{"meta":{"query_hash":"43635461e15e","filters":{"venue":"KI - Künstliche Intelligenz"},"cohort_total":8,"direct_labels_cover":0,"predictions_cover":8,"exported":8,"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/43635461e15e","api":"https://metacan.xera.ac/api/v1/cohort?venue=KI+-+K%C3%BCnstliche+Intelligenz"},"results":[{"id":"W1961849562","doi":"10.1007/s13218-015-0399-3","title":"Abductive Conjunctive Query Answering w.r.t. Ontologies","year":2015,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Semantic Web and Ontologies","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":"Concordia University","funders":"","keywords":"Conjunctive query; Computer science; Question answering; Abductive reasoning; Information retrieval; Query expansion; Perspective (graphical); Boolean conjunctive query; Query language; Feature (linguistics); Key (lock); Query optimization; Web search query; Web query classification; Search engine; Artificial intelligence; Relational database","score_opus":0.09401379178979191,"score_gpt":0.2892354825306391,"score_spread":0.19522169074084716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1961849562","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":[0.04826502,0.002307003,0.91089046,0.0021484778,0.002813013,0.00034957012,0.000003428317,0.0012603995,0.03196263],"genre_scores_gemma":[0.938817,0.00007987439,0.059094798,0.00080693647,0.00022570117,0.000042580705,0.0000028555744,0.00002221766,0.0009079853],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9977987,0.00012714951,0.00038375813,0.0006859345,0.00041433173,0.0005901502],"domain_scores_gemma":[0.99804467,0.00033960573,0.00014897843,0.00090114377,0.0003477175,0.00021788923],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055107224,0.0003186758,0.0004108889,0.0001824154,0.00012042682,0.00023123722,0.0014860682,0.00017728501,0.000019380695],"category_scores_gemma":[0.0007992435,0.00026910906,0.0001306921,0.00045391044,0.00025601912,0.00084211957,0.0005570345,0.0003065516,0.0005245553],"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":[0.00014689197,0.00037946424,0.0140565755,0.000052421903,0.00040577853,0.00044933666,0.022494007,0.0011809235,0.0017879537,0.77427626,0.031230284,0.1535401],"study_design_scores_gemma":[0.0020403417,0.0019016545,0.015798282,0.00023798725,0.00012781443,0.00077015633,0.03594299,0.030938093,0.23959883,0.50334805,0.16564326,0.0036525263],"about_ca_topic_score_codex":0.00057329756,"about_ca_topic_score_gemma":0.0002356554,"teacher_disagreement_score":0.89055204,"about_ca_system_score_codex":0.00018056756,"about_ca_system_score_gemma":0.00024612452,"threshold_uncertainty_score":0.9999761},"labels":[],"label_agreement":null},{"id":"W2036143097","doi":"10.1007/s13218-011-0125-8","title":"Eine Zukunftsperspektive der Künstlichen Intelligenz am Beispiel menschlicher Kommunikation mit Rechnern","year":2011,"lang":"de","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Flexible and Reconfigurable Manufacturing Systems","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 Calgary","funders":"","keywords":"Humanities; Philosophy; Political science; Art","score_opus":0.05721502093420781,"score_gpt":0.2442313979307832,"score_spread":0.1870163769965754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036143097","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":[0.14148606,0.20980728,0.06274684,0.001691151,0.031818334,0.0076487144,0.000428236,0.006260902,0.53811246],"genre_scores_gemma":[0.9509104,0.0086558405,0.004763199,0.00071073516,0.0031970658,0.00031576335,0.00029330284,0.00091186544,0.030241841],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99134165,0.0005503101,0.0027505558,0.0018325034,0.001092609,0.0024323962],"domain_scores_gemma":[0.99425006,0.00048306718,0.0007709815,0.0029018908,0.0006864985,0.0009075121],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.001699187,0.0021147935,0.0019196954,0.0011753523,0.0005678365,0.00046226618,0.0022887613,0.002048629,0.0059134746],"category_scores_gemma":[0.00033593466,0.0021079443,0.0010111704,0.0013499961,0.0005512001,0.0008532108,0.00047816624,0.0028410943,0.017534522],"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":[0.0017752572,0.0051362286,0.0048913197,0.0072088423,0.05315657,0.0012148847,0.24537615,0.027595526,0.044128794,0.031052923,0.1526843,0.4257792],"study_design_scores_gemma":[0.0011055854,0.00072684314,0.0009598645,0.0019133177,0.0027127962,0.00012207487,0.0052594137,0.011333437,0.6300513,0.002481865,0.33929646,0.004037022],"about_ca_topic_score_codex":0.00496702,"about_ca_topic_score_gemma":0.0005146218,"teacher_disagreement_score":0.80942434,"about_ca_system_score_codex":0.001186058,"about_ca_system_score_gemma":0.00027013005,"threshold_uncertainty_score":0.9994594},"labels":[],"label_agreement":null},{"id":"W2081075046","doi":"10.1007/s13218-015-0355-2","title":"Is