{"meta":{"query_hash":"8bdf710f9008","filters":{"venue":"National Conference on Artificial Intelligence"},"cohort_total":168,"direct_labels_cover":1,"predictions_cover":168,"exported":168,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/8bdf710f9008","api":"https://metacan.xera.ac/api/v1/cohort?venue=National+Conference+on+Artificial+Intelligence"},"results":[{"id":"W104575591","doi":"","title":"Can we work around numerical methods? an insight","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Van der Pol oscillator; Oscillation (cell signaling); Resistor; Stability (learning theory); Physics; Relaxation oscillator; Work (physics); Hill differential equation; Mathematics; Differential equation; Computer science; Nonlinear system; Ordinary differential equation; Quantum mechanics; Voltage; Exact differential equation","score_opus":0.1645280250281555,"score_gpt":0.40720624538023387,"score_spread":0.24267822035207837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W104575591","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004290864,0.00005300741,0.981323,0.0079019,0.0006482713,0.0001576332,0.0000070892233,0.00018141857,0.009298583],"genre_scores_gemma":[0.47714195,0.000010700451,0.521624,0.0006288928,0.0003572415,0.000028221359,0.000007463487,0.000011354904,0.00019017752],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968005,0.0005306596,0.00057194073,0.0007748796,0.00088949996,0.00043252826],"domain_scores_gemma":[0.9979671,0.000657766,0.00017409286,0.00042174544,0.0005688253,0.00021045131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009330149,0.0002697423,0.00029122183,0.00021370033,0.00027239663,0.0005312213,0.0011398557,0.00012815253,0.00017087585],"category_scores_gemma":[0.00032801589,0.00024073347,0.00009955155,0.0012738098,0.00015463778,0.0004579942,0.00013286762,0.00039036738,0.00025521294],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009380983,0.00015476739,0.00001111232,0.0000011924504,0.0000034284024,0.000004576804,0.00008549039,0.00066585006,0.00062664587,0.5650364,0.000032976634,0.43336815],"study_design_scores_gemma":[0.000019000525,0.00014303674,0.00029970214,0.00001873241,0.0000026888551,0.000004636401,0.000050984665,0.22095859,0.015723359,0.7596275,0.0028592602,0.00029251617],"about_ca_topic_score_codex":0.00016665914,"about_ca_topic_score_gemma":0.000024931986,"teacher_disagreement_score":0.47671285,"about_ca_system_score_codex":0.0001294493,"about_ca_system_score_gemma":0.00027594742,"threshold_uncertainty_score":0.98168254},"labels":[],"label_agreement":null},{"id":"W104649228","doi":"","title":"Interfacing Issues for Information Extraction","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Information retrieval; Query expansion; Domain (mathematical analysis); Information extraction; Task (project management); Query language; User interface; World Wide Web; Programming language","score_opus":0.1555867381301594,"score_gpt":0.38288216419909377,"score_spread":0.22729542606893435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W104649228","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052680974,0.000009941094,0.9675332,0.0036378347,0.0003312732,0.0002401474,0.000007572238,0.0001386065,0.02283334],"genre_scores_gemma":[0.9681762,0.0000146114735,0.030692559,0.0005544134,0.0001368779,0.000048442264,0.0000103660395,0.0000037151694,0.00036282087],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869925,0.000029317549,0.00039099468,0.00024581375,0.0004495957,0.00018504052],"domain_scores_gemma":[0.9990174,0.00013175611,0.00008582901,0.00018472286,0.0005294306,0.000050822622],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00041005417,0.00011344793,0.000098357785,0.0001576845,0.00016555875,0.0003438754,0.00044406703,0.00006976637,0.00037835535],"category_scores_gemma":[0.00020635464,0.00011582371,0.00004767387,0.00019970749,0.000028380657,0.0010977216,0.000034781224,0.0001446847,0.0008228227],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014525395,0.000015828322,7.525459e-7,0.0000029764042,0.000001992018,1.0433652e-7,0.00025238213,0.0051512476,0.00027042488,0.5208718,0.00006425492,0.47335365],"study_design_scores_gemma":[0.000018142871,0.000059409656,0.000023652163,0.000021447879,0.0000010681102,0.0000023020375,0.00006543339,0.6415901,0.019931259,0.33327946,0.0048946505,0.00011308448],"about_ca_topic_score_codex":0.000030734802,"about_ca_topic_score_gemma":0.000013759662,"teacher_disagreement_score":0.9629081,"about_ca_system_score_codex":0.000084900974,"about_ca_system_score_gemma":0.00012327087,"threshold_uncertainty_score":0.9999552},"labels":[],"label_agreement":null},{"id":"W105450294","doi":"","title":"A Pragmatic Global Vision System for Educational Robotics","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Robotics; Artificial intelligence; Computer science; Machine vision; Robot; Cognitive neuroscience of visual object recognition; Robot vision; Object (grammar); Overhead (engineering); Server; Computer vision; Human–computer interaction; Mobile robot; World Wide Web","score_opus":0.058087371642274774,"score_gpt":0.3414758550813853,"score_spread":0.2833884834391105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W105450294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020411178,0.000020325777,0.98005676,0.00065984984,0.000879045,0.00034494136,0.00005577237,0.00013950521,0.01580268],"genre_scores_gemma":[0.98670983,0.0000046386363,0.01272293,0.00008209278,0.00028051613,0.000020919355,0.00011359198,0.000016328393,0.000049122304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985725,0.000018645806,0.00047065422,0.00020958249,0.00048170995,0.0002468777],"domain_scores_gemma":[0.9987309,0.00031001354,0.00006573292,0.00011349204,0.0006812531,0.000098636774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048461228,0.00015617599,0.00013916887,0.00011392357,0.00012974135,0.00010333222,0.00015948982,0.00011143263,0.000056021938],"category_scores_gemma":[0.00023986667,0.0001629849,0.000058711863,0.00029795524,0.00004730009,0.000099957564,0.000009365468,0.00009892567,0.00017907548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020044541,0.00004102756,0.000010603498,0.000043266453,0.000007696489,4.1368142e-7,0.00002795735,0.39329058,0.00044932612,0.5963259,0.00016135268,0.009621846],"study_design_scores_gemma":[0.000035773173,0.00007020233,0.00023580038,0.000090677044,0.000008489334,0.000004069595,0.00018560089,0.9084106,0.004929528,0.085669704,0.00017396222,0.00018555911],"about_ca_topic_score_codex":0.000005158394,"about_ca_topic_score_gemma":0.000053637825,"teacher_disagreement_score":0.98466873,"about_ca_system_score_codex":0.0004063235,"about_ca_system_score_gemma":0.00014428076,"threshold_uncertainty_score":0.6646331},"labels":[],"label_agreement":null},{"id":"W112014004","doi":"","title":"Optimal scheduling of contract algorithms for anytime problems","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Waterloo","funders":"","keywords":"Computer science; Acceleration; Computation; Algorithm; Scheduling (production processes); Mathematical optimization; Schedule; Matching (statistics); Approximation algorithm; Mathematics","score_opus":0.14399056113021236,"score_gpt":0.3591819608483168,"score_spread":0.21519139971810441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112014004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015946723,0.000024505103,0.9902942,0.0015654975,0.00017280075,0.0005000458,0.00003135376,0.000086050626,0.0057308753],"genre_scores_gemma":[0.79548055,0.000008099284,0.20402831,0.00011902688,0.0000936854,0.00006213038,0.000024750583,0.00000815506,0.00017531244],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979568,0.000054132095,0.00058431184,0.00041164027,0.0006944349,0.00029867783],"domain_scores_gemma":[0.9975334,0.00032592882,0.00021568256,0.00019620781,0.0016544092,0.00007436932],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007602522,0.00015547224,0.00020115369,0.00022013059,0.00015299978,0.00020322044,0.00064494973,0.00009376388,0.00011736468],"category_scores_gemma":[0.00027262044,0.0001519511,0.00009052898,0.00041667797,0.00011914448,0.0004022568,0.000059998507,0.00015026006,0.000053397474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015859705,0.00016251276,0.000008198255,0.000010217778,0.0000063656876,4.3814833e-7,0.000076028795,0.24576539,0.0035361764,0.73075575,0.000043216307,0.019619843],"study_design_scores_gemma":[0.000061275394,0.00017051355,0.00006959971,0.000034048207,0.000001960556,0.0000015356021,0.000017987084,0.83802557,0.027222631,0.13398497,0.0002610901,0.00014879846],"about_ca_topic_score_codex":0.000046344547,"about_ca_topic_score_gemma":0.000020143569,"teacher_disagreement_score":0.7938859,"about_ca_system_score_codex":0.00005762712,"about_ca_system_score_gemma":0.00037324242,"threshold_uncertainty_score":0.6196386},"labels":[],"label_agreement":null},{"id":"W123045425","doi":"","title":"Conservative belief revision","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Belief revision; Artificial intelligence","score_opus":0.17133188383235118,"score_gpt":0.3617073778012017,"score_spread":0.19037549396885053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W123045425","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027501243,0.000028018156,0.9677611,0.009832743,0.00030111623,0.00016364967,0.000008669978,0.00020306824,0.018951539],"genre_scores_gemma":[0.9662158,0.00003886103,0.03131921,0.0021799349,0.00009532648,0.000015678032,0.0000072610405,0.000008364143,0.000119599514],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976591,0.00007617921,0.0004620433,0.0006020403,0.00088457594,0.00031605497],"domain_scores_gemma":[0.99810255,0.00017275642,0.00014173711,0.00034165493,0.0010919853,0.00014930614],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00056487264,0.00021758718,0.0001935726,0.00018366044,0.00025966796,0.00032310677,0.0009880963,0.000117230156,0.00011306736],"category_scores_gemma":[0.00043801218,0.0002058909,0.00007535389,0.0005981295,0.00014690896,0.0005053413,0.00012479941,0.00033785249,0.0014340815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012773552,0.00011135193,0.0000021011858,0.0000032446262,0.0000055694563,0.000005480401,0.00028051826,0.0037009078,0.0008432978,0.90731907,0.00009097751,0.087624684],"study_design_scores_gemma":[0.00004025683,0.00019239217,0.00008568568,0.000110697314,0.0000020542013,0.0000075604516,0.00006122021,0.082094565,0.031273622,0.88552594,0.00034233963,0.0002636559],"about_ca_topic_score_codex":0.00005406979,"about_ca_topic_score_gemma":0.000029775429,"teacher_disagreement_score":0.96346563,"about_ca_system_score_codex":0.00015956421,"about_ca_system_score_gemma":0.0006660591,"threshold_uncertainty_score":0.9993434},"labels":[],"label_agreement":null},{"id":"W125764232","doi":"","title":"Exploring more realistic evaluation measures for collaborative filtering","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collaborative filtering; Computer science; Quality (philosophy); Simple (philosophy); Empirical research; Recommender system; Data mining; Artificial intelligence; Information retrieval; Machine learning; Mathematics; Epistemology; Statistics","score_opus":0.5878091667199781,"score_gpt":0.42411983990793906,"score_spread":0.16368932681203902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W125764232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014520843,0.00002212625,0.9884026,0.003513216,0.00047235627,0.00068942015,0.000022252143,0.00016347895,0.005262442],"genre_scores_gemma":[0.96577394,0.00002374193,0.03306332,0.0001891657,0.00013691834,0.0007758665,0.000014655284,0.000008610902,0.0000137898505],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980232,0.00007389516,0.00038766538,0.00042761085,0.0008765535,0.0002110683],"domain_scores_gemma":[0.9973768,0.00017688335,0.00014638802,0.00022989605,0.0020026946,0.00006736235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011097104,0.00015655458,0.00016490063,0.00019545456,0.00023157711,0.00029872157,0.00050105643,0.000054304248,0.000014862091],"category_scores_gemma":[0.00060273963,0.00015162851,0.000053827127,0.00040972268,0.000045938632,0.00067727495,0.000053169908,0.000110359164,0.000027869224],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013566928,0.00004438739,0.0000010586825,0.0000065939375,0.0000088287625,7.1120616e-7,0.00084477745,0.004488743,0.001533996,0.85741377,0.00005394395,0.13558964],"study_design_scores_gemma":[0.000066642046,0.00020764073,0.00010655296,0.00012922518,0.000005681357,0.000003260049,0.00047165,0.11617464,0.14735006,0.7345959,0.0006295951,0.0002591371],"about_ca_topic_score_codex":0.00006561182,"about_ca_topic_score_gemma":0.00008873727,"teacher_disagreement_score":0.96432185,"about_ca_system_score_codex":0.00029921357,"about_ca_system_score_gemma":0.00050931284,"threshold_uncertainty_score":0.61832315},"labels":[],"label_agreement":null},{"id":"W14532928","doi":"10.1155/2003/213213","title":"Mining and re-ranking for answering biographical queries on the web","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Association of Gastroenterology","keywords":"Computer science; Bootstrapping (finance); Ranking (information retrieval); Information retrieval; Question answering; Knowledge base; Web mining; World Wide Web; Web page; Artificial intelligence","score_opus":0.14680838160324203,"score_gpt":0.3276615497888354,"score_spread":0.18085316818559335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W14532928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08903495,0.000034830977,0.89053583,0.013189588,0.00030274945,0.00026349345,0.000007552352,0.000105422965,0.0065255747],"genre_scores_gemma":[0.98308206,0.000010725193,0.016066724,0.00058466056,0.00016570133,0.000044615583,0.0000023897833,0.0000061349538,0.000037020327],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985937,0.00004677613,0.00029450015,0.00039955383,0.00044812448,0.00021737014],"domain_scores_gemma":[0.99865097,0.0007645458,0.000082094135,0.00019615103,0.00027069976,0.000035535773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006071382,0.00013202587,0.00010950142,0.00016311108,0.0003377909,0.00033793514,0.00044211495,0.000065504864,0.000021373146],"category_scores_gemma":[0.00028999968,0.00010451964,0.0000517823,0.00027988115,0.00012556075,0.00017824878,0.00006768326,0.00013015162,0.000008854798],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001357657,0.000021938169,0.000055038454,0.0000032899013,0.0000033938502,7.102278e-7,0.00014466005,0.0010915753,0.0012349169,0.9596836,0.00006697733,0.03768034],"study_design_scores_gemma":[0.000018851271,0.000070444956,0.00016500673,0.00004835734,0.0000017598396,0.000001543107,0.00014011237,0.62936175,0.009682511,0.35974964,0.00062580744,0.00013419992],"about_ca_topic_score_codex":0.000027942719,"about_ca_topic_score_gemma":0.00017057356,"teacher_disagreement_score":0.8940471,"about_ca_system_score_codex":0.000028988024,"about_ca_system_score_gemma":0.00010076018,"threshold_uncertainty_score":0.42621875},"labels":[],"label_agreement":null},{"id":"W15128715","doi":"10.1093/heapro/dah211","title":"Design of a mechanism for promoting honesty in E-marketplaces","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","keywords":"Reputation; Honesty; Forward auction; Business; Profit (economics); Incentive; Mechanism (biology); Order (exchange); Trustworthiness; Ask price; E-commerce; Internet privacy; Computer science; Marketing; World Wide Web; Microeconomics; Economics","score_opus":0.3735224080372475,"score_gpt":0.4656751373509016,"score_spread":0.09215272931365409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W15128715","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023853388,0.000002907564,0.9714028,0.0009227455,0.0001326057,0.00050082355,0.000015264312,0.000018821203,0.0031505923],"genre_scores_gemma":[0.9898163,0.0000023992504,0.009706237,0.00012041723,0.000056378933,0.000063013526,0.000002745163,0.00000563271,0.00022687156],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974607,0.0001400351,0.0008930841,0.00036944592,0.00094064465,0.00019609186],"domain_scores_gemma":[0.9935266,0.0042759664,0.0003511367,0.00017364108,0.0016150457,0.000057634166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0092192935,0.000104011364,0.00017831076,0.00039996443,0.00013540444,0.000066532804,0.0004602877,0.00008493966,0.00031464192],"category_scores_gemma":[0.006975885,0.00008957854,0.000054349734,0.00081951113,0.0001266123,0.00016688913,0.000028071438,0.00012005952,0.00012529887],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019469469,0.0001219567,0.000020880923,0.0000021398432,0.0000024640308,3.287447e-7,0.00039811706,0.0016701502,0.015663147,0.9185079,0.000022808885,0.063395455],"study_design_scores_gemma":[0.000021659884,0.000068309004,0.00013072132,0.0000145749,0.0000012182975,9.3837616e-7,0.00082590943,0.07068575,0.20711106,0.7210293,0.00004107375,0.000069518166],"about_ca_topic_score_codex":0.0000071657655,"about_ca_topic_score_gemma":0.00006298209,"teacher_disagreement_score":0.96596295,"about_ca_system_score_codex":0.000048708454,"about_ca_system_score_gemma":0.0001911579,"threshold_uncertainty_score":0.83512896},"labels":[],"label_agreement":null},{"id":"W1527133581","doi":"","title":"Using Pattern Databases to Find Macro Operators","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Heuristics; Macro; Table (database); Simple (philosophy); Operator (biology); Notation; Heuristic; Computer science; Space (punctuation); Theoretical computer science; Mathematics; Algorithm; Sequence (biology); Abstraction; State (computer science); Data mining; Mathematical optimization; Artificial intelligence; Arithmetic; Programming language","score_opus":0.22378880669472126,"score_gpt":0.3662642560339946,"score_spread":0.14247544933927334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527133581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19592725,0.00003410342,0.7370683,0.0007737402,0.00082223443,0.00049091305,0.00033617037,0.00044980334,0.064097516],"genre_scores_gemma":[0.99542546,0.000028358112,0.0034330175,0.0006212913,0.0001084607,0.000011358189,0.00004170447,0.000019672534,0.00031068551],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897087,0.000023662868,0.0002669459,0.00021291719,0.00033026346,0.00019532186],"domain_scores_gemma":[0.999521,0.000050397037,0.00001638137,0.00012729947,0.00018365664,0.000101248195],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013738683,0.00014181969,0.00010702073,0.00012573284,0.000103336075,0.000119225,0.00016853202,0.00004492653,0.008402381],"category_scores_gemma":[0.000053668926,0.00015040326,0.000027083848,0.00030392662,0.000030007675,0.0001451162,0.000013576592,0.00013463746,0.0016716354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009404169,0.000028486229,0.000032985034,0.0000065022878,0.0000074178843,0.0000016381231,0.00021926331,0.8343249,0.002028202,0.018764589,0.00032159855,0.14425504],"study_design_scores_gemma":[0.00002019942,0.000031048774,0.000140763,0.000072535746,0.0000036300933,0.0000030707859,0.000056421115,0.96681726,0.026300272,0.0020456433,0.0042206636,0.00028847886],"about_ca_topic_score_codex":0.000038486087,"about_ca_topic_score_gemma":0.000055406977,"teacher_disagreement_score":0.7994982,"about_ca_system_score_codex":0.00006696852,"about_ca_system_score_gemma":0.000061444305,"threshold_uncertainty_score":0.9991057},"labels":[],"label_agreement":null},{"id":"W1534091696","doi":"","title":"The Language of Stories: A Conceptual Integration Approach","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Conceptual blending; Narrative; Interpretation (philosophy); Cognitive linguistics; Meaning (existential); Computer science; Linguistics; Natural language processing; Cognition; Cognitive science; Epistemology; Psychology; Programming language; Philosophy","score_opus":0.1109899210194331,"score_gpt":0.37630409100503026,"score_spread":0.26531416998559715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1534091696","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4720874,0.00015517868,0.08322369,0.0008561701,0.0032872467,0.00068614224,0.00013259905,0.00012293208,0.43944862],"genre_scores_gemma":[0.9976193,0.0000061159376,0.00041806363,0.00015907263,0.0003096671,0.000077586046,0.000053077078,0.0000094807465,0.0013476821],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99855083,0.00012783351,0.0003711342,0.0002611433,0.0005050782,0.00018395796],"domain_scores_gemma":[0.99841976,0.00042625994,0.00017527987,0.00022605491,0.0007027208,0.000049910497],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006814312,0.00012883924,0.00013121082,0.00008704927,0.00018063298,0.0000647727,0.0003156804,0.0001330226,0.0015321284],"category_scores_gemma":[0.0007388027,0.00009135216,0.00006854334,0.00024400806,0.0004962385,0.000085134176,0.000019442821,0.00042167923,0.0003153412],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108364526,0.0001407509,0.000008585482,0.0000015667691,0.00001687439,8.567532e-7,0.010224197,0.000004758608,0.013456857,0.86663884,0.00017575928,0.109222576],"study_design_scores_gemma":[0.00024808777,0.0006809338,0.0019335115,0.000050342056,0.000058184163,0.000025956442,0.3113173,0.009862679,0.23701398,0.43466863,0.0034029982,0.0007374014],"about_ca_topic_score_codex":0.00018829774,"about_ca_topic_score_gemma":0.00051112165,"teacher_disagreement_score":0.5255318,"about_ca_system_score_codex":0.000022647517,"about_ca_system_score_gemma":0.00013000883,"threshold_uncertainty_score":0.9993806},"labels":[],"label_agreement":null},{"id":"W1556059795","doi":"","title":"A Bayesian Kernel logistic discriminant model: an improvement to the Kernel Fisher's discriminant","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke; University of Windsor","funders":"","keywords":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel (algebra); Fisher kernel; Pattern recognition (psychology); Artificial intelligence; Discriminant; Kernel method; Mathematics; Optimal discriminant analysis; Bayesian probability; Covariance matrix; Covariance; Computer science; Statistics; Machine learning; Support vector machine; Combinatorics","score_opus":0.2148833050964134,"score_gpt":0.3546056745877623,"score_spread":0.13972236949134892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556059795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015275189,0.0000075133303,0.9704312,0.01060664,0.00041876224,0.00044503322,0.000038099548,0.0001115469,0.0026660305],"genre_scores_gemma":[0.98950464,0.000030221741,0.007135421,0.0025546323,0.0001331446,0.00016235554,0.000023877137,0.000013980877,0.00044175852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972363,0.00009631396,0.0005113223,0.00074905314,0.0009656422,0.00044135025],"domain_scores_gemma":[0.9983409,0.00012440402,0.00013765301,0.0005594829,0.00058888446,0.00024867835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004678996,0.0002702414,0.00019907045,0.00016289989,0.00062222936,0.00029774476,0.0013587679,0.00009173021,0.00009909505],"category_scores_gemma":[0.00028388435,0.0001877916,0.00008835102,0.00036453313,0.00015550741,0.00057021464,0.00024127627,0.00027807805,0.00054013054],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011495816,0.0007222793,0.00002215452,0.000012416269,0.000015079883,0.000023966762,0.005017184,0.047886986,0.00906922,0.71283853,0.0027057524,0.22157146],"study_design_scores_gemma":[0.000029687495,0.00032820055,0.00032580065,0.000047177695,0.0000053124027,0.000010330717,0.000339019,0.8228869,0.016300319,0.15911934,0.00032492774,0.00028297713],"about_ca_topic_score_codex":0.00018225326,"about_ca_topic_score_gemma":0.0003585172,"teacher_disagreement_score":0.9742294,"about_ca_system_score_codex":0.00012192281,"about_ca_system_score_gemma":0.0003849618,"threshold_uncertainty_score":0.7657919},"labels":[],"label_agreement":null},{"id":"W160047510","doi":"","title":"Analogical path planning","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Motion planning; Path (computing); Computer science; Probabilistic logic; Robot; Trajectory; Mathematical optimization; Set (abstract data type); Mobile robot; Probability distribution; Probabilistic roadmap; Markov process; Configuration space; Artificial intelligence; Mathematics","score_opus":0.2076154789963048,"score_gpt":0.37192380708654105,"score_spread":0.16430832809023624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W160047510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002359312,0.00001981147,0.9762454,0.0028315876,0.00043392487,0.000119869124,0.000006341849,0.0002361717,0.017747559],"genre_scores_gemma":[0.8891264,0.000003455253,0.10984914,0.00080511556,0.00013780079,0.000016284057,0.000008125966,0.0000065473137,0.000047086858],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99763554,0.00006151522,0.00039160936,0.00055827637,0.000996411,0.00035667233],"domain_scores_gemma":[0.99881697,0.00018937812,0.00012014189,0.00028313693,0.00043892296,0.00015147979],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00051986636,0.00019449096,0.00017840603,0.00020549384,0.00022732215,0.00027957288,0.0010049308,0.00011057029,0.000058376314],"category_scores_gemma":[0.00046849012,0.00018032147,0.00006482267,0.0005544969,0.00010696318,0.00034945182,0.000114665054,0.00033793223,0.00094036217],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007787605,0.00010079466,0.000022903288,0.0000016434565,0.00000586506,0.000048630096,0.00031256917,0.08604413,0.00039734435,0.89083874,0.000062562554,0.022157008],"study_design_scores_gemma":[0.000042081065,0.00019698474,0.0012573685,0.000078723824,0.0000019124373,0.000024783812,0.00007857259,0.289243,0.005844115,0.7028329,0.0001240835,0.00027547663],"about_ca_topic_score_codex":0.0000184437,"about_ca_topic_score_gemma":0.0000012536551,"teacher_disagreement_score":0.88676715,"about_ca_system_score_codex":0.00016149067,"about_ca_system_score_gemma":0.00040831038,"threshold_uncertainty_score":0.9998375},"labels":[],"label_agreement":null},{"id":"W1604446916","doi":"","title":"Bayesian reputation modeling in E-marketplaces sensitive to subjecthity, deception and change","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":114,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Deception; Computer science; Reputation; Heuristics; Interpretation (philosophy); Probabilistic logic; Artificial intelligence; Subjectivity; Bayesian probability; Machine learning; Psychology; Social psychology","score_opus":0.37358274091608257,"score_gpt":0.4420146932122569,"score_spread":0.06843195229617433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1604446916","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55577815,0.0000054848824,0.42932048,0.0052468292,0.000084435414,0.00040499854,0.00002132348,0.000034269877,0.009104028],"genre_scores_gemma":[0.99797654,0.000005058059,0.0012419915,0.00041579184,0.00012213433,0.0000778108,0.000014087357,0.0000062561708,0.00014034758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975265,0.00023019894,0.0005822986,0.00053465966,0.0009400001,0.00018633206],"domain_scores_gemma":[0.99812794,0.0006997781,0.00012096836,0.00015683191,0.0008198122,0.000074695556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026849636,0.00012965187,0.00015861847,0.000509516,0.00017514307,0.00032193685,0.00021619245,0.000080970036,0.00016519059],"category_scores_gemma":[0.001506273,0.00012148972,0.00003183897,0.00087468344,0.000084635,0.00043008558,0.000053652646,0.00014830087,0.00036449058],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015625649,0.00010767139,0.00019079521,0.0000017006702,0.0000017713875,0.0000020785733,0.0013353927,0.038559593,0.0037664662,0.78383493,0.000050497678,0.17199285],"study_design_scores_gemma":[0.0000143688185,0.00003108987,0.0027585768,0.000017825918,0.0000012020677,0.0000017953873,0.001000109,0.42815647,0.003810327,0.5640832,0.000026578717,0.00009841295],"about_ca_topic_score_codex":0.00027180256,"about_ca_topic_score_gemma":0.0025575096,"teacher_disagreement_score":0.4421984,"about_ca_system_score_codex":0.00007865379,"about_ca_system_score_gemma":0.00007466379,"threshold_uncertainty_score":0.4954207},"labels":[],"label_agreement":null},{"id":"W164082874","doi":"","title":"The proteome analyst suite of automated function prediction tools","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Proteome; Gene ontology; Computer science; Function (biology); Suite; Ontology; Sequence (biology); Computational biology; Subcellular localization; Data mining; Bioinformatics; Gene; Biology; Biochemistry; Genetics","score_opus":0.05568293676456858,"score_gpt":0.3325883504638718,"score_spread":0.2769054136993032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W164082874","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.637756,0.00017967944,0.27765414,0.0065073296,0.001138919,0.0020362802,0.00047919757,0.00042871115,0.073819794],"genre_scores_gemma":[0.9983211,0.00002884479,0.00077474007,0.00014410714,0.00020691901,0.000028672268,0.00016719919,0.000006857563,0.0003215403],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988096,0.00006407488,0.0004180316,0.00016357475,0.00040621628,0.00013851366],"domain_scores_gemma":[0.9989202,0.000063202424,0.00020179978,0.00017564613,0.0006033518,0.000035793586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054181385,0.000103848266,0.00008231565,0.000062325256,0.00016929112,0.00007172797,0.00020438302,0.00010114859,0.00007076116],"category_scores_gemma":[0.00081413315,0.00008089697,0.000054365435,0.00016238181,0.00011926361,0.000015498075,0.000037388592,0.00013475298,0.0000839894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074511935,0.00024279972,0.0006309911,0.000032323165,0.00014148981,2.637826e-7,0.00020346598,0.07880952,0.39199173,0.2837529,0.0024373785,0.24101204],"study_design_scores_gemma":[0.00005692688,0.0005742612,0.0053259404,0.0000276074,0.000015348302,0.0000039137853,0.0001452515,0.66245586,0.3132683,0.00495676,0.0129886875,0.00018115272],"about_ca_topic_score_codex":0.0000049908435,"about_ca_topic_score_gemma":0.000053546642,"teacher_disagreement_score":0.58364636,"about_ca_system_score_codex":0.00003024165,"about_ca_system_score_gemma":0.00014525995,"threshold_uncertainty_score":0.32988825},"labels":[],"label_agreement":null},{"id":"W164088426","doi":"","title":"ScriptEase: motivational behaviors for interactive characters in computer role-playing games","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Digital Games and Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Scripting language; Computer science; Character (mathematics); Guard (computer science); Human–computer interaction; Multimedia; Abstraction; Programming language","score_opus":0.08096414252500254,"score_gpt":0.35938319194162166,"score_spread":0.2784190494166191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W164088426","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.65310514,0.00004596961,0.06780262,0.008772528,0.0028174554,0.0020463301,0.0003269391,0.00021920679,0.26486382],"genre_scores_gemma":[0.99645334,0.0000060625775,0.00097547786,0.00039034872,0.0005492498,0.000094387,0.00009871755,0.000009679073,0.0014227339],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826026,0.000064378364,0.00038145357,0.0003272488,0.00065678963,0.00030987],"domain_scores_gemma":[0.9986741,0.00042595607,0.00013375116,0.00006527338,0.0006197951,0.00008114914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050420495,0.00013807745,0.00015895093,0.00023824994,0.00014256268,0.00023272508,0.00023092542,0.00009397097,0.0002246611],"category_scores_gemma":[0.00039887102,0.00014726317,0.0000764255,0.00026359974,0.00021813746,0.0004619767,0.000022304797,0.00016627631,0.00008237267],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007716294,0.00026534745,0.00085203623,0.0000030126484,0.0000047394396,0.0000019172214,0.0016257087,0.0005477269,0.0006891042,0.79370964,0.00014618045,0.20207742],"study_design_scores_gemma":[0.00041364026,0.0005042656,0.10312414,0.00054168573,0.00002768926,0.0000031503405,0.01129926,0.07809776,0.01256501,0.73900956,0.052880634,0.0015332253],"about_ca_topic_score_codex":0.00045566054,"about_ca_topic_score_gemma":0.0017092318,"teacher_disagreement_score":0.34334823,"about_ca_system_score_codex":0.00027407872,"about_ca_system_score_gemma":0.00038641252,"threshold_uncertainty_score":0.60052174},"labels":[],"label_agreement":null},{"id":"W165596894","doi":"","title":"A polynomial-time algorithm for action-graph games","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Game Theory and Applications","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Graph; Time complexity; Representation (politics); Mathematics; Polynomial; Bounded function; Theoretical computer science; Context (archaeology); Computer science; Algorithm; Discrete mathematics","score_opus":0.275005480657108,"score_gpt":0.44427386357591075,"score_spread":0.16926838291880275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W165596894","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01577238,0.000020671427,0.92345166,0.005708926,0.0004723719,0.00072443666,0.00039361214,0.00014445119,0.053311504],"genre_scores_gemma":[0.98233646,0.0000036498996,0.0087411525,0.00037283002,0.00053194066,0.00019628285,0.000049279868,0.000013107523,0.0077553135],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99703944,0.0001173818,0.0007520819,0.00059615367,0.0012173785,0.0002775687],"domain_scores_gemma":[0.99568003,0.0021106543,0.00026877428,0.00031316595,0.0015385008,0.00008884898],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001790717,0.00018303207,0.00023019136,0.00038446818,0.00039730722,0.00039507757,0.00069332763,0.0001113625,0.0021549554],"category_scores_gemma":[0.001186717,0.00015750893,0.00017339492,0.0007841294,0.00025578265,0.0002566307,0.00003813329,0.00015200902,0.0036235186],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004640556,0.0001713672,0.0000069069715,7.395284e-7,0.0000062688428,3.2768912e-7,0.00003776781,0.0009561442,0.0056213117,0.54740864,0.013062763,0.43268138],"study_design_scores_gemma":[0.000035127527,0.00007216541,0.00025959002,0.000007107263,0.0000054664342,0.0000024423184,0.00022348142,0.0878851,0.032164678,0.86121285,0.017946064,0.00018592623],"about_ca_topic_score_codex":0.000038228303,"about_ca_topic_score_gemma":0.000060051738,"teacher_disagreement_score":0.96656406,"about_ca_system_score_codex":0.000061500476,"about_ca_system_score_gemma":0.00024158295,"threshold_uncertainty_score":0.9987572},"labels":[],"label_agreement":null},{"id":"W1666476","doi":"","title":"Speeding up learning in real-time search via automatic state abstraction","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Abstraction; Heuristic; Path (computing); Beam search; State (computer science); Search algorithm; Bidirectional search; Domain (mathematical analysis); Artificial intelligence; Incremental heuristic search; Machine learning; Algorithm; Programming language; Mathematics","score_opus":0.1356894275948526,"score_gpt":0.37130966447218483,"score_spread":0.23562023687733222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1666476","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08302116,0.000008759369,0.896251,0.003032662,0.00052353385,0.0003174663,0.000003779642,0.0005188519,0.01632275],"genre_scores_gemma":[0.9493226,0.000017478256,0.04960012,0.000088474124,0.00014020762,0.000013704621,0.000008611313,0.0000117929285,0.0007970121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99720484,0.00015933922,0.0005867786,0.0005305751,0.0010882476,0.00043023351],"domain_scores_gemma":[0.9986137,0.00046508526,0.00016229117,0.00019903512,0.0004431724,0.00011670305],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014508497,0.0001933736,0.0002026783,0.00047997702,0.0001863887,0.00030515133,0.0006512057,0.00009334555,0.00022304694],"category_scores_gemma":[0.0003809694,0.00020891694,0.00004586501,0.0006734386,0.0000694149,0.0007591053,0.00009347584,0.00056874525,0.0029536246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013527175,0.00010875924,0.00008188446,0.0000073745177,0.0000073483748,0.000015195985,0.0014452316,0.34388494,0.010039772,0.04896372,0.00004593795,0.59538627],"study_design_scores_gemma":[0.000033119,0.00007973662,0.002623888,0.000072791045,0.0000011699069,0.000012055361,0.00007047093,0.96232796,0.0150286555,0.019504959,0.00003624996,0.00020896035],"about_ca_topic_score_codex":0.0001236414,"about_ca_topic_score_gemma":0.000023265033,"teacher_disagreement_score":0.8663014,"about_ca_system_score_codex":0.00036899373,"about_ca_system_score_gemma":0.00034242924,"threshold_uncertainty_score":0.9978227},"labels":[],"label_agreement":null},{"id":"W167277932","doi":"","title":"Dual search in permutation state spaces","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Dual (grammatical number); Duality (order theory); State (computer science); Permutation (music); Computer science; Symmetry (geometry); Search algorithm; Theoretical computer science; Mathematics; Algorithm; Combinatorics; Physics","score_opus":0.07908508805931216,"score_gpt":0.35123582287096733,"score_spread":0.2721507348116552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W167277932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16637877,0.000023584025,0.8006171,0.007211635,0.00036596676,0.00021451384,0.000011526198,0.000175422,0.025001483],"genre_scores_gemma":[0.99378324,0.0000049473415,0.0053089107,0.00013638516,0.00012352374,0.000012460278,0.00001371266,0.000005734155,0.0006110612],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980892,0.00013460162,0.0003233088,0.000397078,0.0007913481,0.0002644882],"domain_scores_gemma":[0.99917495,0.00019600664,0.00006989841,0.00014379808,0.00036678274,0.000048577294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066881865,0.00012905538,0.00011592309,0.00031218232,0.00012263701,0.00035367155,0.00038082685,0.000048836646,0.00010343899],"category_scores_gemma":[0.00013593133,0.0001264323,0.000033823326,0.00057603576,0.000069470545,0.0003153152,0.00006371716,0.00030459405,0.00044695206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010324536,0.00010344638,0.00024179168,0.0000030459369,0.0000015940748,0.000012590587,0.00034342555,0.05437947,0.0006979308,0.8347312,0.000045039837,0.10943014],"study_design_scores_gemma":[0.000025446969,0.00006668628,0.005198099,0.00001792282,4.0615322e-7,0.0000036454378,0.00006446303,0.69825506,0.0075338613,0.28856254,0.0001302312,0.00014163063],"about_ca_topic_score_codex":0.0005652992,"about_ca_topic_score_gemma":0.00032574678,"teacher_disagreement_score":0.8274045,"about_ca_system_score_codex":0.00008657825,"about_ca_system_score_gemma":0.00023413765,"threshold_uncertainty_score":0.57448137},"labels":[],"label_agreement":null},{"id":"W19153916","doi":"10.1080/00048670802607154","title":"A framework for representing and solving NP search problems","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Constraint satisfaction problem; Answer set programming; Constraint programming; Parameterized complexity; Computer science; Constraint satisfaction; Extension (predicate logic); Satisfiability; Boolean satisfiability problem; Set (abstract data type); Theoretical computer science; Solver; Strengths and weaknesses; Backtracking; Constraint (computer-aided design); Mathematical optimization; Programming language; Mathematics; Artificial intelligence; Algorithm; Stochastic programming","score_opus":0.20830229007824574,"score_gpt":0.3866366578885083,"score_spread":0.17833436781026257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W19153916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032453125,0.00009548604,0.9782361,0.0048345984,0.00019754515,0.0003234149,0.0000040537293,0.000112178626,0.012951324],"genre_scores_gemma":[0.90196884,0.000026315898,0.0969623,0.00034379974,0.0003441234,0.000054377488,0.0000027772471,0.000007847427,0.00028963023],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99817747,0.000060336064,0.00033258912,0.0005692844,0.0005023201,0.00035801635],"domain_scores_gemma":[0.9979953,0.0008015443,0.00009865077,0.00022969767,0.0007628423,0.00011191858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096452085,0.00015027176,0.0001524644,0.00014918092,0.00035558315,0.00048943964,0.0005948741,0.00011243073,0.00006720248],"category_scores_gemma":[0.0013526914,0.00014120294,0.00005788861,0.0003037931,0.000109215594,0.00041078648,0.00017937346,0.00026133258,0.00011700014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000008458258,0.00006072026,0.000059002927,0.000010485134,0.0000058582486,5.2969983e-7,0.0009449069,0.00068750407,0.00055325194,0.84168094,0.00007571036,0.15591262],"study_design_scores_gemma":[0.000019650271,0.00006729896,0.000108025466,0.000049530812,0.0000016614739,0.000004450539,0.00007024685,0.4523407,0.0075875674,0.5384727,0.0011322561,0.00014589743],"about_ca_topic_score_codex":0.000013671708,"about_ca_topic_score_gemma":0.000049617327,"teacher_disagreement_score":0.89872354,"about_ca_system_score_codex":0.00006389032,"about_ca_system_score_gemma":0.00020946919,"threshold_uncertainty_score":0.5758089},"labels":[],"label_agreement":null},{"id":"W1919500120","doi":"","title":"Vesselness features and the inverse compositional AAM for robust face recognition sing thermal IR","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Artificial intelligence; Image warping; Facial expression; Computer science; Pattern recognition (psychology); Computer vision; Face (sociological concept); Active appearance model; Expression (computer science); Facial recognition system; Local binary patterns; Image (mathematics); Histogram","score_opus":0.11694965693318955,"score_gpt":0.32794215122885784,"score_spread":0.2109924942956683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1919500120","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8304457,0.00025935264,0.08233833,0.051635463,0.00097137556,0.0048457817,0.00019059128,0.00020741073,0.029105991],"genre_scores_gemma":[0.9923354,0.000021468191,0.0036470285,0.0030025728,0.0003923816,0.0002539262,0.00017448646,0.000016545857,0.00015622273],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984847,0.00009547054,0.0003403276,0.0002917803,0.0005664021,0.00022136224],"domain_scores_gemma":[0.9976808,0.00074911624,0.00012811177,0.00013051392,0.001211043,0.00010042018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005422017,0.00018161467,0.00023290789,0.0001619086,0.0003067811,0.0001047098,0.00013339893,0.000120129844,0.00064345193],"category_scores_gemma":[0.0005824815,0.00012716872,0.00008708076,0.00021092738,0.0006482866,0.00016560224,0.000022976856,0.000330123,0.000104002705],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004856808,0.0006220879,0.00019640468,0.0001818895,0.00028492927,0.000009161692,0.003734213,0.002064776,0.064615384,0.5966624,0.0067157256,0.32005623],"study_design_scores_gemma":[0.002149115,0.0010245234,0.021676349,0.0009491817,0.00023850243,0.00017693064,0.005867646,0.20572129,0.094201736,0.66685665,0.00033696514,0.000801082],"about_ca_topic_score_codex":0.000045910514,"about_ca_topic_score_gemma":0.000016891561,"teacher_disagreement_score":0.31925514,"about_ca_system_score_codex":0.000054777294,"about_ca_system_score_gemma":0.00014561233,"threshold_uncertainty_score":0.70453477},"labels":[],"label_agreement":null},{"id":"W1988996388","doi":"10.5555/777092.777258","title":"Generalized features: their application to classification","year":2002,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; sort; Focus (optics); Set (abstract data type); Feature (linguistics); Process (computing); Artificial intelligence; Machine learning; Information retrieval; Data