144329,"about_ca_topic_score_gemma":0.007607658,"domain_scores_codex":[0.9987249,0.0003209963,0.00004853073,0.000371793,0.0004083762,0.0001253848],"domain_scores_gemma":[0.9968451,0.001121343,0.0003748066,0.001008089,0.0005637494,0.0000869127],"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":[0.00056077,0.0003257584,0.003977492,0.00009750643,0.00006587801,0.000177883,0.0001567906,0.4716508,0.04298292,0.005983512,0.001688391,0.4723322],"study_design_scores_gemma":[0.00001052005,0.00005720924,0.0006206747,0.000003649077,0.000005949633,0.00007388656,0.00001944799,0.9887116,0.007687399,0.002418159,0.0003816698,0.000009809456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06109751,0.0001065063,0.9367132,0.00008410273,0.00002526959,0.00005250066,0.00004469709,0.00131944,0.0005569429],"genre_scores_gemma":[0.6186655,0.0001093442,0.3787183,0.00008026721,0.00005144161,0.0001042206,0.0004610387,0.0002273141,0.001582507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005144329,"threshold_uncertainty_score":0.01022875,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W186506571","doi":"","title":"Using core beliefs for point-based value iteration","year":2005,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Simplex; Computer science; Mathematical optimization; Partially observable Markov decision process; Set (abstract data type); Heuristic; Core (optical fiber); Simplex algorithm; Bellman equation; Point (geometry); Value (mathematics); Function (biology); Algorithm; Artificial intelligence; Mathematics; Linear programming; Machine learning; Markov chain; Combinatorics; Markov model","authors":[{"name":"Masoumeh T. Izadi","is_ca":true},{"name":"Ajit Rajwade","is_ca":false},{"name":"Doina Precup","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.29352949057106,"gpt":0.3825340826528269,"spread":0.08900459208176686,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003398046,0.001304005,0.001467434,0.00112519,0.0007280046,0.001777152,0.002222852,0.001431184,0.004050381],"category_scores_gemma":[0.01343316,0.0008955863,0.001085566,0.0008868449,0.002072656,0.002911749,0.003259156,0.002606089,0.0009565377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334013,"about_ca_system_score_gemma":0.001550045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002382527,"about_ca_topic_score_gemma":0.002719816,"domain_scores_codex":[0.9974948,0.001041759,0.0001267798,0.0003354088,0.0007965818,0.0002046517],"domain_scores_gemma":[0.9950606,0.003065085,0.0003470139,0.0006747434,0.0006993707,0.0001531475],"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":[0.000243734,0.0001086713,0.0008123941,0.0002203281,0.00009199502,0.0001079308,0.0004192802,0.6623033,0.003304459,0.2140874,0.001511893,0.1167887],"study_design_scores_gemma":[0.00003131107,0.00005106386,0.00004233856,0.00002580545,0.00001154404,0.00002092976,0.00002339538,0.9130246,0.001500901,0.08408537,0.001171007,0.00001172487],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003824142,0.00006779552,0.9943398,0.00004864527,0.00001139868,0.00004253307,0.0000145567,0.0001962879,0.001454868],"genre_scores_gemma":[0.3108754,0.000241227,0.6862475,0.0001174582,0.00003814576,0.0004212005,0.0001258401,0.000198717,0.001734617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004050381,"threshold_uncertainty_score":0.0179708,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W46433006","doi":"","title":"Learning algorithms for software agents in uncertain and untrusted market environments","year":2003,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Reputation; Reinforcement learning; Order (exchange); Software; Software agent; Multi-agent system; Intelligent agent; Algorithm; Artificial intelligence; Computer security; Business","authors":[{"name":"Thomas Tran","is_ca":true},{"name":"Robin Cohen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08983223663751178,"gpt":0.3175322691727702,"spread":0.2277000325352584,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003602148,0.001106574,0.001425823,0.0008538812,0.0008708602,0.001616628,0.002169507,0.002319657,0.001856503],"category_scores_gemma":[0.01525852,0.0006004949,0.0006141054,0.0009368901,0.002324015,0.003530962,0.002261085,0.00213072,0.0004522167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001537762,"about_ca_system_score_gemma":0.001655768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003878151,"about_ca_topic_score_gemma":0.003237971,"domain_scores_codex":[0.9987153,0.0005532196,0.0001037927,0.0002317825,0.0002258877,0.0001700107],"domain_scores_gemma":[0.9912187,0.006764357,0.0007616886,0.0003640559,0.0006545412,0.0002365993],"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":[0.0000382527,0.00003207639,0.0004660909,0.00005301885,0.00003085659,0.00002560149,0.00008813183,0.9461401,0.00018371,0.02842364,0.0004728061,0.0240459],"study_design_scores_gemma":[0.00001415956,0.00001097388,0.00002528681,0.000005594847,0.000003169248,0.000004647426,0.000008925681,0.9785251,0.00007503999,0.02115927,0.0001651132,0.000002722535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01368952,0.0003563109,0.9839633,0.0004312322,0.00002908223,0.00005183064,0.0000182926,0.0002268239,0.001233493],"genre_scores_gemma":[0.5436005,0.0007481944,0.4507716,0.0002206647,0.0001365125,0.000506084,0.000141053,0.0001081569,0.003767231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003878151,"threshold_uncertainty_score":0.01905018,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W11533555","doi":"","title":"Towards