it Research or is it Spying? Thinking-Through Ethics in Big Data AI and Other Knowledge Sciences","year":2015,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":16,"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":"Espionage; Action (physics); Big data; Digital humanities; Engineering ethics; Sociology; Position (finance); Epistemology; Position paper; Data science; Computer science; Political science; Law; Engineering; Library science; World Wide Web; Philosophy","score_opus":0.8794542601558473,"score_gpt":0.6416509481292203,"score_spread":0.23780331202662697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081075046","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":[0.027576191,0.0034193797,0.0005138778,0.4478033,0.0015642477,0.00079322106,0.00010630715,0.00010807858,0.5181154],"genre_scores_gemma":[0.8954379,0.005354595,0.0015657232,0.07323033,0.0020966027,0.00002035779,0.000007930182,0.00006958648,0.02221696],"study_design_codex":"qualitative","study_design_gemma":"not_applicable","domain_scores_codex":[0.99402076,0.0013640551,0.0005238487,0.0008814637,0.002111265,0.0010986099],"domain_scores_gemma":[0.99458045,0.0029599504,0.00013143728,0.00077124167,0.0011251167,0.0004317775],"candidate_categories":["metaresearch","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.03052322,0.00024987932,0.00037163403,0.00028676435,0.0020214359,0.001344406,0.0024884646,0.00085682655,0.0004924023],"category_scores_gemma":[0.016084068,0.00020781907,0.000058033565,0.0020460424,0.0041073463,0.0011704174,0.001116549,0.0024655333,0.00044993576],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003175935,0.00014706934,0.003494573,0.000041083065,0.000030070278,0.000012097131,0.69716877,0.0000018125307,0.000010102556,0.06050548,0.23310383,0.005453373],"study_design_scores_gemma":[0.00017405032,0.00014628052,0.000044314034,0.00020538262,0.000010974864,0.000001776864,0.10108951,0.00019227751,0.00031384084,0.110305496,0.7872268,0.00028930942],"about_ca_topic_score_codex":0.063737124,"about_ca_topic_score_gemma":0.16028136,"teacher_disagreement_score":0.86786175,"about_ca_system_score_codex":0.0002644664,"about_ca_system_score_gemma":0.0048646554,"threshold_uncertainty_score":0.9998358},"labels":[],"label_agreement":null},{"id":"W2114336530","doi":"10.1007/s13218-010-0081-8","title":"A GGP Feature Learning Algorithm","year":2011,"lang":"de","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":16,"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":"Feature (linguistics); Computer science; Artificial intelligence; Simple (philosophy); Algorithm; Machine learning; Quality (philosophy)","score_opus":0.0644535964446665,"score_gpt":0.28788759482533405,"score_spread":0.22343399838066755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114336530","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":[0.0018026849,0.017105283,0.933032,0.001372726,0.011854655,0.0009765996,0.000034440658,0.0012700442,0.03255158],"genre_scores_gemma":[0.4531605,0.0075600524,0.446201,0.004263278,0.008394563,0.00025130017,0.00009227727,0.0006685493,0.07940847],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.992534,0.00064111035,0.0013616835,0.002125003,0.0012500216,0.0020882252],"domain_scores_gemma":[0.9950098,0.00052170426,0.0007176487,0.0021690452,0.0007670256,0.00081478764],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014533425,0.0011688503,0.0009983844,0.00057938776,0.0007949644,0.0008411304,0.0043395828,0.0010221661,0.0028538506],"category_scores_gemma":[0.00077017874,0.0011805899,0.000692728,0.0019683454,0.0008183851,0.0011793511,0.0015514104,0.0028546255,0.031112075],"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":[0.00005469307,0.00056897546,0.0014421677,0.000072647505,0.00047388478,0.00058976526,0.03325447,0.0001859625,0.0005101577,0.019628128,0.019347752,0.9238714],"study_design_scores_gemma":[0.00017221896,0.0010586253,0.0006410039,0.00043818326,0.0002792894,0.00015551051,0.002894315,0.18250343,0.148504,0.014779334,0.6462194,0.0023547225],"about_ca_topic_score_codex":0.000964825,"about_ca_topic_score_gemma":0.00010584873,"teacher_disagreement_score":0.92151666,"about_ca_system_score_codex":0.00031926183,"about_ca_system_score_gemma":0.0004759273,"threshold_uncertainty_score":0.99944586},"labels":[],"label_agreement":null},{"id":"W2497286804","doi":"10.1007/s13218-016-0450-z","title":"Full-Body Motion Planning for Humanoid Robots using Rapidly Exploring Random