mining","score_opus":0.16865849311093994,"score_gpt":0.3426979531147663,"score_spread":0.17403946000382636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988996388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017820856,0.00003560359,0.9482105,0.03091697,0.00022899245,0.00044871948,0.0000111766585,0.0006274742,0.017738514],"genre_scores_gemma":[0.9834061,0.00004962458,0.014015427,0.0014409473,0.000086521315,0.00024565187,0.000014148418,0.000009118272,0.0007324096],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979612,0.00006811323,0.00040816824,0.00064044056,0.00065927004,0.0002627585],"domain_scores_gemma":[0.99842685,0.000121767385,0.00015814457,0.000596325,0.0005865328,0.000110390574],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003304261,0.0001992613,0.00015255428,0.00036643964,0.00024362761,0.0003669927,0.0013316544,0.00013384767,0.0002285433],"category_scores_gemma":[0.00031410164,0.00018010757,0.00006347066,0.0009658728,0.000083793275,0.00040709518,0.000111344896,0.0002071023,0.0025038738],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052032083,0.00008194943,0.0000049780165,0.0000012466794,0.0000035513258,2.0182094e-7,0.000235945,0.0001745904,0.007004846,0.69956064,0.0016758704,0.291251],"study_design_scores_gemma":[0.00003801417,0.000120979945,0.0011266188,0.000015766287,0.0000021999915,0.000003351266,0.00024957547,0.42875525,0.0798503,0.4782806,0.011194696,0.00036264554],"about_ca_topic_score_codex":0.00001018626,"about_ca_topic_score_gemma":0.000021777792,"teacher_disagreement_score":0.98162407,"about_ca_system_score_codex":0.0001498503,"about_ca_system_score_gemma":0.00006252169,"threshold_uncertainty_score":0.9982728},"labels":[],"label_agreement":null},{"id":"W2039203087","doi":"10.5555/777092.777262","title":"A reputation-oriented reinforcement learning approach for agents in electronic marketplaces","year":2002,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reputation; Reinforcement learning; Quality (philosophy); Purchasing; Product (mathematics); Value (mathematics); Function (biology); Set (abstract data type); Order (exchange); Computer science; Business; Microeconomics; Marketing; Economics; Artificial intelligence","score_opus":0.3203309259973149,"score_gpt":0.4355815457183797,"score_spread":0.11525061972106482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2039203087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010688454,0.000018417928,0.9354265,0.001333837,0.000112545255,0.0006441717,0.000009390588,0.000051439005,0.051715266],"genre_scores_gemma":[0.9924437,0.00002465181,0.0010375298,0.00024217318,0.000070957016,0.0003110854,0.000036373283,0.0000082784645,0.005825248],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970026,0.0001970456,0.00079433125,0.0005565847,0.0011457842,0.00030366395],"domain_scores_gemma":[0.9975596,0.001031711,0.00029056292,0.0001964427,0.0008515858,0.00007010525],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0026711065,0.00014298502,0.00017321437,0.00040373608,0.0003319957,0.00017621873,0.00042261096,0.00008203733,0.0026403735],"category_scores_gemma":[0.0039366884,0.0001304797,0.00008218606,0.0010110325,0.00011109243,0.0002677432,0.000034483015,0.00026554757,0.000543766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008907625,0.00014106013,0.00006666172,0.0000018264495,0.000005771513,1.8270977e-7,0.0003832693,0.16712308,0.000094069015,0.78521955,0.00055103184,0.046324443],"study_design_scores_gemma":[0.000046057703,0.00008749243,0.00011490037,0.0000065704176,0.0000021085748,0.0000011477636,0.00091785553,0.6913399,0.0010526967,0.30147073,0.0048463484,0.00011416265],"about_ca_topic_score_codex":0.0000074467002,"about_ca_topic_score_gemma":0.000019156838,"teacher_disagreement_score":0.98175526,"about_ca_system_score_codex":0.0001670987,"about_ca_system_score_gemma":0.000097911674,"threshold_uncertainty_score":0.99827135},"labels":[],"label_agreement":null},{"id":"W2090036526","doi":"","title":"Distance Metric Learning Versus Fisher Discriminant Analysis.","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Linear discriminant analysis; Optimal discriminant analysis; Metric (unit); Artificial intelligence; Statistics; Kernel Fisher discriminant analysis; Mathematics; Discriminant; Computer science; Multiple discriminant analysis; Fisher kernel; Pattern recognition (psychology); Engineering","score_opus":0.19794439780867815,"score_gpt":0.35058680429940026,"score_spread":0.1526424064907221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090036526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014110983,0.00003883367,0.9602107,0.0013928728,0.00051638915,0.00011603937,0.0000064701,0.00015062354,0.02345712],"genre_scores_gemma":[0.99535364,0.00007863323,0.0036279873,0.00020694527,0.0000685544,0.000025532787,0.000026308258,0.0000060552934,0.0006063633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788886,0.00011147749,0.0003680915,0.00052903243,0.00082492625,0.0002776106],"domain_scores_gemma":[0.99854887,0.00035473425,0.00014976156,0.00023866727,0.00059469364,0.00011327471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031396296,0.000172582,0.00020973412,0.00052671315,0.0004194108,0.0001631129,0.00062295847,0.000081641265,0.00036366953],"category_scores_gemma":[0.0005923878,0.00015559861,0.0001393332,0.002077351,0.0001092251,0.00050581025,0.000093595365,0.0002960317,0.0007587821],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017109333,0.00029179882,0.0004348766,0.0000059316562,0.00011719525,0.000024827277,0.00078673824,0.016763104,0.0008956029,0.8626682,0.0008383855,0.11700221],"study_design_scores_gemma":[0.00013052636,0.0003665696,0.007215251,0.000041509178,0.000058869853,0.0000066590546,0.00035791015,0.9027795,0.02314134,0.061112076,0.0041198093,0.0006699489],"about_ca_topic_score_codex":0.000051216095,"about_ca_topic_score_gemma":0.000071327595,"teacher_disagreement_score":0.98124266,"about_ca_system_score_codex":0.000089684494,"about_ca_system_score_gemma":0.00016659396,"threshold_uncertainty_score":0.9752862},"labels":[],"label_agreement":null},{"id":"W209492218","doi":"","title":"Hidden naive Bayes","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Naive Bayes classifier; Bayesian programming; Artificial intelligence; Bayesian network; Machine learning; Computer science; Conditional independence; Bayes' theorem; Bayesian probability; Estimator; Bayes error rate; Pattern recognition (psychology); Algorithm; Data mining; Mathematics; Bayes factor; Bayes classifier; Statistics; Support vector machine","score_opus":0.1332457761803776,"score_gpt":0.3481089768628088,"score_spread":0.21486320068243117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W209492218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022075786,0.000039336144,0.9378863,0.011960433,0.00030136897,0.00013377714,0.000009955771,0.0002652136,0.047196053],"genre_scores_gemma":[0.9603268,0.0000283172,0.037038535,0.0018009256,0.0003225366,0.000028021639,0.0000061233914,0.000009622076,0.0004391147],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975525,0.00008389781,0.0004623935,0.0006148189,0.0009177798,0.0003685869],"domain_scores_gemma":[0.99831367,0.00020804619,0.00012480441,0.00035427828,0.00083309296,0.00016613943],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005105722,0.00022577195,0.00018378177,0.00021710756,0.00023156776,0.00040017837,0.0012311297,0.00012345143,0.0004247722],"category_scores_gemma":[0.00027850267,0.00021823155,0.00008259813,0.00045056842,0.000116015726,0.0006452909,0.00013723539,0.0003349476,0.0031478447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007979001,0.0000760165,0.000003979003,0.0000014904452,0.000005357285,0.0000017940098,0.00019844777,0.00087384117,0.00070081983,0.6893758,0.0003481296,0.30840632],"study_design_scores_gemma":[0.000020059382,0.000091894595,0.00008714061,0.000031092073,0.0000021265173,0.000006797943,0.0000466778,0.4772179,0.028940752,0.4922667,0.0010218701,0.00026695692],"about_ca_topic_score_codex":0.000019309959,"about_ca_topic_score_gemma":0.00003817644,"teacher_disagreement_score":0.9581192,"about_ca_system_score_codex":0.00012813932,"about_ca_system_score_gemma":0.00039350294,"threshold_uncertainty_score":0.99762833},"labels":[],"label_agreement":null},{"id":"W2113932434","doi":"","title":"Clause learning can effectively P-simulate general propositional resolution","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"DPLL algorithm; Mathematical proof; Computer science; Resolution (logic); Class (philosophy); Artificial intelligence; Algorithm; Mathematics","score_opus":0.1641521181636323,"score_gpt":0.3675864209641359,"score_spread":0.20343430280050362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113932434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047742784,0.000012818763,0.9415364,0.001039643,0.00045483257,0.00032413314,0.000009462222,0.00025124027,0.008628651],"genre_scores_gemma":[0.88157547,0.000021292622,0.117517956,0.0002836619,0.00018619664,0.000053492306,0.000022887449,0.000010944274,0.00032812255],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971208,0.000402107,0.00045476446,0.0005933681,0.0010908525,0.00033809504],"domain_scores_gemma":[0.9981141,0.00022355093,0.00020795388,0.00025708423,0.0010767498,0.00012053667],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000883504,0.00021242154,0.00017472536,0.00028268973,0.00066448806,0.0001419168,0.00067169353,0.000134221,0.00007863159],"category_scores_gemma":[0.00085237063,0.00021728803,0.000078799116,0.0006237621,0.00023267836,0.00056600664,0.00010774653,0.0004844342,0.00040286937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052697655,0.0001063072,0.00006589812,0.000004411057,0.000009491743,0.000007885247,0.00039607883,0.035301562,0.0045473855,0.9363451,0.00005380775,0.023109378],"study_design_scores_gemma":[0.00004284507,0.00026013044,0.0043794434,0.000027554437,0.000002542346,0.00003591526,0.000018014935,0.817826,0.04702513,0.12986584,0.00025486073,0.00026172723],"about_ca_topic_score_codex":0.00004844966,"about_ca_topic_score_gemma":0.000013105978,"teacher_disagreement_score":0.8338327,"about_ca_system_score_codex":0.0003173907,"about_ca_system_score_gemma":0.00046630672,"threshold_uncertainty_score":0.8860749},"labels":[],"label_agreement":null},{"id":"W2125336041","doi":"","title":"Generalized nogoods in CSPs","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Solver; Computer science; Boolean satisfiability problem; Mathematical optimization; Yield (engineering); Artificial intelligence; Theoretical computer science; Mathematics; Programming language","score_opus":0.11459982372095619,"score_gpt":0.3476652843411281,"score_spread":0.23306546062017192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125336041","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007964488,0.000013728925,0.9436952,0.012700483,0.0003265194,0.00019170762,0.0000053288263,0.00014326884,0.034959275],"genre_scores_gemma":[0.9621134,0.000026525795,0.036238614,0.0012914517,0.00010510487,0.000021147336,0.0000066258353,0.000004910748,0.00019221581],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842227,0.00007768898,0.00039814474,0.00036734887,0.000524693,0.00020983414],"domain_scores_gemma":[0.999227,0.000108310174,0.00008696192,0.00018315946,0.00031904588,0.00007555398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038548175,0.00013063887,0.00012282266,0.00031317078,0.00009213348,0.00018463802,0.00043050578,0.00007813715,0.0007465421],"category_scores_gemma":[0.0001938194,0.00013598366,0.000044272187,0.0005435183,0.000059614325,0.00049127784,0.00005111668,0.00018779766,0.0006811395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000081701855,0.000056519177,0.00006331361,7.5035587e-7,0.0000017248937,0.0000011601952,0.00013933753,0.0183187,0.0003856324,0.7184158,0.00006182664,0.26254708],"study_design_scores_gemma":[0.000058168618,0.000037084552,0.0015273268,0.000016965692,8.460962e-7,0.000004765955,0.000035190966,0.8696401,0.012864075,0.114318065,0.0012959742,0.00020141211],"about_ca_topic_score_codex":0.000021141013,"about_ca_topic_score_gemma":0.00033905488,"teacher_disagreement_score":0.9541489,"about_ca_system_score_codex":0.00015565068,"about_ca_system_score_gemma":0.00024571078,"threshold_uncertainty_score":0.8754898},"labels":[],"label_agreement":null},{"id":"W2130302372","doi":"","title":"Adapting Autonomous Behavior Based on an Estimate of an Operator's Trust","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Robot; Trustworthiness; Task (project management); Context (archaeology); Computer science; Human–computer interaction; Human–robot interaction; Order (exchange); Operator (biology); Task analysis; Artificial intelligence; Computer security; Engineering","score_opus":0.1526836798605626,"score_gpt":0.44804369716433745,"score_spread":0.2953600173037748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130302372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7647631,0.0000025947566,0.06686478,0.0011822439,0.0024013054,0.00057517295,0.00012630245,0.0003450644,0.16373944],"genre_scores_gemma":[0.99702376,2.8861598e-7,0.0015621473,0.0007243278,0.00027076705,0.00008356698,0.000079707155,0.000020019343,0.00023544017],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978229,0.0003358975,0.0006296517,0.00044944385,0.0005433753,0.00021875088],"domain_scores_gemma":[0.9983357,0.00022235834,0.00024199126,0.00034090946,0.00071139564,0.00014760446],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00079674326,0.00019203058,0.0002108802,0.00028520732,0.00019674448,0.00009883908,0.0003336123,0.00013942877,0.011417752],"category_scores_gemma":[0.00024558144,0.00019350061,0.00006559925,0.00017848173,0.00011688172,0.00023244829,0.000012841885,0.00027460247,0.0011019547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002644185,0.000889548,0.00011529422,0.00000353479,0.000007081489,0.0000025875815,0.00059261685,0.008573483,0.0011006303,0.8068046,0.000035353285,0.18161084],"study_design_scores_gemma":[0.00012438365,0.001480452,0.013292377,0.000039958297,0.000012686185,0.00000489148,0.0008695778,0.9645766,0.011756213,0.0063556917,0.0011692734,0.00031786528],"about_ca_topic_score_codex":0.00007810614,"about_ca_topic_score_gemma":0.0001148068,"teacher_disagreement_score":0.9560031,"about_ca_system_score_codex":0.000087535525,"about_ca_system_score_gemma":0.00016973239,"threshold_uncertainty_score":0.9996758},"labels":[],"label_agreement":null},{"id":"W2160725942","doi":"","title":"The Consolidation of Task Knowledge for Lifelong Machine Learning","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Computer science; Lifelong learning; Task (project management); Consolidation (business); Domain knowledge; Knowledge transfer; Knowledge management; Artificial intelligence; Human–computer interaction; Engineering; Psychology; Systems engineering","score_opus":0.12035925711131597,"score_gpt":0.34744418537688576,"score_spread":0.2270849282655698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160725942","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016452867,0.00007740549,0.9802201,0.0035205286,0.00033659048,0.00042593756,0.000004875376,0.00007692645,0.0136923855],"genre_scores_gemma":[0.99252534,0.00003282017,0.006266255,0.00018228978,0.00007221176,0.00009758325,0.000013644095,0.0000072454263,0.0008026359],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852043,0.00013779239,0.00044065175,0.00027670257,0.00041114885,0.00021324825],"domain_scores_gemma":[0.9967052,0.0013600073,0.00024020599,0.00016970176,0.0014564757,0.00006843288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084253674,0.00012066137,0.00012608604,0.00012355752,0.000451743,0.0003137585,0.00060147006,0.000057773148,0.000106729276],"category_scores_gemma":[0.0015383352,0.00009614036,0.000066037646,0.00032549986,0.00013868556,0.0003348638,0.00006781,0.00020732215,0.00037184096],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011778853,0.00003820587,0.000019967834,0.000004266365,0.0000072361763,7.039314e-8,0.00043936473,0.0015584935,0.0019170002,0.7672854,0.00013813394,0.2285801],"study_design_scores_gemma":[0.000037561364,0.0001177875,0.00030886332,0.000020472668,0.000001923364,0.0000010002784,0.00024768477,0.83006513,0.009574478,0.15284193,0.006666181,0.00011699556],"about_ca_topic_score_codex":0.000026157522,"about_ca_topic_score_gemma":0.000037575755,"teacher_disagreement_score":0.99088,"about_ca_system_score_codex":0.000044860626,"about_ca_system_score_gemma":0.00023878414,"threshold_uncertainty_score":0.47793874},"labels":[],"label_agreement":null},{"id":"W2165796360","doi":"","title":"Distance metric learning vs. Fisher discriminant analysis","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metric (unit); Linear discriminant analysis; Discriminant; Computer science; Semidefinite programming; Artificial intelligence; Class (philosophy); Kernel Fisher discriminant analysis; Iterative method; Machine learning; Optimal discriminant analysis; Mathematical optimization; Algorithm; Mathematics; Pattern recognition (psychology)","score_opus":0.13735980511357815,"score_gpt":0.3309964840608148,"score_spread":0.19363667894723663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165796360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013919912,0.000030703173,0.9696442,0.0019066386,0.0002421377,0.00011288388,0.0000068504733,0.00014229729,0.013994382],"genre_scores_gemma":[0.99421453,0.00009071775,0.0042281863,0.0004347352,0.00006083679,0.000028583205,0.0000258259,0.000006431922,0.0009101698],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978019,0.00011923077,0.00039855595,0.00055040576,0.0008470072,0.00028290835],"domain_scores_gemma":[0.99857926,0.00025134513,0.00015911073,0.0002461747,0.0006472588,0.000116871306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034672892,0.00017772584,0.00023071862,0.00060532254,0.0004421214,0.00018076957,0.00064922165,0.000084185354,0.0004298315],"category_scores_gemma":[0.00048715287,0.00015825257,0.00014902187,0.002298491,0.00011112995,0.00052867224,0.000096796626,0.000299479,0.0007517807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008041418,0.0003852652,0.0017137618,0.000009401915,0.000115497955,0.000032251628,0.0011564055,0.019265631,0.0012899595,0.83813643,0.0012689226,0.13654608],"study_design_scores_gemma":[0.000049819417,0.00021580726,0.0127550885,0.000044937107,0.00004305588,0.00001026435,0.00021930807,0.8766533,0.02458731,0.08140088,0.003463313,0.0005569278],"about_ca_topic_score_codex":0.000063139654,"about_ca_topic_score_gemma":0.000056925128,"teacher_disagreement_score":0.9802946,"about_ca_system_score_codex":0.000080893515,"about_ca_system_score_gemma":0.00015972203,"threshold_uncertainty_score":0.966287},"labels":[],"label_agreement":null},{"id":"W2167994996","doi":"","title":"Partial pathfinding using map abstraction and refinement","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":120,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Abstraction; Pathfinding; Computer science; Heuristic; Domain (mathematical analysis); Process (computing); Path (computing); Function (biology); Motion planning; Algorithm; Theoretical computer science; Mathematical optimization; Artificial intelligence; Programming language; Mathematics; Shortest path problem; Robot","score_opus":0.19083000481911286,"score_gpt":0.3618077080581525,"score_spread":0.17097770323903963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167994996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044852756,0.00005637815,0.94516665,0.005632508,0.0004412541,0.0001463107,0.0000085602505,0.00013490205,0.0035606737],"genre_scores_gemma":[0.96702933,0.000007858142,0.032269698,0.0003685495,0.00023058025,0.000007698942,0.000005924791,0.000005347178,0.00007501959],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984971,0.000052673142,0.00033376864,0.00037953124,0.0005022947,0.00023458255],"domain_scores_gemma":[0.9992464,0.00015610512,0.00013575972,0.00014198692,0.00023230634,0.000087424094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060209614,0.00013698591,0.00010827391,0.00015069782,0.0003047046,0.00028693388,0.00026436686,0.000074781594,0.00012968776],"category_scores_gemma":[0.000103023856,0.00013956986,0.000029825354,0.00016939643,0.0000488965,0.00043511967,0.000063931766,0.00021779377,0.00019249982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020175581,0.00006894961,0.000082028404,0.0000074587315,0.0000079596375,0.0000038191,0.00055533886,0.02238016,0.0047784657,0.72192043,0.00011428536,0.25006092],"study_design_scores_gemma":[0.000026785396,0.00007355007,0.00020178485,0.00006967636,0.000003519275,0.000008403925,0.000063397034,0.93185526,0.026773233,0.03836534,0.00235012,0.00020893321],"about_ca_topic_score_codex":0.00003824419,"about_ca_topic_score_gemma":0.000019746156,"teacher_disagreement_score":0.92217654,"about_ca_system_score_codex":0.0001125068,"about_ca_system_score_gemma":0.00017563437,"threshold_uncertainty_score":0.56914943},"labels":[],"label_agreement":null},{"id":"W2171494278","doi":"","title":"Software testing by active learning for commercial games","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Software Testing and Debugging Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Computer science; Correctness; Video game; Context (archaeology); Software engineering; Software performance testing; Artificial intelligence; White-box testing; Software; Active learning (machine learning); Machine learning; Human–computer interaction; Software construction; Software system; Multimedia; Programming language","score_opus":0.16779529998615517,"score_gpt":0.36722164474622604,"score_spread":0.19942634476007087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171494278","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014629374,0.000020456033,0.9905042,0.0019440001,0.0001419034,0.00024210646,0.000017422375,0.003142001,0.0025249417],"genre_scores_gemma":[0.7733667,0.0000028308086,0.22544648,0.00075152604,0.00019100559,0.000085426065,0.000016819284,0.000012460418,0.00012674308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981986,0.00007444854,0.00035199957,0.0005054307,0.00053920614,0.00033029445],"domain_scores_gemma":[0.99621195,0.0022247692,0.00018207487,0.00019298804,0.001096351,0.00009185479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054920255,0.0001975351,0.00017751707,0.00015614353,0.00041960992,0.0002746581,0.00074435456,0.000104854145,0.000038534996],"category_scores_gemma":[0.0063814204,0.00020445742,0.000079228994,0.00038840843,0.00009354356,0.00039473362,0.00010627808,0.00032857675,0.0000902721],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020273139,0.00009055308,0.0001461742,0.0000043072314,0.000006952087,3.965962e-7,0.00020694309,0.0012085706,0.0003215058,0.117777295,0.004473443,0.87574357],"study_design_scores_gemma":[0.000047469046,0.00035738107,0.00048915605,0.00009350755,0.000005122787,0.000004809984,0.00002556935,0.5101072,0.036864243,0.44774458,0.0038421573,0.00041875514],"about_ca_topic_score_codex":0.000030121491,"about_ca_topic_score_gemma":0.000011510942,"teacher_disagreement_score":0.87532485,"about_ca_system_score_codex":0.00013969191,"about_ca_system_score_gemma":0.00024974957,"threshold_uncertainty_score":0.83375317},"labels":[],"label_agreement":null},{"id":"W2176672739","doi":"","title":"Dysregulated Learning with Advanced Learning Technologies","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Metacognition; Computer science; Premise; Task (project management); Cognition; Proactive learning; Active learning (machine learning); Multi-task learning; Class (philosophy); Self-regulated learning; Artificial intelligence; Cognitive psychology; Robot learning; Psychology; Neuroscience; Engineering; Mathematics education","score_opus":0.04175282869208486,"score_gpt":0.31850769705943494,"score_spread":0.2767548683673501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2176672739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2066663,0.00002713723,0.738388,0.025733076,0.0005848063,0.00031114486,0.0000030450747,0.0031567889,0.025129694],"genre_scores_gemma":[0.9766032,0.000014490106,0.022519875,0.00007216596,0.000054847147,0.0000117335285,0.000008330823,0.000012582336,0.00070277473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809587,0.00006449194,0.00029021292,0.0005201524,0.00070611155,0.0003231344],"domain_scores_gemma":[0.9984645,0.0002380037,0.00018309528,0.00024718567,0.0008008859,0.00006631304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004377785,0.00020272114,0.00017702053,0.0002756786,0.0003847959,0.00031611265,0.0008165431,0.0001493109,0.000071011695],"category_scores_gemma":[0.0011716312,0.0001730203,0.000045652672,0.00078129536,0.00020348585,0.00033908102,0.00011953025,0.0015140469,0.0003027961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016438997,0.000053519787,0.00016648805,0.0000026454604,0.000008253316,0.0000061541737,0.00013107425,0.03340529,0.0080877775,0.6738121,0.0000059710173,0.28430432],"study_design_scores_gemma":[0.00004595885,0.00044772046,0.00030118757,0.00005272904,0.0000040661153,0.000015153098,0.00052379287,0.8192997,0.032409035,0.14449511,0.0020563123,0.00034923726],"about_ca_topic_score_codex":0.000007002184,"about_ca_topic_score_gemma":0.00003919437,"teacher_disagreement_score":0.7858944,"about_ca_system_score_codex":0.00003338193,"about_ca_system_score_gemma":0.00025114111,"threshold_uncertainty_score":0.7055563},"labels":[],"label_agreement":null},{"id":"W2182569073","doi":"","title":"On Microeconomic Errors and Ordinal Group Decision Making","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Decision-Making and Behavioral Economics","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Impossibility; Mathematical economics; Von Neumann architecture; Group (periodic table); Group decision-making; Pareto principle; Game theory; Decision theory; Social choice theory; Computer science; Economics; Mathematics; Microeconomics; Mathematical optimization; Psychology; Social psychology","score_opus":0.3247082787357758,"score_gpt":0.46449957791930896,"score_spread":0.13979129918353317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182569073","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9586214,0.0000371184,0.027638711,0.0006089874,0.0013283808,0.00017982944,0.00004149779,0.0000410929,0.011502979],"genre_scores_gemma":[0.9957537,0.000012067978,0.0030418239,0.0008301126,0.00022405568,0.000013496181,0.000004676394,0.000016329763,0.00010372883],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99658424,0.0001274838,0.0010014289,0.0007099663,0.0011511995,0.0004256601],"domain_scores_gemma":[0.99518204,0.0034105498,0.00035472718,0.00037464485,0.00046458357,0.00021346315],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003291665,0.00026337677,0.00033376613,0.0006203374,0.00034945278,0.0006044721,0.00069387973,0.00016115778,0.0022186565],"category_scores_gemma":[0.0036021436,0.00021875884,0.000118843964,0.00038125223,0.00021948836,0.00061084493,0.00015897358,0.00033075718,0.004766563],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018301465,0.00014327034,0.00064662425,5.7140767e-7,0.0000034887178,0.0000015365098,0.00012178307,0.0003966905,0.00024068014,0.3284515,0.00059405103,0.6692168],"study_design_scores_gemma":[0.000045856337,0.00019821698,0.0059190127,0.00007737673,0.000006217721,0.000017752243,0.0005008219,0.0131356,0.000900639,0.97672224,0.0021584767,0.00031779762],"about_ca_topic_score_codex":0.000011424802,"about_ca_topic_score_gemma":0.000067528905,"teacher_disagreement_score":0.668899,"about_ca_system_score_codex":0.00018789603,"about_ca_system_score_gemma":0.00010928872,"threshold_uncertainty_score":0.99869347},"labels":[],"label_agreement":null},{"id":"W2182624225","doi":"","title":"Ambulatory Assessment of Lifestyle Factors for Alzheimer's Disease and Related Dementias","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Disease; Dementia; Ambulatory; Medicine; Physical medicine and rehabilitation; Gerontology; Physical therapy; Psychological intervention; Sleep (system call); Alzheimer's disease; Computer science; Psychiatry; Internal medicine","score_opus":0.24796266547656462,"score_gpt":0.3706317929553854,"score_spread":0.12266912747882078,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2182624225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14368072,0.00011552557,0.8423753,0.00063291687,0.00094104244,0.0011998287,0.00012415752,0.00017805406,0.010752496],"genre_scores_gemma":[0.99660033,0.000007849597,0.0031796442,0.000072789044,0.000024635,0.000061559025,0.0000130823555,0.000008242975,0.000031841297],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829334,0.00010358998,0.00048543717,0.00042345634,0.0005146598,0.00017953616],"domain_scores_gemma":[0.99809164,0.00044448065,0.00026591812,0.00022625805,0.0008167588,0.00015496339],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052905537,0.00015726236,0.00019386048,0.00019818012,0.00012630373,0.00007696765,0.0003931486,0.00006882219,0.00016723215],"category_scores_gemma":[0.0002832709,0.00015483,0.0000805439,0.00021315283,0.00012368792,0.00043670268,0.00009160722,0.00011667467,0.000023323757],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003115084,0.0002543967,0.0019655116,0.000013306959,0.000088490546,0.0000017822204,0.0007822733,0.000030222112,0.00060633157,0.9555863,0.000032800894,0.040607426],"study_design_scores_gemma":[0.000187719,0.00056023715,0.13391629,0.00017326797,0.00009581876,0.00000486368,0.0006797788,0.3007433,0.04887268,0.5138631,0.00020515482,0.00069779204],"about_ca_topic_score_codex":0.000035444395,"about_ca_topic_score_gemma":0.000021730768,"teacher_disagreement_score":0.85291964,"about_ca_system_score_codex":0.00004085403,"about_ca_system_score_gemma":0.00038343988,"threshold_uncertainty_score":0.6313785},"labels":[],"label_agreement":null},{"id":"W2183708446","doi":"","title":"Population Health Record: An Informatics Infrastructure for Management, Integration, and Analysis of Large Scale Population Health Data","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Health informatics; Public health informatics; Population health; Data science; Population; Health indicator; Scale (ratio); Health care; Computer science; Informatics; Data integration; Public health; Data quality; Knowledge management; HRHIS; Environmental health; Data mining; Health policy; Business; Medicine; Geography; Engineering; Nursing; Marketing","score_opus":0.10248553284834452,"score_gpt":0.4116339901271095,"score_spread":0.30914845727876494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183708446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03744408,0.000014921691,0.95653385,0.004631184,0.00014781722,0.00086902536,0.0001673004,0.00007422635,0.00011756488],"genre_scores_gemma":[0.8358657,0.00003474878,0.1601371,0.0015311737,0.000035998895,0.000034749966,0.002338413,0.0000069684374,0.000015157115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99737763,0.0002137945,0.0010772313,0.00044293475,0.0006080812,0.00028030635],"domain_scores_gemma":[0.99757177,0.0001598643,0.00077862886,0.0006223054,0.0007126111,0.00015482974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001512541,0.00016668579,0.0003507278,0.00067430036,0.00032190434,0.0002342847,0.0007636346,0.00008003291,0.00006656403],"category_scores_gemma":[0.00023639468,0.00016192424,0.000041815412,0.0010515911,0.00003117816,0.0012940133,0.00015774838,0.00019824076,0.0000073836154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010585923,0.00005709308,0.010981744,0.00008888048,0.0000359137,2.561523e-8,0.0014570282,0.006260878,0.0000026109033,0.54329216,0.00022077086,0.4375923],"study_design_scores_gemma":[0.000027000995,0.00014465388,0.17035471,0.000038543472,0.000009383665,3.542699e-7,0.00044928194,0.7541449,0.000009589407,0.074638195,0.000081611484,0.00010179661],"about_ca_topic_score_codex":0.0020235244,"about_ca_topic_score_gemma":0.0030946617,"teacher_disagreement_score":0.7984216,"about_ca_system_score_codex":0.00015639132,"about_ca_system_score_gemma":0.00016532524,"threshold_uncertainty_score":0.6603079},"labels":[],"label_agreement":null},{"id":"W2186795831","doi":"","title":"Flexible Multi-Robot Formation Control: Partial Formations as Physical Data Structures","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Distributed computing; Population; Robot; Human–computer interaction; Graph; Range (aeronautics); Control (management); Perception; Artificial intelligence; Theoretical computer science; Engineering","score_opus":0.37191005140904626,"score_gpt":0.38985972766401517,"score_spread":0.017949676254968905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186795831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014898625,0.00001206861,0.9890841,0.0008067775,0.0005991941,0.0005571323,0.00024917035,0.0003024475,0.0068992064],"genre_scores_gemma":[0.9909189,0.0000042400993,0.008076157,0.0004326934,0.000206951,0.00008106078,0.00020185178,0.000012251976,0.00006594129],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968538,0.00019132518,0.00072713813,0.0006691642,0.0011063637,0.000452162],"domain_scores_gemma":[0.99735445,0.00021501574,0.000358726,0.0009487323,0.0009330039,0.0001900742],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064190733,0.00029372776,0.00029765183,0.00023258248,0.00034946916,0.00044071875,0.002478154,0.00012178438,0.00017189981],"category_scores_gemma":[0.0006793166,0.00027524668,0.00009397644,0.00046418645,0.00013209973,0.002597162,0.00027200373,0.00030058646,0.0016425507],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059210102,0.00030033934,0.000014574351,0.000007902232,0.00003102664,0.0000036534539,0.000986611,0.0014235136,0.0013975037,0.9594798,0.00019697275,0.036098868],"study_design_scores_gemma":[0.00017235149,0.00012284481,0.000648052,0.000022591,0.000013261028,0.000010442378,0.00015471237,0.8348196,0.025689436,0.13769019,0.00036597202,0.00029051866],"about_ca_topic_score_codex":0.00012991423,"about_ca_topic_score_gemma":0.00007062871,"teacher_disagreement_score":0.989429,"about_ca_system_score_codex":0.00012338065,"about_ca_system_score_gemma":0.00036297514,"threshold_uncertainty_score":0.99996996},"labels":[],"label_agreement":null},{"id":"W2186840067","doi":"","title":"Automatic Seizure Detection in an In-Vivo Model of Epilepsy","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Electroencephalography; Epilepsy; Artificial intelligence; Latency (audio); Machine learning; Epileptic seizure; False positive rate; Pattern recognition (psychology); Neuroscience; Psychology","score_opus":0.2395078593216924,"score_gpt":0.3533868260161978,"score_spread":0.1138789666945054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186840067","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9710804,0.0000020141877,0.020547185,0.00008923535,0.00017330036,0.00020429541,0.000018190833,0.00004648368,0.007838851],"genre_scores_gemma":[0.9988007,0.0000045430234,0.0008907912,0.00021418702,0.0000232838,0.000023475295,7.718945e-7,0.000008809094,0.00003344746],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982604,0.00014252304,0.000528985,0.00040535314,0.0004512954,0.00021148492],"domain_scores_gemma":[0.9992776,0.00018623473,0.00014324763,0.00015865437,0.00017945658,0.000054793698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003751704,0.00014300033,0.0001788147,0.0004015366,0.00004575928,0.000036632595,0.0003960776,0.00009869305,0.00039348248],"category_scores_gemma":[0.0004081158,0.00014044612,0.00003757263,0.00043406576,0.00013961142,0.00034721787,0.00003844762,0.0002466705,0.000052296455],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012889171,0.00065546116,0.0002613247,0.000020541214,0.0000016794507,0.000007327848,0.003391039,0.015875146,0.6191072,0.28498137,0.0000054845273,0.07556453],"study_design_scores_gemma":[0.000016188955,0.000098931625,0.00026648736,0.000032662978,5.6293806e-7,0.0000012922102,0.00009527097,0.45442382,0.4541646,0.09082873,7.7459856e-7,0.00007070235],"about_ca_topic_score_codex":0.00007081301,"about_ca_topic_score_gemma":0.0008145416,"teacher_disagreement_score":0.43854865,"about_ca_system_score_codex":0.00007055334,"about_ca_system_score_gemma":0.00014129948,"threshold_uncertainty_score":0.5727227},"labels":[],"label_agreement":null},{"id":"W2188156513","doi":"","title":"Symbol Recognition and Artificial Emotion for Making an Autonomoius Robot Attend the AAAI Conference.","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Robot; Computer science; Human–computer interaction; Mobile robot; sort; Artificial intelligence; Compass; Pentium; Social robot; Frame (networking); Robot control; Computer vision","score_opus":0.275299898719469,"score_gpt":0.3672453488869614,"score_spread":0.09194545016749239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188156513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021521924,0.000017748522,0.9677881,0.0047371285,0.0006410143,0.00076479133,0.000077951125,0.00023517277,0.004216198],"genre_scores_gemma":[0.96194386,0.000015359057,0.036566548,0.0007162447,0.0003660622,0.000117772695,0.00007521128,0.000016605205,0.00018230436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972446,0.00020921355,0.00060802547,0.0008020134,0.00069646613,0.000439661],"domain_scores_gemma":[0.99805784,0.0004538913,0.0001995467,0.00037108056,0.0007895376,0.00012812686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011198091,0.00028477795,0.00024393953,0.0001820872,0.00065376,0.000844525,0.00080953975,0.00016369448,0.0003913783],"category_scores_gemma":[0.00030076894,0.00024777022,0.00007344723,0.0003691924,0.00023114884,0.00077258353,0.000059913345,0.0003297041,0.0005987948],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005858457,0.0001257712,0.000005559213,0.000007270374,0.000013385188,0.000003298427,0.0011050468,0.0044057034,0.0009408861,0.23114197,0.000058107013,0.76213443],"study_design_scores_gemma":[0.000040113715,0.0002515111,0.0005273636,0.00006930433,0.000009543337,0.000020865733,0.00020353662,0.6851203,0.0030975484,0.31025657,0.00013279979,0.00027056006],"about_ca_topic_score_codex":0.00003080582,"about_ca_topic_score_gemma":0.000033253666,"teacher_disagreement_score":0.94042194,"about_ca_system_score_codex":0.000103877996,"about_ca_system_score_gemma":0.00032559232,"threshold_uncertainty_score":0.99999744},"labels":[],"label_agreement":null},{"id":"W2199618325","doi":"","title":"Preface: Meta-Cognitive Educational Systems: One Step Forward","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Metacognition; Computer science; Cognition; Field (mathematics); Cognitive science; Cognitive systems; Artificial intelligence; Mathematics education; Management science; Psychology; Engineering; Mathematics","score_opus":0.3376795242404403,"score_gpt":0.47790099048237006,"score_spread":0.14022146624192977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199618325","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09333579,0.00012931823,0.39749396,0.0065726694,0.0062997825,0.0010179818,0.00021975937,0.00025945116,0.4946713],"genre_scores_gemma":[0.9867448,0.000001643926,0.0046891025,0.00051627,0.00073790725,0.0002979392,0.000073772964,0.00002559891,0.0069129616],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9968688,0.000790492,0.0005805103,0.00059949356,0.00080820016,0.00035248548],"domain_scores_gemma":[0.9957667,0.0017019892,0.00029298745,0.0002426977,0.0018854091,0.00011022203],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0029970182,0.0002608843,0.0003495366,0.00032903597,0.00032199046,0.00015868622,0.00036327587,0.00023108181,0.014409655],"category_scores_gemma":[0.002811405,0.00024125977,0.00013403941,0.00043342594,0.00027916476,0.00013505614,0.000035052577,0.0013303396,0.0033300575],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012179834,0.00045209477,0.00010605071,0.000007832712,0.00040334652,0.0000011659806,0.001071616,0.000082693696,0.0022899972,0.95003873,0.0005305982,0.044894077],"study_design_scores_gemma":[0.00051868154,0.0015849106,0.06105663,0.0003166249,0.001199506,0.00009179709,0.0107862,0.024029179,0.022701541,0.83834904,0.036292862,0.0030730357],"about_ca_topic_score_codex":0.00018334795,"about_ca_topic_score_gemma":0.000056506236,"teacher_disagreement_score":0.893409,"about_ca_system_score_codex":0.00006221882,"about_ca_system_score_gemma":0.00039324988,"threshold_uncertainty_score":0.99744594},"labels":[],"label_agreement":null},{"id":"W2205601656","doi":"","title":"Addressing Preemption Costs in Multi-Agent Resource Allocation for Medical Applications","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Preemption; Computer science; Scheduling (production processes); Task (project management); Resource allocation; Operations research; Budget constraint; Risk analysis (engineering); Resource use; Distributed computing; Operations management; Business; Engineering; Microeconomics; Economics; Computer network; Systems engineering","score_opus":0.23762634520700787,"score_gpt":0.3978710104169647,"score_spread":0.16024466520995684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205601656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014604613,0.000041300325,0.98720926,0.0031259877,0.00014542782,0.0009871989,0.0000049098003,0.00009849209,0.0069269803],"genre_scores_gemma":[0.9842773,0.00001681279,0.013731646,0.00051450415,0.00013601955,0.0010495278,0.000038485334,0.000008577562,0.0002270913],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787736,0.00010676014,0.0004838233,0.0005202634,0.0007257849,0.0002860094],"domain_scores_gemma":[0.9981637,0.00044410277,0.00014952983,0.00025132557,0.0008391741,0.00015217016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083245465,0.00015562227,0.00014852025,0.00022093214,0.00020544206,0.0002875966,0.0008089987,0.00016150395,0.00019222009],"category_scores_gemma":[0.00091548456,0.00014911694,0.00005624193,0.00042140024,0.00010158203,0.00040600114,0.00009642527,0.00022232415,0.0006060987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074482214,0.00023508203,0.000058119662,0.000008517575,0.0000033785436,3.8845448e-7,0.0002745444,0.00033992666,0.000581457,0.7088453,0.00021841243,0.2894274],"study_design_scores_gemma":[0.0000905566,0.000073150244,0.001995122,0.000093857896,0.0000021198964,0.00000290504,0.00014039048,0.8832988,0.005390216,0.10692151,0.0017660253,0.00022531679],"about_ca_topic_score_codex":0.00007156846,"about_ca_topic_score_gemma":0.0003154097,"teacher_disagreement_score":0.9828169,"about_ca_system_score_codex":0.0002554369,"about_ca_system_score_gemma":0.00039710733,"threshold_uncertainty_score":0.7790375},"labels":[],"label_agreement":null},{"id":"W2210064905","doi":"","title":"Estimating People's Subjective Experiences of Robot Behavior","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Predictability; Robot; Metric (unit); Computer science; Measure (data warehouse); Human–computer interaction; Social robot; Artificial intelligence; Behavior-based robotics; Mobile robot; Robot control; Engineering; Data mining; Mathematics","score_opus":0.17011178704241334,"score_gpt":0.4446471919842012,"score_spread":0.27453540494178785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2210064905","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63851476,0.000010662878,0.19598974,0.0005443388,0.004261568,0.0003869287,0.00001236839,0.000113202754,0.16016644],"genre_scores_gemma":[0.99736124,7.043488e-7,0.0015834885,0.00020144162,0.0002796156,0.0002016131,0.000008287936,0.000010908017,0.00035270737],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9983424,0.00014726484,0.00046024917,0.0003325845,0.00051181327,0.00020569183],"domain_scores_gemma":[0.9983183,0.00050874037,0.00024865757,0.0001474953,0.00070534775,0.00007146167],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00038501405,0.00014440044,0.00021351136,0.00016520222,0.0001554734,0.000051796513,0.00026067792,0.000110533474,0.0073443293],"category_scores_gemma":[0.00079605257,0.0001452179,0.00008599897,0.00028774288,0.0001737457,0.00011408496,0.000025055908,0.00021726638,0.00077098235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000100850535,0.00035574735,0.0003760499,0.00000341123,0.000016664804,9.232872e-7,0.038936213,0.0006510657,0.0012586155,0.8745474,0.00010165553,0.08365137],"study_design_scores_gemma":[0.00034230945,0.0023659512,0.08004029,0.00027571997,0.00009506614,0.00004069345,0.34228837,0.16417381,0.10488901,0.30322334,0.00050311367,0.0017623407],"about_ca_topic_score_codex":0.0001802717,"about_ca_topic_score_gemma":0.0001490075,"teacher_disagreement_score":0.5713241,"about_ca_system_score_codex":0.000057051213,"about_ca_system_score_gemma":0.000094452225,"threshold_uncertainty_score":0.9935631},"labels":[],"label_agreement":null},{"id":"W2219002109","doi":"","title":"Learning