cooperative negotiation for decentralized resource allocation in autonomic computing systems","year":2003,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Advanced Software Engineering Methodologies","field":"Computer Science","cited_by":7,"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":"Autonomic computing; Negotiation; Computer science; Resource allocation; Distributed computing; Resource management (computing); Resource (disambiguation); Computer network; Cloud computing; Operating system","authors":[{"name":"Craig Boutilier","is_ca":true},{"name":"Rajarshi Das","is_ca":false},{"name":"Jeffrey O. Kephart","is_ca":false},{"name":"William E. Walsh","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.157422937616512,"gpt":0.3588051013070076,"spread":0.2013821636904956,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00801875,0.000611197,0.001299442,0.0005957096,0.001659426,0.004154578,0.002400954,0.001859942,0.002738642],"category_scores_gemma":[0.01198859,0.0006725644,0.0006942376,0.0007089569,0.001911796,0.004059989,0.005223653,0.002754281,0.0005801473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009583585,"about_ca_system_score_gemma":0.00176495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149864,"about_ca_topic_score_gemma":0.001784535,"domain_scores_codex":[0.9960091,0.002107111,0.0002626693,0.0004242691,0.0008252984,0.0003715623],"domain_scores_gemma":[0.9958413,0.002319633,0.0002931214,0.0006007604,0.0005783211,0.0003667438],"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":[0.0004271404,0.0001480751,0.001104574,0.0002047714,0.0001346663,0.0003712262,0.001212977,0.4720224,0.005652111,0.4211383,0.005246479,0.0923373],"study_design_scores_gemma":[0.00005086313,0.00004316467,0.00006958096,0.00001814141,0.00001423633,0.00004122453,0.0001215739,0.8598431,0.0006214777,0.135553,0.003610197,0.00001344704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01357709,0.0003789575,0.9797016,0.0009632858,0.0001081266,0.0001164055,0.00001452726,0.0002287954,0.00491117],"genre_scores_gemma":[0.6839982,0.0005126146,0.3097632,0.0003409084,0.00024754,0.0004341783,0.00007804971,0.0001194158,0.004505987],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00801875,"threshold_uncertainty_score":0.04240769,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1966910741","doi":"","title":"Semi-supervised learning of visual classifiers from web images and text","year":2009,"lang":"en","type":"article","venue":"International Joint Conference on Artificial Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Generative model; Machine learning; Probabilistic logic; Object (grammar); Image (mathematics); Visualization; Generative grammar; Supervised learning; Information retrieval; Pattern recognition (psychology); Artificial neural network","authors":[{"name":"Nicholas Morsillo","is_ca":false},{"name":"Christopher Pal","is_ca":true},{"name":"Randal C. Nelson","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06446601243891999,"gpt":0.3204279420706577,"spread":0.2559619296317377,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002344387,0.001004124,0.00145532,0.001852864,0.0005316467,0.00135294,0.002146964,0.001341347,0.0008989979],"category_scores_gemma":[0.008923959,0.0004855423,0.0009786726,0.001080296,0.001019632,0.002348223,0.0009887057,0.001623513,0.001158679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000713863,"about_ca_system_score_gemma":0.0009623478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001806266,"about_ca_topic_score_gemma":0.003078945,"domain_scores_codex":[0.9984485,0.0005131047,0.0001188907,0.0004640837,0.0003383781,0.0001170464],"domain_scores_gemma":[0.9909852,0.004901567,0.0009947445,0.001362243,0.001558447,0.0001979164],"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":[0.0005585126,0.001067549,0.008769918,0.0003574405,0.000200147,0.0002611131,0.0003016025,0.1599033,0.01701397,0.00384811,0.01033934,0.797379],"study_design_scores_gemma":[0.00001692107,0.0000699547,0.0009479388,0.00001649157,0.00001971451,0.00007760579,0.00004167855,0.9868189,0.007048212,0.004384414,0.0005469514,0.00001118992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1345393,0.000493523,0.8580686,0.0003280632,0.00006875811,0.0002266404,0.000515766,0.003523135,0.002236275],"genre_scores_gemma":[0.7331917,0.0002207132,0.2600894,0.0002491938,0.0001670001,0.0003994751,0.002985244,0.0001511366,0.002546174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002344387,"threshold_uncertainty_score":0.01239842,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}