Trees","year":2016,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":8,"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; Robot; Motion (physics); Computer science; Motion planning; Obstacle; Context (archaeology); Artificial intelligence; Planner; Computer vision; Tree (set theory); Random tree; Degrees of freedom (physics and chemistry); Simulation; Mathematics; Geography","score_opus":0.1430913394168787,"score_gpt":0.32465077586430086,"score_spread":0.18155943644742215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2497286804","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":[0.037717402,0.0004415793,0.958338,0.00033417006,0.0017914289,0.0005393774,0.0000074236423,0.0005667733,0.0002637991],"genre_scores_gemma":[0.5488717,0.00004970504,0.44895434,0.00018378314,0.0010323006,0.00023022869,0.0000104524315,0.00009614011,0.00057132944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685335,0.00014862137,0.000708737,0.0009239655,0.00047771228,0.00088759814],"domain_scores_gemma":[0.9974821,0.0008668269,0.00030734058,0.00091049145,0.00020463397,0.00022859598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010121411,0.00043125078,0.0005116812,0.00038352795,0.0004087435,0.00026644074,0.0012960364,0.00017112256,0.000015282621],"category_scores_gemma":[0.00043307777,0.00033655885,0.00024929346,0.0004275593,0.00009418482,0.0013282577,0.00027653817,0.0001995798,0.00013438026],"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":[0.00054519606,0.00037841738,0.0027079522,0.00016240023,0.00045965714,0.00019348486,0.0085079055,0.28288332,0.2796351,0.009594477,0.002444677,0.41248742],"study_design_scores_gemma":[0.0035708742,0.0005949348,0.0013749909,0.0009947551,0.00008862321,0.00019524671,0.00027583604,0.8123364,0.17202017,0.0039236546,0.0032174434,0.0014070595],"about_ca_topic_score_codex":0.000028986637,"about_ca_topic_score_gemma":0.000001982238,"teacher_disagreement_score":0.5294531,"about_ca_system_score_codex":0.00021680884,"about_ca_system_score_gemma":0.0001070106,"threshold_uncertainty_score":0.9999086},"labels":[],"label_agreement":null},{"id":"W3000965188","doi":"10.1007/s13218-020-00636-z","title":"Measuring the Quality of Explanations: The System Causability Scale (SCS)","year":2020,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":386,"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":"Karl-Franzens-Universität Graz; Medizinische Universität Graz; Austrian Science Fund","keywords":"Computer science; Variety (cybernetics); Relevance (law); Artificial intelligence; Transparency (behavior); Scale (ratio); Quality (philosophy); Domain (mathematical analysis); Usability; Traceability; Data science; Machine learning; Human–computer interaction; Software engineering; Mathematics","score_opus":0.16656570928716258,"score_gpt":0.32200664110880406,"score_spread":0.15544093182164148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000965188","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":[0.19441977,0.000883113,0.7683439,0.022811007,0.00081418635,0.0013575973,0.0000221003,0.00060690864,0.01074141],"genre_scores_gemma":[0.996641,0.000035532674,0.0020052814,0.0009480364,0.00019383596,0.000087299886,0.000001917706,0.000019288282,0.0000678265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9959581,0.0008141426,0.001147464,0.0006586618,0.0009327299,0.00048890075],"domain_scores_gemma":[0.99571013,0.0014489287,0.00040610498,0.0017292687,0.00052278617,0.00018278575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003230297,0.00027353424,0.00039754252,0.000061361025,0.00055894186,0.000246363,0.0033360317,0.00011170217,0.000042514817],"category_scores_gemma":[0.0013274595,0.00016971407,0.00024968607,0.0012952241,0.00037450052,0.00061405456,0.00061398844,0.00044358088,0.0003926704],"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":[0.00008677775,0.00029046452,0.008520423,0.0005902613,0.0002216484,0.00001840057,0.050161704,0.008339814,0.015910225,0.8729082,0.0026681705,0.04028395],"study_design_scores_gemma":[0.00018226697,0.00022024623,0.004096488,0.00016667182,0.00006212161,0.00004021449,0.028909573,0.15855365,0.7814516,0.008146344,0.017310897,0.0008599282],"about_ca_topic_score_codex":0.0011448527,"about_ca_topic_score_gemma":0.0005459733,"teacher_disagreement_score":0.8647618,"about_ca_system_score_codex":0.00017106206,"about_ca_system_score_gemma":0.00017202522,"threshold_uncertainty_score":0.6920739},"labels":[],"label_agreement":null},{"id":"W3023191393","doi":"10.1007/s13218-020-00659-6","title":"Interactive Transfer Learning in Relational Domains","year":2020,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Domain Adaptation and Few-Shot Learning","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 