a Cost Function for Interactive Microscope Image Segmentation","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ottawa Hospital","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Microscopy; Image segmentation; Active contour model; Microscope; Pattern recognition (psychology); Optics; Physics","score_opus":0.04041222913413865,"score_gpt":0.3627384528981455,"score_spread":0.3223262237640068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219002109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03389452,0.000006560197,0.95652103,0.00022990617,0.00006785782,0.0003837065,0.000008302277,0.00003502487,0.008853107],"genre_scores_gemma":[0.9936754,0.00001856692,0.0045856754,0.0004237348,0.00019690034,0.00014667628,0.00037731449,0.000013969491,0.0005617405],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990016,0.00008185198,0.00023495225,0.00034755786,0.0001937725,0.00014022991],"domain_scores_gemma":[0.99881595,0.00007588692,0.00013363751,0.00011088257,0.000824683,0.000038943646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040561717,0.00012293636,0.00010081838,0.000094454124,0.00013547111,0.00010383268,0.000127922,0.000081341605,0.000109063454],"category_scores_gemma":[0.00077369437,0.00012929949,0.00007692309,0.00009757967,0.000070664166,0.000017474047,0.00003296184,0.00011607336,0.000082450504],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002499233,0.00006621391,0.000032968386,0.0000049616115,0.000022450671,8.7335046e-8,0.000040813007,0.00030852406,0.90647554,0.008699283,0.0007011471,0.08339811],"study_design_scores_gemma":[0.000041129355,0.00054705335,0.00007597297,0.000013173316,0.0000137705,8.1357075e-7,0.00019759222,0.017458266,0.9644931,0.011145505,0.0058688656,0.00014476398],"about_ca_topic_score_codex":0.000010862204,"about_ca_topic_score_gemma":0.000048310798,"teacher_disagreement_score":0.95978093,"about_ca_system_score_codex":0.00004463527,"about_ca_system_score_gemma":0.00006584954,"threshold_uncertainty_score":0.527268},"labels":[],"label_agreement":null},{"id":"W2219965243","doi":"","title":"The RhetFig project: computational rhetorics and models of persuasion","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Persuasion; Automatic summarization; Topos theory; Ontology; Computer science; Rhetoric; Annotation; Ontology engineering; Natural language processing; Artificial intelligence; Epistemology; Linguistics; Process ontology; Semantic Web; Philosophy","score_opus":0.26881621305356274,"score_gpt":0.3706189476740427,"score_spread":0.10180273462047995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2219965243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014646022,0.00004379741,0.9861689,0.0003581302,0.00007562307,0.00017435772,0.0000049187743,0.000078284,0.011631348],"genre_scores_gemma":[0.9290635,0.00008655383,0.07067804,0.00007175803,0.000018003826,0.000021996406,0.0000027060114,0.000005074702,0.0000523761],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99835044,0.000068345784,0.00039572574,0.00032842328,0.00069370534,0.0001633402],"domain_scores_gemma":[0.9981488,0.0003158177,0.00020435952,0.00022171339,0.0010651964,0.000044107263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051875756,0.00012834203,0.00013601172,0.00017568539,0.00024445198,0.00008732741,0.0006612054,0.000059625305,0.000013051206],"category_scores_gemma":[0.00019925492,0.000098519326,0.00005093744,0.00046833465,0.00023461801,0.00044288527,0.00014572845,0.00015241234,0.000013641167],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017011063,0.00005953351,0.000010261875,0.0000022210486,0.0000079556585,5.341087e-7,0.00068111724,0.001042108,0.00014524042,0.9262269,0.000030716958,0.07177641],"study_design_scores_gemma":[0.000007943608,0.00006629045,0.00008134915,0.000010425014,0.0000018693283,0.000001649841,0.00009253281,0.4059813,0.005787253,0.58785933,0.00003958122,0.000070438036],"about_ca_topic_score_codex":0.000035342342,"about_ca_topic_score_gemma":0.000025426349,"teacher_disagreement_score":0.9275989,"about_ca_system_score_codex":0.000050825292,"about_ca_system_score_gemma":0.00022271341,"threshold_uncertainty_score":0.40175015},"labels":[],"label_agreement":null},{"id":"W2222917154","doi":"","title":"Integrating Representation Learning and Temporal Difference Learning: A Matrix Factorization Approach","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Computer science; Temporal difference learning; Artificial intelligence; Feature learning; Representation (politics); Formalism (music); Semi-supervised learning; Factorization; Machine learning; Mathematical optimization; Algorithm; Mathematics","score_opus":0.092088676256048,"score_gpt":0.3547959311461222,"score_spread":0.2627072548900742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222917154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07297154,0.0000052462547,0.90019435,0.00024143916,0.00012158211,0.00016079354,0.0000028262066,0.00007083708,0.026231404],"genre_scores_gemma":[0.99660885,0.000006602946,0.0016332654,0.000024238323,0.00035052878,0.000027586708,0.00012205672,0.000012233038,0.0012146574],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852896,0.000201783,0.0003210355,0.0003883947,0.00038062435,0.00017922612],"domain_scores_gemma":[0.9991679,0.0001842899,0.00018994599,0.00006572468,0.0003080456,0.00008404749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002789061,0.00016744221,0.00016107316,0.000108645545,0.00031815426,0.00020193572,0.00010289634,0.000061390034,0.00030775918],"category_scores_gemma":[0.00015253872,0.0001557415,0.000046333556,0.00022624979,0.00008184036,0.00015102277,0.0000347715,0.0004797773,0.000037438655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029253115,0.00006106569,0.00819443,0.000004612051,0.000008759882,6.7914385e-8,0.00031241283,0.01084144,0.0021708393,0.7670682,0.000010247481,0.21129869],"study_design_scores_gemma":[0.000040451298,0.000099659,0.0007778719,0.000026070938,0.000005853606,9.280455e-7,0.0011462449,0.88653547,0.0038319852,0.1069475,0.00038810252,0.0001998521],"about_ca_topic_score_codex":0.00007364414,"about_ca_topic_score_gemma":0.0000034129828,"teacher_disagreement_score":0.9236373,"about_ca_system_score_codex":0.000025137017,"about_ca_system_score_gemma":0.00004841392,"threshold_uncertainty_score":0.63509536},"labels":[],"label_agreement":null},{"id":"W2262976093","doi":"","title":"Designing Intelligent Wheelchairs: Reintegrating AI","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Wheelchair; Engineering management; Human–computer interaction; Artificial intelligence; Knowledge management; Engineering; World Wide Web","score_opus":0.11807837501570881,"score_gpt":0.3369628519752738,"score_spread":0.218884476959565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2262976093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023479438,0.000022638626,0.97296536,0.011361908,0.00034891212,0.00025824754,0.0000022259833,0.0004567522,0.01223603],"genre_scores_gemma":[0.95358974,0.000009178008,0.044656444,0.0013096802,0.0000894806,0.00009541669,0.000004263245,0.000011757128,0.00023402103],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975433,0.00011354232,0.00054579094,0.0006544469,0.00070933875,0.00043359917],"domain_scores_gemma":[0.9976944,0.00032819336,0.0001726989,0.0003765396,0.0013038862,0.0001243243],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005449907,0.0002547578,0.00021939928,0.00035147942,0.00028389329,0.00055969745,0.0012156869,0.00015204106,0.00045780404],"category_scores_gemma":[0.00076734973,0.00023214522,0.00008688012,0.00061780616,0.0001738738,0.00060386554,0.00016076175,0.00056428905,0.0029554265],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003056568,0.00007128224,0.000030550236,0.0000026944294,0.000007992218,0.0000030440824,0.00015817287,0.00046903183,0.007888692,0.76792747,0.0004458818,0.22299215],"study_design_scores_gemma":[0.000015938494,0.00015857324,0.00022583865,0.00007646313,0.0000019884278,0.000008436513,0.0002752177,0.25470352,0.18872206,0.55522645,0.000304529,0.00028099035],"about_ca_topic_score_codex":0.00007763071,"about_ca_topic_score_gemma":0.00002956388,"teacher_disagreement_score":0.9512418,"about_ca_system_score_codex":0.00014840359,"about_ca_system_score_gemma":0.00024745354,"threshold_uncertainty_score":0.9978209},"labels":[],"label_agreement":null},{"id":"W2271015133","doi":"","title":"An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Machine learning; Artificial intelligence; Computer science; Autoencoder; Population; Health care; Deep learning; Medicine","score_opus":0.11718683833947711,"score_gpt":0.3568039385338846,"score_spread":0.23961710019440746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2271015133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013663583,0.000010123292,0.97136724,0.0008764298,0.0004192518,0.00056780514,0.000012764852,0.0008709121,0.012211902],"genre_scores_gemma":[0.9682165,9.433057e-7,0.03098847,0.00032361876,0.00016756916,0.00014725936,0.000043566848,0.000016006828,0.00009607151],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971118,0.00033171193,0.00051741855,0.00059239316,0.0010169031,0.00042974736],"domain_scores_gemma":[0.9978633,0.000547385,0.00018381329,0.00035744376,0.0007436053,0.00030444178],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014758377,0.00023636539,0.00026193904,0.00043677253,0.00027323217,0.0003611919,0.0007217227,0.000118072,0.00009782397],"category_scores_gemma":[0.0006870156,0.00024523356,0.00006476895,0.000736438,0.000063677064,0.0010133964,0.00010032782,0.00033943757,0.0011832818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005456935,0.0007319564,0.00037020553,0.000015979047,0.000019614357,4.926131e-7,0.0028969378,0.04244515,0.01288431,0.77587163,0.000079373734,0.16462977],"study_design_scores_gemma":[0.000041613952,0.00027999506,0.0018891096,0.000022931266,0.000004501331,0.0000067667715,0.00045686675,0.98174477,0.007505956,0.007406951,0.000287531,0.00035298592],"about_ca_topic_score_codex":0.000052562278,"about_ca_topic_score_gemma":0.00003218752,"teacher_disagreement_score":0.9545529,"about_ca_system_score_codex":0.00021246595,"about_ca_system_score_gemma":0.00016811902,"threshold_uncertainty_score":1},"labels":[],"label_agreement":null},{"id":"W2272412163","doi":"","title":"Smart Homes or Smart Occupants? Reframing Computational Design Models for the Green Home","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Home automation; Cognitive reframing; Architectural engineering; Computer science; Sustainability; Sustainable design; Sustainable development; Building automation; Sustainable living; Efficient energy use; Risk analysis (engineering); Environmental economics; Engineering; Business; Telecommunications","score_opus":0.24299510174187688,"score_gpt":0.31255029138292284,"score_spread":0.06955518964104596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2272412163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009122215,0.000037006597,0.99608326,0.00021284999,0.00033503177,0.0002767714,0.000026837766,0.00016737333,0.0019486344],"genre_scores_gemma":[0.96889496,0.000043461503,0.030340994,0.00022112124,0.00008903157,0.0001320629,0.00003687405,0.000024489684,0.00021702106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883384,0.000032612537,0.00032748093,0.00022322293,0.00036110065,0.00022173031],"domain_scores_gemma":[0.99885195,0.0005150286,0.000060246744,0.00013039156,0.00039038982,0.000052014246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037892666,0.00017196122,0.00013661817,0.00012691041,0.00024738727,0.0000735449,0.00031499725,0.000101922364,0.00029523455],"category_scores_gemma":[0.00006993822,0.00013201006,0.000056332818,0.0002406408,0.00008304224,0.00024555612,0.000020217625,0.00015499382,0.00002568448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001151087,0.000026952473,0.000004438957,0.000006918367,0.00002684672,5.206345e-7,0.00023312944,0.6884928,0.00002470739,0.2887303,0.0001458543,0.02219244],"study_design_scores_gemma":[0.000027259824,0.000058324506,0.00007806294,0.000020752139,0.000008751413,0.0000020978935,0.00007937462,0.75015,0.0018282315,0.24749246,0.00010974743,0.0001449136],"about_ca_topic_score_codex":0.000042102874,"about_ca_topic_score_gemma":0.00006873173,"teacher_disagreement_score":0.9679827,"about_ca_system_score_codex":0.00006764675,"about_ca_system_score_gemma":0.00014318762,"threshold_uncertainty_score":0.5383214},"labels":[],"label_agreement":null},{"id":"W2283744749","doi":"","title":"Complex AI on Small Embedded Systems: Humanoid Robotics using Mobile Phones","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Humanoid robot; Robotics; Payload (computing); Computer science; Accelerometer; Mobile robot; Embedded system; Robot; Artificial intelligence; Mobile phone; Human–computer interaction; Real-time computing; Telecommunications; Computer security; Operating system","score_opus":0.1559145963562388,"score_gpt":0.3359073205466848,"score_spread":0.179992724190446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2283744749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12770192,0.000020857287,0.85901767,0.00018966351,0.0027946136,0.0006323519,0.00006703477,0.0003771656,0.009198731],"genre_scores_gemma":[0.9968262,0.000010207059,0.0024782347,0.00018275475,0.0003125937,0.000021071348,0.000066839275,0.000035395915,0.000066706874],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984532,0.000048396276,0.00046261091,0.00028315917,0.00048769047,0.00026494006],"domain_scores_gemma":[0.9988165,0.00016179228,0.000073556075,0.00021507111,0.0006322122,0.000100873156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573662,0.00023139488,0.00021178396,0.00021627879,0.00018424826,0.0002207063,0.00023418527,0.00016726839,0.0002663455],"category_scores_gemma":[0.00013405363,0.00024113641,0.00005730535,0.00025013174,0.000094947834,0.00008725464,0.000017172126,0.0004148983,0.00031579201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011251367,0.000058600348,0.0000067272754,0.00001720578,0.000009223467,0.0000018249555,0.00007381968,0.68868434,0.035182543,0.27404433,0.000075808406,0.0018343191],"study_design_scores_gemma":[0.000027180997,0.00006839206,0.00003495699,0.000045909153,0.0000073895962,0.0000033162862,0.0001389074,0.9627901,0.02824684,0.0081352545,0.00024196402,0.0002598166],"about_ca_topic_score_codex":0.00003109884,"about_ca_topic_score_gemma":0.00009352134,"teacher_disagreement_score":0.8691243,"about_ca_system_score_codex":0.000099792735,"about_ca_system_score_gemma":0.00011259862,"threshold_uncertainty_score":0.9833257},"labels":[],"label_agreement":null},{"id":"W2284070151","doi":"","title":"What's in a Name? Using First Names as Features for Gender Inference in Twitter","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Classifier (UML); Inference; Computer science; Artificial intelligence; Social media; Natural language processing; Baseline (sea); Information retrieval; Machine learning; World Wide Web","score_opus":0.2696613820475069,"score_gpt":0.4140428855734447,"score_spread":0.14438150352593782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2284070151","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14117488,0.00016541014,0.842651,0.0114235375,0.0011810643,0.0012562006,0.000008435592,0.000109341054,0.0020301442],"genre_scores_gemma":[0.98947877,0.000037515223,0.008753578,0.0013505467,0.00007497396,0.00016201835,0.0000097923985,0.000009419875,0.00012339775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99773777,0.000116485746,0.0005473691,0.0005889277,0.0005698897,0.00043953487],"domain_scores_gemma":[0.99802357,0.0008139715,0.00014469279,0.0002376325,0.0006765059,0.00010361313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075649115,0.00022195902,0.00022518414,0.0004307184,0.0001577659,0.0008565188,0.0007394249,0.00018067966,0.0003452779],"category_scores_gemma":[0.0011845176,0.00021914524,0.000067732886,0.0006438184,0.00009315644,0.001575068,0.00011973773,0.0003809399,0.00034859657],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030382542,0.00012001395,0.0006435221,0.000017655813,0.000005344865,0.0000038602943,0.0023344755,0.007276491,0.0005901304,0.9589701,0.00005722823,0.029950742],"study_design_scores_gemma":[0.000059309994,0.000058025576,0.0030601963,0.0001343322,0.000001187667,0.0000031956479,0.00071549497,0.45268473,0.007317478,0.5355757,0.00012696905,0.00026338044],"about_ca_topic_score_codex":0.00026978322,"about_ca_topic_score_gemma":0.00036405292,"teacher_disagreement_score":0.84830385,"about_ca_system_score_codex":0.00022401499,"about_ca_system_score_gemma":0.00043748325,"threshold_uncertainty_score":0.8936483},"labels":[],"label_agreement":null},{"id":"W2287802714","doi":"","title":"An Intelligent Nutritional Assessment System","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Dementia; Computer science; Cognition; Gerontology; Artificial intelligence; Meal; Medicine; Psychiatry","score_opus":0.1805094647003256,"score_gpt":0.41744016083171603,"score_spread":0.23693069613139042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287802714","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27931815,0.00097309233,0.18191819,0.02070631,0.004378439,0.0034552317,0.0008358632,0.0010593515,0.5073554],"genre_scores_gemma":[0.9932814,0.000057468587,0.004204702,0.00083994155,0.0011902294,0.00011458946,0.0002039947,0.000016623848,0.00009104312],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99754846,0.00007154733,0.00048368238,0.00032468655,0.001185584,0.0003860192],"domain_scores_gemma":[0.99816316,0.00015028672,0.00010143259,0.00017816952,0.0010551488,0.0003517789],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005852647,0.00020892109,0.00026710992,0.00017775381,0.00026960787,0.00006501424,0.00014523286,0.00010948259,0.0011296751],"category_scores_gemma":[0.00010466421,0.00018973516,0.000119095435,0.00025224563,0.00013270511,0.0002234749,0.00003221197,0.0002804906,0.00074519176],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020530942,0.001690167,0.0015899392,0.00004971447,0.000040137103,0.000005432265,0.00005208972,0.000056425073,0.0017257197,0.9854672,0.00066830765,0.008449583],"study_design_scores_gemma":[0.0012599687,0.006885326,0.25014028,0.0024205823,0.00039520508,0.00068616244,0.020996036,0.12361843,0.11194725,0.4323521,0.04601498,0.0032836823],"about_ca_topic_score_codex":0.00002109954,"about_ca_topic_score_gemma":0.000006897969,"teacher_disagreement_score":0.71396327,"about_ca_system_score_codex":0.00048631788,"about_ca_system_score_gemma":0.00024544034,"threshold_uncertainty_score":0.99978346},"labels":[],"label_agreement":null},{"id":"W2287921404","doi":"","title":"A Study of Phase Transitions in Security Games","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Infrastructure Resilience and Vulnerability Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Stackelberg competition; Computer science; Software deployment; Suite; Benchmark (surveying); Computation; Guard (computer science); Game theory; Theoretical computer science; Mathematical optimization; Algorithm; Mathematical economics; Mathematics; Law","score_opus":0.08300799509632655,"score_gpt":0.3629789054404974,"score_spread":0.2799709103441708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287921404","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9838009,0.000020380627,0.011281834,0.00007047798,0.00009751235,0.00017326414,0.00002303197,0.000032986554,0.0044996208],"genre_scores_gemma":[0.99981093,0.0000063673187,0.00007182689,0.000017008808,0.000053884494,0.000025498124,0.000006730866,0.0000051065267,0.0000026169828],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989196,0.00005831457,0.00036441354,0.00012207989,0.0003542207,0.00018138342],"domain_scores_gemma":[0.9995751,0.00007107768,0.000030936393,0.00010573777,0.00016379275,0.000053372463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034672947,0.0001029721,0.00016600663,0.00023895131,0.00004045796,0.000015932215,0.00012341574,0.00005461143,0.00041301502],"category_scores_gemma":[0.00009702424,0.00010214627,0.000043288823,0.00046411197,0.00005971137,0.00019471966,0.0000067500914,0.00019779209,0.00003262975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014320084,0.0064700637,0.004463364,0.00006244676,0.000101826045,0.000004717913,0.032301202,0.39344734,0.008560883,0.4674894,0.00005815021,0.086897425],"study_design_scores_gemma":[0.00033684834,0.00065428694,0.011503171,0.00007850391,0.00005441434,0.00000458496,0.029279169,0.7525236,0.06083959,0.14402989,0.00007791178,0.000618005],"about_ca_topic_score_codex":0.000026777312,"about_ca_topic_score_gemma":0.00036907083,"teacher_disagreement_score":0.3590763,"about_ca_system_score_codex":0.000061299215,"about_ca_system_score_gemma":0.000036686662,"threshold_uncertainty_score":0.45222247},"labels":[],"label_agreement":null},{"id":"W2295323106","doi":"","title":"Self-Managed Access to Personalized Healthcare through Automated Generation of Tailored Health Educational Materials from Electronic Health Records","year":2009,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Multimedia Communication and Technology","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Health care; Personalization; Internet privacy; Variety (cybernetics); Health information; Business; Nursing; Knowledge management; Medicine; Computer science; World Wide Web","score_opus":0.24490061669976437,"score_gpt":0.47856824355299604,"score_spread":0.23366762685323167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295323106","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07613865,0.00048110506,0.01638843,0.89671504,0.0010791919,0.0025420606,0.00028678754,0.00094147865,0.005427246],"genre_scores_gemma":[0.98039865,0.0010292089,0.008788985,0.008921404,0.0002731004,0.00007995692,0.0004187131,0.00001066079,0.00007931439],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99645054,0.0008802584,0.00085494295,0.00044979565,0.00086872024,0.0004957374],"domain_scores_gemma":[0.9977064,0.00027314224,0.0005498336,0.0003012423,0.0009836269,0.00018573314],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0014471292,0.00018127992,0.00037482785,0.000271364,0.0007436184,0.00016389438,0.00085256447,0.00016124974,0.0012072145],"category_scores_gemma":[0.0007885618,0.00020033367,0.00005520327,0.00078001525,0.00017396234,0.00031332302,0.000049136655,0.00025453538,0.0001303561],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006298138,0.00028830505,0.000021197055,0.000008805259,0.000016354354,9.033652e-8,0.007048998,0.000043597254,0.001441282,0.9464294,0.0018825684,0.04275645],"study_design_scores_gemma":[0.0002943101,0.0012932435,0.006258578,0.00025220186,0.000013555099,0.00000127692,0.0052095694,0.024932886,0.025776617,0.91150033,0.023649678,0.0008177593],"about_ca_topic_score_codex":0.0075403033,"about_ca_topic_score_gemma":0.011842407,"teacher_disagreement_score":0.90426,"about_ca_system_score_codex":0.00092261215,"about_ca_system_score_gemma":0.0041677477,"threshold_uncertainty_score":0.9997058},"labels":[],"label_agreement":null},{"id":"W2295787329","doi":"","title":"What is hot in CHI","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Autodesk (Canada)","funders":"","keywords":"Vision; The arts; Human science; Computer science; Engineering ethics; Sociology; Engineering; Social science; Political science","score_opus":0.19117083066406565,"score_gpt":0.35722615859545,"score_spread":0.16605532793138433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295787329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11044019,0.00033505165,0.79992265,0.023245584,0.004643913,0.00060209364,0.00000934242,0.0004385399,0.060362674],"genre_scores_gemma":[0.99489194,0.000030630526,0.002979521,0.0016706416,0.00010772522,0.0000134532,0.0000027881988,0.0000069377093,0.0002963439],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794686,0.000086957785,0.00036765754,0.00048443078,0.0008283821,0.0002856995],"domain_scores_gemma":[0.998739,0.00011416996,0.000089765774,0.00031144614,0.0005929379,0.00015268882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008492834,0.00015662739,0.00015729699,0.00025504385,0.00008492679,0.00073483505,0.0006560673,0.00008998627,0.00006763745],"category_scores_gemma":[0.0002751409,0.0001565009,0.00004544253,0.0005939418,0.000083666615,0.0008547586,0.000104099425,0.0002553398,0.00077393133],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022616057,0.00010238712,0.00003595349,0.0000023568841,0.0000033357487,0.000011232647,0.0021950358,0.0021039178,0.0003505546,0.84123546,0.00045674792,0.15348043],"study_design_scores_gemma":[0.00005473209,0.00012845152,0.0002757003,0.0001330888,0.000001330762,0.000011862127,0.0011285513,0.51645887,0.03727658,0.44276637,0.0014442554,0.00032017866],"about_ca_topic_score_codex":0.00005360577,"about_ca_topic_score_gemma":0.0000857743,"teacher_disagreement_score":0.88445175,"about_ca_system_score_codex":0.0001420361,"about_ca_system_score_gemma":0.0004303447,"threshold_uncertainty_score":0.99475795},"labels":[],"label_agreement":null},{"id":"W2394649188","doi":"","title":"Learning Grounded Communicative Intent from Human-Robot Dialog","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Unobservable; Dialog box; Perception; Computer science; Robot; Human–computer interaction; Artificial intelligence; Cognitive science; Psychology","score_opus":0.17179679521865573,"score_gpt":0.36140449539049324,"score_spread":0.1896077001718375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2394649188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075514585,0.000032794163,0.7353826,0.004929637,0.0031311768,0.0006249181,0.000038664883,0.00061798736,0.17972763],"genre_scores_gemma":[0.9917449,0.000007163059,0.0072907144,0.0003742394,0.00029176005,0.000045857887,0.00006175067,0.00001089967,0.00017270993],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99756616,0.0002289566,0.00053445343,0.0005546323,0.0007962748,0.0003195196],"domain_scores_gemma":[0.9974959,0.0006496389,0.00024807217,0.0005749345,0.0008700162,0.00016142827],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007535561,0.00022288355,0.0002402185,0.00018968616,0.0004622675,0.00048232122,0.0017845633,0.00016292611,0.00039960988],"category_scores_gemma":[0.0009477975,0.0002153417,0.000095517105,0.0003718313,0.0002442648,0.00040977198,0.0002930845,0.00095323316,0.0013507807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001851755,0.00012378633,0.00007749798,0.0000014226281,0.000014087316,0.000003327388,0.0007772609,0.00013881654,0.039730284,0.916488,0.00007269993,0.042554334],"study_design_scores_gemma":[0.00006121214,0.0002244573,0.0027840883,0.00003869832,0.0000038701774,0.0000039576844,0.00033494603,0.05362496,0.058270384,0.8836232,0.0006593961,0.00037085393],"about_ca_topic_score_codex":0.00091433374,"about_ca_topic_score_gemma":0.0015324759,"teacher_disagreement_score":0.9162303,"about_ca_system_score_codex":0.000087053995,"about_ca_system_score_gemma":0.00024699548,"threshold_uncertainty_score":0.9994268},"labels":[],"label_agreement":null},{"id":"W2395123824","doi":"","title":"Reasoning about Chemical Reactions Using the Situation Calculus.","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Heuristics; Set (abstract data type); Task (project management); Situation calculus; Representation (politics); Prolog; Theoretical computer science; Artificial intelligence; Programming language; Systems engineering","score_opus":0.20267425396235095,"score_gpt":0.380526042724773,"score_spread":0.17785178876242203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2395123824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0375703,0.00006346028,0.9505799,0.002713324,0.0006789295,0.0001430098,0.0000023352413,0.000121852725,0.00812692],"genre_scores_gemma":[0.9868715,0.0000105152085,0.012374001,0.00038101507,0.00029128106,0.0000148767285,0.000003412215,0.000004926943,0.000048498692],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984221,0.00008982423,0.0003110285,0.00025153754,0.00061608944,0.00030943373],"domain_scores_gemma":[0.99875766,0.00035018226,0.00013178485,0.0002609598,0.0004203799,0.000079032485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075263943,0.00012806852,0.00010849159,0.000091134476,0.00032636136,0.00021333495,0.00058997853,0.00008427639,0.00007676093],"category_scores_gemma":[0.0009260837,0.000097072196,0.000058224818,0.00038544324,0.00011019823,0.000572909,0.00009960139,0.000234376,0.00020879737],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000057815914,0.00006249486,0.000056082772,0.000001771414,0.000006315937,4.5026408e-7,0.0005472448,0.00066139747,0.008097452,0.96336013,0.000047850608,0.027153028],"study_design_scores_gemma":[0.000018815543,0.000022434917,0.0012119326,0.000046768386,0.000008012778,0.000024661735,0.0004868132,0.7933295,0.09716354,0.10680278,0.00065793324,0.00022683377],"about_ca_topic_score_codex":0.00008661478,"about_ca_topic_score_gemma":0.000022335737,"teacher_disagreement_score":0.9493012,"about_ca_system_score_codex":0.00012322911,"about_ca_system_score_gemma":0.00018165412,"threshold_uncertainty_score":0.39584893},"labels":[],"label_agreement":null},{"id":"W2396740589","doi":"","title":"Procedural approach to mitigating concurrently applied clinical practice guidelines","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; McGill University","funders":"","keywords":"Computer science; Clinical Practice; Process (computing); Decision support system; Medicine; Artificial intelligence","score_opus":0.6350836907760694,"score_gpt":0.582333943221922,"score_spread":0.05274974755414741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396740589","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08055504,0.00005108736,0.32214424,0.30245385,0.0020038676,0.008097314,0.00008656931,0.0005426925,0.28406534],"genre_scores_gemma":[0.8195065,0.000017909748,0.13412984,0.044690087,0.0010399496,0.00028965072,0.00014403128,0.000026305886,0.00015571626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944379,0.00015484686,0.002762743,0.0007618855,0.0014930242,0.00038962645],"domain_scores_gemma":[0.98693657,0.0023315963,0.0006058949,0.00030692594,0.0093626445,0.00045634637],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0029718308,0.00027362502,0.00042178607,0.00018034315,0.00016644804,0.0002056378,0.00027441944,0.00017581557,0.0007694659],"category_scores_gemma":[0.07392899,0.00024786036,0.00013414823,0.00052533404,0.00016471004,0.00045559782,0.00009839096,0.0006639567,0.0031869414],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052423496,0.0012652586,0.00015457338,0.000040191284,0.00008262858,0.0000027674882,0.0002522213,0.00053902157,0.002823949,0.34391272,0.037117068,0.61328536],"study_design_scores_gemma":[0.0021290714,0.0044234297,0.016666312,0.00088304584,0.00067207584,0.0003261874,0.038351666,0.5722303,0.026483174,0.23912321,0.0954996,0.0032119204],"about_ca_topic_score_codex":0.00013287856,"about_ca_topic_score_gemma":0.00001427482,"teacher_disagreement_score":0.7389515,"about_ca_system_score_codex":0.00014699939,"about_ca_system_score_gemma":0.0010071786,"threshold_uncertainty_score":0.9999974},"labels":[],"label_agreement":null},{"id":"W2397298375","doi":"","title":"Estimating quantitative magnitudes using semantic similarity","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Carleton University","funders":"","keywords":"Adjective; Similarity (geometry); Computer science; Semantic similarity; Natural language processing; Analogy; Noun; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Linguistics","score_opus":0.22628478075506617,"score_gpt":0.41391607508895256,"score_spread":0.1876312943338864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397298375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008836873,0.0000054240013,0.9839112,0.0015905772,0.00048964086,0.00017544975,0.00000769742,0.00024303384,0.004740099],"genre_scores_gemma":[0.7006681,0.0000020080276,0.2989863,0.00022039052,0.00006866341,0.000010024545,0.0000031599031,0.000005804445,0.000035557172],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980883,0.00007406693,0.00040775727,0.00046044504,0.00072710245,0.00024231354],"domain_scores_gemma":[0.997913,0.00030552215,0.00018759041,0.00029246561,0.001211675,0.000089760164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007447276,0.0001742228,0.00015817607,0.00020204188,0.00031869352,0.0004147504,0.00081783946,0.00010923789,0.00016766797],"category_scores_gemma":[0.0011461985,0.00016700674,0.00006345294,0.0005450787,0.00019955746,0.00057484134,0.0001133683,0.00046697233,0.00022181032],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007139102,0.00009062605,0.000017385879,0.0000067893657,0.000004558673,0.000001966213,0.00012832931,0.00016238613,0.06474184,0.8951704,0.000010519026,0.039658066],"study_design_scores_gemma":[0.000007374909,0.000041339146,0.00013327031,0.000018539431,0.0000019247805,0.000003843745,0.00002893968,0.56423277,0.122956395,0.31241852,0.00002907667,0.00012803054],"about_ca_topic_score_codex":0.000038739698,"about_ca_topic_score_gemma":0.000039183968,"teacher_disagreement_score":0.69183123,"about_ca_system_score_codex":0.000054352986,"about_ca_system_score_gemma":0.00034528298,"threshold_uncertainty_score":0.68103373},"labels":[],"label_agreement":null},{"id":"W2397806853","doi":"","title":"On the Construction of Trust Metrics","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Harm; Vulnerability (computing); Computer science; Reliability (semiconductor); Variable (mathematics); Identity (music); Subject (documents); Computational trust; Computer security; Knowledge management; Social psychology; Psychology; Mathematics; World Wide Web","score_opus":0.13978900978425807,"score_gpt":0.32038533462402224,"score_spread":0.18059632483976418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2397806853","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02184609,0.000009797725,0.94086933,0.007209134,0.00036285186,0.0002248784,0.000006844069,0.0000699536,0.029401094],"genre_scores_gemma":[0.9905215,0.00001557805,0.0085144425,0.0007710764,0.000044991088,0.000035905156,0.0000019494119,0.000004904497,0.00008964264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981323,0.000102253536,0.00041179484,0.00033141245,0.00082358555,0.00019865054],"domain_scores_gemma":[0.99757195,0.000691901,0.00018108805,0.0003302457,0.0011545774,0.00007025034],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00050208473,0.00014646299,0.00014421185,0.00022618097,0.00016284738,0.00022196001,0.0008935172,0.00008511021,0.0005480303],"category_scores_gemma":[0.00083110813,0.00010565116,0.00006467062,0.0007791419,0.00021199725,0.00027379772,0.00006894403,0.00027340231,0.00081046956],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005168964,0.00005871395,0.0000071800087,0.0000020122723,0.000005788172,2.3346406e-7,0.00009345698,0.000640107,0.000693167,0.89219606,0.00025010938,0.10604803],"study_design_scores_gemma":[0.000008416595,0.00009447871,0.00008534707,0.000020004118,0.0000012877645,0.0000017326796,0.00008754239,0.3342539,0.024750289,0.6405814,0.00002733481,0.000088245375],"about_ca_topic_score_codex":0.000051663475,"about_ca_topic_score_gemma":0.000004193554,"teacher_disagreement_score":0.96867543,"about_ca_system_score_codex":0.000051082876,"about_ca_system_score_gemma":0.00021798979,"threshold_uncertainty_score":0.9999675},"labels":[],"label_agreement":null},{"id":"W2398292744","doi":"","title":"NEH Project: Modeling Acoustic Adaptation in Bird Song","year":2011,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Laurentian University","funders":"","keywords":"Singing; Sparrow; Sociocultural evolution; Adaptation (eye); Selection (genetic algorithm); Darwinism; Variation (astronomy); Computer science; Evolutionary biology; Ecology; Artificial intelligence; Psychology; Biology; Sociology; Anthropology","score_opus":0.40952279505928896,"score_gpt":0.3920580445985265,"score_spread":0.017464750460762457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398292744","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63655245,0.00006930877,0.3421195,0.0002871174,0.00016988318,0.0005172195,0.000017957711,0.00007172272,0.02019487],"genre_scores_gemma":[0.99776113,0.000051418006,0.0017792225,0.00019194449,0.00005089805,0.000045524393,0.000030654333,0.000013511148,0.00007569539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900365,0.00006306136,0.0002897704,0.0002621273,0.00022861018,0.00015276158],"domain_scores_gemma":[0.9993686,0.000014425394,0.00006268772,0.00015423702,0.00036185587,0.000038181508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025262588,0.00011166976,0.00008352529,0.000112969305,0.00007259189,0.000026861007,0.00024931369,0.00011177777,0.00011034993],"category_scores_gemma":[0.00020562488,0.00011311881,0.000038858896,0.0001653478,0.00006188795,0.000011483961,0.000052594376,0.00015346482,0.00011516399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017543582,0.0016020826,0.0012392687,0.000024789508,0.00003100612,0.000008573517,0.0028312192,0.033804126,0.37476185,0.4353642,0.000097750715,0.1484808],"study_design_scores_gemma":[0.00008941522,0.0006890606,0.0018534863,0.00005730972,0.000010662407,0.0000043421987,0.002924633,0.89991057,0.068477646,0.025403982,0.00013720941,0.00044166614],"about_ca_topic_score_codex":0.0002219641,"about_ca_topic_score_gemma":0.0013500833,"teacher_disagreement_score":0.86610645,"about_ca_system_score_codex":0.000030895957,"about_ca_system_score_gemma":0.00023251199,"threshold_uncertainty_score":0.46128514},"labels":[],"label_agreement":null},{"id":"W2398865566","doi":"","title":"AutoFolio: Algorithm Configuration for Algorithm Selection.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Folio; Algorithm; Benchmark (surveying); Computer science; Selection (genetic algorithm); Parameterized complexity; Set (abstract data type); Hyperparameter; State (computer science); Weighted Majority Algorithm; Variety (cybernetics); Machine learning; Artificial intelligence; Artificial neural network; Wake-sleep algorithm","score_opus":0.17037899944649626,"score_gpt":0.3755070636843442,"score_spread":0.20512806423784796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2398865566","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000039399176,0.000014124314,0.9857579,0.004540468,0.00052599114,0.0003795029,0.000041304036,0.0002528507,0.008448466],"genre_scores_gemma":[0.4274206,0.000013060454,0.5682419,0.0011199679,0.00062616484,0.00037786824,0.0003792068,0.000022035192,0.001799193],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978758,0.00011543797,0.000424778,0.0005462086,0.000770197,0.0002675748],"domain_scores_gemma":[0.99708724,0.0002357275,0.0001913835,0.00023478207,0.0020734298,0.0001774279],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000837479,0.00018171866,0.00015579289,0.00024178888,0.00023756697,0.0005254003,0.00061213336,0.00011462151,0.00010886493],"category_scores_gemma":[0.0007841315,0.00017958375,0.000057078603,0.0005303937,0.00006310288,0.0007036471,0.00004815837,0.00021902157,0.0011791028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000051096204,0.000041459654,0.0000020929565,0.0000014484895,0.000004761306,2.0692353e-7,0.000083823084,0.00047361868,0.00021561746,0.5078351,0.0014997851,0.489837],"study_design_scores_gemma":[0.000046985246,0.00018108425,0.0000857777,0.000008601192,0.0000026151538,0.0000046864548,0.00007706311,0.7813656,0.0065081427,0.20291379,0.008633219,0.00017244562],"about_ca_topic_score_codex":0.00007157496,"about_ca_topic_score_gemma":0.000013581114,"teacher_disagreement_score":0.78089195,"about_ca_system_score_codex":0.00016984287,"about_ca_system_score_gemma":0.0006116724,"threshold_uncertainty_score":0.9995986},"labels":[],"label_agreement":null},{"id":"W2400441446","doi":"","title":"The Role of Prompting and Feedback in Facilitating Students' Learning about Science with MetaTutor.","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Session (web analytics); Context (archaeology); Metacognition; Computer science; Self-regulated learning; Hypermedia; Adaptive hypermedia; Software deployment; Control (management); Human–computer interaction; Multimedia; Cognition; Knowledge management; Artificial intelligence; Psychology; Mathematics education; World Wide Web; Software engineering","score_opus":0.08264740034874508,"score_gpt":0.43599977251584066,"score_spread":0.3533523721670956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400441446","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.97203434,0.00003132723,0.004711388,0.00013297949,0.00016928205,0.00016155152,0.0000013581777,0.000023833223,0.02273392],"genre_scores_gemma":[0.995911,0.0000016719207,0.003744104,0.000023738558,0.0000384643,0.000025326664,0.0000010712407,0.000007634104,0.0002470057],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99790466,0.0003860372,0.00037225336,0.00035122872,0.0007104013,0.00027540798],"domain_scores_gemma":[0.9976239,0.0012725823,0.000223083,0.0001295614,0.0007101941,0.000040678617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066727246,0.000120707766,0.00014485419,0.00021643861,0.00051033637,0.00015394112,0.00038526914,0.000057209058,0.000097933415],"category_scores_gemma":[0.0033537177,0.00008644425,0.000016728834,0.00062347826,0.0009546174,0.00011235158,0.000057048386,0.0010804101,0.000027975604],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000097655895,0.000069805494,0.017197007,0.0000030201209,0.00001036264,6.951244e-7,0.0071506416,0.0003324686,0.033738412,0.4290227,4.215648e-7,0.5123768],"study_design_scores_gemma":[0.00015127017,0.0004778817,0.8306544,0.00015879913,0.000009253853,0.000011507761,0.03821547,0.024619421,0.029944032,0.07426902,0.0010854248,0.00040352353],"about_ca_topic_score_codex":0.00014482373,"about_ca_topic_score_gemma":0.00012740435,"teacher_disagreement_score":0.81345737,"about_ca_system_score_codex":0.000029776667,"about_ca_system_score_gemma":0.00019540395,"threshold_uncertainty_score":0.46939042},"labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"},{"model":"gpt","categories":[],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low"}],"label_agreement":"split"},{"id":"W2401626140","doi":"","title":"Treating Epilepsy by Reinforcement Learning Via Manifold-Based Simulation.","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Reinforcement learning; Computer science; Epilepsy; Artificial intelligence; Manifold (fluid mechanics); Psychology; Neuroscience; Engineering; Mechanical engineering","score_opus":0.06475144094737749,"score_gpt":0.32914766654567496,"score_spread":0.26439622559829745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2401626140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011898357,0.0000031161176,0.9760644,0.0008867456,0.000465106,0.00025037333,0.000001690162,0.00024971034,0.020889044],"genre_scores_gemma":[0.98133606,0.000002214366,0.016857017,0.0005673563,0.00012363004,0.000030709856,0.000046125813,0.000017056142,0.0010198596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9969639,0.00009761232,0.0006666725,0.0005633356,0.0012971067,0.00041136885],"domain_scores_gemma":[0.99756646,0.00072091544,0.00034660258,0.00038125025,0.0008337622,0.00015101554],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007579415,0.00027156936,0.00019704692,0.00024779156,0.00047320547,0.00047452273,0.00094247994,0.00015549417,0.00076110224],"category_scores_gemma":[0.0009908922,0.0002805198,0.00008948896,0.00048182768,0.00009118084,0.00048525239,0.0001113641,0.0007824169,0.0008180082],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011314731,0.000035153986,0.000073352145,0.0000038603675,0.0000064632877,0.0000014787377,0.0000917878,0.6794477,0.0041758637,0.29405144,0.00005248095,0.022049097],"study_design_scores_gemma":[0.00005110957,0.00020467308,0.00007042657,0.000023939947,0.000003803688,0.0000013059034,0.000026545267,0.96706754,0.020210104,0.010200332,0.0018435668,0.0002966253],"about_ca_topic_score_codex":0.000031103486,"about_ca_topic_score_gemma":0.00001315474,"teacher_disagreement_score":0.98014617,"about_ca_system_score_codex":0.000117304364,"about_ca_system_score_gemma":0.0002719135,"threshold_uncertainty_score":0.9999647},"labels":[],"label_agreement":null},{"id":"W2402000776","doi":"","title":"Possibilistic behavior recognition in smart homes for cognitive assistance","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi; Université de Sherbrooke","funders":"","keywords":"Home automation; Activity recognition; Cognition; Computer science; Field (mathematics); Moment (physics); Order (exchange); Internet of Things; Cognitive computing; Smart environment; Human–computer interaction; Artificial intelligence; Computer security; Psychology; Telecommunications; Business; Mathematics","score_opus":0.1833253256588282,"score_gpt":0.37657687288200564,"score_spread":0.19325154722317744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2402000776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25481105,0.000013917813,0.7319326,0.0014105247,0.0019843727,0.0016792284,0.00031487655,0.00022064065,0.0076328395],"genre_scores_gemma":[0.99447334,0.000003930281,0.0040838574,0.0003095281,0.00015646189,0.0007557343,0.00007078353,0.000014728931,0.00013162223],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9973991,0.00012443255,0.0006384604,0.000782751,0.0006943958,0.00036088517],"domain_scores_gemma":[0.9959191,0.0015294286,0.00025278993,0.0002599882,0.0019139503,0.00012473561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010526017,0.0002421407,0.00027744478,0.00042867503,0.00018905498,0.00039580485,0.0005945969,0.00018355984,0.00021945861],"category_scores_gemma":[0.0022918673,0.00026152402,0.000107775224,0.00060193165,0.00015481062,0.00076179503,0.00006600647,0.00048231936,0.00046518765],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013115688,0.0006269488,0.00066258525,0.000019472234,0.0000121336525,0.000010555081,0.0003766886,0.000006273781,0.007483955,0.25135845,0.000051969324,0.73925984],"study_design_scores_gemma":[0.00053134095,0.00065449503,0.039084945,0.0004982074,0.000033059663,0.00005652337,0.00086057605,0.1174426,0.10576581,0.73307765,0.0005015227,0.0014932847],"about_ca_topic_score_codex":0.000062191044,"about_ca_topic_score_gemma":0.0021656498,"teacher_disagreement_score":0.7396623,"about_ca_system_score_codex":0.000121952806,"about_ca_system_score_gemma":0.0004806838,"threshold_uncertainty_score":0.99998367},"labels":[],"label_agreement":null},{"id":"W2403646712","doi":"","title":"Assistive Technologies and Children-Robot Interaction.","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Human–computer interaction; Modalities; Mobile robot; Multitude; Humanoid robot; Variety (cybernetics); Robot; Computer science; Autism; Social robot; Mobile interaction; Artificial intelligence; Robot control; Psychology; Mobile device; Developmental psychology; World Wide Web","score_opus":0.18136242048168338,"score_gpt":0.44523951084096397,"score_spread":0.2638770903592806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403646712","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18311901,0.00021340231,0.20476453,0.010443973,0.005300275,0.0008974425,0.000070185575,0.001151874,0.5940393],"genre_scores_gemma":[0.99791986,0.00001839619,0.0004605921,0.00050093816,0.00018660592,0.000024598601,0.000013751303,0.00001183447,0.00086343964],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985461,0.00004873327,0.00038109632,0.0004000158,0.00035947407,0.00026456328],"domain_scores_gemma":[0.9987199,0.00047435815,0.00015380142,0.00013429382,0.0004520457,0.00006561585],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00044856634,0.00016697666,0.00015403806,0.00032016597,0.0002196023,0.00009952428,0.00018984934,0.00018956077,0.002398041],"category_scores_gemma":[0.00055244303,0.00016562716,0.00005546302,0.00029282382,0.00025871737,0.00014282703,0.00004395649,0.00045201875,0.0014268028],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016938076,0.00014540371,0.0005707406,7.8143745e-7,0.00003479741,0.000003918168,0.00040782266,0.000014789239,0.00045535548,0.68379754,0.00050579105,0.31389368],"study_design_scores_gemma":[0.0003073181,0.001057218,0.3913048,0.00018107067,0.000057643705,0.00019090535,0.058238655,0.002008397,0.060056597,0.47661266,0.008413096,0.0015716248],"about_ca_topic_score_codex":0.000117366835,"about_ca_topic_score_gemma":0.00021085024,"teacher_disagreement_score":0.81480086,"about_ca_system_score_codex":0.000114635295,"about_ca_system_score_gemma":0.000054483196,"threshold_uncertainty_score":0.9993507},"labels":[],"label_agreement":null},{"id":"W2403763169","doi":"","title":"Using Metacognitive Tools to Scaffold Medical Students Developing Clinical Reasoning Skills","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Innovative Teaching and Learning Methods","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Metacognition; Palette (painting); Computer science; Cognition; Medical diagnosis; Medical education; Psychology; Multimedia; Human–computer interaction; Mathematics education; Medicine; Pathology","score_opus":0.42419188466445407,"score_gpt":0.5835205447541827,"score_spread":0.15932866008972862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403763169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.550967,0.0000033482138,0.41932786,0.0014322222,0.0027022082,0.0002764829,0.000013413661,0.00009758987,0.025179928],"genre_scores_gemma":[0.93592525,0.0000016312056,0.0591227,0.0036079022,0.00080202834,0.00003442301,0.00001504646,0.00002658046,0.00046441995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99469274,0.0013068843,0.00096193317,0.0007383845,0.0018420498,0.0004579858],"domain_scores_gemma":[0.99551046,0.0022753128,0.00026585738,0.00024420768,0.0014499878,0.00025420188],"candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.012407943,0.00025720155,0.0003731908,0.00034698524,0.0003648173,0.00027557227,0.00088861893,0.00037410305,0.003937573],"category_scores_gemma":[0.020725688,0.00024710307,0.00010795682,0.000669978,0.00025507752,0.00014348423,0.00017598436,0.001722012,0.0016051669],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010147231,0.00033283446,0.0032676302,0.0000019838474,0.00007586154,0.000017405919,0.0011326713,0.000035203604,0.0010642359,0.68015254,0.00010655576,0.31371158],"study_design_scores_gemma":[0.000980723,0.0018422373,0.59076,0.0019229299,0.00018989599,0.0002151631,0.007452352,0.027678652,0.04023229,0.30330765,0.020982895,0.00443521],"about_ca_topic_score_codex":0.00006729508,"about_ca_topic_score_gemma":0.000067745525,"teacher_disagreement_score":0.5874924,"about_ca_system_score_codex":0.000091730624,"about_ca_system_score_gemma":0.0006587869,"threshold_uncertainty_score":0.9999981},"labels":[],"label_agreement":null},{"id":"W2403989326","doi":"","title":"OCR-Based Image Features for Biomedical Image and Article Classification: Identifying Documents Relevant to Genomic Cis-Regulatory Elements.","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Image (mathematics); Artificial intelligence; Contextual image classification; Information retrieval; Computational biology; Pattern recognition (psychology); Biology","score_opus":0.0875309987850259,"score_gpt":0.3880385414947419,"score_spread":0.30050754270971597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2403989326","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5910616,0.00022693118,0.39644876,0.0071078297,0.0007728186,0.0015465004,0.00031851808,0.00004412341,0.0024729504],"genre_scores_gemma":[0.9784865,0.00005126798,0.019755995,0.0007923053,0.00037030762,0.00008839621,0.0002185827,0.000014671572,0.00022201028],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99802434,0.000051755113,0.00047046682,0.00033387417,0.0006714135,0.00044813612],"domain_scores_gemma":[0.9987211,0.00007627428,0.00010503379,0.00020875174,0.0005239067,0.00036493666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093496917,0.00016709603,0.00013475418,0.00014990296,0.0001893208,0.00015308945,0.00027494165,0.00014601795,0.0001228957],"category_scores_gemma":[0.0009022622,0.00015407764,0.00006376277,0.00013421965,0.00031309863,0.000024997753,0.00010967709,0.00012791982,0.00016285585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023662201,0.00021994131,0.00013323998,0.000044543922,0.00002605667,3.9209257e-7,0.000116728224,0.0000045880843,0.9413034,0.011549196,0.0025667902,0.04379854],"study_design_scores_gemma":[0.00026417428,0.0006949129,0.0115136895,0.00005246837,0.000019699406,0.000004418207,0.00070283154,0.007753496,0.95962584,0.008219591,0.010721885,0.00042701446],"about_ca_topic_score_codex":0.000006146165,"about_ca_topic_score_gemma":0.000014975913,"teacher_disagreement_score":0.3874249,"about_ca_system_score_codex":0.00006685556,"about_ca_system_score_gemma":0.00023377586,"threshold_uncertainty_score":0.6283104},"labels":[],"label_agreement":null},{"id":"W2404370375","doi":"","title":"Security Games on Social Networks.","year":2012,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Adversary; Computer science; Misinformation; Oracle; Heuristics; Computer security; Stochastic game; Game theory; Normal-form game; Social network (sociolinguistics); Repeated game; Mathematical economics; Social media; Mathematics","score_opus":0.15966460907257662,"score_gpt":0.3518188858133328,"score_spread":0.1921542767407562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404370375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02713487,0.000066808214,0.90012705,0.006142938,0.003967074,0.00030784772,0.0000127295,0.00045862366,0.061782077],"genre_scores_gemma":[0.9973427,0.0000114160675,0.00043402155,0.0010539447,0.0010568689,0.000020428355,0.0000055118758,0.0000072984035,0.000067785615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982129,0.00011618686,0.00026360227,0.00032428608,0.000726462,0.00035659946],"domain_scores_gemma":[0.9990483,0.00022318702,0.0001105399,0.00017898773,0.0003247755,0.000114197464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006915026,0.00016125262,0.00013024859,0.00014811683,0.00032731265,0.00024961025,0.00055043283,0.00012706454,0.00018277009],"category_scores_gemma":[0.00022640485,0.0001578852,0.00007480534,0.00043417292,0.00007718281,0.00052795047,0.00007008738,0.00033969857,0.0007366431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023091749,0.00014246251,0.000041683586,0.0000015455456,0.000005588665,5.442398e-7,0.0004759461,0.00070398004,0.00008943906,0.9037209,0.0008650178,0.09392976],"study_design_scores_gemma":[0.000049226604,0.000245911,0.0060023195,0.000037641003,0.0000061482765,0.000007383628,0.00014662548,0.42524502,0.020473292,0.54223096,0.004975828,0.0005796214],"about_ca_topic_score_codex":0.000018249435,"about_ca_topic_score_gemma":0.000014394942,"teacher_disagreement_score":0.97020787,"about_ca_system_score_codex":0.000114029164,"about_ca_system_score_gemma":0.00008840485,"threshold_uncertainty_score":0.9468302},"labels":[],"label_agreement":null},{"id":"W2404523947","doi":"","title":"Towards Semantic Integration of Legacy Databases for Homeland Security.","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Computer science; Metadata; Ontology; World Wide Web; Context (archaeology); Vocabulary; Database; Homeland security; Semantic Web; Information retrieval","score_opus":0.1757340789809956,"score_gpt":0.37876546945676454,"score_spread":0.20303139047576893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404523947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01410763,0.000053143907,0.97424275,0.0044061523,0.00030031224,0.000266664,0.000034637247,0.00007956698,0.0065091285],"genre_scores_gemma":[0.96544605,0.000028663131,0.03400917,0.00025725152,0.00014831376,0.000030168729,0.000024580098,0.000004854204,0.000050946335],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998446,0.000044528173,0.00043317434,0.00035628202,0.00051895104,0.00020107061],"domain_scores_gemma":[0.9983701,0.0003549988,0.00016075422,0.0002608757,0.00080289016,0.000050391605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004858216,0.00014430434,0.00020216724,0.00020250575,0.000101757716,0.00015567252,0.0006311291,0.000057316865,0.000060801496],"category_scores_gemma":[0.0008259748,0.00012649917,0.00007480583,0.00027729257,0.00010871911,0.00075217336,0.00007783136,0.0001137461,0.00007012107],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000026081536,0.000102393744,0.000010010133,0.000010019888,0.0000068341724,4.4809076e-7,0.00035041774,0.00020151174,0.0013493503,0.7888897,0.00016518963,0.20888801],"study_design_scores_gemma":[0.00005325832,0.00016597864,0.00036652916,0.00006501148,0.000006290595,0.0000039182146,0.0003219765,0.41981205,0.24338521,0.33454934,0.0010785318,0.00019190673],"about_ca_topic_score_codex":0.00007085622,"about_ca_topic_score_gemma":0.00039393496,"teacher_disagreement_score":0.9513384,"about_ca_system_score_codex":0.000049563903,"about_ca_system_score_gemma":0.00028093578,"threshold_uncertainty_score":0.5158487},"labels":[],"label_agreement":null},{"id":"W2405049669","doi":"","title":"Using a Bottom-Up Approach to Design Computers as Metacognitive Tools to Enhance Learning of History","year":2010,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Educator Training and Historical Pedagogy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Metacognition; Notice; Narrative; Computer science; Recall; Process (computing); Mathematics education; Product (mathematics); Psychology; Cognitive psychology; Cognition; Linguistics","score_opus":0.5210833058302835,"score_gpt":0.4752099609427798,"score_spread":0.04587334488750372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405049669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06707716,0.000026263178,0.7507509,0.0012853809,0.006677618,0.0007547348,0.000011297115,0.00012226196,0.17329438],"genre_scores_gemma":[0.9654448,0.000003563336,0.032124396,0.00062253326,0.00067786133,0.000035714358,0.0000036099698,0.000012689725,0.001074835],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99751645,0.00031495615,0.000383186,0.000446169,0.0010093539,0.0003298723],"domain_scores_gemma":[0.99726003,0.0008419325,0.00016042135,0.00011460493,0.0013202147,0.00030276927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017062433,0.00015481279,0.00024570356,0.00030632722,0.00036637214,0.0000773871,0.0004273494,0.0001563988,0.00055398926],"category_scores_gemma":[0.0049597365,0.0001746588,0.000073588526,0.0004622906,0.00035159042,0.0001670228,0.000035807276,0.00060643884,0.00027749513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011573659,0.00023407182,0.00001028703,0.000005887391,0.000017728016,6.747178e-7,0.047238182,0.0031748952,0.027613653,0.8635108,0.0008733813,0.0572047],"study_design_scores_gemma":[0.00023708143,0.0032472326,0.00059903716,0.0006272158,0.00019574835,0.000012109178,0.1576739,0.0704113,0.24352837,0.24205613,0.27691957,0.004492326],"about_ca_topic_score_codex":0.0012522964,"about_ca_topic_score_gemma":0.0002050956,"teacher_disagreement_score":0.89836764,"about_ca_system_score_codex":0.00047461403,"about_ca_system_score_gemma":0.0021410866,"threshold_uncertainty_score":0.7122379},"labels":[],"label_agreement":null},{"id":"W2405235846","doi":"","title":"A Critical Step in eGovernment Evolution.","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Phase (matter); Popularity; Process management; Process (computing); Service (business); Business; Web service; Investment (military); Computer science; Database transaction; Transformation (genetics); Knowledge management; World Wide Web; Marketing; Political science","score_opus":0.04447568092986656,"score_gpt":0.312469183862709,"score_spread":0.2679935029328424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405235846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076603536,0.000057869376,0.92689294,0.008029729,0.00046052044,0.00018717698,0.000013473482,0.00011866726,0.056579277],"genre_scores_gemma":[0.9905135,0.0000026802481,0.008232269,0.0009845493,0.00016260895,0.000031999236,0.000005867145,0.0000058793858,0.000060635608],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99762577,0.00008875239,0.0004271324,0.0004974325,0.0010345352,0.0003264003],"domain_scores_gemma":[0.99884194,0.00033968667,0.000069356676,0.00025273717,0.0004245104,0.00007175823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034442413,0.00016735282,0.00014308392,0.00023550137,0.0001277637,0.00021744567,0.00077650324,0.00008256451,0.00013243],"category_scores_gemma":[0.00009020782,0.00016165388,0.000051538766,0.00066996476,0.00007863875,0.0003809026,0.00012639843,0.00024094364,0.00039233407],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018310726,0.00022542213,0.00014276209,0.0000062418167,0.0000021397018,0.000008430233,0.00011395263,0.0021006684,0.0006913805,0.9832703,0.000062723375,0.013357658],"study_design_scores_gemma":[0.000034586268,0.000084449704,0.004517013,0.000046013305,0.000001589066,0.0000047899052,0.00014200468,0.2401652,0.00582102,0.74827576,0.00070004625,0.00020750258],"about_ca_topic_score_codex":0.0005328875,"about_ca_topic_score_gemma":0.0016662929,"teacher_disagreement_score":0.9828532,"about_ca_system_score_codex":0.00022696432,"about_ca_system_score_gemma":0.0002361453,"threshold_uncertainty_score":0.6592054},"labels":[],"label_agreement":null},{"id":"W2405342173","doi":"","title":"Evidence for the Cross-Domain Reinterpretation of Creative Ideas","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Creativity in Education and Neuroscience","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Reinterpretation; Painting; Domain (mathematical analysis); Interpretation (philosophy); Computer science; Instrumental music; Visual arts; Artificial intelligence; Art; Psychology; Aesthetics; Musical; Mathematics","score_opus":0.3684637673862136,"score_gpt":0.5098683052349696,"score_spread":0.141404537848756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2405342173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41864717,0.00024203266,0.48770759,0.016183125,0.0031832082,0.003022062,0.00010974626,0.000096995485,0.070808075],"genre_scores_gemma":[0.99612004,0.000018960114,0.00051764335,0.00068052445,0.00011380209,0.00051324366,0.0000049450746,0.000008725817,0.0020221332],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983029,0.00014734142,0.00046461573,0.00037594207,0.0004998211,0.0002094262],"domain_scores_gemma":[0.9944337,0.0035094023,0.00026485347,0.00026383557,0.0014676221,0.00006061324],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007759991,0.00013604162,0.00014169693,0.00012275118,0.0002348011,0.00014273425,0.00047485196,0.000070134985,0.0053646057],"category_scores_gemma":[0.0031712388,0.000103940416,0.00009084986,0.0003419681,0.000590664,0.00029187024,0.00002777011,0.00015663583,0.00066342275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018739307,0.00018414353,0.0002987466,0.00000653222,0.000014728354,1.6493608e-7,0.0029364293,0.00029772398,0.007510149,0.93803644,0.0009998758,0.04952769],"study_design_scores_gemma":[0.00009641707,0.00068164675,0.07637423,0.00019459249,0.000019534045,0.0000064959986,0.007184893,0.03205634,0.052052908,0.8304077,0.0005827805,0.00034246442],"about_ca_topic_score_codex":0.00021858768,"about_ca_topic_score_gemma":0.00003620039,"teacher_disagreement_score":0.57747287,"about_ca_system_score_codex":0.0000647477,"about_ca_system_score_gemma":0.00021230482,"threshold_uncertainty_score":0.9955446},"labels":[],"label_agreement":null},{"id":"W2408039204","doi":"","title":"A Physician's Authoring Tool for Generation of Personalized Health Education in Reconstructive Surgery.","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Personalization; Domain (mathematical analysis); Computer science; Health care; Multimedia; World Wide Web","score_opus":0.1419393657558969,"score_gpt":0.3870617355454141,"score_spread":0.2451223697895172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408039204","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8741971,0.00031288952,0.12089431,0.0020019717,0.00052592164,0.0004490941,0.00008383405,0.000016385442,0.0015184912],"genre_scores_gemma":[0.9942813,0.000028708453,0.0047395565,0.00023785852,0.0003122017,0.000057112553,0.00020204036,0.000005391406,0.0001358122],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990746,0.000059866095,0.0003345656,0.00023315802,0.00016890897,0.00012890718],"domain_scores_gemma":[0.99933535,0.000084735635,0.0001556859,0.00006781235,0.00033628364,0.000020132413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041822135,0.00008320838,0.00014213199,0.00010029679,0.000053916585,0.000015206476,0.000072275456,0.0000843927,0.000012178598],"category_scores_gemma":[0.00040357717,0.000084880376,0.00005886466,0.00011288276,0.00011966553,0.0000049764903,0.000009929903,0.00005520062,0.0000026419327],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014385453,0.00024616058,0.00043144377,0.000024794068,0.000009200705,8.939778e-8,0.00006829414,0.00024961558,0.080951996,0.18217859,0.0015774178,0.7341185],"study_design_scores_gemma":[0.00019929619,0.00072332093,0.0072802054,0.0002788667,0.000009465885,0.0000047510903,0.0013716694,0.023063214,0.7820314,0.17750685,0.0069425353,0.0005884149],"about_ca_topic_score_codex":0.0000716785,"about_ca_topic_score_gemma":0.00018007892,"teacher_disagreement_score":0.73353016,"about_ca_system_score_codex":0.00004667533,"about_ca_system_score_gemma":0.0009991162,"threshold_uncertainty_score":0.34613213},"labels":[],"label_agreement":null},{"id":"W2408500447","doi":"","title":"Telepresence Robot for Home Care Assistance.","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":73,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Teleoperation; Telerobotics; Telehealth; Robot; Telemedicine; Variety (cybernetics); Mobile robot; Computer science; Human–computer interaction; Aging in place; Videoconferencing; Multimedia; Health care; Medicine; Artificial intelligence","score_opus":0.22379085905505805,"score_gpt":0.43988877838270185,"score_spread":0.2160979193276438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408500447","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3779593,0.0003782104,0.57441247,0.008004108,0.002505791,0.0021569387,0.00010591446,0.0005627299,0.03391449],"genre_scores_gemma":[0.99337876,0.000019271652,0.005551339,0.0003857721,0.0003703371,0.00005295759,0.000043026175,0.000013086588,0.00018542336],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99832207,0.000018732837,0.00041674994,0.000360626,0.0005284855,0.00035333203],"domain_scores_gemma":[0.99778116,0.00039271888,0.00009821425,0.00018915784,0.0014004282,0.00013833078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052342337,0.00014502788,0.00019967525,0.00023769081,0.0002070166,0.000021872442,0.00016548946,0.00022051361,0.00009980861],"category_scores_gemma":[0.0008298457,0.00014054794,0.000073841,0.00030454242,0.00014175981,0.00007366007,0.000020368188,0.0003427773,0.00011704544],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000825702,0.0001539939,0.0058700074,0.00009746677,0.000020022419,0.000015755799,0.0004450807,0.00011714322,0.004654925,0.5237831,0.00011132727,0.46390545],"study_design_scores_gemma":[0.00049076445,0.0035299824,0.07003824,0.001020261,0.0000664175,0.000046516307,0.007416526,0.008456294,0.6615977,0.23628542,0.009916119,0.001135743],"about_ca_topic_score_codex":0.00001903938,"about_ca_topic_score_gemma":0.000108443564,"teacher_disagreement_score":0.6569428,"about_ca_system_score_codex":0.00022598382,"about_ca_system_score_gemma":0.00036473238,"threshold_uncertainty_score":0.5731379},"labels":[],"label_agreement":null},{"id":"W2411176276","doi":"","title":"What Does Consciousness Bring to CTS","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Consciousness; Computer science; TUTOR; Task (project management); Human–computer interaction; Context (archaeology); Simple (philosophy); Artificial intelligence; Space (punctuation); Psychology; Engineering; Systems engineering","score_opus":0.09538882220650284,"score_gpt":0.35214615156327655,"score_spread":0.2567573293567737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2411176276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025185915,0.000052515254,0.92722476,0.0031616907,0.0030581048,0.00028815703,0.0000032906046,0.00022064072,0.04080491],"genre_scores_gemma":[0.9931678,0.000026090363,0.004372847,0.0013639856,0.0002920681,0.000011266149,0.0000028536954,0.00000880802,0.0007542902],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99758166,0.000054859785,0.0004354716,0.0006275338,0.0008299092,0.00047057288],"domain_scores_gemma":[0.99777997,0.00047293075,0.00011066347,0.00032347348,0.0010572406,0.00025572535],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012587248,0.0002080029,0.00019064259,0.0003033557,0.00024177291,0.0008482138,0.0010955706,0.00010616466,0.00016907229],"category_scores_gemma":[0.00069086003,0.00016986541,0.00006940498,0.0006700804,0.00011104981,0.0008366507,0.00021134582,0.00022511966,0.0018282644],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019449155,0.00007914971,0.00005017824,0.000003616598,0.000005384626,0.000011557847,0.0011566437,0.00016978166,0.0015284156,0.8315149,0.000084500796,0.16537642],"study_design_scores_gemma":[0.000056774723,0.00023575003,0.002096024,0.00019361528,0.000004683714,0.000016835416,0.0013832966,0.039897643,0.2535573,0.6970776,0.004756735,0.0007237668],"about_ca_topic_score_codex":0.00002883483,"about_ca_topic_score_gemma":0.00075805053,"teacher_disagreement_score":0.9679819,"about_ca_system_score_codex":0.00014326256,"about_ca_system_score_gemma":0.0002889365,"threshold_uncertainty_score":0.99894893},"labels":[],"label_agreement":null},{"id":"W2464628313","doi":"","title":"Activity Prediction Based on Tme Series Forcasting","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Activity recognition; Computer science; Process (computing); Rank (graph theory); Artificial intelligence; Time series; Machine learning; Series (stratigraphy); Activities of daily living; Interval (graph theory); Data mining; Medicine; Mathematics; Biology","score_opus":0.10204370507993565,"score_gpt":0.30122496351966344,"score_spread":0.19918125843972778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2464628313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047906362,0.0000015467999,0.9504939,0.002395912,0.0003073041,0.00010615281,0.000012025961,0.00014641653,0.04174611],"genre_scores_gemma":[0.9918198,0.0000017841201,0.007335866,0.00037441138,0.00020255851,0.000019056042,0.000008310158,0.0000075377907,0.00023067114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979826,0.00011253155,0.00032257556,0.0005062153,0.0008076811,0.00026835408],"domain_scores_gemma":[0.99862957,0.0003302808,0.00018550223,0.0002932913,0.0004695479,0.00009182399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007679907,0.00018357165,0.0001785066,0.00020996919,0.0003838579,0.00033166804,0.00048882735,0.00008057209,0.00019194528],"category_scores_gemma":[0.00077223737,0.00017164451,0.00009311321,0.00049901404,0.000084251085,0.00055508915,0.00006917975,0.00023254847,0.00028092976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004489031,0.00007508699,0.00003834155,0.0000041205717,0.0000058397563,7.481702e-7,0.000050719376,0.034234103,0.000998161,0.7116926,0.000041703748,0.25281373],"study_design_scores_gemma":[0.000021140575,0.00034763446,0.00066393265,0.00004025357,0.000003408548,0.0000016957091,0.000022305127,0.91770583,0.016708575,0.06383855,0.0004934541,0.00015320002],"about_ca_topic_score_codex":0.00001654135,"about_ca_topic_score_gemma":0.000033464836,"teacher_disagreement_score":0.9870292,"about_ca_system_score_codex":0.0000912691,"about_ca_system_score_gemma":0.000101311314,"threshold_uncertainty_score":0.699946},"labels":[],"label_agreement":null},{"id":"W246639213","doi":"","title":"Prediction Driven Behavior: Learning Predictions that Drive Fixed Responses","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Robot; Computer science; Artificial intelligence; Adaptive behavior; Behavior-based robotics; Adaptive control; Reinforcement learning; Latency (audio); Control (management); Control theory (sociology); Simulation; Mobile robot; Psychology","score_opus":0.15524353064616092,"score_gpt":0.3359340618331985,"score_spread":0.1806905311870376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W246639213","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004874456,0.000003220761,0.97738403,0.0010175953,0.0007589433,0.00028027038,0.000009327013,0.00045422337,0.015217922],"genre_scores_gemma":[0.9906122,0.000019572626,0.007575217,0.00021319737,0.00019723109,0.000088813395,0.00003544388,0.000015069789,0.0012432277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99696255,0.0003362664,0.00047969248,0.00058610627,0.0012819023,0.00035346148],"domain_scores_gemma":[0.9978317,0.0006422936,0.00025719526,0.00037325334,0.0007520555,0.00014347918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079309475,0.00023046538,0.00018658821,0.00036775434,0.0005716047,0.00042746938,0.00086143706,0.00015398276,0.00019879063],"category_scores_gemma":[0.0014155513,0.0002399035,0.00009180193,0.00045906403,0.0001664909,0.00062856165,0.0001673078,0.0005936932,0.0006388389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023853489,0.00006603239,0.0015450156,0.0000031351046,0.000011553758,0.0000017693677,0.00037270575,0.427545,0.0009932461,0.55210364,0.00012642464,0.017207598],"study_design_scores_gemma":[0.00004252137,0.00041675367,0.0068238927,0.00004645137,0.000010769591,0.0000072035064,0.0001221755,0.97272104,0.0056296093,0.012157806,0.00179672,0.00022504167],"about_ca_topic_score_codex":0.000013492072,"about_ca_topic_score_gemma":0.000011673275,"teacher_disagreement_score":0.98573774,"about_ca_system_score_codex":0.00018350613,"about_ca_system_score_gemma":0.00024746603,"threshold_uncertainty_score":0.97829807},"labels":[],"label_agreement":null},{"id":"W2483647133","doi":"","title":"The Implementation of a Planning and Scheduling Architecture for Multiple Robots Assisting Multiple Users in a Retirement Home Setting","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Scheduling (production processes); Architecture; Computer science; Schedule; Plan (archaeology); Human–computer interaction; Artificial intelligence; Engineering; Operations management; Operating system","score_opus":0.22448834479337995,"score_gpt":0.39706406352138524,"score_spread":0.17257571872800528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483647133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20398434,0.00004438397,0.7932817,0.0017021574,0.00022302174,0.0005925833,0.000022248325,0.0000382602,0.00011134786],"genre_scores_gemma":[0.98254347,0.0000016601233,0.017203566,0.000067465844,0.000051696406,0.0001112626,0.00001002137,0.0000068178524,0.0000040559953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981974,0.00014521762,0.0005324514,0.00035239736,0.0005387481,0.00023379592],"domain_scores_gemma":[0.9968871,0.0018409872,0.00030764384,0.00014058144,0.0007518503,0.00007186031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001683971,0.0001306149,0.00016183664,0.00018324204,0.0002041358,0.00023622366,0.0003250223,0.000051886702,0.0000018558245],"category_scores_gemma":[0.0017892245,0.0001156087,0.000038755625,0.00028416264,0.00007006136,0.00029671894,0.00009927711,0.00016446644,0.0000027555677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020537258,0.000101233345,0.02240389,0.000051414656,0.00003556206,0.0000028048275,0.010258274,0.030757187,0.0068475963,0.08755426,0.000027562466,0.84175485],"study_design_scores_gemma":[0.00028574732,0.0001719169,0.0061145737,0.00022892524,0.0000037605605,0.000004503484,0.006804527,0.9206029,0.021759363,0.043719206,0.00007645055,0.00022812503],"about_ca_topic_score_codex":0.00019461337,"about_ca_topic_score_gemma":0.0016572269,"teacher_disagreement_score":0.8898457,"about_ca_system_score_codex":0.00012875338,"about_ca_system_score_gemma":0.00030117374,"threshold_uncertainty_score":0.47143862},"labels":[],"label_agreement":null},{"id":"W2528006267","doi":"","title":"Mixed-Integer Linear Programming for Planning with Temporal Logic Tasks [Position Paper]","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nutrasource","funders":"","keywords":"Integer programming; Computer science; Linear programming; Computation; Mathematical optimization; Linear temporal logic; Dynamical systems theory; Task (project management); Integer (computer science); Theoretical computer science; Algorithm; Mathematics; Engineering; Programming language; Systems engineering","score_opus":0.3511121685748741,"score_gpt":0.41381928557568937,"score_spread":0.06270711700081527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2528006267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001046612,0.000017866436,0.9931987,0.0015951376,0.00044259764,0.00052226614,0.000010435641,0.00019775174,0.002968619],"genre_scores_gemma":[0.5517786,8.20715e-7,0.44752172,0.00035404423,0.00012990263,0.00012947114,0.000031776814,0.000009030526,0.000044651304],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977341,0.00012656688,0.00043933632,0.00053674047,0.00083374046,0.00032946],"domain_scores_gemma":[0.99751055,0.00020030531,0.00023384384,0.0002880942,0.001610906,0.00015629937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013972962,0.00021057698,0.00018391764,0.00019407088,0.00020690972,0.00028593873,0.0006843785,0.00012176857,0.000015353695],"category_scores_gemma":[0.00075873284,0.00018086139,0.00005485372,0.00047243398,0.00013056223,0.00082714076,0.000066656874,0.00024646916,0.00010964815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014469071,0.00010708542,0.000030448378,0.00000897851,0.000009043311,0.0000032724863,0.0004197551,0.0072587277,0.00029885533,0.92009985,0.00010720051,0.07151212],"study_design_scores_gemma":[0.00008050828,0.00090378686,0.00007016093,0.00008587777,0.0000061625874,0.000017679622,0.00043410514,0.7288894,0.021264527,0.2453415,0.0025858502,0.00032043556],"about_ca_topic_score_codex":0.00001860193,"about_ca_topic_score_gemma":0.000018944758,"teacher_disagreement_score":0.7216307,"about_ca_system_score_codex":0.00017977414,"about_ca_system_score_gemma":0.00046606056,"threshold_uncertainty_score":0.73753136},"labels":[],"label_agreement":null},{"id":"W2530564561","doi":"","title":"A New Look at Ontology Correctness","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Correctness; Ontology; Computer science; Terminology; Upper ontology; Semantics (computer science); Process ontology; Suggested Upper Merged Ontology; Ontology-based data integration; Ontology components; Theoretical computer science; Programming language; Information retrieval; Semantic Web; Epistemology","score_opus":0.2482856625543623,"score_gpt":0.3670105023094892,"score_spread":0.11872483975512688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2530564561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005454413,0.0000697951,0.9213085,0.0073608067,0.0017163707,0.00016206683,0.0000029379685,0.00023479493,0.0636903],"genre_scores_gemma":[0.9820414,0.000007773161,0.014437882,0.0010694328,0.00017650287,0.000014043269,0.00000507756,0.000006241584,0.0022416546],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99797815,0.00009091477,0.0003460405,0.00049988215,0.0007684439,0.00031656088],"domain_scores_gemma":[0.9983269,0.0002824829,0.00011921484,0.0003269124,0.0007114021,0.00023310524],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044904725,0.00017645444,0.00020804892,0.00016555295,0.00012143485,0.00017195818,0.0010625558,0.00012068915,0.0002569448],"category_scores_gemma":[0.00081545894,0.0001611516,0.0000556458,0.0003548379,0.00011412414,0.00029586317,0.00023028135,0.0001716172,0.0023625083],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000044297005,0.000046573692,0.0000906523,0.00000137546,0.0000062504446,0.000011983094,0.00046660233,0.00036986388,0.00013591093,0.85957813,0.0043749553,0.1348734],"study_design_scores_gemma":[0.00007849518,0.00025714404,0.00056748406,0.000023534496,0.000004039929,0.000046996145,0.00031003935,0.12499157,0.024423743,0.84343964,0.0055160564,0.00034124163],"about_ca_topic_score_codex":0.00018434628,"about_ca_topic_score_gemma":0.0007786244,"teacher_disagreement_score":0.976587,"about_ca_system_score_codex":0.00021327107,"about_ca_system_score_gemma":0.0010081439,"threshold_uncertainty_score":0.9984143},"labels":[],"label_agreement":null},{"id":"W2564892412","doi":"","title":"On the Use of Modular Software and Hardware for Designing Wheelchair Robots.","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of British Columbia; McGill University","funders":"","keywords":"Wheelchair; Modular design; Robot; Embedded system; Software; Computer science; Self-reconfiguring modular robot; Work (physics); Computer architecture; Software engineering; Mobile robot; Engineering; Operating system; Robot control; Artificial intelligence; World Wide Web","score_opus":0.27038643741897544,"score_gpt":0.32832668050513986,"score_spread":0.05794024308616441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564892412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00948704,0.000007739012,0.9831509,0.0068809227,0.00010148355,0.00019321957,0.000025234967,0.000087214445,0.00006624232],"genre_scores_gemma":[0.9585341,0.000008067841,0.040987175,0.00031702907,0.000019659577,0.000040700583,0.0000011429603,0.0000058492305,0.000086276596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988615,0.00005888994,0.00023569957,0.00034363993,0.00032558836,0.00017466153],"domain_scores_gemma":[0.9971056,0.0018742906,0.00012446532,0.00023869959,0.0006210729,0.000035892783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034665593,0.00012060622,0.00012898214,0.00012678611,0.00016130216,0.00008768266,0.00047597967,0.00007536562,0.00002116405],"category_scores_gemma":[0.0022202188,0.0000731763,0.000044867338,0.00016461503,0.00021039453,0.00019311518,0.00007034101,0.000100957026,0.000025252815],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017513557,0.000033049997,0.000034882734,0.0000032424916,0.0000066775206,5.0960074e-7,0.000043158863,0.0006339039,0.0045030406,0.8147307,0.00013357631,0.17985974],"study_design_scores_gemma":[0.000042368913,0.0003063069,0.0009876387,0.00017040233,0.0000035103646,0.0000021167164,0.000026255282,0.07008303,0.14716758,0.7806357,0.00040242207,0.00017263797],"about_ca_topic_score_codex":0.000006053702,"about_ca_topic_score_gemma":0.000008999121,"teacher_disagreement_score":0.949047,"about_ca_system_score_codex":0.000035294266,"about_ca_system_score_gemma":0.00009840338,"threshold_uncertainty_score":0.2984043},"labels":[],"label_agreement":null},{"id":"W2572086656","doi":"","title":"Activity Recognition Through Complex Event Processing: First Findings","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Complex event processing; Leverage (statistics); Computer science; Abstraction; Activity recognition; Event (particle physics); Process (computing); Data stream mining; Task (project management); Representation (politics); Real-time computing; Human–computer interaction; Artificial intelligence; Data mining; Programming language; Engineering","score_opus":0.32075994596115737,"score_gpt":0.37643140796227037,"score_spread":0.055671462001113003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2572086656","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018735081,0.000007183457,0.9559235,0.013186312,0.00042277237,0.00038825223,0.000054299664,0.0002829361,0.010999656],"genre_scores_gemma":[0.9964685,0.000017675919,0.0024687562,0.00050597754,0.00017391068,0.00011348443,0.000009816005,0.000014529748,0.00022736998],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997002,0.00016068872,0.0005138695,0.0008008485,0.0011341746,0.00038843942],"domain_scores_gemma":[0.9972651,0.00065738923,0.0002748585,0.00032102212,0.0013582265,0.00012342868],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006544952,0.00027601182,0.00026980607,0.00021553486,0.0004036786,0.00038499077,0.00080702425,0.00014176802,0.0006649151],"category_scores_gemma":[0.0006679818,0.00022483365,0.00011695298,0.0005818898,0.00016935608,0.0017957825,0.00015130204,0.00020899155,0.0019694266],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000637488,0.00031117533,0.00005390825,0.00001771983,0.00001529862,0.0000044725552,0.00041394413,0.00002052118,0.008964181,0.123099625,0.00046458284,0.86657083],"study_design_scores_gemma":[0.00026768108,0.0005018964,0.0036198508,0.00086869457,0.0000132759105,0.000056758883,0.00016965879,0.12288584,0.2839505,0.5789932,0.007405006,0.0012676108],"about_ca_topic_score_codex":0.000042890588,"about_ca_topic_score_gemma":0.00018814906,"teacher_disagreement_score":0.9777334,"about_ca_system_score_codex":0.00033948346,"about_ca_system_score_gemma":0.00039189562,"threshold_uncertainty_score":0.99880767},"labels":[],"label_agreement":null},{"id":"W2573040134","doi":"","title":"Monitoring Discussion of Vaccine Adverse Events in the Media: Opportunities from the Vaccine Sentimeter.","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University","funders":"","keywords":"Vaccine safety; Adverse effect; Medicine; Computer science; Virology; Immunology; Immunization; Internal medicine; Immune system","score_opus":0.20245996722698606,"score_gpt":0.3757674765733512,"score_spread":0.17330750934636513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573040134","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.89312917,0.00011526762,0.0031432316,0.09591184,0.0010696086,0.00053726,0.00008555638,0.000038591494,0.005969496],"genre_scores_gemma":[0.99842006,0.0004957796,0.000045043922,0.00020362905,0.0005369187,0.000021317679,0.0000057126417,0.00000584475,0.00026571317],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99785346,0.0003736822,0.00043200058,0.00021461393,0.0009115898,0.00021464222],"domain_scores_gemma":[0.99766386,0.0015476933,0.00017474865,0.00018860758,0.00037458146,0.000050522223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012043228,0.00012122389,0.00015431688,0.00008933948,0.00028573172,0.000020354428,0.00061623036,0.00007558784,0.0008842735],"category_scores_gemma":[0.0012842318,0.000053327236,0.000070124894,0.00028071136,0.00003485808,0.00029051513,0.000048273978,0.0001493042,0.00008233287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003713093,0.00042338515,0.07945805,0.0000070550223,0.00004274249,0.000014938656,0.026809791,0.00005287251,0.0037655672,0.57870555,0.0008547247,0.309494],"study_design_scores_gemma":[0.00027618028,0.00013298746,0.3518247,0.00077809195,0.000035176978,9.06032e-7,0.050041337,0.00028992584,0.016536899,0.5772842,0.002408249,0.00039134902],"about_ca_topic_score_codex":0.00062011793,"about_ca_topic_score_gemma":0.0029672536,"teacher_disagreement_score":0.30910262,"about_ca_system_score_codex":0.000081849925,"about_ca_system_score_gemma":0.00025642622,"threshold_uncertainty_score":0.9682175},"labels":[],"label_agreement":null},{"id":"W2573476186","doi":"","title":"On Keeping Secrets: Intelligent Agents and the Ethics of Information Hiding.