Alberta","funders":"Air Force Office of Scientific Research","keywords":"Transfer of learning; Computer science; Task (project management); Knowledge transfer; Inductive transfer; Salient; Process (computing); Domain (mathematical analysis); Transfer (computing); Space (punctuation); Artificial intelligence; Interface (matter); Transfer problem; Human–computer interaction; Machine learning; Knowledge management; Engineering; Robot learning; Mathematics","score_opus":0.04656369151163966,"score_gpt":0.27329853121486974,"score_spread":0.22673483970323008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023191393","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":[0.027911203,0.00010719502,0.9440012,0.005317739,0.00019881841,0.0001773846,0.0000011050997,0.00032120914,0.02196412],"genre_scores_gemma":[0.982676,0.000027477096,0.013972747,0.0026139428,0.00010686329,0.000016716087,0.000009231802,0.000021414166,0.00055555697],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981631,0.0002115902,0.00042072774,0.00051278475,0.00035864208,0.00033315903],"domain_scores_gemma":[0.999098,0.000372411,0.00006338014,0.00019471318,0.000078146106,0.00019334792],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00034092568,0.00019423258,0.00021698441,0.00016653688,0.00012149668,0.00014301701,0.00056981994,0.000103117396,0.0003017903],"category_scores_gemma":[0.00034226428,0.00020046918,0.00011059914,0.0007901138,0.00005258806,0.00068759,0.00012096889,0.00082817854,0.00079101697],"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":[0.00020809335,0.0002593316,0.015243623,0.00005076897,0.0001356162,0.00024420363,0.09233861,0.13353844,0.007996093,0.5380673,0.0013797118,0.21053825],"study_design_scores_gemma":[0.0011250084,0.00033231312,0.013062249,0.00008765913,0.000012199233,0.000034702083,0.0024960516,0.6610363,0.0068559176,0.003194481,0.31096938,0.0007937226],"about_ca_topic_score_codex":0.000024255898,"about_ca_topic_score_gemma":0.000022238944,"teacher_disagreement_score":0.95476484,"about_ca_system_score_codex":0.00009004628,"about_ca_system_score_gemma":0.000102287326,"threshold_uncertainty_score":0.999987},"labels":[],"label_agreement":null},{"id":"W3043748577","doi":"10.1007/s13218-020-00666-7","title":"Using Feature-Based Description Logics to avoid Duplicate Elimination in Object-Relational Query Languages","year":2020,"lang":"en","type":"article","venue":"KI - Künstliche Intelligenz","topic":"Semantic Web and Ontologies","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 Waterloo","funders":"","keywords":"Conjunctive query; Computer science; Schema (genetic algorithms); P; EXPTIME; Description logic; Theoretical computer science; Query language; Inference; Relational database; Feature (linguistics); Programming language; Boolean conjunctive query; Information retrieval; Time complexity; Algorithm; Artificial intelligence; Computational complexity theory; Sargable; PSPACE; Web search query; Linguistics","score_opus":0.13497759540689253,"score_gpt":0.3224946006857382,"score_spread":0.18751700527884568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043748577","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":[0.09213709,0.00039705107,0.89660126,0.009437168,0.00019972512,0.00026483389,0.0000029668856,0.00022904371,0.00073088484],"genre_scores_gemma":[0.9002236,0.00000971125,0.09617436,0.0033571203,0.00011618414,0.000015814196,0.000010558411,0.000012079833,0.000080544174],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851227,0.000099988385,0.00029157707,0.000484622,0.00030898477,0.00030253682],"domain_scores_gemma":[0.99917364,0.0001864083,0.000099400466,0.0003085358,0.000113495036,0.000118547316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030965472,0.00018794657,0.00020720712,0.00020616397,0.000087573666,0.00019193006,0.00055490405,0.00015271716,0.000014742153],"category_scores_gemma":[0.0004608433,0.00017795409,0.000074995645,0.000780084,0.000037531954,0.0004885354,0.00014558966,0.0002529405,0.00012255486],"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":[0.0003186394,0.0006320808,0.052840497,0.00035975422,0.00011010404,0.00036922193,0.017630104,0.23830386,0.18585676,0.35301182,0.009054065,0.14151308],"study_design_scores_gemma":[0.0005387919,0.00028127414,0.06479983,0.00016128762,0.000026086118,0.000027295831,0.00087609084,0.78169453,0.13990474,0.003757133,0.0070997076,0.0008332149],"about_ca_topic_score_codex":0.00015116013,"about_ca_topic_score_gemma":0.00014152836,"teacher_disagreement_score":0.8080865,"about_ca_system_score_codex":0.00014057191,"about_ca_system_score_gemma":0.000114145885,"threshold_uncertainty_score":0.7256757},"labels":[],"label_agreement":null}]}