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Computer science; Computer security; Internet privacy","score_opus":0.24015273069790039,"score_gpt":0.3727058303971242,"score_spread":0.1325530996992238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573476186","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012383078,0.000035958343,0.94938034,0.021410724,0.0008309958,0.0005619429,0.0000131366405,0.00008749642,0.015296303],"genre_scores_gemma":[0.9970555,0.000029135184,0.00050529285,0.0023124174,0.000033971206,0.0000202551,0.000007363618,0.0000032417172,0.00003286853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976918,0.00028679677,0.0005570122,0.00020397676,0.0011170679,0.00014333411],"domain_scores_gemma":[0.9973606,0.00084229314,0.00026742055,0.00026327008,0.0011636676,0.000102759936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024424407,0.0001234094,0.00016177911,0.00020018208,0.00014891668,0.00027109354,0.00064791885,0.00009840984,0.000023315684],"category_scores_gemma":[0.0027468419,0.00009340596,0.00004496273,0.00034108572,0.000216212,0.00046637258,0.000115038616,0.00032798288,0.00021981663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052842446,0.000036973783,0.000008078606,0.000010103209,0.0000087564285,2.1432126e-7,0.030927567,0.00021946375,0.0000096174645,0.95385873,0.000120542245,0.014747108],"study_design_scores_gemma":[0.00006846697,0.00007584035,0.000052719428,0.00005953342,0.0000024689089,0.0000023578086,0.00081546884,0.5162438,0.0023283295,0.47940078,0.0008591557,0.00009105866],"about_ca_topic_score_codex":0.000051681163,"about_ca_topic_score_gemma":0.000018229564,"teacher_disagreement_score":0.98467237,"about_ca_system_score_codex":0.000078052886,"about_ca_system_score_gemma":0.000326277,"threshold_uncertainty_score":0.38089845},"labels":[],"label_agreement":null},{"id":"W2573613785","doi":"","title":"Exploring power storage profiles for vehicle to grid systems","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Electric Vehicles and Infrastructure","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Computer science; Power grid; Grid; Power (physics)","score_opus":0.2399476031522043,"score_gpt":0.31554042945143124,"score_spread":0.07559282629922695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573613785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6217816,0.00020852487,0.34430543,0.0013708664,0.0058665876,0.0017937972,0.00029287738,0.0007401446,0.023640195],"genre_scores_gemma":[0.99831307,0.000007400377,0.00080406794,0.000102123566,0.00043804737,0.00022201447,0.000015686694,0.000023393059,0.000074216434],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987862,0.000017431941,0.0002815066,0.00021653603,0.00043492392,0.00026338638],"domain_scores_gemma":[0.99903035,0.0000798854,0.000031562085,0.00011353895,0.0005747763,0.000169857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027550486,0.00015117401,0.00014686136,0.00014601157,0.00007198733,0.00012282937,0.00020559118,0.00006412161,0.0000451663],"category_scores_gemma":[0.0002071887,0.00014776723,0.00003781493,0.0002520167,0.000019923375,0.00023311052,0.00001718618,0.00015834703,0.00021822099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008562791,0.000037191552,0.000021478678,0.00003101432,0.000023225031,0.0000021414285,0.00073617924,0.42679688,0.010009501,0.52698946,0.0057611424,0.029506171],"study_design_scores_gemma":[0.00008040065,0.00054299674,0.00037689993,0.000100736324,0.000007471702,0.000004919678,0.001385558,0.840526,0.107253045,0.032409742,0.016733838,0.00057839055],"about_ca_topic_score_codex":0.000012978407,"about_ca_topic_score_gemma":0.000007711734,"teacher_disagreement_score":0.4945797,"about_ca_system_score_codex":0.00019028907,"about_ca_system_score_gemma":0.000106108004,"threshold_uncertainty_score":0.60257727},"labels":[],"label_agreement":null},{"id":"W2574286572","doi":"","title":"Efficient Appliances Recognition in Smart Homes Based on Active and Reactive Power, Fast Fourier Transform and Decision Trees.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; AC power; Decision tree; Fourier transform; Artificial intelligence; Power (physics); Computer vision; Speech recognition; Mathematics","score_opus":0.07435391982588865,"score_gpt":0.29191103222261383,"score_spread":0.21755711239672518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574286572","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7580152,0.000056409703,0.1799256,0.0007730667,0.00069832546,0.00069755316,0.00008115834,0.00015227872,0.059600413],"genre_scores_gemma":[0.99845064,0.00003242932,0.0012784041,0.00008825715,0.000038752856,0.000068317866,0.00001935051,0.000013913461,0.000009910794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871385,0.00002548229,0.00025034847,0.00030852997,0.0005201022,0.00018166268],"domain_scores_gemma":[0.9993409,0.00024745663,0.000036576996,0.000088318644,0.00018562256,0.00010115049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003106268,0.00017850808,0.00015652987,0.00031866907,0.000050019935,0.0000659601,0.00008475226,0.000080598176,0.00005122669],"category_scores_gemma":[0.0001409714,0.00017591887,0.000022123233,0.0002581346,0.00008664991,0.00010201805,0.000014079664,0.00018458793,0.00004999619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003945637,0.000117755946,0.000040157236,0.000007956165,0.0000114108925,0.000004459152,0.00044437364,0.33969975,0.0001668274,0.010990609,0.000055484135,0.64806664],"study_design_scores_gemma":[0.00015656589,0.00019772859,0.002198825,0.00012310321,0.000005340414,9.477909e-7,0.000881508,0.95008767,0.010938216,0.034938406,0.00024332148,0.00022835148],"about_ca_topic_score_codex":0.000028342118,"about_ca_topic_score_gemma":0.00032925763,"teacher_disagreement_score":0.6478383,"about_ca_system_score_codex":0.00020792647,"about_ca_system_score_gemma":0.00005604138,"threshold_uncertainty_score":0.71737635},"labels":[],"label_agreement":null},{"id":"W2574625309","doi":"","title":"Exploiting Environmental Sounds for Activity Recognition in Smart Homes.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Speech recognition","score_opus":0.34439884933354287,"score_gpt":0.3580037930964423,"score_spread":0.013604943762899446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574625309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14515087,0.000015958172,0.8448114,0.0018845103,0.0007523901,0.00069515296,0.000087411776,0.00014538445,0.0064569716],"genre_scores_gemma":[0.9965976,0.0000060686643,0.0026198688,0.0002417533,0.00014684469,0.0002444124,0.000039283612,0.000012400365,0.00009176377],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976715,0.0001731965,0.0004399045,0.00060039747,0.00079196633,0.000323044],"domain_scores_gemma":[0.9984657,0.0005813694,0.00020722975,0.00021778034,0.00037722985,0.0001506531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012836304,0.00020221877,0.00023229605,0.00034431345,0.0001256903,0.00028117283,0.00048251037,0.00011963856,0.000063979205],"category_scores_gemma":[0.0007296218,0.00022788194,0.00008330613,0.00035532666,0.00008080543,0.0010952691,0.00010191377,0.00023914034,0.00054070837],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014162196,0.0004893079,0.00031816086,0.0000114559,0.000013892937,0.0000051759516,0.00095222244,0.0001573345,0.0046188827,0.06417401,0.00014555728,0.92897236],"study_design_scores_gemma":[0.00034200595,0.00050091447,0.0018940018,0.00015364542,0.000006116889,0.000024518693,0.001413688,0.34458935,0.08391259,0.56530064,0.0010735616,0.0007889929],"about_ca_topic_score_codex":0.0000709693,"about_ca_topic_score_gemma":0.00023484154,"teacher_disagreement_score":0.9281834,"about_ca_system_score_codex":0.00045705115,"about_ca_system_score_gemma":0.0003508596,"threshold_uncertainty_score":0.9292756},"labels":[],"label_agreement":null},{"id":"W2575473210","doi":"","title":"An Exploratory Study into the Use of an Emotionally Aware Cognitive Assistant.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI in Service Interactions","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Cognition; Exploratory research; Human–computer interaction; Psychology","score_opus":0.3810335401888722,"score_gpt":0.4166106884691983,"score_spread":0.0355771482803261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2575473210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43010607,0.000005296642,0.56626713,0.001651032,0.00062119117,0.0005052254,0.000039049177,0.00013010809,0.0006748745],"genre_scores_gemma":[0.9950468,0.0000016880244,0.004003128,0.0006933285,0.000116026145,0.00006772959,0.000025343757,0.000011308855,0.000034657412],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967237,0.00056691427,0.0005492179,0.0005400681,0.0014246625,0.00019542329],"domain_scores_gemma":[0.9937655,0.00067223987,0.0002637357,0.0005279455,0.0045762365,0.00019437756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009712911,0.00019524613,0.00017683761,0.00021789724,0.00026246678,0.00045597032,0.0012993148,0.00007338173,0.0000930463],"category_scores_gemma":[0.00076502346,0.0001603714,0.000050087092,0.00063736684,0.00019506663,0.0024727292,0.00015647881,0.00031704095,0.0002127749],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002580233,0.0041232677,0.000495413,0.0000060539505,0.00007837894,0.000021373724,0.0572731,0.014549327,0.0008874299,0.8096933,0.0001970372,0.11241732],"study_design_scores_gemma":[0.00011085916,0.0021128394,0.004136452,0.0000796955,0.000020755897,0.000008557773,0.03670453,0.77605605,0.009041635,0.17097202,0.00030581385,0.00045079045],"about_ca_topic_score_codex":0.00023526135,"about_ca_topic_score_gemma":0.003409627,"teacher_disagreement_score":0.76150674,"about_ca_system_score_codex":0.00014563336,"about_ca_system_score_gemma":0.0009338372,"threshold_uncertainty_score":0.6539756},"labels":[],"label_agreement":null},{"id":"W2576053140","doi":"","title":"Self-Driving Aircraft Towing Vehicles: A Preliminary Report","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Towing; Runway; Crew; Workload; Engineering; Aeronautics; Work (physics); Computer science; Simulation; Automotive engineering; Marine engineering; Transport engineering","score_opus":0.08285315254577422,"score_gpt":0.2975444781248063,"score_spread":0.21469132557903206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576053140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026649116,0.00006846406,0.77418035,0.0011231789,0.0012395222,0.00050625455,0.0000055532037,0.0016615621,0.19456603],"genre_scores_gemma":[0.9910306,0.000022491216,0.008258689,0.00008044721,0.00019474572,0.000030100422,0.000036252204,0.00002106478,0.00032564986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985144,0.000026145579,0.00038616065,0.0002530768,0.0006082973,0.00021189885],"domain_scores_gemma":[0.9991993,0.00008656765,0.00005632682,0.00014910841,0.00039806828,0.00011067776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004498687,0.00016069197,0.00012719385,0.00016859184,0.00008283702,0.00010985436,0.00020266912,0.00008504305,0.00011434343],"category_scores_gemma":[0.0003032672,0.00017477343,0.000042792304,0.00028285684,0.000034361983,0.00028079067,0.00003643869,0.00017413293,0.00032191],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032650558,0.000114515795,0.00010613387,0.000033659777,0.000039863688,0.00005469541,0.0010276883,0.7989087,0.00010217932,0.15709834,0.0026687568,0.0398128],"study_design_scores_gemma":[0.000027010294,0.000077086625,0.00012404485,0.000043044205,0.000009661277,0.000009054577,0.0005144672,0.98302454,0.0018099152,0.012002383,0.0021424862,0.00021630041],"about_ca_topic_score_codex":0.0000037424752,"about_ca_topic_score_gemma":0.000011657488,"teacher_disagreement_score":0.96438146,"about_ca_system_score_codex":0.00016719267,"about_ca_system_score_gemma":0.00011233833,"threshold_uncertainty_score":0.7127054},"labels":[],"label_agreement":null},{"id":"W2576201175","doi":"","title":"Discovering Relevant Hashtags for Health Concepts: A Case Study of Twitter","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Advanced Text Analysis Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thomson Reuters (Canada)","funders":"","keywords":"Computer science; Search engine indexing; Baseline (sea); Cluster analysis; Information retrieval; Social media; Word (group theory); Natural language processing; Artificial intelligence; Data science; World Wide Web; Linguistics","score_opus":0.19415731578588039,"score_gpt":0.45427406322844993,"score_spread":0.2601167474425695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576201175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06189565,0.000010142505,0.9343851,0.0027321556,0.00009617804,0.00056535454,0.000017207401,0.000103589,0.00019460195],"genre_scores_gemma":[0.9769977,0.00001134715,0.022382686,0.0003330643,0.00004897033,0.00012775135,0.0000013461324,0.000009900367,0.000087242246],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99777085,0.000101159574,0.0006963488,0.0005522319,0.0006092938,0.00027012386],"domain_scores_gemma":[0.99778485,0.0005587689,0.00036816002,0.00040615367,0.000792928,0.00008911957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064942054,0.00017634791,0.00029601916,0.00023993451,0.0001621133,0.000077745426,0.0006293086,0.00004680061,0.000032840697],"category_scores_gemma":[0.00044464023,0.00012947763,0.00008590865,0.0003704631,0.00011374012,0.00048172585,0.00012739576,0.00009761561,0.000021193251],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046724846,0.00059498375,0.000077541285,0.000012879685,0.000034106703,0.000032954675,0.0027195124,0.0003018763,0.0038336231,0.71176356,0.00014830814,0.2804339],"study_design_scores_gemma":[0.0004267312,0.0046157106,0.00019652597,0.00040444915,0.000021362632,0.00013344934,0.006349756,0.121142924,0.13171117,0.7332522,0.0008353141,0.0009103942],"about_ca_topic_score_codex":0.00012547297,"about_ca_topic_score_gemma":0.00042057154,"teacher_disagreement_score":0.91510206,"about_ca_system_score_codex":0.0001713363,"about_ca_system_score_gemma":0.0002867841,"threshold_uncertainty_score":0.52799445},"labels":[],"label_agreement":null},{"id":"W2576324802","doi":"","title":"Reconfiguration Control and Decision, Application to Smart Environments.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Control reconfiguration; Computer science; Control (management); Embedded system; Artificial intelligence","score_opus":0.09283982261975483,"score_gpt":0.3052443757092383,"score_spread":0.21240455308948347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576324802","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034994945,0.000018109442,0.95227385,0.0054252986,0.00014876269,0.00023610277,0.0000052987525,0.000054650507,0.0068429774],"genre_scores_gemma":[0.99587715,0.0000058068517,0.0006334048,0.0029776427,0.0003470642,0.000060348473,0.000032228854,0.000010253261,0.00005612781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986152,0.0000075734292,0.00032477747,0.0003590997,0.0005501032,0.00014322193],"domain_scores_gemma":[0.9990111,0.00005667653,0.00013145075,0.00012071208,0.00064047484,0.000039599257],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00055413437,0.00013559689,0.00014332718,0.00027454906,0.00014051968,0.0002978368,0.00016512437,0.00006389174,0.00007868423],"category_scores_gemma":[0.00051328406,0.00012947124,0.000027282205,0.00038158675,0.00004588014,0.00049452647,0.00003328913,0.00008973789,0.00132084],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018383007,0.000107675434,0.0004656043,0.000008522058,0.000012955761,6.3798456e-7,0.000043381795,0.023576152,0.0015478145,0.44484138,0.0005102091,0.52870184],"study_design_scores_gemma":[0.00007059524,0.00001613727,0.0007714943,0.00002443117,0.000018711757,5.3943853e-7,0.00017245323,0.61818534,0.0003975321,0.37507352,0.005074098,0.00019515026],"about_ca_topic_score_codex":0.000189595,"about_ca_topic_score_gemma":0.00013012651,"teacher_disagreement_score":0.9608822,"about_ca_system_score_codex":0.000047814723,"about_ca_system_score_gemma":0.00005246133,"threshold_uncertainty_score":0.99945676},"labels":[],"label_agreement":null},{"id":"W2576392126","doi":"","title":"A Trust Establishment Model in Multi-Agent Systems.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Access Control and Trust","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Multi-agent system; Artificial intelligence","score_opus":0.38717559040168587,"score_gpt":0.42798533170565056,"score_spread":0.0408097413039647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576392126","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060945276,0.00037969352,0.35656428,0.014111757,0.0030278275,0.002414973,0.00013660063,0.0003719616,0.56204766],"genre_scores_gemma":[0.9975402,0.000034059623,0.0005514377,0.0002725828,0.00015513526,0.00010004333,0.000009064791,0.0000085606125,0.0013289549],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99738693,0.00019266886,0.00044905144,0.0003521304,0.001256489,0.00036275078],"domain_scores_gemma":[0.99842596,0.00012435394,0.00012383264,0.00012587826,0.00095866894,0.00024128486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016035862,0.00014835286,0.00019629534,0.00023240027,0.00023254797,0.00033089682,0.00044216675,0.00013088965,0.00013770339],"category_scores_gemma":[0.0012007365,0.00014519459,0.00004546319,0.0004286783,0.00020522176,0.00039853083,0.000044930963,0.00022774767,0.00043388156],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005673957,0.00024553036,0.00034331088,0.000002948617,0.000005164723,0.000005235539,0.0030632336,0.06343184,0.000021870637,0.9226073,0.0001940242,0.0100228265],"study_design_scores_gemma":[0.00011375892,0.000045018656,0.00023825571,0.00004105406,0.0000043690925,4.030556e-7,0.0075425413,0.8907297,0.00015089475,0.09891856,0.0019773247,0.00023809003],"about_ca_topic_score_codex":0.0029804024,"about_ca_topic_score_gemma":0.004941787,"teacher_disagreement_score":0.9365949,"about_ca_system_score_codex":0.0005341553,"about_ca_system_score_gemma":0.0013702043,"threshold_uncertainty_score":0.5920863},"labels":[],"label_agreement":null},{"id":"W2576618580","doi":"","title":"Graphical View of Blog Content Using B2G.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Microblogging; Content (measure theory); Social media; Multimedia; World Wide Web; Mathematics","score_opus":0.4270729361813214,"score_gpt":0.3803942316333476,"score_spread":0.04667870454797379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2576618580","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014883779,0.000052177373,0.9788712,0.0013510082,0.00026436846,0.00011173094,0.0000057642455,0.000039232433,0.004420723],"genre_scores_gemma":[0.99191123,0.00001846843,0.0076514063,0.00031506573,0.00005343189,0.00000498875,0.0000069071893,0.00000421755,0.00003427078],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790573,0.00012114287,0.000534252,0.00032795177,0.0009446174,0.00016633308],"domain_scores_gemma":[0.99751824,0.00010774963,0.00019238767,0.00023247115,0.0018230414,0.00012611445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008131758,0.00012404608,0.00022033976,0.000245206,0.000076058175,0.00011617832,0.0005734719,0.00007797555,0.000053788153],"category_scores_gemma":[0.00042762916,0.00011128183,0.00009167299,0.0007499461,0.000113873306,0.00028767734,0.00009002356,0.00013050064,0.00006783478],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000128997135,0.00011005886,0.00013820318,0.0000031858622,0.000013186451,0.0000015468408,0.00012891824,0.0023137582,0.002803744,0.9678223,0.00002490476,0.026627306],"study_design_scores_gemma":[0.000026806427,0.00009848813,0.00016927106,0.000033367876,0.000006832851,0.0000025088668,0.000116780364,0.6636668,0.015784713,0.31984112,0.0001239866,0.00012936635],"about_ca_topic_score_codex":0.00007717423,"about_ca_topic_score_gemma":0.00004882641,"teacher_disagreement_score":0.9770275,"about_ca_system_score_codex":0.00006696367,"about_ca_system_score_gemma":0.00040115035,"threshold_uncertainty_score":0.45379412},"labels":[],"label_agreement":null},{"id":"W2577358226","doi":"","title":"The Budgeted Biomarker Discovery Problem: A Variant of Association Studies.","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Association (psychology); Biomarker; Biomarker discovery; Computer science; Computational biology; Biology; Genetics; Psychology; Gene","score_opus":0.545521470065721,"score_gpt":0.47300737763099776,"score_spread":0.07251409243472329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577358226","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064017855,0.0022635201,0.3464481,0.43393883,0.0057372265,0.0036176976,0.001066298,0.00023196015,0.14267853],"genre_scores_gemma":[0.9946012,0.00017993117,0.00082399795,0.0029322794,0.00020591926,0.00010188604,0.000018766457,0.000014186026,0.0011218588],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954866,0.00044315378,0.0029996366,0.00041799265,0.00035045325,0.0003021366],"domain_scores_gemma":[0.9904883,0.005904436,0.0024451106,0.000282855,0.0008096386,0.000069678936],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.020809028,0.00015910079,0.00052802404,0.00020157044,0.00032862404,0.00016541178,0.00035462776,0.0001259784,0.0001258991],"category_scores_gemma":[0.027474115,0.00014990247,0.00010531471,0.00030413535,0.0001463907,0.00036856695,0.0000492236,0.00017770207,0.0010369727],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027946862,0.000075903125,0.00045874077,0.00003722947,0.00008638036,1.0004934e-7,0.0004486875,0.0003147488,0.000035192883,0.9917653,0.004057281,0.0026924715],"study_design_scores_gemma":[0.00007512749,0.000109067674,0.002756983,0.00008697037,0.0000061804867,8.0379573e-7,0.0008346111,0.059038978,0.0003285703,0.9253612,0.011171225,0.00023028396],"about_ca_topic_score_codex":0.00016756283,"about_ca_topic_score_gemma":0.00027150277,"teacher_disagreement_score":0.9305833,"about_ca_system_score_codex":0.00070690556,"about_ca_system_score_gemma":0.0002545306,"threshold_uncertainty_score":0.99974084},"labels":[],"label_agreement":null},{"id":"W2577609954","doi":"","title":"Explorations of Quantum-Classical Approaches to Scheduling a Mars Lander Activity Problem","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Mars landing; Mathematical optimization; Scheduling (production processes); Quantum; Quantum computer; Mars Exploration Program; Algorithm; Theoretical computer science; Mathematics; Exploration of Mars","score_opus":0.49408787309912444,"score_gpt":0.3803586932931276,"score_spread":0.11372917980599684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2577609954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018899718,0.0000028777154,0.976422,0.017129038,0.000118242606,0.00033558934,0.000019764915,0.00006736878,0.0040151607],"genre_scores_gemma":[0.869601,0.000008051005,0.12992525,0.00007981045,0.000054514123,0.000086274275,0.0000023522891,0.000008188813,0.00023455716],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973372,0.00019432846,0.0004390192,0.00053908123,0.0012056107,0.0002847108],"domain_scores_gemma":[0.9977773,0.00062519824,0.00014631635,0.00034729336,0.0009197162,0.00018421892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090367405,0.00015755066,0.00020924199,0.0003692025,0.00013352429,0.00016101386,0.0008098346,0.0000810833,0.00021128966],"category_scores_gemma":[0.001397243,0.0001176866,0.00006007122,0.0008316388,0.00014460122,0.0004915978,0.00019525451,0.00016177745,0.00043032033],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030364617,0.00018741078,0.000011089044,0.000005750896,0.000009785523,9.197909e-7,0.00015560183,0.00557941,0.0032196224,0.83633757,0.00005807723,0.15440443],"study_design_scores_gemma":[0.00003946278,0.00013955926,0.00015873008,0.00005452146,0.0000019758156,0.0000015211917,0.00004130751,0.767632,0.047633495,0.18402457,0.00010657139,0.00016626513],"about_ca_topic_score_codex":0.000008803863,"about_ca_topic_score_gemma":0.000015310798,"teacher_disagreement_score":0.867711,"about_ca_system_score_codex":0.00010477692,"about_ca_system_score_gemma":0.0005660072,"threshold_uncertainty_score":0.55310404},"labels":[],"label_agreement":null},{"id":"W2578841805","doi":"","title":"Pathological Effects of Variance on Classification-Based Policy Iteration.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Policy Transfer and Learning","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Variance (accounting); Computer science; Pathological; Artificial intelligence; Statistics; Mathematics; Economics; Accounting","score_opus":0.28538154867400883,"score_gpt":0.442588942644232,"score_spread":0.15720739397022315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2578841805","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07813626,0.00004772516,0.32897988,0.05044158,0.0011604371,0.0012389199,0.000072589755,0.00032938973,0.5395932],"genre_scores_gemma":[0.99756885,0.000009859078,0.00046580247,0.0012788542,0.0003743273,0.000037658592,0.000012144626,0.000006594853,0.00024589596],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977678,0.0004310468,0.0003189625,0.00027217402,0.00097263546,0.00023738584],"domain_scores_gemma":[0.9978685,0.00089333224,0.00010226741,0.00012620912,0.00083980116,0.00016989397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009626824,0.00012035611,0.00015225411,0.00023466769,0.0002499849,0.000086536835,0.00029827488,0.00013465933,0.00012579454],"category_scores_gemma":[0.0054788375,0.00011468408,0.000058632773,0.0005467647,0.00035889805,0.00012143696,0.00000814213,0.00022586908,0.0002590143],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000692432,0.00018932392,0.00009653705,0.0000060112748,0.0000035375533,0.0000017228023,0.0022705302,0.00201442,0.0010580078,0.97430724,0.0000819531,0.019901454],"study_design_scores_gemma":[0.000273605,0.0011917094,0.011416475,0.00025606476,0.000017447876,8.508274e-7,0.002291142,0.05597493,0.046657413,0.8742489,0.007027414,0.00064400816],"about_ca_topic_score_codex":0.000384592,"about_ca_topic_score_gemma":0.00014231821,"teacher_disagreement_score":0.9194326,"about_ca_system_score_codex":0.00020096762,"about_ca_system_score_gemma":0.0016656048,"threshold_uncertainty_score":0.65590763},"labels":[],"label_agreement":null},{"id":"W2579011828","doi":"","title":"Lazy Arithmetic Circuits.","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Arithmetic; Computer science; Electronic circuit; Mathematics; Electrical engineering; Engineering","score_opus":0.10841834535894951,"score_gpt":0.33997025881175547,"score_spread":0.23155191345280596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579011828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011020659,0.00001272387,0.9497434,0.012632757,0.00052517094,0.00009800355,0.000008505612,0.0002227947,0.035654586],"genre_scores_gemma":[0.99258655,0.000015055712,0.005115903,0.00068780186,0.00020381318,0.00001917517,0.0000018747896,0.00000823187,0.0013615945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979407,0.000106069085,0.0003251005,0.00051420706,0.0008127788,0.00030113885],"domain_scores_gemma":[0.99855787,0.00038572142,0.000107192194,0.00031140013,0.0005137918,0.00012402088],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005628605,0.00016709525,0.00014514598,0.00020451074,0.00017353613,0.00021177648,0.0009338783,0.0000755574,0.00046585654],"category_scores_gemma":[0.0006948212,0.00011896967,0.00006708882,0.000397672,0.000111921094,0.00032213406,0.00010342443,0.00019559424,0.0025778296],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000018909193,0.00003985523,0.000015994985,0.0000010793367,0.0000033241195,0.0000029317675,0.00004300051,0.00006520942,0.0011142823,0.57547987,0.00009055081,0.423142],"study_design_scores_gemma":[0.000051345934,0.00018851901,0.00083795266,0.000079475794,0.0000024129795,0.000013802352,0.000022387367,0.14639942,0.022679903,0.8256326,0.0037247746,0.0003674596],"about_ca_topic_score_codex":0.000017365744,"about_ca_topic_score_gemma":0.0000102102385,"teacher_disagreement_score":0.99148446,"about_ca_system_score_codex":0.00007886108,"about_ca_system_score_gemma":0.00023307523,"threshold_uncertainty_score":0.9981988},"labels":[],"label_agreement":null},{"id":"W2579334949","doi":"","title":"Modeling Trust Evaluating Agents: Towards a Comprehensive Trust Management for Multi-agent Systems.","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Access Control and Trust","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Trust management (information system); Computer science; Multi-agent system; Knowledge management; Process management; Business; Computer security; Artificial intelligence","score_opus":0.4533757847758716,"score_gpt":0.4713224804144626,"score_spread":0.017946695638591026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2579334949","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022999592,0.00011368105,0.94123656,0.0039046495,0.001544388,0.0021970551,0.00016145105,0.00017991378,0.027662711],"genre_scores_gemma":[0.99366146,0.00013053125,0.0030147173,0.00033571475,0.00043000642,0.00044025132,0.000012936272,0.000019377807,0.0019550226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99668044,0.00023064691,0.00064264506,0.00057384116,0.0013620615,0.0005103959],"domain_scores_gemma":[0.99720865,0.0002723837,0.00019228217,0.00018226959,0.0019709792,0.00017342455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012683959,0.00023654952,0.00027787607,0.0002077068,0.0009152683,0.0003091028,0.0005642658,0.00013652233,0.0004869918],"category_scores_gemma":[0.00082964014,0.00018740518,0.00013813011,0.00026180287,0.00021862212,0.00034655194,0.00007392412,0.00011993805,0.00031113566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001267617,0.00012882406,0.00002281214,0.000022744578,0.00005310772,0.0000023461735,0.00075645687,0.009699065,0.00016207348,0.77269185,0.00006356107,0.2162704],"study_design_scores_gemma":[0.000271497,0.00010994668,0.00014977572,0.00019797566,0.000036638125,4.8435055e-7,0.006206684,0.9461996,0.0002534282,0.0432623,0.0029635245,0.00034815999],"about_ca_topic_score_codex":0.0005771366,"about_ca_topic_score_gemma":0.0002995125,"teacher_disagreement_score":0.9706619,"about_ca_system_score_codex":0.00045152288,"about_ca_system_score_gemma":0.00040555396,"threshold_uncertainty_score":0.7642161},"labels":[],"label_agreement":null},{"id":"W2585083595","doi":"","title":"A Preliminary Study of Transfer Learning between Unicycle Robots","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Control Systems and Identification","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Robot; Nonlinear system; Computer science; Scalar (mathematics); Control theory (sociology); LTI system theory; Transformation (genetics); Transfer of learning; Linear system; Invariant (physics); Artificial intelligence; Mathematics; Control (management)","score_opus":0.09902850839958162,"score_gpt":0.31012332881850635,"score_spread":0.21109482041892474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585083595","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9153923,0.000019925425,0.08027637,0.00018000418,0.0001772464,0.0003574487,0.000018383802,0.00011412967,0.0034642017],"genre_scores_gemma":[0.9996718,0.000008746625,0.000021916783,0.0000032186297,0.00009573059,0.000037224672,0.0000049992345,0.000013386115,0.00014293694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987381,0.000068809306,0.00044657802,0.00019284865,0.00041718376,0.00013647533],"domain_scores_gemma":[0.9992653,0.00020780961,0.00003976408,0.00012317218,0.00031766534,0.000046274192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031590243,0.000113501446,0.0001720745,0.00014985958,0.000061427825,0.000029706507,0.00017373628,0.00006126,0.00015530414],"category_scores_gemma":[0.00012433491,0.00009228355,0.00004098072,0.00017737785,0.000034227298,0.00014104258,0.0000111836025,0.000112759626,0.00011697999],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001936439,0.0004573377,0.0039569815,0.00005500903,0.00015755546,0.0000028372465,0.0031303586,0.100514844,0.1148536,0.13362087,0.00007827687,0.64297867],"study_design_scores_gemma":[0.0011089627,0.0050584003,0.22035472,0.0010634889,0.00017009018,0.0000055816886,0.0071257693,0.34875083,0.32897663,0.08398776,0.0013843981,0.0020133385],"about_ca_topic_score_codex":0.000036562546,"about_ca_topic_score_gemma":0.000073129835,"teacher_disagreement_score":0.64096534,"about_ca_system_score_codex":0.000062523555,"about_ca_system_score_gemma":0.00003639423,"threshold_uncertainty_score":0.3763214},"labels":[],"label_agreement":null},{"id":"W2588920321","doi":"","title":"How Do We Extract Solutions of Unmet Needs from the Vast Sea of Big Data","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vale (Canada)","funders":"","keywords":"Big data; Radar; Computer science; Scope (computer science); Analytics; Field (mathematics); Data science; White paper; Space (punctuation); Operations research; Data mining; Geography; Mathematics; Telecommunications","score_opus":0.45912215844557547,"score_gpt":0.355687216803226,"score_spread":0.10343494164234945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588920321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031943657,0.0009279153,0.8363636,0.100699864,0.003778399,0.0009375473,0.005265199,0.0001650562,0.019918764],"genre_scores_gemma":[0.9978933,0.00021246391,0.00018816773,0.00024656067,0.0011187491,0.0000099320405,0.00017291907,0.0000136816025,0.0001442667],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99787813,0.00003147842,0.0005253223,0.00039902236,0.0009070503,0.00025898134],"domain_scores_gemma":[0.9968395,0.0006917692,0.00048675662,0.00076655066,0.0011975197,0.000017867997],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007420839,0.00020252011,0.00023945716,0.00027818122,0.00018059644,0.000266712,0.0016626747,0.00010440437,0.0009970329],"category_scores_gemma":[0.0015682951,0.00012464575,0.00007277514,0.0009283592,0.0004894001,0.001234795,0.0004773531,0.00015727828,0.00025844047],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006381896,0.00014925422,0.00056101877,0.000018280363,0.000027954022,5.271453e-7,0.00003044108,0.000020422662,0.007233347,0.58397835,0.0023531923,0.4055634],"study_design_scores_gemma":[0.00013914767,0.00005656484,0.0048922882,0.0011873471,0.00013865031,0.0000022712927,0.0019643118,0.02445863,0.039709147,0.8209301,0.10572582,0.00079567387],"about_ca_topic_score_codex":0.0005229952,"about_ca_topic_score_gemma":0.00051825214,"teacher_disagreement_score":0.9659496,"about_ca_system_score_codex":0.000026861884,"about_ca_system_score_gemma":0.00018761371,"threshold_uncertainty_score":0.9999162},"labels":[],"label_agreement":null},{"id":"W2591794136","doi":"","title":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16)","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.16208401831451955,"score_gpt":0.33707740503262607,"score_spread":0.17499338671810652,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591794136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08614654,0.00006265093,0.72782993,0.05780819,0.0035605486,0.0015312406,0.00008948195,0.00059403694,0.12237737],"genre_scores_gemma":[0.9961903,0.00006132558,0.0020966616,0.00083677884,0.00019679156,0.000052482952,0.000001486873,0.000016918088,0.000547247],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9954046,0.00010537536,0.0010558221,0.0009993372,0.0018245335,0.000610321],"domain_scores_gemma":[0.99582934,0.0007514117,0.0005659446,0.00058463466,0.0021124633,0.00015619662],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001077037,0.00043149956,0.0004322725,0.00030724317,0.00035435645,0.00035188257,0.0030025174,0.00025106987,0.00041641053],"category_scores_gemma":[0.0018682067,0.0002677974,0.00021521987,0.0009466424,0.00081753207,0.00064409035,0.0004154257,0.00044923255,0.00067906553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000084864354,0.000196104,0.000077206045,0.000012001973,0.000016260296,0.0000013951897,0.00038031087,0.000046524332,0.011261569,0.83236885,0.00030562942,0.15524928],"study_design_scores_gemma":[0.000024693169,0.00028260236,0.00066929485,0.0002564832,0.000006960637,0.0000061333817,0.00038675775,0.01222373,0.34317335,0.64245623,0.00020370116,0.00031007046],"about_ca_topic_score_codex":0.0000571211,"about_ca_topic_score_gemma":0.00011999735,"teacher_disagreement_score":0.9100438,"about_ca_system_score_codex":0.00022163049,"about_ca_system_score_gemma":0.00064231554,"threshold_uncertainty_score":0.9999774},"labels":[],"label_agreement":null},{"id":"W2598528464","doi":"","title":"Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada.","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Telecommunications","score_opus":0.08604630794363108,"score_gpt":0.2975613497503107,"score_spread":0.2115150418066796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598528464","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14506379,0.00021609529,0.36629754,0.15852576,0.009379835,0.0029934896,0.00018516096,0.0008202655,0.31651807],"genre_scores_gemma":[0.99586695,0.000029065875,0.0010389774,0.0017311428,0.00024929244,0.000040466562,0.0000044359012,0.000018062396,0.0010215944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949558,0.00011893082,0.0011556848,0.0010198674,0.0020945447,0.00065521296],"domain_scores_gemma":[0.9955218,0.0008359806,0.00064650085,0.0006043992,0.0021761444,0.00021518365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010415739,0.00049451314,0.0005340756,0.0002851431,0.00054225075,0.0004245964,0.003161346,0.00021490511,0.00029847852],"category_scores_gemma":[0.0017441,0.00038164505,0.00017888442,0.00082626124,0.00065532856,0.00041921463,0.0004115612,0.0006947474,0.00037511467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006864586,0.00022078365,0.00027759795,0.000021482838,0.000025447887,0.0000017759535,0.0005013604,0.0003917199,0.00100332,0.9487687,0.007792469,0.040926713],"study_design_scores_gemma":[0.00006882065,0.00059825886,0.0031639072,0.00033945017,0.000033111857,0.000017013237,0.00083452574,0.16760218,0.21559277,0.5940239,0.016627483,0.0010986154],"about_ca_topic_score_codex":0.22641361,"about_ca_topic_score_gemma":0.7114514,"teacher_disagreement_score":0.8508032,"about_ca_system_score_codex":0.00047481305,"about_ca_system_score_gemma":0.005296285,"threshold_uncertainty_score":0.99986356},"labels":[],"label_agreement":null},{"id":"W2602746987","doi":"","title":"The Workshops of the Thirtieth AAAI Conference on Artificial Intelligence Incentives and Trust in Electronic Communities: Technical Report WS-16-09","year":2016,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Incentive","score_opus":0.09367570293162544,"score_gpt":0.3126327684150878,"score_spread":0.21895706548346236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602746987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74960846,0.00027005904,0.06960152,0.035551824,0.001971252,0.0028088924,0.000085797605,0.0003673029,0.1397349],"genre_scores_gemma":[0.9986271,0.00020057561,0.000042543063,0.00042922466,0.00019834889,0.000084399086,0.0000102103795,0.000027309807,0.00038024818],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99649686,0.00009656967,0.0011720225,0.00056019553,0.001043618,0.0006307303],"domain_scores_gemma":[0.99662364,0.001047922,0.00074630405,0.00058703264,0.0009658024,0.000029296929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017549287,0.00038132924,0.0003645242,0.0003258903,0.00053652405,0.0005305216,0.0012621483,0.00020825001,0.00017637052],"category_scores_gemma":[0.0026128385,0.0002381493,0.00012813191,0.0010528904,0.0016067242,0.00076342246,0.00045132657,0.0007904813,0.0001538947],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002731203,0.0001793402,0.0010439833,0.000017473674,0.0000113900705,0.0000038978665,0.00008705553,0.000057416815,0.0011119803,0.88560283,0.00008033372,0.11153115],"study_design_scores_gemma":[0.000055458702,0.000113992115,0.0051274337,0.00089846243,0.000014061298,0.000011529888,0.0022255522,0.0076255496,0.013487475,0.96797913,0.0019938177,0.00046755892],"about_ca_topic_score_codex":0.00034306268,"about_ca_topic_score_gemma":0.004850114,"teacher_disagreement_score":0.24901868,"about_ca_system_score_codex":0.0003176822,"about_ca_system_score_gemma":0.00037887087,"threshold_uncertainty_score":0.9711446},"labels":[],"label_agreement":null},{"id":"W2605703804","doi":"","title":"Keystone rescue techniques","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Keystone species; Biology","score_opus":0.10363478090037884,"score_gpt":0.35601805817508964,"score_spread":0.25238327727471077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605703804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010205697,0.000007001769,0.9583215,0.006407953,0.00008679027,0.00023210878,0.0000050662334,0.00062837143,0.03329064],"genre_scores_gemma":[0.9229295,0.000025056042,0.07585459,0.0007292086,0.0000969701,0.00013213548,0.0000038779813,0.0000074455947,0.00022117053],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984673,0.000027841563,0.00033729424,0.00043286177,0.00052962394,0.00020503665],"domain_scores_gemma":[0.9988904,0.000040993844,0.00008436984,0.00034413632,0.00055352406,0.00008657542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031910455,0.00014402931,0.000117629395,0.000212649,0.0002493033,0.00019091406,0.0007994949,0.000099997524,0.00010999439],"category_scores_gemma":[0.00010028473,0.00014192708,0.00006572117,0.0005918137,0.00010864623,0.00031018452,0.000097441276,0.0002256491,0.0005892319],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000042470406,0.000086460735,8.3947094e-7,0.0000012399448,0.0000024186168,0.0000013793108,0.000049946488,0.0003020706,0.0037527578,0.89908075,0.0000642884,0.09665362],"study_design_scores_gemma":[0.000009645849,0.000105329564,0.000037650974,0.000018460549,9.2617864e-7,0.0000070915403,0.000020806347,0.007784896,0.35370895,0.6366261,0.0015360996,0.00014400146],"about_ca_topic_score_codex":0.00006372756,"about_ca_topic_score_gemma":0.000036632126,"teacher_disagreement_score":0.921909,"about_ca_system_score_codex":0.00016369032,"about_ca_system_score_gemma":0.00025385135,"threshold_uncertainty_score":0.757358},"labels":[],"label_agreement":null},{"id":"W2611975171","doi":"","title":"Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada.","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Library science","score_opus":0.08604630794363108,"score_gpt":0.2975613497503107,"score_spread":0.2115150418066796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2611975171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14506379,0.00021609529,0.36629754,0.15852576,0.009379835,0.0029934896,0.00018516096,0.0008202655,0.31651807],"genre_scores_gemma":[0.99586695,0.000029065875,0.0010389774,0.0017311428,0.00024929244,0.000040466562,0.0000044359012,0.000018062396,0.0010215944],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9949558,0.00011893082,0.0011556848,0.0010198674,0.0020945447,0.00065521296],"domain_scores_gemma":[0.9955218,0.0008359806,0.00064650085,0.0006043992,0.0021761444,0.00021518365],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010415739,0.00049451314,0.0005340756,0.0002851431,0.00054225075,0.0004245964,0.003161346,0.00021490511,0.00029847852],"category_scores_gemma":[0.0017441,0.00038164505,0.00017888442,0.00082626124,0.00065532856,0.00041921463,0.0004115612,0.0006947474,0.00037511467],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006864586,0.00022078365,0.00027759795,0.000021482838,0.000025447887,0.0000017759535,0.0005013604,0.0003917199,0.00100332,0.9487687,0.007792469,0.040926713],"study_design_scores_gemma":[0.00006882065,0.00059825886,0.0031639072,0.00033945017,0.000033111857,0.000017013237,0.00083452574,0.16760218,0.21559277,0.5940239,0.016627483,0.0010986154],"about_ca_topic_score_codex":0.22641361,"about_ca_topic_score_gemma":0.7114514,"teacher_disagreement_score":0.8508032,"about_ca_system_score_codex":0.00047481305,"about_ca_system_score_gemma":0.005296285,"threshold_uncertainty_score":0.99986356},"labels":[],"label_agreement":null},{"id":"W2613743574","doi":"","title":"The Macro Architecture Hypothesis: A Theoretical Framework for Integrated Cognition","year":2013,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Macro; Cognitive architecture; Architecture; Cognition; Computer science; Cognitive science; Enterprise architecture framework; Reference architecture; Solution architecture; Software architecture; Psychology; Programming language; Software","score_opus":0.09969611806029081,"score_gpt":0.3317140024712873,"score_spread":0.23201788441099647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2613743574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00093728956,0.000013667403,0.97240543,0.01833449,0.000332022,0.0006228735,0.0000151767545,0.00010023934,0.007238785],"genre_scores_gemma":[0.95822066,0.000016845972,0.039168365,0.0019955114,0.000140201,0.00036388318,0.000005227405,0.0000072848234,0.000082009894],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980427,0.00011438076,0.00033806023,0.00047129294,0.00063871825,0.00039487172],"domain_scores_gemma":[0.99483347,0.0030843557,0.00011315962,0.00024213805,0.001616595,0.00011029305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007209411,0.00017747181,0.00012834238,0.0001263779,0.0006192073,0.0009130797,0.0010162826,0.00010249695,0.00026244923],"category_scores_gemma":[0.0039928528,0.00011998264,0.00009472235,0.0005385993,0.0005202482,0.0003102078,0.0000938075,0.00033043342,0.0006289235],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014441395,0.000028619743,5.1911394e-7,0.0000015076293,0.000005603342,2.7711144e-7,0.00013358759,0.000022997585,0.00063406734,0.63923895,0.0000995259,0.35981992],"study_design_scores_gemma":[0.00001713468,0.00010444251,0.00008135837,0.00005798997,0.0000031310815,0.000003036824,0.00031094518,0.10148564,0.012666517,0.8846176,0.0004999668,0.00015219102],"about_ca_topic_score_codex":0.000007290558,"about_ca_topic_score_gemma":0.000015382102,"teacher_disagreement_score":0.9572834,"about_ca_system_score_codex":0.00005036594,"about_ca_system_score_gemma":0.00028534207,"threshold_uncertainty_score":0.8804848},"labels":[],"label_agreement":null},{"id":"W26292495","doi":"","title":"Decision Making under Uncertainty: Operations Research Meets AI (Again)","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Artificial intelligence; Machine learning; Probabilistic logic; Markov decision process; Representation (politics); Partially observable Markov decision process; Bayesian network; Automated planning and scheduling; Influence diagram; Markov process; Markov chain; Markov model; Decision tree; Mathematics","score_opus":0.27366028222817224,"score_gpt":0.4565537726636735,"score_spread":0.18289349043550124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W26292495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010225266,0.000036797053,0.9595007,0.011907787,0.00030797868,0.00025377652,0.000012865945,0.00018717643,0.017567677],"genre_scores_gemma":[0.9755259,0.00006668552,0.021137098,0.0025492376,0.00016670105,0.000054913206,0.000009601605,0.00001420234,0.0004756643],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956841,0.00031336982,0.0006114916,0.0008842164,0.0019245107,0.0005822999],"domain_scores_gemma":[0.9965058,0.0006768487,0.000039906918,0.0005603886,0.0020397024,0.00017735113],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0018693819,0.0002459593,0.00021251808,0.00041923564,0.0008291237,0.0010317203,0.0015521776,0.00018553749,0.0017227259],"category_scores_gemma":[0.0005043851,0.00023497276,0.00008421324,0.0012047047,0.00024176092,0.0007265515,0.00015259082,0.0007558518,0.0029737297],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002312183,0.000075587726,8.9581516e-7,0.0000011721476,0.000004256639,0.0000030592107,0.00014024854,0.18282034,0.0002224263,0.5188541,0.00017318054,0.2976816],"study_design_scores_gemma":[0.00001737533,0.00007948123,0.000045375727,0.000088852146,0.0000011919159,0.0000047584354,0.000039159688,0.5385236,0.00089671713,0.45977607,0.00036328443,0.00016409931],"about_ca_topic_score_codex":0.0000887767,"about_ca_topic_score_gemma":0.00037785945,"teacher_disagreement_score":0.9653006,"about_ca_system_score_codex":0.00024515748,"about_ca_system_score_gemma":0.0008937279,"threshold_uncertainty_score":0.99918985},"labels":[],"label_agreement":null},{"id":"W26302103","doi":"10.1037/pas0000140","title":"Coordination and adaptation in impromptu teams","year":2005,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundación Florencio Fiorini","keywords":"Impromptu; Computer science; Teamwork; Joins; Adaptation (eye); Robot; Adversarial system; Key (lock); Multi-agent system; Human–computer interaction; Domain (mathematical analysis); Knowledge management; Artificial intelligence; Computer security","score_opus":0.12425227980378631,"score_gpt":0.33370749396293753,"score_spread":0.20945521415915122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W26302103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095767,0.000048625978,0.88893706,0.007940492,0.00033368534,0.0004558826,0.000005660998,0.00011000854,0.0064016087],"genre_scores_gemma":[0.9911941,0.000014182882,0.008265118,0.00024690392,0.0001040978,0.000031081247,0.000007649629,0.0000045282536,0.00013229632],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847627,0.00007668272,0.00042445946,0.00036847332,0.0004901813,0.00016393012],"domain_scores_gemma":[0.9992238,0.000121612145,0.00013847236,0.00012848827,0.00033076416,0.000056825542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006406448,0.00011747975,0.00011312636,0.0002672599,0.00009781923,0.00017654042,0.0002491736,0.00007639336,0.000050559946],"category_scores_gemma":[0.00020527268,0.00011807578,0.000022452996,0.00032622396,0.00003595492,0.0006845994,0.00004155933,0.00013424267,0.000189099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007410503,0.00006982928,0.00012815319,0.0000033642154,0.0000017793708,5.549931e-7,0.00063808274,0.0023411445,0.0017563184,0.6919901,0.000030064302,0.3030332],"study_design_scores_gemma":[0.000047430924,0.000054220996,0.003928248,0.00002876082,8.0493766e-7,0.00000240085,0.00013762408,0.9455524,0.005579915,0.044243474,0.00029128167,0.00013340943],"about_ca_topic_score_codex":0.00009357456,"about_ca_topic_score_gemma":0.00061786507,"teacher_disagreement_score":0.94321126,"about_ca_system_score_codex":0.00015075684,"about_ca_system_score_gemma":0.00012710296,"threshold_uncertainty_score":0.48149905},"labels":[],"label_agreement":null},{"id":"W2703543743","doi":"","title":"Towards Tractable Inference for Resource-Bounded Agents","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Inference; sort; Commonsense reasoning; Epistemic modal logic; Commonsense knowledge; Semantics (computer science); Non-monotonic logic; Artificial intelligence; Common sense; Rule of inference; Epistemology; Theoretical computer science; Description logic; Cognitive science; Knowledge representation and reasoning; Programming language; Multimodal logic; Psychology; Philosophy; Information retrieval","score_opus":0.28474744631534155,"score_gpt":0.3935910568604578,"score_spread":0.10884361054511626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2703543743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022568,0.000040173967,0.8394925,0.0023053363,0.0007462423,0.0004342695,0.000022820586,0.00021831422,0.15448353],"genre_scores_gemma":[0.98402536,0.000008236376,0.01359936,0.00077191007,0.00027859217,0.00010886591,0.000026527277,0.000013787867,0.0011673832],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997185,0.000111835856,0.0005018824,0.0006812552,0.0010320018,0.00048802988],"domain_scores_gemma":[0.99690074,0.00041009302,0.00019657896,0.00038851294,0.0017874215,0.00031664496],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012278106,0.00026568226,0.00026732186,0.00022338887,0.0002700777,0.0005917724,0.0014094318,0.00016408043,0.00012964249],"category_scores_gemma":[0.002538052,0.00024572996,0.0001146539,0.0005394471,0.00014225394,0.0005812339,0.00017526904,0.0002545435,0.00066275],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005496336,0.00018745576,0.000016628044,0.0000063459893,0.000011090756,0.0000026896682,0.0008601703,0.00048061155,0.0001307186,0.9164745,0.0021798909,0.07959494],"study_design_scores_gemma":[0.000097736614,0.00032263773,0.00013987943,0.00003025695,0.0000050336926,0.000004492174,0.00019644927,0.24537443,0.0120464545,0.7183626,0.02309383,0.00032618752],"about_ca_topic_score_codex":0.000047073547,"about_ca_topic_score_gemma":0.000076294236,"teacher_disagreement_score":0.98176855,"about_ca_system_score_codex":0.00024717939,"about_ca_system_score_gemma":0.0013567024,"threshold_uncertainty_score":0.9999995},"labels":[],"label_agreement":null},{"id":"W27158770","doi":"10.1002/mus.25180","title":"Lookahead Pathology in Real-Time Path-Finding.","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Incremental heuristic search; Computer science; Heuristic; Path (computing); Artificial intelligence; Machine learning; Limit (mathematics); Minimax; Iterative deepening depth-first search; Search algorithm; Mathematical optimization; Beam search; Algorithm; Mathematics","score_opus":0.10429304688397582,"score_gpt":0.34982573794473376,"score_spread":0.24553269106075792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W27158770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17022362,0.00007646389,0.5812294,0.008655443,0.0016017494,0.0010523711,0.000046233934,0.0008813078,0.23623341],"genre_scores_gemma":[0.9893902,0.000035189718,0.0091066,0.00035446833,0.0002472441,0.00006894255,0.000018937566,0.000020819885,0.000757602],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995916,0.00024868717,0.0010567199,0.0009975324,0.0010822488,0.00069878495],"domain_scores_gemma":[0.99775505,0.0006211197,0.0002653379,0.00054528343,0.0006948423,0.00011834517],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001171769,0.0003542688,0.00037376056,0.0006530509,0.00020642746,0.00033561845,0.0015326599,0.00025387525,0.00048929645],"category_scores_gemma":[0.0005046328,0.0003699083,0.00011581034,0.0011394316,0.00032264626,0.00060121156,0.00020331828,0.00044849885,0.0040880684],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028633542,0.00029278576,0.00023085468,0.0000037687776,0.0000034438388,0.00007796364,0.00025242366,0.0031656602,0.013657422,0.9317919,0.00036635136,0.050128803],"study_design_scores_gemma":[0.000027085984,0.00016270937,0.001872595,0.000058567974,0.000002256238,0.000017893299,0.000089488734,0.2109129,0.058973167,0.7271907,0.00026790728,0.00042476348],"about_ca_topic_score_codex":0.00043041186,"about_ca_topic_score_gemma":0.00048892526,"teacher_disagreement_score":0.81916654,"about_ca_system_score_codex":0.00031138666,"about_ca_system_score_gemma":0.00045056903,"threshold_uncertainty_score":0.9998753},"labels":[],"label_agreement":null},{"id":"W2794468939","doi":"","title":"Rewards Structure in Games: Learning a Compact Representation for Action Space.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Representation (politics); Space (punctuation); Action (physics); Theoretical computer science; Artificial intelligence; Human–computer interaction; Physics","score_opus":0.28377712910933206,"score_gpt":0.43801295742776347,"score_spread":0.1542358283184314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794468939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14683093,0.000009797305,0.8268301,0.008314607,0.0013479076,0.0007255656,0.00003258515,0.00017957628,0.015728964],"genre_scores_gemma":[0.99764687,0.000019222269,0.0016975156,0.00012335744,0.00017400361,0.000022651358,0.000029345523,0.000006891544,0.00028012376],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99854374,0.00007883371,0.0003006876,0.00042148322,0.000455577,0.00019967745],"domain_scores_gemma":[0.99860466,0.00022348744,0.00029826668,0.0002675614,0.0005488176,0.00005720434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003667894,0.00013216014,0.00014606552,0.000246616,0.00054451847,0.0007102915,0.0005042879,0.00009784877,0.00014223838],"category_scores_gemma":[0.0012729262,0.00013597276,0.00006202344,0.00014290282,0.000068582216,0.0009839771,0.000039205468,0.00028280469,0.00008596405],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000080278274,0.00009382722,0.00033544426,0.000013233995,0.00001117302,0.0000031439051,0.00057641196,0.0053046686,0.009154926,0.6526434,0.00024088618,0.33154264],"study_design_scores_gemma":[0.0001050752,0.00015865416,0.008612138,0.000080617334,0.0000033907695,0.0000040226864,0.00029729694,0.3990624,0.09490294,0.4954887,0.001048497,0.00023625263],"about_ca_topic_score_codex":0.000082535014,"about_ca_topic_score_gemma":0.00031870895,"teacher_disagreement_score":0.85081595,"about_ca_system_score_codex":0.00013298493,"about_ca_system_score_gemma":0.0001685759,"threshold_uncertainty_score":0.6849357},"labels":[],"label_agreement":null},{"id":"W2794831924","doi":"","title":"Households, The Homeless and Slums Towards a Standard for Representing City Shelter Open Data.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Geographic Information Systems Studies","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Open data; Computer science; Computer security; Business; Socioeconomics; Internet privacy; World Wide Web; Sociology","score_opus":0.5409938325291249,"score_gpt":0.49588350071894727,"score_spread":0.04511033181017765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794831924","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15687694,0.00016703675,0.0858349,0.11310767,0.0037273243,0.008039775,0.0022726576,0.00027513062,0.6296986],"genre_scores_gemma":[0.99826616,0.00008225211,0.0004951679,0.00026255852,0.00028199473,0.00011607612,0.000018057746,0.00000627626,0.00047146642],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979982,0.00009702609,0.00039752605,0.000364695,0.0008661455,0.00027643543],"domain_scores_gemma":[0.9973523,0.00037885856,0.00037905332,0.0005817701,0.0012388541,0.00006916083],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004582913,0.00012116552,0.00021039211,0.000058018628,0.004229691,0.0025215014,0.0023188253,0.00007915514,0.000088931585],"category_scores_gemma":[0.0039527058,0.000093650204,0.00003856818,0.0001136296,0.0008553622,0.0011352336,0.0009156356,0.00014874537,0.000020983369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006440636,0.000018465567,0.0013169352,0.00000814834,0.000026628819,3.5996317e-7,0.005738079,0.000009961784,0.000007666994,0.90548724,0.0008928365,0.086429276],"study_design_scores_gemma":[0.00023602096,0.00013698818,0.0255654,0.00020662347,0.000036141282,0.0000021767974,0.04856072,0.010179134,0.0010885656,0.8017459,0.11158271,0.0006596525],"about_ca_topic_score_codex":0.0039210124,"about_ca_topic_score_gemma":0.010157769,"teacher_disagreement_score":0.84138924,"about_ca_system_score_codex":0.000050864535,"about_ca_system_score_gemma":0.000400869,"threshold_uncertainty_score":0.998514},"labels":[],"label_agreement":null},{"id":"W2794851298","doi":"","title":"The Senior Transportation Problem.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science","score_opus":0.11009375435604372,"score_gpt":0.35990514637520304,"score_spread":0.2498113920191593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794851298","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017379947,0.000045974815,0.85881394,0.0059251683,0.0014018201,0.0006011537,0.00006454895,0.00054078124,0.11522668],"genre_scores_gemma":[0.990215,0.00005333866,0.009266945,0.000047891448,0.00011755123,0.000034994733,0.000010127053,0.000016338874,0.00023779226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988993,0.000037182574,0.0003022961,0.0001646662,0.00041368234,0.00018291066],"domain_scores_gemma":[0.99902,0.0001923927,0.00009493575,0.0002482222,0.0003934113,0.00005102796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059607043,0.00011732247,0.0000876664,0.00004570976,0.00079520594,0.00040179727,0.00042821057,0.000069176676,0.0001012298],"category_scores_gemma":[0.0003716687,0.00009746928,0.00004083261,0.00007388052,0.00013670177,0.00019121003,0.000007997607,0.00019516292,0.00018804792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016406635,0.00001575316,0.00006019935,0.0000068892214,0.000012612841,0.0000010180331,0.00017997998,0.14660613,0.0013902675,0.6522621,0.0001907657,0.19925787],"study_design_scores_gemma":[0.000027358286,0.000028501532,0.0029566342,0.000034026223,0.0000060824,9.189176e-7,0.00012220374,0.80054975,0.03145903,0.1626777,0.0019221996,0.0002156243],"about_ca_topic_score_codex":0.000012035927,"about_ca_topic_score_gemma":0.00014422681,"teacher_disagreement_score":0.97283506,"about_ca_system_score_codex":0.00005599802,"about_ca_system_score_gemma":0.00006832177,"threshold_uncertainty_score":0.6116162},"labels":[],"label_agreement":null},{"id":"W2795231283","doi":"","title":"Knowledge-Based Provision of Goods and Services for People with Social Needs: Towards a Virtual Marketplace.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Community Development and Social Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Ottawa","funders":"","keywords":"Business; Knowledge management; Goods and services; Computer science; Internet privacy; Economics","score_opus":0.15752645370021265,"score_gpt":0.34702139828092565,"score_spread":0.189494944580713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795231283","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8942542,0.000085914595,0.030719029,0.005449856,0.00034848085,0.0008209491,0.0004449437,0.00004425659,0.06783238],"genre_scores_gemma":[0.9989491,0.000017753997,0.0005434587,0.00007319058,0.000075264725,0.000035522957,0.000026765705,0.000011341083,0.00026757066],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99914306,0.000021657232,0.000369309,0.0001978558,0.00010160666,0.00016649708],"domain_scores_gemma":[0.99890906,0.00015964695,0.00039976556,0.00015047641,0.0003293518,0.000051728195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070446153,0.00013442314,0.00029794205,0.00021293196,0.0006376336,0.00025540256,0.00036382704,0.00009921417,0.00013509174],"category_scores_gemma":[0.000286598,0.00013813228,0.0000569858,0.00012636407,0.00017087962,0.00024707857,0.00006481428,0.00012009466,0.000025855266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040303252,0.00020488938,0.0037995032,0.000069087124,0.000031849908,1.3595971e-7,0.004302822,0.000022084329,0.000052389743,0.9211474,0.00005243655,0.06991436],"study_design_scores_gemma":[0.0008814197,0.0016990701,0.2599589,0.00033286313,0.000027416952,0.000001174442,0.006843043,0.13855585,0.0064830654,0.5802477,0.003823167,0.0011463197],"about_ca_topic_score_codex":0.0002769532,"about_ca_topic_score_gemma":0.0010915131,"teacher_disagreement_score":0.34089968,"about_ca_system_score_codex":0.000066982386,"about_ca_system_score_gemma":0.00023749167,"threshold_uncertainty_score":0.5632871},"labels":[],"label_agreement":null},{"id":"W2795257399","doi":"","title":"Formalizing Anthropomorphism Through Games: A Study in Deep Neural Networks.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence; Cognitive science; Psychology","score_opus":0.20896466148286322,"score_gpt":0.40576896193554923,"score_spread":0.19680430045268602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795257399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04907427,0.00007128899,0.92885035,0.0041535944,0.001787605,0.0009685421,0.0000054814195,0.0002198482,0.0148690315],"genre_scores_gemma":[0.99542326,0.000041249135,0.003519397,0.00056249235,0.00026085068,0.00011197275,0.0000036275503,0.000022591772,0.000054562992],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956362,0.00021883594,0.001050173,0.0010561823,0.0012790334,0.0007595522],"domain_scores_gemma":[0.99690163,0.00039435743,0.0005321151,0.0012517197,0.00077333074,0.00014684885],"candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012201843,0.00041580887,0.00043664413,0.00029886095,0.0010901506,0.0016105752,0.003596044,0.00017325934,0.00022168524],"category_scores_gemma":[0.0012626449,0.00041252203,0.00012704097,0.0004763074,0.00057056814,0.0025403646,0.00063653593,0.00067076395,0.00035180428],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006184662,0.00068692956,0.001936565,0.0000041221274,0.000017462598,0.00008952985,0.0030903902,0.0271731,0.00008080578,0.7568985,0.000037669903,0.20992306],"study_design_scores_gemma":[0.00004833353,0.00032481414,0.0031550946,0.000041834635,0.0000040347395,0.000008413854,0.0014951695,0.7986646,0.0026667533,0.19312018,0.00007186534,0.00039889748],"about_ca_topic_score_codex":0.00070922385,"about_ca_topic_score_gemma":0.0029692466,"teacher_disagreement_score":0.94634897,"about_ca_system_score_codex":0.00023033384,"about_ca_system_score_gemma":0.00020104308,"threshold_uncertainty_score":0.9998327},"labels":[],"label_agreement":null},{"id":"W2797934678","doi":"","title":"On keeping secrets","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"British Columbia Institute of Technology","funders":"","keywords":"Computer science; Intelligent agent; Secrecy; Software agent; Private information retrieval; Computer security; Obligation; Order (exchange); Internet privacy; Artificial intelligence; Business","score_opus":0.4651903065479184,"score_gpt":0.4825425060457487,"score_spread":0.017352199497830345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797934678","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019631572,0.000014436815,0.0040374924,0.035146195,0.0010272283,0.00024357697,0.000016177755,0.00012874487,0.9397546],"genre_scores_gemma":[0.99398494,0.000030971885,0.00020106832,0.004230857,0.0005640565,0.000010433486,0.0000073047513,0.000009761898,0.00096058054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972026,0.00024415142,0.00027379565,0.00028100173,0.0016571463,0.0003413313],"domain_scores_gemma":[0.99676573,0.0006662715,0.00010814603,0.00011513393,0.0020056844,0.00033904568],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002323056,0.00012848021,0.00014321374,0.00014054062,0.0005974698,0.00037461743,0.00037357112,0.00020043958,0.0005536728],"category_scores_gemma":[0.009831256,0.00013342357,0.00006160812,0.00034108508,0.0004056352,0.00031141343,0.000029173467,0.00040772837,0.0013136582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004239513,0.0000783056,0.000008035352,0.000001056117,0.000005845109,0.0000027407132,0.0060844305,0.00033044838,0.000043875483,0.977009,0.0010851752,0.01530872],"study_design_scores_gemma":[0.0000233269,0.00014682431,0.000044584925,0.000037763144,0.0000024214328,1.9739667e-7,0.0045336373,0.0017072343,0.0009257048,0.9867489,0.005651924,0.00017748128],"about_ca_topic_score_codex":0.0007532888,"about_ca_topic_score_gemma":0.0016348929,"teacher_disagreement_score":0.9743534,"about_ca_system_score_codex":0.00034118467,"about_ca_system_score_gemma":0.0016016663,"threshold_uncertainty_score":0.9994639},"labels":[],"label_agreement":null},{"id":"W2809942986","doi":"","title":"Predicting the performance of IDA* with conditional distributions","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Heuristic; Set (abstract data type); Computer science; Sample (material); Distribution (mathematics); Algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Statistics","score_opus":0.09917547778702003,"score_gpt":0.2979532252419059,"score_spread":0.19877774745488586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809942986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21028496,0.000018370998,0.78241116,0.0025512155,0.00011265306,0.00014971841,0.0000691381,0.000080961996,0.0043218373],"genre_scores_gemma":[0.99608415,0.000009864797,0.003563689,0.00014882424,0.000060018054,0.000022311066,0.000037312147,0.0000037080063,0.00007011005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850386,0.00005973415,0.00029425984,0.00024249285,0.0007145986,0.00018505426],"domain_scores_gemma":[0.9984475,0.00043077423,0.00016513806,0.00019582637,0.0007105297,0.000050242365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038961417,0.0001138155,0.00010633226,0.00007741118,0.0005825134,0.000053712734,0.00058075687,0.0000448524,0.0000572229],"category_scores_gemma":[0.00015603885,0.00008040389,0.000035127003,0.0004202416,0.0002915541,0.00027564768,0.00005028088,0.00024181257,0.00006341987],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028531811,0.00006701595,0.0031491912,0.000006211021,0.000012679081,0.0000030671854,0.00046248295,0.017612305,0.00031502225,0.9745822,0.000110797206,0.0036505018],"study_design_scores_gemma":[0.000044918645,0.0004239685,0.0127498675,0.000119137054,0.000005478913,0.000069883434,0.00009676897,0.8841427,0.041175693,0.060734637,0.0002152335,0.00022171895],"about_ca_topic_score_codex":0.000021875008,"about_ca_topic_score_gemma":0.000009437121,"teacher_disagreement_score":0.91384757,"about_ca_system_score_codex":0.000040618244,"about_ca_system_score_gemma":0.0004906793,"threshold_uncertainty_score":0.4480281},"labels":[],"label_agreement":null},{"id":"W2886935593","doi":"","title":"Using Natural Language Processing for Documentation Assist.","year":2018,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Software Engineering Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Documentation; Computer science; Internal documentation; World Wide Web; Technical documentation; Cursor (databases); Natural language; Software engineering; Database; Programming language; Software; Natural language processing; Software development","score_opus":0.17915399153434525,"score_gpt":0.43928851831647003,"score_spread":0.2601345267821248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886935593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015497432,0.000042043135,0.98244625,0.00050912565,0.0004737174,0.00022951288,0.0000054097486,0.00015652542,0.0006399573],"genre_scores_gemma":[0.9303134,7.153862e-7,0.069057554,0.00016774166,0.00030364248,0.000030188397,0.0000075059443,0.000009071464,0.00011020009],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984189,0.000032759614,0.00023862578,0.00037186252,0.00067154947,0.0002662663],"domain_scores_gemma":[0.99817663,0.00039345684,0.000077000535,0.0001640114,0.0011271494,0.00006175501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004894718,0.000120494435,0.00009638849,0.0002131652,0.00024290425,0.00044951524,0.0005617975,0.00005329486,0.000067310895],"category_scores_gemma":[0.0012289541,0.00011942647,0.000040034633,0.00046420787,0.000100659476,0.00057797745,0.00006846676,0.00014427578,0.000120632176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000041116462,0.0000632408,0.000060987975,0.000020734124,0.0000096406375,0.000002136699,0.0010270796,0.00075979915,0.017507164,0.5151722,0.00009141173,0.4652445],"study_design_scores_gemma":[0.00002845384,0.00009223258,0.00043208047,0.00004184746,0.0000017870578,0.0000037367874,0.00009837691,0.88088745,0.08172247,0.036455557,0.00007989717,0.0001560898],"about_ca_topic_score_codex":0.000017932745,"about_ca_topic_score_gemma":0.000018169023,"teacher_disagreement_score":0.91481596,"about_ca_system_score_codex":0.00017625163,"about_ca_system_score_gemma":0.00032650045,"threshold_uncertainty_score":0.48700702},"labels":[],"label_agreement":null},{"id":"W2887633513","doi":"","title":"Output Encoding by Compressed Sensing for Cell Detection with Deep Convnet.","year":2018,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Molecular Communication and Nanonetworks","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Encoding (memory); Computer science; Compressed sensing; Artificial intelligence; Pattern recognition (psychology); Computer vision","score_opus":0.06931034880109846,"score_gpt":0.28653758121228473,"score_spread":0.21722723241118627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887633513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005838886,0.000048533886,0.97526544,0.000100131554,0.00021490376,0.00025763636,0.000011478501,0.00014460881,0.01811837],"genre_scores_gemma":[0.9947645,0.000022601538,0.004763743,0.00016680361,0.00012572677,0.00001918404,0.000033997614,0.000021950405,0.000081467886],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990684,0.000036390495,0.00024088427,0.00019505792,0.0002658924,0.00019341048],"domain_scores_gemma":[0.9989949,0.00016790228,0.000058259997,0.00016587027,0.0005508957,0.00006216771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018878936,0.00014195067,0.000117884956,0.00009157668,0.00019755153,0.00009299373,0.00017731544,0.00008942903,0.000077150595],"category_scores_gemma":[0.000051382827,0.00014329521,0.000033460463,0.00018464407,0.00010518055,0.00007775611,0.000015331121,0.00016198968,0.00009005938],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038888457,0.00015829365,0.000008843387,0.000054975382,0.000067048364,0.000001673331,0.00078059017,0.046832692,0.47385582,0.050317112,0.0014669435,0.4260671],"study_design_scores_gemma":[0.000036834375,0.000096861506,0.0000042771676,0.000019648101,0.0000042913307,0.0000011229616,0.000060862043,0.6039518,0.39115685,0.0024683885,0.002073838,0.00012520287],"about_ca_topic_score_codex":0.000008016303,"about_ca_topic_score_gemma":0.0001303424,"teacher_disagreement_score":0.98892564,"about_ca_system_score_codex":0.00006922921,"about_ca_system_score_gemma":0.000036130317,"threshold_uncertainty_score":0.58434093},"labels":[],"label_agreement":null},{"id":"W2902062187","doi":"","title":"Adaptive Treatment Allocation Using Sub-Sampled Gaussian Processes.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Université Laval","funders":"","keywords":"Computer science; Gaussian process; Gaussian; Mathematical optimization; Mathematics; Physics","score_opus":0.49829403498757796,"score_gpt":0.4459295368269759,"score_spread":0.05236449816060207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902062187","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9060936,0.00015888346,0.07538212,0.0061148335,0.0007853609,0.0012754498,0.00003622629,0.0003417439,0.009811794],"genre_scores_gemma":[0.99727714,0.000029849345,0.002125976,0.00017194016,0.00022879399,0.000047560487,0.000042116608,0.000011207132,0.00006539982],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99860454,0.000049783233,0.00032628092,0.00029969713,0.00050379074,0.00021589814],"domain_scores_gemma":[0.9979752,0.000104753875,0.00011105641,0.00013754003,0.0014926939,0.00017880953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026335384,0.00015233169,0.00018843496,0.00020810412,0.00011844904,0.000022340622,0.00008440644,0.00015900124,0.000043718574],"category_scores_gemma":[0.0008076076,0.00013484976,0.000032408156,0.0003991751,0.00010458268,0.000106076244,0.000013654154,0.00018655845,0.00018235286],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020434663,0.0015214212,0.0046586646,0.00009083117,0.00012956433,0.000050135142,0.0029058272,0.0027181287,0.008239743,0.5413743,0.00008903474,0.43617883],"study_design_scores_gemma":[0.00053710357,0.006056167,0.0019210892,0.00072629185,0.000107107604,0.00007949691,0.0045756013,0.17510949,0.6433213,0.16576146,0.0010609014,0.000744002],"about_ca_topic_score_codex":0.00017887782,"about_ca_topic_score_gemma":0.00017134563,"teacher_disagreement_score":0.6350815,"about_ca_system_score_codex":0.00061408506,"about_ca_system_score_gemma":0.0020280522,"threshold_uncertainty_score":0.54990137},"labels":[],"label_agreement":null},{"id":"W2902062907","doi":"","title":"Nonlinear Optimization and Symbolic Dynamic Programming for Parameterized Hybrid Markov Decision Processes.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Parameterized complexity; Markov decision process; Computer science; Mathematical optimization; Dynamic programming; Nonlinear system; Markov process; Markov chain; Nonlinear programming; Mathematics; Algorithm; Machine learning","score_opus":0.08557820677108352,"score_gpt":0.3627951243467675,"score_spread":0.27721691757568395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902062907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031482065,0.000013163705,0.9944566,0.0008966205,0.00030283563,0.0005759243,0.000009169749,0.000091341404,0.00050616864],"genre_scores_gemma":[0.6324118,0.00005590521,0.367228,0.000091006,0.00004212803,0.000065347325,0.000019982894,0.000010063724,0.00007580429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813217,0.000030502995,0.00042284408,0.00052650744,0.0006049556,0.00028299756],"domain_scores_gemma":[0.9975454,0.0004579496,0.00036494984,0.0004239057,0.0011124568,0.00009532688],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000570996,0.00019074194,0.00019287421,0.00015879456,0.0007244019,0.0015197977,0.0010113544,0.00007473079,0.00002002722],"category_scores_gemma":[0.0033110627,0.00018707481,0.00004509869,0.00013673362,0.00016576645,0.00072416104,0.00019788604,0.00015974439,0.000027715807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010248103,0.00009447585,0.00004507399,0.000056377863,0.000018250665,0.0000029948455,0.00014184667,0.36272317,0.00021475107,0.107229546,0.000016831784,0.5293542],"study_design_scores_gemma":[0.000075730786,0.00017689826,0.00008543318,0.00009293173,0.0000053695653,0.0000049183595,0.000017990056,0.97579163,0.002661178,0.020664982,0.00021504615,0.0002078811],"about_ca_topic_score_codex":0.0000066613893,"about_ca_topic_score_gemma":0.000009924099,"teacher_disagreement_score":0.6292635,"about_ca_system_score_codex":0.000070120426,"about_ca_system_score_gemma":0.0003116529,"threshold_uncertainty_score":0.9995167},"labels":[],"label_agreement":null},{"id":"W2902160170","doi":"","title":"Towards a Framework for Testing Learning from Observation of State-Based Agents.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; State (computer science); Artificial intelligence; Programming language","score_opus":0.38210236330653463,"score_gpt":0.3942640635304206,"score_spread":0.012161700223885963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902160170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02685131,0.0000088593015,0.9696001,0.0017520858,0.00029116555,0.00018810111,0.000034773715,0.00008112673,0.0011924979],"genre_scores_gemma":[0.7580874,0.0000011224039,0.2415585,0.00019540332,0.00006995526,0.000025023215,0.00002437979,0.0000071002128,0.00003112048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982899,0.00007155541,0.0004167002,0.00040850745,0.00058532046,0.00022803656],"domain_scores_gemma":[0.99633884,0.0014882684,0.00056402467,0.00035734978,0.001183168,0.000068341324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077920954,0.00015140868,0.00018445872,0.0001061848,0.00062665826,0.0004491255,0.0010447837,0.0001025383,0.00004271133],"category_scores_gemma":[0.006719028,0.00015644473,0.00006450871,0.00016415429,0.00009277654,0.000366685,0.00008272738,0.00028292957,0.000031109055],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005550354,0.00006698027,0.0025205538,0.000018113991,0.000013328606,0.0000015716134,0.00045391786,0.052380003,0.0014450591,0.66315156,0.000029266683,0.27986416],"study_design_scores_gemma":[0.00002421848,0.00011155515,0.004335203,0.00016202334,0.000002446895,8.906942e-8,0.000020893776,0.55871236,0.02333089,0.41313806,0.000053710937,0.000108566244],"about_ca_topic_score_codex":0.00031370076,"about_ca_topic_score_gemma":0.000023880326,"teacher_disagreement_score":0.7312361,"about_ca_system_score_codex":0.000057205132,"about_ca_system_score_gemma":0.00053896365,"threshold_uncertainty_score":0.8043789},"labels":[],"label_agreement":null},{"id":"W2902163128","doi":"","title":"Multidimensional and Longitudinal Indicators in Population Health.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Population; Environmental health; Medicine","score_opus":0.45720004861892083,"score_gpt":0.5408046215233402,"score_spread":0.08360457290441936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902163128","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9442297,0.00007378515,0.00082695275,0.035679538,0.0011466602,0.001287106,0.00010600156,0.00008199909,0.016568244],"genre_scores_gemma":[0.997556,0.000067927234,0.00064206694,0.0012871006,0.0001641419,0.000060385853,0.00005534427,0.000008994339,0.00015808284],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99775267,0.00023780375,0.00071281876,0.00037661943,0.0005393112,0.00038075267],"domain_scores_gemma":[0.9984973,0.0003402836,0.00048802936,0.00022854753,0.0002675057,0.00017831444],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0012661141,0.0001414985,0.00023330798,0.00050475966,0.0026173647,0.000044521446,0.00020916807,0.0001814742,0.0005969564],"category_scores_gemma":[0.0011557485,0.00014504597,0.00002865384,0.00017717695,0.00016443167,0.00028535884,0.000105132836,0.0006589381,0.00036257243],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007615566,0.000080433405,0.14364994,0.00003486933,0.0000035750659,0.000001534745,0.0009084299,0.00007204824,0.000007401961,0.8402212,0.00015101836,0.014793339],"study_design_scores_gemma":[0.000094613846,0.00007953159,0.85904086,0.00023585821,0.0000017625478,5.4074303e-7,0.0004360866,0.009248784,0.000016259753,0.12988673,0.00082032214,0.00013863036],"about_ca_topic_score_codex":0.0024831435,"about_ca_topic_score_gemma":0.0070600044,"teacher_disagreement_score":0.7153909,"about_ca_system_score_codex":0.00027481196,"about_ca_system_score_gemma":0.0005173074,"threshold_uncertainty_score":0.99868107},"labels":[],"label_agreement":null},{"id":"W2902215381","doi":"","title":"Proposal to Add Emotion to the Standard Model","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Emotions and Moral Behavior","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Standard Model (mathematical formulation); Cognitive psychology; Psychology; History","score_opus":0.29582828159210134,"score_gpt":0.46492694199667756,"score_spread":0.16909866040457622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902215381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27059186,0.000007387913,0.50169617,0.08430158,0.0026703717,0.0018274474,0.00048283464,0.0001390981,0.13828324],"genre_scores_gemma":[0.9923693,0.0000011530727,0.002255947,0.0011405637,0.00024411624,0.00013813625,0.000010766473,0.0000130308035,0.0038269744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998423,0.000058456004,0.0002858248,0.00039734386,0.00057370745,0.0002616703],"domain_scores_gemma":[0.998391,0.00004271617,0.00010253613,0.0005298077,0.00078823627,0.00014572399],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005466552,0.00014416677,0.00012650111,0.00012060757,0.0007444939,0.00030966903,0.0006933752,0.00008561364,0.0019442756],"category_scores_gemma":[0.00042188895,0.00011056416,0.000056900866,0.000115291376,0.00010739526,0.0001182652,0.0000950314,0.00021500172,0.004467445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015205702,0.00010055956,0.000059147504,9.133648e-7,0.000005862029,0.0000020681798,0.0007987545,0.0023610308,0.00067554926,0.8467243,0.0051562074,0.14396359],"study_design_scores_gemma":[0.00026952702,0.0029766979,0.050669856,0.0002746183,0.000073632014,0.00002116262,0.004133219,0.071656026,0.04443469,0.7994612,0.024327958,0.0017014208],"about_ca_topic_score_codex":0.000102987586,"about_ca_topic_score_gemma":0.0006303198,"teacher_disagreement_score":0.72177744,"about_ca_system_score_codex":0.00009564865,"about_ca_system_score_gemma":0.00023545466,"threshold_uncertainty_score":0.99896806},"labels":[],"label_agreement":null},{"id":"W2902338434","doi":"","title":"Crowdsourcing the Pronunciation of Out-of-Vocabulary Words.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Crowdsourcing; Pronunciation; Computer science; Vocabulary; Natural language processing; Artificial intelligence; Speech recognition; Linguistics; World Wide Web","score_opus":0.17418239703140648,"score_gpt":0.35554039245367786,"score_spread":0.18135799542227138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902338434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032325827,0.00005780902,0.9174136,0.003949375,0.0020451036,0.00051929354,0.000019262514,0.000058361453,0.043611374],"genre_scores_gemma":[0.9979943,0.0000053593794,0.0016880672,0.000064121705,0.0001392443,0.000014722025,0.0000024966555,0.0000039963356,0.0000876989],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99829304,0.00007736505,0.00043712967,0.00024757694,0.00078880094,0.00015607386],"domain_scores_gemma":[0.99778533,0.00022758385,0.0005424614,0.0005814746,0.0008205698,0.000042575095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010092955,0.000105133215,0.00016121306,0.00009520554,0.00029890082,0.00020920392,0.0014653946,0.00006642806,0.000027448239],"category_scores_gemma":[0.0010979634,0.000079663274,0.00007105095,0.00011982634,0.0002018695,0.0003405903,0.00014138022,0.00013112841,0.00009144896],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024365374,0.000058803536,0.00021924544,0.000008454063,0.000009966701,8.599455e-7,0.0009621924,0.00035657475,0.0037256228,0.87058705,0.000056094286,0.12399077],"study_design_scores_gemma":[0.00006544197,0.0002203595,0.009369179,0.00018915617,0.000007734335,0.000004211212,0.00036168448,0.1543061,0.3409871,0.49391308,0.00029885833,0.00027713375],"about_ca_topic_score_codex":0.000107911124,"about_ca_topic_score_gemma":0.00013126356,"teacher_disagreement_score":0.96566844,"about_ca_system_score_codex":0.000039084433,"about_ca_system_score_gemma":0.00034158138,"threshold_uncertainty_score":0.3248574},"labels":[],"label_agreement":null},{"id":"W2902387896","doi":"","title":"Foundations of Human-Agent Collaboration: Situation-Relevant Information Sharing.","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Knowledge management; Information sharing; Human–computer interaction; World Wide Web","score_opus":0.11014330541967347,"score_gpt":0.354698715458783,"score_spread":0.24455541003910952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902387896","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049294718,0.000005310069,0.97027886,0.0029280393,0.00031959979,0.00018896557,0.000015963507,0.000103007806,0.0212308],"genre_scores_gemma":[0.9892823,0.0000030028534,0.010018605,0.0003919711,0.000076126315,0.000026924192,0.00009328456,0.000004064116,0.00010372888],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981231,0.00007388766,0.0006161643,0.0002592565,0.00075859553,0.00016899145],"domain_scores_gemma":[0.99741083,0.00028915916,0.00033295565,0.0002870006,0.0016108123,0.00006924543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000902799,0.00013401665,0.00014462616,0.00028112228,0.00034084122,0.00036623594,0.00063016854,0.00007809948,0.00015658919],"category_scores_gemma":[0.00066951575,0.00013822936,0.000044479002,0.0006063742,0.000069376714,0.0010524102,0.00007792532,0.00015430742,0.00038305754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006892327,0.000045001576,0.000054388678,0.000010045746,0.000006496015,1.3026232e-7,0.0010270869,0.0135041345,0.0008120341,0.95457166,0.00023113008,0.029731],"study_design_scores_gemma":[0.000036615686,0.00017027829,0.00044269834,0.000057976817,0.0000036308222,8.906138e-7,0.00012728023,0.6214898,0.010562731,0.3658657,0.0010776302,0.00016473189],"about_ca_topic_score_codex":0.00003871373,"about_ca_topic_score_gemma":0.00005310112,"teacher_disagreement_score":0.9843528,"about_ca_system_score_codex":0.00008769624,"about_ca_system_score_gemma":0.00030681456,"threshold_uncertainty_score":0.563683},"labels":[],"label_agreement":null},{"id":"W2902425499","doi":"","title":"Exploring Synergies between Visual Analytical Flow and Language Pragmatics.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Language, Metaphor, and Cognition","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Pragmatics; Computer science; Linguistics; Natural language processing; Philosophy","score_opus":0.2906613784778585,"score_gpt":0.429665654346898,"score_spread":0.13900427586903952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902425499","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9048936,0.000052830463,0.015007404,0.0011588606,0.0006091608,0.00021455291,0.00006537093,0.000088721295,0.07790953],"genre_scores_gemma":[0.9984174,0.000022415983,0.00035284657,0.00012754039,0.00050146814,0.000052590214,0.0000326209,0.000013690534,0.00047943],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984762,0.000099706296,0.00032985333,0.0003721484,0.0004706716,0.0002514259],"domain_scores_gemma":[0.9988966,0.00030531987,0.00015527011,0.0002541702,0.00028080255,0.00010783221],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005279972,0.00016705196,0.00021231703,0.00015303951,0.00039111692,0.000315435,0.00028323315,0.00009770384,0.0011965941],"category_scores_gemma":[0.00089598657,0.00015428162,0.00005532603,0.0000774359,0.00025936382,0.00033058127,0.00006894777,0.00025025528,0.0007300282],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006628195,0.00011105295,0.00044519038,0.000010623893,0.000059613947,0.00002241659,0.0034338508,0.000012177679,0.0002806753,0.6101902,0.00005646252,0.38531145],"study_design_scores_gemma":[0.00060926145,0.0013150015,0.26639453,0.0005378392,0.00035172183,0.000044179353,0.055957638,0.10055097,0.055217016,0.5157383,0.00090381166,0.002379692],"about_ca_topic_score_codex":0.00013381297,"about_ca_topic_score_gemma":0.00007999281,"teacher_disagreement_score":0.38293177,"about_ca_system_score_codex":0.000032529842,"about_ca_system_score_gemma":0.00005931422,"threshold_uncertainty_score":0.99971646},"labels":[],"label_agreement":null},{"id":"W2902566158","doi":"","title":"Towards Ontologies in Variation.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Variation (astronomy); Computer science; Physics","score_opus":0.28689797060671046,"score_gpt":0.37726698774564327,"score_spread":0.0903690171389328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902566158","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071792877,0.000067568566,0.89487296,0.016176889,0.00094729813,0.00019710441,0.0000038188255,0.00024381433,0.080311276],"genre_scores_gemma":[0.9802615,0.00001190359,0.018849868,0.00069585134,0.000062843654,0.000024464542,0.0000024223846,0.0000033710648,0.00008777991],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99819684,0.00009776425,0.00036074285,0.0003824716,0.0007159478,0.0002462068],"domain_scores_gemma":[0.9987584,0.00020048227,0.000088716086,0.00024089472,0.00062946934,0.0000820592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079846976,0.0001354128,0.00016653494,0.00025126513,0.000055726516,0.00021298241,0.00089379534,0.00009646997,0.00005302483],"category_scores_gemma":[0.0017409233,0.00012241467,0.000035181554,0.00050336384,0.0000847924,0.00044579955,0.00012511057,0.00017735886,0.00044169897],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016316388,0.00007980327,0.00008099962,0.0000013921996,0.0000029166818,0.000008228467,0.0007273341,0.0012916998,0.000073404655,0.9261336,0.0002226093,0.07136171],"study_design_scores_gemma":[0.00003664603,0.00009846667,0.0052368417,0.000014837425,8.5763264e-7,0.0000041917883,0.0003293295,0.14901143,0.0034550903,0.841347,0.00030942666,0.00015591756],"about_ca_topic_score_codex":0.00027252463,"about_ca_topic_score_gemma":0.00047678902,"teacher_disagreement_score":0.9730822,"about_ca_system_score_codex":0.00016117029,"about_ca_system_score_gemma":0.0006927895,"threshold_uncertainty_score":0.5677294},"labels":[],"label_agreement":null},{"id":"W2902644086","doi":"","title":"Using Deep Learning to Automate Feature Modeling in Learning by Observation: A Preliminary Study.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Deep learning; Machine learning; Feature learning","score_opus":0.25705089330873315,"score_gpt":0.4028845113484676,"score_spread":0.14583361803973444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902644086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17482066,0.00001964584,0.8208339,0.0027156929,0.0001384554,0.0005085924,0.0000016188426,0.00016200321,0.0007993845],"genre_scores_gemma":[0.9680873,0.000007866657,0.03132462,0.00017423068,0.00008149253,0.00009792861,0.00000678873,0.000015679056,0.00020409483],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765575,0.00013948901,0.00041549624,0.0007334254,0.0006988121,0.00035705575],"domain_scores_gemma":[0.9984458,0.00018203983,0.00025934496,0.00045738844,0.00053677754,0.00011865753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056569633,0.0002210488,0.00020661556,0.00019761728,0.0011442073,0.0006295298,0.0013790366,0.00009406772,0.00001626401],"category_scores_gemma":[0.000830354,0.00024347285,0.000040745985,0.00048336372,0.000049603736,0.00089442596,0.00037683916,0.0006418398,0.00009658958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035179106,0.00013630252,0.00076805864,0.0000027979474,0.000004792805,0.0000063141647,0.00093411066,0.854387,0.0023843045,0.06381081,0.00001238669,0.077517964],"study_design_scores_gemma":[0.000038567367,0.00019056897,0.001477049,0.00005397678,0.0000024973394,0.0000027623248,0.00037880076,0.97158283,0.0012263849,0.02471692,0.00008276554,0.0002468533],"about_ca_topic_score_codex":0.000058373025,"about_ca_topic_score_gemma":0.00011033944,"teacher_disagreement_score":0.79326665,"about_ca_system_score_codex":0.00018877666,"about_ca_system_score_gemma":0.00012166704,"threshold_uncertainty_score":0.99285346},"labels":[],"label_agreement":null},{"id":"W2902711140","doi":"","title":"An Activity-Based Ontology for Dates.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ontology; Computer science; Information retrieval; Epistemology","score_opus":0.35600831424850854,"score_gpt":0.4200492926185642,"score_spread":0.06404097837005568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902711140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009702949,0.0000096808635,0.9790957,0.0058151516,0.00057279284,0.00022812947,0.000012934123,0.00017605635,0.0043866355],"genre_scores_gemma":[0.9636377,0.0000011087934,0.035105262,0.0010321537,0.00010645443,0.000058255708,0.000012980061,0.000005775545,0.00004027878],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840903,0.00009693455,0.00023646578,0.00048097514,0.0004994669,0.00027711436],"domain_scores_gemma":[0.99800944,0.00040252393,0.00010512498,0.0003472945,0.0009867059,0.00014889709],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006934731,0.0001493921,0.00017649216,0.00016586114,0.0001221995,0.00021781363,0.0009938414,0.00010814365,0.000028694794],"category_scores_gemma":[0.00077996124,0.00013864503,0.000050292936,0.00022999832,0.00012240067,0.00045487602,0.00004364043,0.00012803494,0.00015764663],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000079490266,0.00018444294,0.00002842416,0.0000029734435,0.000004389593,0.0000022228733,0.0001246857,0.0018202389,0.0010382996,0.89770603,0.00024096858,0.098767824],"study_design_scores_gemma":[0.0000502573,0.00040039083,0.00018075871,0.00000678628,0.0000020553857,0.000001917425,0.00006086131,0.5782904,0.04495232,0.375081,0.0008215412,0.00015171399],"about_ca_topic_score_codex":0.000055521803,"about_ca_topic_score_gemma":0.00023740197,"teacher_disagreement_score":0.9539348,"about_ca_system_score_codex":0.000086119595,"about_ca_system_score_gemma":0.0008043208,"threshold_uncertainty_score":0.565378},"labels":[],"label_agreement":null},{"id":"W2902908185","doi":"","title":"Continuous and Parallel: Challenges for a Standard Model of the Mind.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Cognitive Science and Education Research","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.5372705007154877,"score_gpt":0.46900121231440206,"score_spread":0.06826928840108559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902908185","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8031137,0.00007612004,0.046392757,0.07407099,0.0008073225,0.0019601318,0.0005246059,0.00003134473,0.073023],"genre_scores_gemma":[0.9988149,0.00012864995,0.00035844214,0.00020546412,0.000041409614,0.0000534921,4.808669e-7,0.00000431206,0.00039284825],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986308,0.00004933679,0.00019795097,0.00032489627,0.0006156116,0.00018138089],"domain_scores_gemma":[0.99839723,0.00044687884,0.0001651397,0.00022693249,0.00070614717,0.000057666133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057965284,0.00008382213,0.00011351635,0.00006485267,0.0006125636,0.00016724737,0.00057436066,0.000042108735,0.000065081054],"category_scores_gemma":[0.005977896,0.000061533414,0.00004638711,0.00005730341,0.0007721131,0.00017274561,0.000092626404,0.00011220176,0.000017067116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012644881,0.000066889595,0.00006678642,0.0000094229035,0.0000017968317,1.4835477e-7,0.00054107065,0.00024383381,0.028165562,0.8024638,0.00004226928,0.16827196],"study_design_scores_gemma":[0.000047072397,0.00012927421,0.0008574264,0.00004051864,0.0000027844987,0.0000010032477,0.0005327254,0.09838497,0.4361761,0.46348062,0.00024789287,0.00009959398],"about_ca_topic_score_codex":0.000007485193,"about_ca_topic_score_gemma":0.00004738954,"teacher_disagreement_score":0.40801054,"about_ca_system_score_codex":0.000022832628,"about_ca_system_score_gemma":0.00040757185,"threshold_uncertainty_score":0.7156532},"labels":[],"label_agreement":null},{"id":"W2902965061","doi":"","title":"Missteps in Robot Social Navigation.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Social robot; Mobile robot navigation; Robot; Human–computer interaction; Mobile robot; Artificial intelligence; Computer vision; Robot control","score_opus":0.4553784267618705,"score_gpt":0.4907067368120759,"score_spread":0.03532831005020537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902965061","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05798191,0.000044025128,0.014476924,0.014623121,0.004090322,0.0004894472,0.000037131827,0.00019368948,0.9080634],"genre_scores_gemma":[0.9960776,0.0000012802025,0.00020852312,0.00091459625,0.00046927706,0.000060871196,0.000035659086,0.000013504128,0.0022186914],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981384,0.0001883904,0.00045327275,0.00034141482,0.0006179068,0.00026060417],"domain_scores_gemma":[0.9987565,0.00019108928,0.00012889622,0.000101747995,0.0007040439,0.000117716794],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005691351,0.00014961224,0.00017396205,0.00021724956,0.00011541513,0.00008748398,0.0002401452,0.00017297066,0.003860057],"category_scores_gemma":[0.00039703777,0.0001633171,0.00006579642,0.00042873697,0.00011944853,0.00013413552,0.000023931501,0.00037538022,0.005562355],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019658302,0.00028937196,0.00012065632,0.0000015031284,0.000011832991,0.000010692469,0.004627184,0.00030860136,0.00019958502,0.9548724,0.0030400355,0.036321532],"study_design_scores_gemma":[0.0003014294,0.0002932905,0.00735725,0.000067592664,0.000010280908,0.000017462344,0.019986505,0.007345731,0.0029256272,0.9507015,0.010344506,0.0006488775],"about_ca_topic_score_codex":0.00020823468,"about_ca_topic_score_gemma":0.00018014522,"teacher_disagreement_score":0.9380957,"about_ca_system_score_codex":0.00025642288,"about_ca_system_score_gemma":0.00026052978,"threshold_uncertainty_score":0.9970505},"labels":[],"label_agreement":null},{"id":"W2903157661","doi":"","title":"Dynamic Goal Recognition Using Windowed Action Sequences.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Action recognition; Action (physics); Pattern recognition (psychology); Speech recognition; Class (philosophy)","score_opus":0.311107938398646,"score_gpt":0.4056791871074276,"score_spread":0.09457124870878164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903157661","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062286094,0.000015692334,0.9256362,0.0021873505,0.0011060926,0.00021258973,0.000027612343,0.00017277985,0.008355609],"genre_scores_gemma":[0.9741177,0.000012637965,0.02540015,0.00023081672,0.00010423466,0.000012459135,0.000021828073,0.000008324957,0.000091873284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980756,0.00009436686,0.00034909608,0.00051375234,0.00067409896,0.00029305465],"domain_scores_gemma":[0.9983011,0.00016817027,0.00036633413,0.00042443382,0.00064158277,0.000098416946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007212388,0.00018383432,0.00015333027,0.00018421028,0.001132319,0.00097548286,0.0010381739,0.00013159633,0.00013165409],"category_scores_gemma":[0.0005608054,0.00019077258,0.000065353655,0.00015898513,0.00015197493,0.001133236,0.00010666205,0.00032987603,0.00045697697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000070268165,0.0001208427,0.00019834675,0.000018232126,0.000025054816,0.000021754575,0.00044935598,0.009380961,0.01678024,0.2380521,0.000059166152,0.73482364],"study_design_scores_gemma":[0.000028166165,0.000069668815,0.00077733956,0.00010756009,0.0000041447192,0.000011668163,0.000048341833,0.74948454,0.01367634,0.23550661,0.000058365826,0.00022724029],"about_ca_topic_score_codex":0.00014785652,"about_ca_topic_score_gemma":0.00009967357,"teacher_disagreement_score":0.91183156,"about_ca_system_score_codex":0.00019579256,"about_ca_system_score_gemma":0.00045033713,"threshold_uncertainty_score":0.94066036},"labels":[],"label_agreement":null},{"id":"W2903180102","doi":"","title":"Knowledge-Based Provisioning of Goods and Services: Towards a Virtual Social Needs Marketplace.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Cooperative Studies and Economics","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Ottawa","funders":"","keywords":"Provisioning; Computer science; Knowledge management; Business; Goods and services; Commerce; Internet privacy; Telecommunications; Economics","score_opus":0.12190858612589273,"score_gpt":0.3367683560709164,"score_spread":0.21485976994502365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903180102","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.635991,0.000052255426,0.0052108024,0.010183917,0.0009224661,0.0005275654,0.000046333436,0.00007843156,0.34698722],"genre_scores_gemma":[0.99865705,0.00000881469,0.000088894005,0.0005275486,0.00054389745,0.000014401714,0.000011198665,0.000009964071,0.00013821833],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908227,0.0000109045095,0.00030351215,0.00022823254,0.00021113531,0.0001639636],"domain_scores_gemma":[0.9989083,0.000065379514,0.00029473554,0.00012042348,0.00059834734,0.000012856731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041099498,0.0001501819,0.00020454204,0.0001782105,0.00085535325,0.0005399857,0.00031357596,0.0000645747,0.00024947425],"category_scores_gemma":[0.00025503198,0.00014283613,0.00005179104,0.000116227384,0.00023598221,0.00050198805,0.00021143614,0.000117038915,0.00008585428],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014794736,0.000088127774,0.000595156,0.00006452238,0.000017516548,4.1119023e-7,0.00027186662,0.00017310905,0.00018396672,0.8736615,0.000108664404,0.12468722],"study_design_scores_gemma":[0.0003462411,0.00017036067,0.03519161,0.00035403017,0.000050857885,5.447489e-7,0.006073722,0.842867,0.0027828268,0.09679652,0.014586356,0.00077990355],"about_ca_topic_score_codex":0.00023176872,"about_ca_topic_score_gemma":0.00050298806,"teacher_disagreement_score":0.8426939,"about_ca_system_score_codex":0.000035729834,"about_ca_system_score_gemma":0.0001225505,"threshold_uncertainty_score":0.6578772},"labels":[],"label_agreement":null},{"id":"W2903401122","doi":"","title":"An AI Planning-Based Approach to the Multi-Agent Plan Recognition Problem (Preliminary Report).","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Plan (archaeology); Artificial intelligence; Machine learning","score_opus":0.2694730179531619,"score_gpt":0.3839999608576474,"score_spread":0.11452694290448551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903401122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022469016,0.000013033475,0.9814519,0.008075017,0.0004056459,0.0005213201,0.00003873652,0.00017885865,0.007068565],"genre_scores_gemma":[0.91816616,9.3437404e-7,0.07945561,0.0018122423,0.00018173293,0.0001436544,0.000119460405,0.00001352136,0.00010666632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972049,0.00018810062,0.0005189511,0.0008208694,0.000884044,0.0003831365],"domain_scores_gemma":[0.997396,0.0002339669,0.00039359432,0.0010029342,0.0007657654,0.00020776228],"candidate_categories":["sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0017187882,0.00025488329,0.0001860656,0.00019231433,0.0014780619,0.0013283328,0.0021345706,0.00013399262,0.00003279503],"category_scores_gemma":[0.0006346975,0.00020785497,0.00007074458,0.00020014435,0.000119894394,0.0006081134,0.00014174618,0.000468194,0.00034417614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035918108,0.0012733378,0.0009276949,0.00004569401,0.00004082401,0.00015216075,0.0040143933,0.40777466,0.0007647966,0.24762256,0.0031635452,0.33386117],"study_design_scores_gemma":[0.0000527938,0.00041654715,0.001535525,0.0001540521,0.0000065658887,0.000031770644,0.00012390513,0.95927817,0.0053337067,0.031540103,0.0011647171,0.00036217435],"about_ca_topic_score_codex":0.0001357704,"about_ca_topic_score_gemma":0.0000405018,"teacher_disagreement_score":0.9159193,"about_ca_system_score_codex":0.000085810134,"about_ca_system_score_gemma":0.00048471146,"threshold_uncertainty_score":0.9998219},"labels":[],"label_agreement":null},{"id":"W2903442974","doi":"","title":"Towards a Preference Formalism for Modular Systems.","year":2015,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Modular design; Formalism (music); Computer science; Programming language; Art","score_opus":0.41133872333254645,"score_gpt":0.37580808777993224,"score_spread":0.03553063555261421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903442974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027549546,0.000086574975,0.9732673,0.002578334,0.0011609974,0.00047681367,0.000022823064,0.00021297413,0.019439206],"genre_scores_gemma":[0.9792432,0.0000112869775,0.019771546,0.00039215066,0.00017259741,0.00016652206,0.000011502745,0.000007993764,0.00022319156],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976868,0.000073468684,0.00045095396,0.00050409004,0.00091906043,0.00036564324],"domain_scores_gemma":[0.9973444,0.0002059072,0.00014989574,0.0003583068,0.0017676454,0.0001738796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009446041,0.0002026969,0.00023720157,0.00018451914,0.00014426128,0.00044563194,0.0012165097,0.0001260915,0.000015520101],"category_scores_gemma":[0.0010821164,0.00017877627,0.00007509316,0.00031427114,0.00009549841,0.00057736103,0.00013832952,0.00014821178,0.00022104615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035765534,0.000065376036,0.000006261782,0.000011006979,0.0000095408695,0.0000021875944,0.00037934282,0.0029066382,0.00013749948,0.9580953,0.0005419513,0.037809137],"study_design_scores_gemma":[0.00004773686,0.00020390362,0.00007845976,0.000028797644,0.0000030413323,0.000006825539,0.00026196637,0.49963406,0.006075927,0.49177736,0.0016918745,0.00019005324],"about_ca_topic_score_codex":0.00008655949,"about_ca_topic_score_gemma":0.000030117488,"teacher_disagreement_score":0.97648823,"about_ca_system_score_codex":0.00013145995,"about_ca_system_score_gemma":0.00076623756,"threshold_uncertainty_score":0.72902846},"labels":[],"label_agreement":null},{"id":"W2903461828","doi":"","title":"Intelligent and Affectively Aligned Evaluation of Online Health Information for Older Adults.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; Data science; World Wide Web; Internet privacy","score_opus":0.28888179908052225,"score_gpt":0.5453131383301174,"score_spread":0.25643133924959516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903461828","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.67643267,0.00009649555,0.2731629,0.01791622,0.0019227932,0.010597286,0.001571778,0.00011236468,0.018187478],"genre_scores_gemma":[0.99535805,0.000052670883,0.0019085741,0.0019823634,0.000121080186,0.00026292726,0.00027488847,0.0000063181374,0.000033128315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996454,0.0003894058,0.0017273924,0.00023593231,0.00089676405,0.00029651108],"domain_scores_gemma":[0.9913723,0.00057982706,0.0018149179,0.0003039792,0.0057852836,0.00014373263],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.006762704,0.00015392831,0.00028091957,0.0002123242,0.0013470077,0.000075014716,0.00028912965,0.00015546769,0.0004108834],"category_scores_gemma":[0.006545785,0.00013718229,0.000052046064,0.000095741925,0.00014844381,0.0015217961,0.00007193785,0.00022925642,0.00007916818],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003658131,0.0001375886,0.00070945284,0.00039882158,0.000009662492,1.0818142e-8,0.007058561,0.000070913666,0.00000980221,0.21303806,0.00029913778,0.7779022],"study_design_scores_gemma":[0.0007862575,0.00053015613,0.18737131,0.0014910834,0.000021330501,5.541795e-7,0.0136479335,0.6599566,0.001848432,0.13120362,0.0027876478,0.0003550963],"about_ca_topic_score_codex":0.0002915226,"about_ca_topic_score_gemma":0.0003360611,"teacher_disagreement_score":0.77754706,"about_ca_system_score_codex":0.00031790402,"about_ca_system_score_gemma":0.0018289491,"threshold_uncertainty_score":0.9999531},"labels":[],"label_agreement":null},{"id":"W2903517279","doi":"","title":"General Model of Human Motivation and Goal Ranking","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Maslow's hierarchy of needs; Fidelity; Ranking (information retrieval); Emulation; Plan (archaeology); Cognition; Domain (mathematical analysis); Goal orientation; Cognitive model; Behavioral modeling; Process (computing); Artificial intelligence; Human–computer interaction; Knowledge management; Psychology; Social psychology","score_opus":0.6006814403389338,"score_gpt":0.4955674240951952,"score_spread":0.10511401624373856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903517279","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72545314,0.000019271803,0.23053282,0.0006105111,0.00029334673,0.00019867861,0.00003248849,0.000020139463,0.04283963],"genre_scores_gemma":[0.9975433,0.0000017706094,0.0013823046,0.00007456294,0.0001276051,0.0000063817733,0.0000015121605,0.000006279785,0.00085633295],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99638593,0.00008536567,0.0008906342,0.00046686968,0.002011276,0.00015993012],"domain_scores_gemma":[0.99644846,0.0006702609,0.0006922332,0.0004923054,0.0016221766,0.000074546406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022726331,0.00013447477,0.00027603304,0.00030737865,0.0007677674,0.00075084064,0.00083910924,0.00007911657,0.00030093858],"category_scores_gemma":[0.0057617812,0.00011023287,0.0000769825,0.00013668607,0.00026325494,0.00042226107,0.00019547775,0.0001380237,0.0000668606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030222413,0.000031262334,0.0008272119,0.000001643664,0.0000037138645,5.60292e-7,0.00020104893,0.0054378356,0.016564783,0.9249491,0.00012125248,0.051831335],"study_design_scores_gemma":[0.000024347055,0.000032819786,0.006542077,0.000024008814,0.0000012997098,8.1570704e-7,0.00009631695,0.4059121,0.0039240667,0.5833445,0.000022348013,0.00007530488],"about_ca_topic_score_codex":0.00007890999,"about_ca_topic_score_gemma":0.00012145699,"teacher_disagreement_score":0.40047425,"about_ca_system_score_codex":0.000032605152,"about_ca_system_score_gemma":0.00012522512,"threshold_uncertainty_score":0.72403735},"labels":[],"label_agreement":null},{"id":"W2905103471","doi":"","title":"Group Fairness for Indivisible Goods Allocation","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Fair division; Set (abstract data type); Group (periodic table); Mathematical economics; Computer science; Fairness measure; Public good; Nash equilibrium; Max-min fairness; Function (biology); Microeconomics; Mathematics; Resource allocation; Economics","score_opus":0.17861673615374524,"score_gpt":0.32247238477174256,"score_spread":0.14385564861799732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905103471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2958802,0.00011864831,0.52531046,0.0021994165,0.003008108,0.001726526,0.00039243838,0.00017389446,0.17119032],"genre_scores_gemma":[0.99755347,0.000006001764,0.00049694174,0.0002416592,0.00016975192,0.00008360733,0.00006125188,0.000015534572,0.001371798],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986382,0.000026867769,0.0005796862,0.00042283454,0.00010940785,0.00022301066],"domain_scores_gemma":[0.99905974,0.0002297956,0.00028885086,0.00018144035,0.00018836533,0.000051809242],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013733492,0.00013564843,0.00023983495,0.00021622257,0.000109818924,0.00012976436,0.00029466656,0.00011342328,0.00088853843],"category_scores_gemma":[0.00033949135,0.00015671641,0.0000854696,0.00022820145,0.00004657852,0.00020985375,0.000022648517,0.00012756839,0.0040910053],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045974863,0.00008887183,0.0008625656,0.000024070045,0.000013585626,9.0268955e-8,0.00018526828,0.0005021076,0.00039330064,0.99324095,0.000058149933,0.0045850887],"study_design_scores_gemma":[0.00006106696,0.00017865558,0.0010657029,0.0000369716,0.0000016674505,7.7311546e-7,0.0002000633,0.034752585,0.0033681018,0.9570862,0.0030223455,0.00022588378],"about_ca_topic_score_codex":0.000036152596,"about_ca_topic_score_gemma":0.000020548468,"teacher_disagreement_score":0.70167327,"about_ca_system_score_codex":0.000095599346,"about_ca_system_score_gemma":0.000046390403,"threshold_uncertainty_score":0.99668443},"labels":[],"label_agreement":null},{"id":"W2911995474","doi":"","title":"Exploring EPCG in The Witness.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Witness; Computer science; Programming language","score_opus":0.3372396978299097,"score_gpt":0.3425922878487284,"score_spread":0.005352590018818715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911995474","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5343381,0.000065021406,0.2955126,0.029251121,0.0017003308,0.0006464067,0.0000043590317,0.00027594008,0.1382061],"genre_scores_gemma":[0.9982725,0.000025629075,0.000635925,0.00086089416,0.000040359508,0.00006660068,8.9520313e-7,0.0000024813003,0.00009471775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987068,0.00007026742,0.0002293275,0.00030814367,0.0004906327,0.00019481218],"domain_scores_gemma":[0.99916244,0.00031271434,0.000053176045,0.00028157193,0.00017315875,0.000016967451],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00065225374,0.00010395997,0.00011294281,0.0001634833,0.00012582124,0.0001055734,0.0010220404,0.00005088432,0.00008013747],"category_scores_gemma":[0.00019252495,0.00007668658,0.000032928572,0.0005368147,0.00010421117,0.0003721707,0.000099356104,0.00028427623,0.0007912375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000055378805,0.000046138965,0.00013125125,0.0000019486438,0.0000028140273,0.00000295484,0.0015145635,0.00020122819,0.000091787675,0.95752364,0.000033352942,0.04044478],"study_design_scores_gemma":[0.000025057514,0.00009335228,0.004366813,0.000029348806,8.783291e-7,0.0000043797045,0.001207096,0.032045957,0.0024248287,0.95837355,0.0012628604,0.00016589432],"about_ca_topic_score_codex":0.000021045229,"about_ca_topic_score_gemma":0.000081512604,"teacher_disagreement_score":0.46393436,"about_ca_system_score_codex":0.000040049144,"about_ca_system_score_gemma":0.000089902074,"threshold_uncertainty_score":0.99998677},"labels":[],"label_agreement":null},{"id":"W2912145295","doi":"","title":"Interaction and learning in a humanoid robot magic performance","year":2018,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Teaching and Learning Programming","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Humanoid robot; Computer science; MAGIC (telescope); Robot; Human–computer interaction; Artificial intelligence; Physics","score_opus":0.12484669535864168,"score_gpt":0.3573165159349851,"score_spread":0.2324698205763434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912145295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38134676,0.0000271752,0.59935564,0.0023221332,0.0005695878,0.00018198845,4.123366e-7,0.00030966316,0.015886648],"genre_scores_gemma":[0.9889944,0.000010579687,0.01037697,0.00017672052,0.00011964103,0.000008975988,0.000001923171,0.000005833953,0.00030492304],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986257,0.00015865816,0.000260727,0.00038674843,0.00034434182,0.00022380931],"domain_scores_gemma":[0.9993132,0.00016948918,0.00009787902,0.00011852602,0.0002481204,0.000052820484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089329394,0.000121125406,0.00010654846,0.000274417,0.00029069788,0.0003075045,0.00032744804,0.00005877195,0.0000549727],"category_scores_gemma":[0.00038335827,0.00012268631,0.000020612573,0.0003415488,0.00011973644,0.00046035755,0.00009014254,0.00060957385,0.00031878913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020535268,0.00004860016,0.0004995652,0.000004812959,0.0000023432287,0.0000015245507,0.0017963942,0.0019280079,0.00045077575,0.21472166,0.000007600285,0.7805182],"study_design_scores_gemma":[0.00005428721,0.00068841386,0.012342133,0.00016753345,0.0000019024549,0.000019119838,0.0005437208,0.9585231,0.0051595904,0.01675972,0.0054022004,0.00033829885],"about_ca_topic_score_codex":0.000059852453,"about_ca_topic_score_gemma":0.0000658789,"teacher_disagreement_score":0.95659506,"about_ca_system_score_codex":0.00007466391,"about_ca_system_score_gemma":0.0001059176,"threshold_uncertainty_score":0.5003002},"labels":[],"label_agreement":null},{"id":"W2912245354","doi":"","title":"Intuitive Rules Design Evaluation Methods and Case Study.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Persona Design and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; St. Francis Xavier University","funders":"","keywords":"Computer science","score_opus":0.3572289834746186,"score_gpt":0.47668608108330757,"score_spread":0.11945709760868894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912245354","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060807746,0.000024020623,0.9334632,0.0006304236,0.00012533265,0.0009679335,0.000004812537,0.00006692003,0.0039095706],"genre_scores_gemma":[0.9068439,0.000005289434,0.092692055,0.00019943675,0.00003233101,0.00015778498,0.000002795948,0.0000052508103,0.00006117099],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778706,0.00054813834,0.00029187734,0.00054988835,0.0006536191,0.00016939304],"domain_scores_gemma":[0.9979307,0.00073546264,0.00010633016,0.00026857108,0.0008752338,0.00008367058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025503074,0.00014764695,0.00014390977,0.00018508274,0.0002150767,0.00027755264,0.00038139435,0.000059747817,0.00016728202],"category_scores_gemma":[0.00033117222,0.00014063291,0.000029033057,0.00035937608,0.00006799632,0.00036202886,0.000091410024,0.00016506332,0.00044068438],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000923582,0.00015405222,0.000016210322,0.0000015650595,0.000010256892,0.000010316581,0.001510591,0.00043884554,0.0022233047,0.6051982,0.000018754557,0.39040866],"study_design_scores_gemma":[0.000043347067,0.00021688892,0.00021444069,0.000009733138,0.0000071951526,0.000072878254,0.0017073549,0.70995057,0.0068060183,0.28077576,0.00003522577,0.0001605881],"about_ca_topic_score_codex":0.000058074755,"about_ca_topic_score_gemma":0.000019646448,"teacher_disagreement_score":0.84603614,"about_ca_system_score_codex":0.00009386844,"about_ca_system_score_gemma":0.00032321384,"threshold_uncertainty_score":0.57348436},"labels":[],"label_agreement":null},{"id":"W2912371510","doi":"","title":"Inconsistent heuristics","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Heuristics; Heuristic; Consistency (knowledge bases); Computer science; Mathematical optimization; Quality (philosophy); Field (mathematics); Incremental heuristic search; Consistent heuristic; Null-move heuristic; Artificial intelligence; Mathematics; Algorithm; Beam search; Search algorithm","score_opus":0.15358805703241057,"score_gpt":0.35392359311149396,"score_spread":0.2003355360790834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912371510","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001643813,0.000024187038,0.9496124,0.0021855799,0.0004897408,0.00009865915,0.0000068965874,0.00015759282,0.04578113],"genre_scores_gemma":[0.97003424,0.0000049492864,0.028530722,0.0010277865,0.00013692712,0.0000048592706,0.000008209756,0.0000062753206,0.00024605033],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99804264,0.00005164325,0.0004304535,0.00039006508,0.00075632485,0.00032886307],"domain_scores_gemma":[0.9981987,0.00061578647,0.00012874539,0.00025044646,0.0006684188,0.0001379292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013788698,0.00015325414,0.00012925139,0.00021469522,0.00024124762,0.0002171479,0.00068969734,0.00009240538,0.000102447986],"category_scores_gemma":[0.0005346451,0.00015135232,0.000054533793,0.00042784694,0.0000851149,0.00019205209,0.00009082241,0.00028649563,0.0006749113],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016239608,0.000060486247,0.000069108035,0.0000034039028,0.0000053868275,0.000014819207,0.00019690122,0.0010583402,0.00037151264,0.89051074,0.00030395243,0.10738911],"study_design_scores_gemma":[0.00003327517,0.00021554656,0.000944539,0.0000815984,0.000003995863,0.000017743492,0.00013510071,0.28838882,0.030382043,0.67611814,0.0032597664,0.00041939897],"about_ca_topic_score_codex":0.000022511758,"about_ca_topic_score_gemma":0.00004028708,"teacher_disagreement_score":0.9683904,"about_ca_system_score_codex":0.000098252254,"about_ca_system_score_gemma":0.00029127946,"threshold_uncertainty_score":0.86748445},"labels":[],"label_agreement":null},{"id":"W2912712767","doi":"","title":"Human-Agent Teaming as a Common Problem for Goal Reasoning.","year":2018,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Multi-agent system; Human–computer interaction; Artificial intelligence","score_opus":0.11159756347113302,"score_gpt":0.3768966286957244,"score_spread":0.26529906522459135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912712767","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011577917,0.000027282382,0.93521225,0.0028649338,0.00051218923,0.0005776012,0.000023418357,0.00030798325,0.0488964],"genre_scores_gemma":[0.95632017,0.0000016969619,0.04163336,0.00086654845,0.00035157855,0.000089177534,0.00002097269,0.000013612223,0.0007029053],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99772114,0.000091790615,0.00047741467,0.0006090671,0.0006794859,0.00042113516],"domain_scores_gemma":[0.99802387,0.00033338068,0.00021535934,0.00030794498,0.0009874749,0.00013196666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010695904,0.00022021965,0.00020002181,0.00019837527,0.00078296283,0.00042575545,0.00095072365,0.00012256972,0.00015045248],"category_scores_gemma":[0.00036676982,0.00022005397,0.00008543743,0.00036559577,0.00016445343,0.00032569107,0.0001246214,0.00025892185,0.00057731447],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000030097915,0.00008502191,0.000047013735,0.000010165931,0.000011810307,0.0000025530264,0.00080327544,0.0004896568,0.001775047,0.9208908,0.0009875454,0.07486698],"study_design_scores_gemma":[0.00006259668,0.00095960934,0.00016960186,0.0002272105,0.0000065724553,0.000010759139,0.00013001569,0.34864387,0.036893304,0.60868627,0.0038015912,0.0004086137],"about_ca_topic_score_codex":0.00011194162,"about_ca_topic_score_gemma":0.000120934965,"teacher_disagreement_score":0.9447422,"about_ca_system_score_codex":0.00012670744,"about_ca_system_score_gemma":0.0003836781,"threshold_uncertainty_score":0.89735407},"labels":[],"label_agreement":null},{"id":"W2913117992","doi":"","title":"From Algorithms to Heuristics: Will Androids Ever Make Freudian Slips?","year":2018,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Advanced Malware Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Heuristics; Computer science; Freudian slip; Algorithm; Artificial intelligence; Operating system; Psychology","score_opus":0.08553899886646475,"score_gpt":0.3573360199423693,"score_spread":0.27179702107590453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913117992","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019849772,0.000007967101,0.98686665,0.0026429764,0.0010281961,0.00028301377,0.0000761068,0.00048347414,0.006626639],"genre_scores_gemma":[0.8364986,0.000008365289,0.1603685,0.0019220187,0.0008070767,0.000059043457,0.000010804399,0.000016507716,0.00030903702],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9972698,0.00007939347,0.00048807674,0.00084101944,0.00094849337,0.00037320412],"domain_scores_gemma":[0.9972072,0.00025468075,0.00014164048,0.0005444312,0.0016198758,0.00023213123],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00036242325,0.00025602902,0.00021594606,0.00033997052,0.00027294454,0.00031212505,0.0012034347,0.00013892459,0.00049175136],"category_scores_gemma":[0.0007937642,0.0002674686,0.000065056534,0.0007677916,0.00019346096,0.0004999429,0.00025382853,0.00027869793,0.0018728347],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039243092,0.00009564193,0.000008472416,0.0000018647509,0.000011799143,0.000009324355,0.0005351357,0.00014733328,0.0032303652,0.53880435,0.0010659429,0.45605052],"study_design_scores_gemma":[0.000025709005,0.00042152943,0.00021114363,0.000043302287,0.000002790778,0.000004992849,0.00006950211,0.08397706,0.21293317,0.6917514,0.010179054,0.00038030694],"about_ca_topic_score_codex":0.00013695986,"about_ca_topic_score_gemma":0.0002206112,"teacher_disagreement_score":0.83451366,"about_ca_system_score_codex":0.00019475755,"about_ca_system_score_gemma":0.00023641868,"threshold_uncertainty_score":0.99997777},"labels":[],"label_agreement":null},{"id":"W2914331068","doi":"","title":"An Architecture for a Military AI System with Ethical Rules.","year":2018,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Architecture; Computer science; Artificial intelligence; Software engineering; Geography","score_opus":0.1503040814801666,"score_gpt":0.4432512319635142,"score_spread":0.2929471504833476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914331068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047621783,0.000039820432,0.5824244,0.19113347,0.0013383549,0.0019882238,0.00035860128,0.0005548909,0.17454048],"genre_scores_gemma":[0.9907943,0.00000878581,0.0024807418,0.005158786,0.0013519321,0.00004718562,0.000022742846,0.000015466538,0.00012003592],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974506,0.0002907652,0.00029300555,0.00040370706,0.0011564544,0.00040545917],"domain_scores_gemma":[0.99534774,0.0005563175,0.00008022484,0.00016513209,0.0035853817,0.00026520147],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0021158797,0.00016247909,0.0001902232,0.00012352891,0.0013281774,0.00024862413,0.00046869903,0.0004559518,0.00018705762],"category_scores_gemma":[0.0015721503,0.00014079991,0.00006789012,0.0002607711,0.0014240483,0.00024848897,0.000017173312,0.0006930762,0.00014253231],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022873917,0.00007783855,0.000007957152,0.000011622517,0.000013110607,0.0000014209737,0.008330516,0.0001234059,0.00018298811,0.9772635,0.00030472613,0.01345419],"study_design_scores_gemma":[0.00006780902,0.001378594,0.00015279265,0.00016162336,0.00001527044,0.0000017826673,0.011237617,0.016240545,0.0020929368,0.9595674,0.0086696865,0.00041393787],"about_ca_topic_score_codex":0.0015307722,"about_ca_topic_score_gemma":0.018299205,"teacher_disagreement_score":0.9431725,"about_ca_system_score_codex":0.00020086802,"about_ca_system_score_gemma":0.0015234968,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W2914623941","doi":"","title":"Subjective mapping","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Computer science; Representation (politics); Key (lock); Dimensionality reduction; Artificial intelligence; Variety (cybernetics); Embedding; Divide and conquer algorithms; Action (physics); Machine learning; Theoretical computer science; Domain (mathematical analysis); Algorithm; Mathematics","score_opus":0.07815407986884743,"score_gpt":0.28066734330041176,"score_spread":0.20251326343156434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914623941","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030169485,0.00002488963,0.85164016,0.0003092508,0.0004141185,0.00015217376,0.000018231365,0.00025052842,0.11702115],"genre_scores_gemma":[0.9986558,0.000009574802,0.0008852645,0.00007660537,0.00018950256,0.0000098786395,0.000036414167,0.000013992384,0.00012294539],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990548,0.00001871088,0.0002561006,0.00016297962,0.00034365314,0.00016375216],"domain_scores_gemma":[0.9994651,0.0000759465,0.000028650176,0.00007697297,0.00031941617,0.00003392819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012307735,0.000116688825,0.00009396946,0.00014859375,0.000082995946,0.00007771353,0.000103070444,0.000069282294,0.00021734572],"category_scores_gemma":[0.000057635203,0.00012492275,0.000035242454,0.00026759648,0.00004462338,0.000081784216,0.0000069828425,0.00012617819,0.00037775378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041307067,0.000022274131,0.000032729335,0.0000039553693,0.0000038257317,0.0000012574566,0.000032513097,0.4357971,0.0037298864,0.5535983,0.00014917117,0.006624876],"study_design_scores_gemma":[0.000012314324,0.000018165618,0.0010202065,0.000018668585,0.0000016580368,9.908904e-7,0.00007180611,0.77036923,0.039445944,0.18848322,0.00040530658,0.00015250091],"about_ca_topic_score_codex":0.000034521352,"about_ca_topic_score_gemma":0.00006578201,"teacher_disagreement_score":0.9684863,"about_ca_system_score_codex":0.00010802167,"about_ca_system_score_gemma":0.000045390247,"threshold_uncertainty_score":0.5094202},"labels":[],"label_agreement":null},{"id":"W2918219359","doi":"","title":"Temporally Extended Metrics for Markov Decision Processes.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Markov decision process; Markov process; Markov chain; Machine learning; Mathematics; Statistics","score_opus":0.1379975308682148,"score_gpt":0.3690931353242502,"score_spread":0.2310956044560354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918219359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024362002,0.000047569945,0.9788093,0.010834966,0.0007156456,0.00068317016,0.000060741593,0.00041937263,0.0059930333],"genre_scores_gemma":[0.6848344,0.000031371314,0.314507,0.00035925355,0.000045867044,0.00007164518,0.000024126939,0.000011459712,0.00011485437],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.996967,0.00004037065,0.00053855847,0.00087732536,0.0011921747,0.00038457214],"domain_scores_gemma":[0.99367964,0.0015702317,0.00023947188,0.0024842524,0.0019402234,0.0000861565],"candidate_categories":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.0011111406,0.00022919485,0.00023626134,0.00052952796,0.00015628873,0.00041864003,0.014432705,0.00019046491,0.0001623257],"category_scores_gemma":[0.055335004,0.00021666467,0.00007139045,0.001611777,0.00009486242,0.0008346667,0.006749267,0.00028285966,0.00066300924],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000072452814,0.00015613333,0.000048212547,0.000033674492,0.000011285566,0.0000020885907,0.00002543172,0.00009732175,0.00078211643,0.6306281,0.009709002,0.3584342],"study_design_scores_gemma":[0.000037471098,0.0001622293,0.00010559962,0.00004907683,0.0000016897012,0.0000022669276,0.000026953781,0.29519877,0.017344605,0.685679,0.0011944326,0.00019789656],"about_ca_topic_score_codex":0.000009986521,"about_ca_topic_score_gemma":0.000023289766,"teacher_disagreement_score":0.6823982,"about_ca_system_score_codex":0.00018487981,"about_ca_system_score_gemma":0.00082106167,"threshold_uncertainty_score":0.9908997},"labels":[],"label_agreement":null},{"id":"W2918865870","doi":"","title":"Towards international standards for evaluating machine learning.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Computer science","score_opus":0.13433544121054686,"score_gpt":0.41564346603083413,"score_spread":0.2813080248202873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2918865870","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013560936,0.000010105924,0.9560742,0.0035211202,0.0003957706,0.00045003265,0.000068747264,0.00023388454,0.03789008],"genre_scores_gemma":[0.95468885,0.000017971252,0.043758404,0.00039209373,0.000097945776,0.00016381835,0.0000313881,0.000009902954,0.00083962036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793595,0.000046095392,0.0003579776,0.00046721112,0.0010005853,0.00019220421],"domain_scores_gemma":[0.99769515,0.00017404919,0.00016528771,0.00021785847,0.001685662,0.00006197292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010775682,0.00014045798,0.00013296117,0.00018111724,0.0001980799,0.00026248777,0.00080277293,0.00007796086,0.00063830405],"category_scores_gemma":[0.00039471657,0.00013858336,0.00009191778,0.00029383443,0.000041256862,0.00028310873,0.00011160248,0.0002235453,0.00025825636],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000025810645,0.0000412703,0.000017311875,0.0000032308678,0.0000073792557,1.185167e-7,0.00005108702,0.0012234931,0.0019412063,0.7321508,0.000082792045,0.26445553],"study_design_scores_gemma":[0.00003814025,0.0002923552,0.000053938827,0.0000158642,0.0000017622052,0.0000021595717,0.00003273449,0.70933723,0.02709944,0.24762535,0.015352264,0.00014874118],"about_ca_topic_score_codex":0.000019545178,"about_ca_topic_score_gemma":0.00000955731,"teacher_disagreement_score":0.9533328,"about_ca_system_score_codex":0.00020191421,"about_ca_system_score_gemma":0.00044173995,"threshold_uncertainty_score":0.69889814},"labels":[],"label_agreement":null},{"id":"W2920076287","doi":"","title":"Towards Robust End-to-End Alignment.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"End-to-end principle; Computer science; Robustness (evolution); Artificial intelligence; Chemistry","score_opus":0.1495320608333968,"score_gpt":0.3435094578686649,"score_spread":0.1939773970352681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920076287","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068436055,0.000015975007,0.87363786,0.060725983,0.0012141807,0.00050977926,0.00007335482,0.0006300713,0.05634921],"genre_scores_gemma":[0.8807689,0.000016042437,0.11739876,0.0014058893,0.00007638307,0.000043928805,0.00002015919,0.000012637308,0.00025731057],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99650514,0.000081796796,0.00048121472,0.000944058,0.0015055628,0.0004822332],"domain_scores_gemma":[0.99568695,0.0002749379,0.00014190467,0.0031659144,0.00058217277,0.00014810094],"candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":["open_science","insufficient_payload"],"category_scores_codex":[0.00085165055,0.0002588885,0.0002349287,0.0003919215,0.00014106736,0.00042693556,0.016722899,0.00016487185,0.0015633061],"category_scores_gemma":[0.008349595,0.0002572816,0.00007156775,0.00091941515,0.00012317041,0.000657741,0.01354067,0.00037950478,0.0061108973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001571544,0.00009123615,0.00003542026,0.000005353632,0.000012138558,0.000004880996,0.0000673269,0.00097222714,0.002561162,0.83096147,0.00874671,0.15652639],"study_design_scores_gemma":[0.000024102721,0.00015795449,0.00027474822,0.00004281507,0.0000018115828,0.000004344139,0.0000680919,0.24077094,0.0652687,0.6906705,0.0024179497,0.00029804965],"about_ca_topic_score_codex":0.00006363928,"about_ca_topic_score_gemma":0.000034994402,"teacher_disagreement_score":0.87392527,"about_ca_system_score_codex":0.00029058658,"about_ca_system_score_gemma":0.00047216672,"threshold_uncertainty_score":0.99998796},"labels":[],"label_agreement":null},{"id":"W2940670759","doi":"","title":"Happiness Ingredients Detection using Multi-Task Deep Learning.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Happiness; Task (project management); Computer science; Artificial intelligence; Deep learning; Machine learning; Psychology; Engineering; Social psychology","score_opus":0.22129384116215908,"score_gpt":0.37219621312787804,"score_spread":0.15090237196571896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2940670759","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.936852,0.0000115743915,0.060316224,0.00016520362,0.00032696492,0.00017442544,0.00001633899,0.00005347412,0.002083833],"genre_scores_gemma":[0.99784267,0.000006910215,0.0016408772,0.00013250938,0.00013972654,0.00000613021,0.000022966764,0.000001213522,0.00020697284],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983961,0.00020843255,0.00031539865,0.00038351773,0.00048079714,0.00021574472],"domain_scores_gemma":[0.9987802,0.00044741304,0.00012662621,0.000040779258,0.0005228365,0.00008217676],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00047356353,0.00014007432,0.0001764592,0.00004005923,0.00024268331,0.00011081278,0.00018060551,0.000103061975,0.0017507047],"category_scores_gemma":[0.00078108115,0.000068258596,0.00007927768,0.00044207455,0.000063669555,0.00010824569,0.00003147761,0.00024017281,0.00052485307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007296651,0.00013872964,0.0009976766,0.000003575128,0.0000146382745,0.0000015039702,0.000059044214,0.0038481453,0.3745762,0.034728065,6.5234065e-7,0.58555883],"study_design_scores_gemma":[0.000027743154,0.00025690705,0.022527015,0.0000268131,0.000011881732,0.0000027712779,0.0005543128,0.92007077,0.033141285,0.022569435,0.0005324415,0.0002786349],"about_ca_topic_score_codex":0.00011492256,"about_ca_topic_score_gemma":0.0002896711,"teacher_disagreement_score":0.91622263,"about_ca_system_score_codex":0.000058146004,"about_ca_system_score_gemma":0.00001367812,"threshold_uncertainty_score":0.99916184},"labels":[],"label_agreement":null},{"id":"W3012962091","doi":"","title":"Simple Continual Learning Strategies for Safer Classifers.","year":2020,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"SAFER; Simple (philosophy); Computer science; Risk analysis (engineering); Computer security; Business","score_opus":0.1673988167164265,"score_gpt":0.3643801908165334,"score_spread":0.19698137410010688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012962091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008083271,0.000005056265,0.98177856,0.0067497753,0.000054633834,0.00028323906,0.000014121055,0.0003285565,0.00997774],"genre_scores_gemma":[0.98604715,0.0000064522255,0.012047206,0.0014823398,0.00015527157,0.00013054471,0.000013612433,0.000008091204,0.00010934709],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986837,0.000037991522,0.00031412745,0.00041819442,0.00034660893,0.0001993629],"domain_scores_gemma":[0.99885464,0.0001900123,0.00011838271,0.00012275597,0.0006085719,0.00010565751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020700868,0.00013231099,0.0001342027,0.000070007,0.00028294153,0.00035185253,0.0005408273,0.000078473386,0.00014100353],"category_scores_gemma":[0.00022550797,0.00013387534,0.00007614413,0.00034251195,0.000072481154,0.00035860107,0.00006846457,0.00022325442,0.00019166361],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018685247,0.00002644699,0.000007240275,0.0000035665914,0.0000046266696,3.1902127e-7,0.00019909514,0.0012859685,0.0032600958,0.9405762,0.0005594362,0.054058306],"study_design_scores_gemma":[0.000024301728,0.00027880503,0.00007698972,0.000005541066,0.0000023066602,8.408826e-7,0.00048337283,0.5454111,0.03008191,0.4071272,0.016325174,0.00018242415],"about_ca_topic_score_codex":0.00000838351,"about_ca_topic_score_gemma":0.000009462474,"teacher_disagreement_score":0.9852388,"about_ca_system_score_codex":0.00003901788,"about_ca_system_score_gemma":0.0002679172,"threshold_uncertainty_score":0.54592776},"labels":[],"label_agreement":null},{"id":"W3013800767","doi":"","title":"Anticipatory Thinking: A Metacognitive Capability","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Operationalization; Metacognition; Cognition; Process (computing); Thinking processes; Cognitive architecture; Computer science; Plan (archaeology); Systems thinking; Cognitive psychology; Psychology; Cognitive science; Knowledge management; Process management; Management science; Artificial intelligence; Engineering; Mathematics education; Epistemology","score_opus":0.48646036549942356,"score_gpt":0.4847353956811919,"score_spread":0.0017249698182316697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013800767","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.50345975,0.00009859061,0.13263372,0.0018423618,0.0027079973,0.0010893734,0.000100292855,0.0001666573,0.35790128],"genre_scores_gemma":[0.99666214,0.0000011167793,0.00068962015,0.0007608986,0.00014918641,0.000020042798,0.0000027481735,0.000011241302,0.0017030061],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923413,0.00044501876,0.0012541357,0.0009788855,0.0046405075,0.00034015044],"domain_scores_gemma":[0.9912742,0.0039978568,0.00042200662,0.00060169917,0.0035486396,0.000155625],"candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.005991595,0.00023651542,0.00045309207,0.0005239633,0.0002674534,0.00069966086,0.0011411951,0.00012473339,0.009837075],"category_scores_gemma":[0.0084868185,0.00018218081,0.00021438858,0.0010357719,0.00017541231,0.00041029268,0.00019289964,0.00034243826,0.016294746],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008575141,0.00011052304,0.00087779213,0.0000023777334,0.0000117235695,0.0000034121565,0.0005719051,0.00078605255,0.0009896694,0.96026397,0.00050575467,0.0357911],"study_design_scores_gemma":[0.00003439872,0.0001098258,0.005122252,0.000038663697,0.000003855907,0.0000039808465,0.0017948867,0.055509433,0.0024976884,0.9325,0.002153973,0.00023101702],"about_ca_topic_score_codex":0.000049370676,"about_ca_topic_score_gemma":0.000095842,"teacher_disagreement_score":0.49320242,"about_ca_system_score_codex":0.000117709045,"about_ca_system_score_gemma":0.00043103442,"threshold_uncertainty_score":0.9998651},"labels":[],"label_agreement":null},{"id":"W3013856963","doi":"","title":"PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML.","year":2020,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Perception; Computer science; Psychology; Neuroscience","score_opus":0.11260758439789258,"score_gpt":0.34128833553054494,"score_spread":0.22868075113265235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013856963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065587284,0.000003533793,0.9577022,0.025264202,0.0002191712,0.00034371586,0.00004148065,0.00029255412,0.009574379],"genre_scores_gemma":[0.9808699,0.000004668484,0.0159743,0.002804732,0.00021918215,0.000015312338,0.00002581229,0.000016662625,0.00006944823],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960615,0.0004732637,0.0005279413,0.0010589722,0.0014683789,0.00040994308],"domain_scores_gemma":[0.9970314,0.0005871727,0.0002066535,0.00039463706,0.0015061215,0.00027402013],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009410538,0.00032745083,0.00033140794,0.00013275197,0.0004012662,0.00028403965,0.0010995186,0.00014258214,0.00034348096],"category_scores_gemma":[0.002249592,0.00028846887,0.000091695336,0.0008956552,0.00034845292,0.0005798494,0.00031590345,0.00076512154,0.00039358763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008832268,0.00008691153,0.00004009608,0.000008851131,0.000026516664,0.000040404502,0.0034783308,0.14353408,0.00044366825,0.7592189,0.00009078095,0.09214829],"study_design_scores_gemma":[0.00009196299,0.00054145785,0.00103688,0.00005197293,0.000007738397,0.000011381325,0.0016434395,0.9658916,0.004501927,0.025180101,0.00057361304,0.0004679377],"about_ca_topic_score_codex":0.00009152474,"about_ca_topic_score_gemma":0.00008443227,"teacher_disagreement_score":0.9743112,"about_ca_system_score_codex":0.0002908225,"about_ca_system_score_gemma":0.0012562694,"threshold_uncertainty_score":0.9999567},"labels":[],"label_agreement":null},{"id":"W3013916032","doi":"","title":"Avoiding Surprise: Augmenting Anticipatory Thinking with Scenario Explorer.","year":2019,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Education and Critical Thinking Development","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Surprise; Computer science; Astrobiology; Psychology; Physics; Communication","score_opus":0.19097838440392859,"score_gpt":0.3969890593943407,"score_spread":0.20601067499041212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013916032","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.60729676,0.000047338992,0.023402661,0.016553879,0.0020215411,0.000845075,0.0000059092645,0.00036540226,0.34946144],"genre_scores_gemma":[0.99571687,0.000030301058,0.0017411456,0.0010987996,0.00019582958,0.000026405993,0.000008181657,0.000013582023,0.0011689042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99701726,0.00029124337,0.0003442108,0.00039801345,0.0015381842,0.00041110572],"domain_scores_gemma":[0.9982289,0.0005505866,0.00011595296,0.0001265423,0.0008282988,0.00014971287],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0022680203,0.00015404724,0.00016273481,0.00015918401,0.0011419876,0.0003700244,0.00038666985,0.00009911636,0.0025358365],"category_scores_gemma":[0.0010193931,0.0001446587,0.000044991382,0.0004155665,0.00024312927,0.00031199146,0.000041022555,0.0004757518,0.0014124361],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027125976,0.00010608078,0.0011067572,0.000008224296,0.000012097585,0.0000031423153,0.018722009,0.00012737629,0.00024073712,0.9675711,0.000055396147,0.012019988],"study_design_scores_gemma":[0.00011086479,0.00017677338,0.0023721287,0.0008331782,0.000019943143,0.000004487889,0.09538372,0.003840592,0.013980418,0.8709105,0.01138644,0.0009809129],"about_ca_topic_score_codex":0.00011275614,"about_ca_topic_score_gemma":0.00019830841,"teacher_disagreement_score":0.3884201,"about_ca_system_score_codex":0.00031197994,"about_ca_system_score_gemma":0.0011534046,"threshold_uncertainty_score":0.9993651},"labels":[],"label_agreement":null},{"id":"W3037416863","doi":"","title":"Detecting Disclosure and Support via Deep Multi-Task Learning.","year":2020,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Access Control and Trust","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Task (project management); Deep learning; Artificial intelligence; Multi-task learning; Machine learning; Engineering","score_opus":0.14437708979097558,"score_gpt":0.3714222994364682,"score_spread":0.22704520964549263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037416863","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07813336,0.0002062311,0.7237778,0.044601675,0.00091332645,0.0013532544,0.000044578974,0.00073172065,0.15023805],"genre_scores_gemma":[0.99809784,0.000038445163,0.00042276568,0.0008376161,0.00034839692,0.000017728424,0.000007044386,0.0000098244855,0.00022033718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982256,0.00016802682,0.0002914744,0.0003742277,0.0006602902,0.00028035365],"domain_scores_gemma":[0.99886745,0.00027392156,0.00012879143,0.000053704553,0.00045190682,0.00022421381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005453584,0.00013783164,0.00016099488,0.00006385946,0.00079692947,0.00023796348,0.00026031418,0.00011372959,0.00091013545],"category_scores_gemma":[0.002086284,0.00013542983,0.00004980541,0.00029311056,0.00029745535,0.0002730098,0.00005320782,0.00037098615,0.00038185556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001029366,0.00006976962,0.002776926,0.000010257829,0.000023085173,0.000010237908,0.011342436,0.00097160955,0.0013541997,0.45727822,0.000020154963,0.52604014],"study_design_scores_gemma":[0.00032796556,0.00086160883,0.007571237,0.00006523553,0.000061028688,0.0000070874303,0.024404103,0.79155296,0.007015948,0.14429359,0.022383392,0.0014558551],"about_ca_topic_score_codex":0.0002639272,"about_ca_topic_score_gemma":0.0013401082,"teacher_disagreement_score":0.9199645,"about_ca_system_score_codex":0.00006296542,"about_ca_system_score_gemma":0.00027611436,"threshold_uncertainty_score":0.9965345},"labels":[],"label_agreement":null},{"id":"W3133934064","doi":"","title":"Classification Confidence Scores with Point-wise Guarantees.","year":2021,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Point (geometry); Confidence interval; Artificial intelligence; Machine learning; Statistics; Mathematics","score_opus":0.09752630771783521,"score_gpt":0.3380556511823711,"score_spread":0.24052934346453586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133934064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004412466,0.000053564883,0.96108276,0.0111112585,0.00034031627,0.00014558581,0.000009460504,0.00021401753,0.02263058],"genre_scores_gemma":[0.98030406,0.000032348875,0.017801028,0.0009587807,0.0001380048,0.000028229515,0.00002089278,0.000010650636,0.0007060227],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973905,0.00016118301,0.00038982494,0.0007188813,0.0010410718,0.0002985493],"domain_scores_gemma":[0.99741143,0.00031078394,0.0001744965,0.00044896145,0.00153549,0.0001188606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048720348,0.00021165427,0.00019401246,0.00016300548,0.0002649906,0.0005149736,0.00075813703,0.000082653205,0.00039036968],"category_scores_gemma":[0.000605143,0.0001863074,0.000060370334,0.000743362,0.00016505117,0.00047664702,0.00009478304,0.00039124908,0.00063773204],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000022316275,0.00011715322,0.00008152518,0.0000062557015,0.000010275532,0.000029080988,0.0002222166,0.0011972636,0.002507705,0.921377,0.00013144394,0.07429773],"study_design_scores_gemma":[0.00006564823,0.00020617947,0.002459525,0.00014195668,0.0000057520774,0.00006424584,0.0002759195,0.653001,0.04570741,0.29695266,0.0007142028,0.00040547492],"about_ca_topic_score_codex":0.000036577087,"about_ca_topic_score_gemma":0.00007917155,"teacher_disagreement_score":0.9758916,"about_ca_system_score_codex":0.00007224597,"about_ca_system_score_gemma":0.00076391647,"threshold_uncertainty_score":0.8196968},"labels":[],"label_agreement":null},{"id":"W3137772419","doi":"10.5555/777092.777247","title":"An extended alternating-offers bargaining protocol for automated negotiation in multi-agent systems","year":2002,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Negotiation; Protocol (science); Context (archaeology); Computer science; Multi-agent system; Artificial intelligence","score_opus":0.327525288131118,"score_gpt":0.428275465677662,"score_spread":0.10075017754654403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137772419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019044499,0.0000039702586,0.96015507,0.00030528882,0.00052424154,0.035437316,0.000016481914,0.0004873316,0.0011658776],"genre_scores_gemma":[0.94538516,0.0000012546843,0.011054056,0.00013607083,0.00013071318,0.04317178,0.000018116796,0.000017451926,0.000085424654],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968937,0.0002541508,0.0009049475,0.0007190506,0.00084007706,0.00038806995],"domain_scores_gemma":[0.9981326,0.0002284826,0.00040991037,0.00033646298,0.00075815275,0.00013440267],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010245763,0.00025710798,0.00025172168,0.00041936085,0.00023237792,0.0005280186,0.0007571325,0.00014353529,0.00008120522],"category_scores_gemma":[0.00040353442,0.00025467985,0.00006861291,0.0004694427,0.000037405447,0.00086967094,0.000043033255,0.00018374807,0.00017614965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006047803,0.0011214697,0.0002709822,0.00007324322,0.00001918715,0.0000068944328,0.0021330453,0.12522624,0.007101281,0.82860774,0.00020482593,0.035174616],"study_design_scores_gemma":[0.00026188398,0.00023734946,0.0012725466,0.00014026313,0.0000016366369,0.000002875347,0.00015123843,0.9892394,0.0044636577,0.0038164577,0.00013276741,0.00027991744],"about_ca_topic_score_codex":0.00013220504,"about_ca_topic_score_gemma":0.00009627972,"teacher_disagreement_score":0.949101,"about_ca_system_score_codex":0.0003043477,"about_ca_system_score_gemma":0.00011700028,"threshold_uncertainty_score":0.9999905},"labels":[],"label_agreement":null},{"id":"W3210125627","doi":"","title":"Learning High-Dimensional Hilbert-Valued Functions With Deep Neural Networks From Limited Data.","year":2021,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Simon Fraser University","funders":"","keywords":"Computer science; Artificial neural network; Deep learning; Artificial intelligence; Deep neural networks","score_opus":0.24666348722630996,"score_gpt":0.35980335415809317,"score_spread":0.11313986693178321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210125627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014746769,0.00008058197,0.98042613,0.0019384413,0.000888236,0.00014120567,0.00009927128,0.00011401199,0.0015653594],"genre_scores_gemma":[0.9898947,0.0000051408647,0.0077874027,0.0004770001,0.00035822127,0.000015078395,0.00062722253,0.000018593844,0.0008166651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99546134,0.00033586612,0.0007106238,0.0010490371,0.0021087779,0.0003343716],"domain_scores_gemma":[0.9937215,0.002889658,0.00021117303,0.00069007353,0.0023037451,0.00018386549],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012135438,0.00025117383,0.00031454721,0.00019546047,0.0004213651,0.00046213434,0.0008680082,0.00014985132,0.0027921053],"category_scores_gemma":[0.007283509,0.00019480793,0.00006191251,0.0011414198,0.00019701677,0.0003850324,0.00023619055,0.000619667,0.00061089493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013091235,0.000115025425,0.000106524676,8.0929823e-7,0.00003175896,0.000025161271,0.000055748413,0.85064024,0.00020658333,0.09713991,0.0005801134,0.050967213],"study_design_scores_gemma":[0.0000559677,0.000111391295,0.00111326,0.000024905381,0.000019261919,0.000010591509,0.00025535133,0.9433432,0.00036766625,0.054095335,0.0003579794,0.00024508513],"about_ca_topic_score_codex":0.00007646807,"about_ca_topic_score_gemma":0.00028749363,"teacher_disagreement_score":0.9751479,"about_ca_system_score_codex":0.000080367274,"about_ca_system_score_gemma":0.0004070514,"threshold_uncertainty_score":0.9981195},"labels":[],"label_agreement":null},{"id":"W3210385601","doi":"","title":"Model Reduction for the Material Point Method on Nonlinear Manifolds Using Deep Learning.","year":2021,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Fluid Dynamics Simulations and Interactions","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Reduction (mathematics); Nonlinear system; Point (geometry); Deep learning; Computer science; Multi point; Artificial intelligence; Mathematics; Applied mathematics; Physics; Geometry","score_opus":0.12854817963421628,"score_gpt":0.3685409075050492,"score_spread":0.23999272787083292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210385601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00932786,0.000011226767,0.98485863,0.00054223323,0.0009866646,0.00019199443,0.00007590608,0.00009218184,0.003913303],"genre_scores_gemma":[0.9577282,0.000025480853,0.041402895,0.00007567436,0.00039399957,0.000039002167,0.00009351495,0.000025259684,0.00021597467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899906,0.00004044386,0.00030629398,0.00022448972,0.00026692107,0.00016276962],"domain_scores_gemma":[0.9988875,0.00025223475,0.000045435867,0.00013409063,0.00064449763,0.000036253357],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025056562,0.00013632892,0.00010939965,0.000102636004,0.00030063928,0.00015940476,0.00011472407,0.00007076474,0.0002795382],"category_scores_gemma":[0.0002256188,0.00012415605,0.000081105645,0.00017743219,0.000028644037,0.00013775977,0.000016905658,0.00023125249,0.00004927735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035949743,0.000033816,2.819817e-7,0.0000044150984,0.000016749196,3.2954748e-7,0.000088635716,0.7425568,0.025455546,0.21702144,0.000020207966,0.014765794],"study_design_scores_gemma":[0.000021893984,0.00003418527,0.0000048795887,0.000017285254,0.000012728912,0.0000075374132,0.00023455442,0.91113,0.052205507,0.035839412,0.00036862437,0.00012335523],"about_ca_topic_score_codex":0.000014258831,"about_ca_topic_score_gemma":0.000037184378,"teacher_disagreement_score":0.9484003,"about_ca_system_score_codex":0.00019958934,"about_ca_system_score_gemma":0.000104205086,"threshold_uncertainty_score":0.50629365},"labels":[],"label_agreement":null},{"id":"W3210980152","doi":"","title":"A Deep Learning Algorithm for Piecewise Linear Interface Construction (PLIC).","year":2021,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Lattice Boltzmann Simulation Studies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computational fluid dynamics; Polyhedron; Bottleneck; Piecewise linear function; Position (finance); Computational complexity theory; Volume fraction; Algorithm; Computer science; Mathematics; Geometry; Physics; Mechanics","score_opus":0.09301818647450365,"score_gpt":0.3453158350330443,"score_spread":0.25229764855854064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210980152","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002715096,0.00015064378,0.98918617,0.00031686333,0.0006097925,0.000201637,0.000030939464,0.00026149407,0.00652736],"genre_scores_gemma":[0.955609,0.00008769759,0.0434808,0.00008582269,0.00028765612,0.00006577884,0.000051314753,0.000029031287,0.0003028612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986377,0.00003886589,0.00040153114,0.00030506603,0.0003839485,0.00023292133],"domain_scores_gemma":[0.9980688,0.00038129155,0.00006411525,0.00011035186,0.0013094087,0.00006603538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021148183,0.00018364414,0.00019739619,0.00011483393,0.00019663188,0.00009907497,0.00011831478,0.000102240396,0.00032404004],"category_scores_gemma":[0.0005214,0.00020513733,0.00007650669,0.00028483084,0.00009224178,0.00017263071,0.000034819142,0.0002668766,0.0002975588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018313847,0.000043417967,0.00001635155,0.000022165197,0.00006038596,0.0000023469493,0.00038288673,0.34270474,0.0024224548,0.1105666,0.00007297554,0.54368734],"study_design_scores_gemma":[0.000050610106,0.00004197374,0.00004014867,0.000032639564,0.000011581951,0.0000059510653,0.00085334695,0.91811806,0.049840093,0.027895419,0.002901926,0.00020822775],"about_ca_topic_score_codex":0.000002305914,"about_ca_topic_score_gemma":0.000024543182,"teacher_disagreement_score":0.9528939,"about_ca_system_score_codex":0.00013020771,"about_ca_system_score_gemma":0.00008849784,"threshold_uncertainty_score":0.8365258},"labels":[],"label_agreement":null},{"id":"W32549655","doi":"10.4103/iju.iju_84_20","title":"Solving a stochastic queueing design and control problem with constraint programming","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Queueing theory; Computer science; Mathematical optimization; Constraint programming; Constraint (computer-aided design); Stochastic programming; Decomposition; Benders' decomposition; Variety (cybernetics); Constraint satisfaction; Operations research; Mathematics; Artificial intelligence; Computer network","score_opus":0.06487848277850274,"score_gpt":0.3006697614736803,"score_spread":0.23579127869517758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W32549655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00065725454,0.000010225203,0.99582595,0.0010895905,0.000081218226,0.0005518762,0.0000016903432,0.00015378027,0.001628433],"genre_scores_gemma":[0.79350835,0.0000020558052,0.20617968,0.00023540476,0.000031141968,0.000023349108,0.0000014527432,0.0000063733246,0.0000122152915],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982493,0.00006993715,0.00037619504,0.00043305033,0.0005569634,0.00031454238],"domain_scores_gemma":[0.99835306,0.00057924306,0.0001607474,0.00012413267,0.0006434471,0.00013938037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011862201,0.00017688145,0.00015099924,0.00024303116,0.00026470542,0.00039234478,0.00022772004,0.00007241316,0.000047498197],"category_scores_gemma":[0.00021324761,0.00016025317,0.000025279856,0.00036846858,0.00022064062,0.00040067706,0.00003248174,0.00022095046,0.000024768487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005062202,0.00003612407,0.00003352103,0.0000040391774,0.000010616333,0.000006262332,0.00032232012,0.024079377,0.0005914552,0.60415447,0.0000017960407,0.3707094],"study_design_scores_gemma":[0.00013017174,0.00028954513,0.00042280596,0.00011616159,0.000007738885,0.000062567575,0.00035317952,0.9420835,0.0031777022,0.05301795,0.000025522091,0.00031318958],"about_ca_topic_score_codex":0.00001607535,"about_ca_topic_score_gemma":0.00007369498,"teacher_disagreement_score":0.9180041,"about_ca_system_score_codex":0.00009093934,"about_ca_system_score_gemma":0.0003586957,"threshold_uncertainty_score":0.6534934},"labels":[],"label_agreement":null},{"id":"W33587638","doi":"10.1021/acs.jpclett.1c00121","title":"Topological mapping with weak sensory data","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Science and Engineering Research Board; Vetenskapsrådet","keywords":"Vertex (graph theory); Computer science; Robot; Bounded function; Topology (electrical circuits); Heuristic; occam; Process (computing); Set (abstract data type); Theoretical computer science; Algorithm; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.24228405410845064,"score_gpt":0.3409676202144604,"score_spread":0.09868356610600978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W33587638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016047725,0.000017627133,0.93905073,0.00038954802,0.00023411594,0.0001279851,0.00002983331,0.00017889461,0.04392356],"genre_scores_gemma":[0.99475205,0.000021535669,0.004679598,0.00016646397,0.00016962345,0.00000224068,0.00009221833,0.000014316546,0.00010195511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869716,0.000020235102,0.00030274104,0.00027373896,0.0004678986,0.00023822185],"domain_scores_gemma":[0.99918544,0.00017667403,0.000036867554,0.00024056538,0.00027886344,0.00008156053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044824416,0.0001399954,0.00011948775,0.00013870903,0.000095661075,0.000071070506,0.00028755443,0.00009612299,0.0002585549],"category_scores_gemma":[0.00015109572,0.00012311932,0.000017628327,0.0002597713,0.000099469995,0.00012392129,0.000030222727,0.00020840243,0.0002048267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005102265,0.000059401365,0.00008495864,0.0000119791375,0.0000193165,0.000014178327,0.00008884891,0.22337559,0.0036546565,0.72465813,0.00019435491,0.047787555],"study_design_scores_gemma":[0.000029392557,0.00007082496,0.000667268,0.000039328246,0.0000047841995,0.000008067831,0.00047853924,0.9580509,0.017072557,0.021760741,0.0015540584,0.00026355564],"about_ca_topic_score_codex":0.000009498666,"about_ca_topic_score_gemma":0.00010482319,"teacher_disagreement_score":0.97870433,"about_ca_system_score_codex":0.00007107564,"about_ca_system_score_gemma":0.00005617203,"threshold_uncertainty_score":0.502066},"labels":[],"label_agreement":null},{"id":"W35529797","doi":"10.21037/tlcr-21-765","title":"A logical theory of coordination and joint ability","year":2007,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Computer science; Logical consequence; Joint (building); Situation calculus; Plan (archaeology); State (computer science); Logical framework; Artificial intelligence; Algorithm; Programming language; Engineering","score_opus":0.1509613474423319,"score_gpt":0.33999822856199413,"score_spread":0.18903688111966224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W35529797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044065367,0.000037618425,0.916885,0.00063639437,0.00019078818,0.00015008257,0.000003280892,0.00005682229,0.037974622],"genre_scores_gemma":[0.9957293,0.000007677598,0.0039615436,0.00014418321,0.000057917805,0.000005332707,0.0000022170475,0.0000029452779,0.00008884623],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984095,0.00010881349,0.00042935094,0.00036729986,0.00048856274,0.00019648038],"domain_scores_gemma":[0.9981329,0.0005952515,0.00015323889,0.00018031677,0.00084732025,0.00009097539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025653322,0.000121108154,0.0001707973,0.00015675189,0.00010964753,0.000075344076,0.0003232177,0.00009573934,0.00011810723],"category_scores_gemma":[0.00138194,0.00010178412,0.000052905532,0.00031740655,0.00026409357,0.00020520025,0.00010894165,0.00016416426,0.00006158263],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028964014,0.00012526882,0.0001076368,0.0000059034833,0.000003927663,0.0000014660164,0.00036595383,0.000018163091,0.0033829988,0.8482387,0.000014174459,0.14770685],"study_design_scores_gemma":[0.000026739519,0.00017211659,0.013862508,0.000018961708,0.000002171276,0.0000048028473,0.00013618542,0.028365118,0.04524888,0.9119624,0.00007328991,0.00012682981],"about_ca_topic_score_codex":0.00000925602,"about_ca_topic_score_gemma":0.000028103605,"teacher_disagreement_score":0.951664,"about_ca_system_score_codex":0.00006291333,"about_ca_system_score_gemma":0.00014668108,"threshold_uncertainty_score":0.4150636},"labels":[],"label_agreement":null},{"id":"W39392249","doi":"10.1111/sms.14740","title":"Automatic Generation of Memory Based Search Heuristics","year":2000,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Heuristics; Cardinality (data modeling); Heuristic; Computer science; Beam search; Incremental heuristic search; Space (punctuation); Ranking (information retrieval); Operator (biology); Table (database); Algorithm; Simple (philosophy); State (computer science); State space; Search algorithm; Theoretical computer science; Sequence (biology); Mathematics; Artificial intelligence; Mathematical optimization; Data mining","score_opus":0.20522953965088014,"score_gpt":0.35037923976499435,"score_spread":0.1451497001141142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W39392249","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04541674,0.000026518583,0.93626034,0.0023080267,0.00021997516,0.0002007324,0.00002349558,0.00014796144,0.015396187],"genre_scores_gemma":[0.969935,0.000006205623,0.029337674,0.0003855297,0.00008799462,0.000010401458,0.000023494486,0.0000061995547,0.00020748594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980589,0.00014987982,0.00043120582,0.00032488772,0.00082687323,0.00020822494],"domain_scores_gemma":[0.99864614,0.0003521394,0.00009304409,0.00026062032,0.00057098863,0.00007705713],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00076023425,0.00013213242,0.00014903866,0.00017635683,0.00016831483,0.00013485407,0.00057205977,0.00007938707,0.0015295082],"category_scores_gemma":[0.00019779029,0.00013242027,0.000050450915,0.00041656464,0.00008761519,0.00018977458,0.000024867477,0.00019630321,0.0004026132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013564402,0.00012280875,0.000017701626,0.000018558389,0.0000067328947,0.000003833517,0.00034740142,0.1019645,0.0021799076,0.34123856,0.00029238933,0.553794],"study_design_scores_gemma":[0.000021841674,0.0001140809,0.00008958926,0.000051013307,0.00000224328,0.0000015179952,0.000015730904,0.91702986,0.056770846,0.025706027,0.000069336274,0.0001279244],"about_ca_topic_score_codex":0.000037848287,"about_ca_topic_score_gemma":0.000011065779,"teacher_disagreement_score":0.9245183,"about_ca_system_score_codex":0.000055650304,"about_ca_system_score_gemma":0.00052739686,"threshold_uncertainty_score":0.9993832},"labels":[],"label_agreement":null},{"id":"W41899231","doi":"","title":"Conservative belief change","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Belief revision; Proposition; Context (archaeology); Interpretation (philosophy); Belief structure; State (computer science); Computer science; Set (abstract data type); Representation (politics); Mathematics; Artificial intelligence; Epistemology; Algorithm; Philosophy","score_opus":0.24965957017784085,"score_gpt":0.3577612197210079,"score_spread":0.10810164954316703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W41899231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005809327,0.000091390706,0.8549311,0.012515105,0.0010369367,0.00047471316,0.000018488916,0.0003416963,0.12478123],"genre_scores_gemma":[0.99154484,0.000028559163,0.005708378,0.002138247,0.00025732422,0.000071409755,0.0000077603045,0.000007611544,0.00023587488],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998083,0.000063411026,0.0003251352,0.00052154996,0.0006797668,0.00032713686],"domain_scores_gemma":[0.9983155,0.00018159978,0.00013047775,0.0002693395,0.0009682078,0.00013488976],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040834793,0.00020135495,0.00017784572,0.00017411252,0.00025296173,0.00023950181,0.000925825,0.00010647878,0.00017050553],"category_scores_gemma":[0.0004431064,0.00018468921,0.000074779564,0.00056506775,0.00017060529,0.00054759986,0.00013762582,0.00024718983,0.0020086756],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010789895,0.00016040566,0.000021482225,0.0000029432852,0.000007307164,0.0000078285875,0.0012794578,0.00012335257,0.00025199336,0.95034415,0.00007794398,0.04771235],"study_design_scores_gemma":[0.00005863163,0.00024100703,0.0012719927,0.000054037013,0.0000025248248,0.00000852674,0.00012341724,0.018948454,0.026331946,0.95136297,0.001291539,0.0003049663],"about_ca_topic_score_codex":0.00010366859,"about_ca_topic_score_gemma":0.00021305196,"teacher_disagreement_score":0.98573554,"about_ca_system_score_codex":0.00017623151,"about_ca_system_score_gemma":0.00049803185,"threshold_uncertainty_score":0.9987684},"labels":[],"label_agreement":null},{"id":"W585824775","doi":"","title":"Geometric ordering of concepts, logical disjunction, and learning by induction","year":2004,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Mereology; Computer science; Distributive property; Theoretical computer science; Convexity; Distributive lattice; Representation (politics); Boolean algebra; Closure (psychology); Artificial intelligence; Algorithm; Mathematics; Pure mathematics","score_opus":0.10093473530874908,"score_gpt":0.32903458272633357,"score_spread":0.2280998474175845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W585824775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021330414,0.000042492225,0.97450626,0.0011025786,0.00022497022,0.00010127531,0.000005352055,0.00006573808,0.0026209322],"genre_scores_gemma":[0.99351096,0.00006920581,0.006132955,0.00009491937,0.000047853813,0.000007677532,0.000018607474,0.0000035098572,0.00011431844],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987045,0.00004434597,0.00027274602,0.0003346607,0.0004961139,0.00014764522],"domain_scores_gemma":[0.9993235,0.00009555685,0.00012105039,0.000108370434,0.00029863155,0.00005290422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042469334,0.00010590136,0.0001156601,0.00024045275,0.00016213761,0.00016413724,0.00032851342,0.000052491778,0.000060779064],"category_scores_gemma":[0.00040630487,0.00010046694,0.000023895427,0.00077669293,0.00012247024,0.0005661846,0.00012307924,0.0002160041,0.000050060637],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064771352,0.00007635309,0.000037951937,0.0000049416003,0.0000064754045,7.478725e-7,0.00009647039,0.002625106,0.0012414809,0.7465611,0.000048086422,0.24929479],"study_design_scores_gemma":[0.00028149944,0.0011958898,0.004305908,0.00013712274,0.000016013028,0.000014167477,0.0011307831,0.18767221,0.09386558,0.70538294,0.005141131,0.0008567385],"about_ca_topic_score_codex":0.000045756544,"about_ca_topic_score_gemma":0.0000057307807,"teacher_disagreement_score":0.97218055,"about_ca_system_score_codex":0.000048663962,"about_ca_system_score_gemma":0.000060817798,"threshold_uncertainty_score":0.4096923},"labels":[],"label_agreement":null},{"id":"W83133245","doi":"","title":"Thresholding for making classifiers cost-sensitive","year":2006,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":143,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Thresholding; Computer science; Artificial intelligence; Balanced histogram thresholding; Pattern recognition (psychology); Machine learning; Sensitivity (control systems); Histogram; Image (mathematics); Engineering","score_opus":0.19450877797409752,"score_gpt":0.3871194660419463,"score_spread":0.19261068806784878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W83133245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021396086,0.000006200183,0.97063863,0.003659766,0.00029741594,0.0005790643,0.00007109621,0.00034193293,0.024191905],"genre_scores_gemma":[0.9190659,0.0000046395594,0.07944835,0.00086429337,0.00017985838,0.00020079856,0.00005654,0.000012420433,0.00016722806],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978239,0.00005808276,0.00048263915,0.00062507764,0.00065693917,0.0003533873],"domain_scores_gemma":[0.9977926,0.00044087094,0.00022915677,0.00036129123,0.0011240465,0.000052054922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064107415,0.00019822903,0.0001764227,0.00028106323,0.00030372545,0.00040311186,0.0008458019,0.0001194509,0.000031530304],"category_scores_gemma":[0.0004026912,0.00020681725,0.000081044716,0.00045763853,0.00014343778,0.0005680525,0.000094165094,0.00020224752,0.00013968014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000021530057,0.000060012964,0.000009546999,0.000003918525,0.000004168054,0.0000015234582,0.000043530574,0.00042839,0.0060445815,0.91925627,0.0014353784,0.07269118],"study_design_scores_gemma":[0.00002502334,0.00005540259,0.0002910609,0.000037774476,0.0000022175886,0.0000032809623,0.000056678084,0.2909472,0.1518375,0.5535893,0.0029301422,0.00022444954],"about_ca_topic_score_codex":0.000014871321,"about_ca_topic_score_gemma":0.00003227656,"teacher_disagreement_score":0.9188519,"about_ca_system_score_codex":0.00024299744,"about_ca_system_score_gemma":0.00026736603,"threshold_uncertainty_score":0.8433762},"labels":[],"label_agreement":null},{"id":"W980983867","doi":"","title":"Recognizing Blind Spot Check Activity with Car Drivers Based on Decision Tree Classifier Approach","year":2014,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Sleep and Work-Related Fatigue","field":"Psychology","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ","funders":"","keywords":"Blind spot; Decision tree; Computer science; Artificial intelligence; Vigilance (psychology); Classifier (UML); Machine learning; Pattern recognition (psychology); Psychology","score_opus":0.1722857300194718,"score_gpt":0.3626618372337513,"score_spread":0.19037610721427947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W980983867","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27141052,0.0000051154307,0.3696371,0.00087220763,0.00080877315,0.000499383,0.000023378996,0.00012688809,0.35661662],"genre_scores_gemma":[0.99616325,0.0000024772376,0.0027896932,0.0005186903,0.00019515707,0.000060026643,0.00003651233,0.000028720931,0.00020546607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973466,0.00024387451,0.00035479284,0.0007734685,0.0009042878,0.00037699923],"domain_scores_gemma":[0.99783283,0.00094055355,0.00019688255,0.00037300267,0.00049766595,0.00015904142],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007402555,0.0002972963,0.0002608701,0.00036034043,0.00024830535,0.00010543318,0.00036120036,0.00033668525,0.0012432592],"category_scores_gemma":[0.00042131453,0.00025493102,0.000093224065,0.0005585787,0.00021159007,0.00012570256,0.000022028533,0.0006966848,0.0012470844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028079236,0.0008010036,0.00046917665,0.0000025297309,0.0000553186,0.0000062410386,0.00039749383,0.009200808,0.00040924043,0.23303437,0.0003765485,0.7524393],"study_design_scores_gemma":[0.0026871252,0.004252409,0.021092316,0.001173983,0.00018739108,0.00001238267,0.0026460302,0.87482625,0.04765203,0.041003708,0.0017230461,0.0027433517],"about_ca_topic_score_codex":0.00004630635,"about_ca_topic_score_gemma":0.00012298257,"teacher_disagreement_score":0.86562544,"about_ca_system_score_codex":0.00014188362,"about_ca_system_score_gemma":0.00017542059,"threshold_uncertainty_score":0.9999903},"labels":[],"label_agreement":null}]}