{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":8,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":8,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"43635461e15e","filters":{"venue":"KI - Künstliche Intelligenz"}},"results":[{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Andreas Holzinger","is_ca":false},{"name":"André Carrington","is_ca":true},{"name":"Heimo Müller","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1665657092871626,"gpt":0.3220066411088041,"spread":0.1554409318216415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003230297,0.0002735342,0.0003975425,0.00006136102,0.0005589419,0.000246363,0.003336032,0.0001117022,0.00004251482],"category_scores_gemma":[0.001327459,0.0001697141,0.0002496861,0.001295224,0.0003745005,0.0006140546,0.0006139884,0.0004435809,0.0003926704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710621,"about_ca_system_score_gemma":0.0001720252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144853,"about_ca_topic_score_gemma":0.0005459733,"domain_scores_codex":[0.9959581,0.0008141426,0.001147464,0.0006586618,0.0009327299,0.0004889008],"domain_scores_gemma":[0.9957101,0.001448929,0.000406105,0.001729269,0.0005227862,0.0001827857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008677775,0.0002904645,0.008520423,0.0005902613,0.0002216484,0.00001840057,0.0501617,0.008339814,0.01591022,0.8729082,0.00266817,0.04028395],"study_design_scores_gemma":[0.000182267,0.0002202462,0.004096488,0.0001666718,0.00006212161,0.00004021449,0.02890957,0.1585536,0.7814516,0.008146344,0.0173109,0.0008599282],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1944198,0.000883113,0.7683439,0.02281101,0.0008141864,0.001357597,0.0000221003,0.0006069086,0.01074141],"genre_scores_gemma":[0.996641,0.00003553267,0.002005281,0.0009480364,0.000193836,0.00008729989,0.000001917706,0.00001928828,0.0000678265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8647618,"threshold_uncertainty_score":0.6920739,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Bettina Berendt","is_ca":false},{"name":"Marco Büchler","is_ca":false},{"name":"Geoffrey Rockwell","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.8794542601558473,"gpt":0.6416509481292203,"spread":0.237803312026627,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaresearch","sts"],"category_scores_codex":[0.03052322,0.0002498793,0.000371634,0.0002867643,0.002021436,0.001344406,0.002488465,0.0008568265,0.0004924023],"category_scores_gemma":[0.01608407,0.0002078191,0.00005803357,0.002046042,0.004107346,0.001170417,0.001116549,0.002465533,0.0004499358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002644664,"about_ca_system_score_gemma":0.004864655,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06373712,"about_ca_topic_score_gemma":0.1602814,"domain_scores_codex":[0.9940208,0.001364055,0.0005238487,0.0008814637,0.002111265,0.00109861],"domain_scores_gemma":[0.9945804,0.00295995,0.0001314373,0.0007712417,0.001125117,0.0004317775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003175935,0.0001470693,0.003494573,0.00004108307,0.00003007028,0.00001209713,0.6971688,0.000001812531,0.00001010256,0.06050548,0.2331038,0.005453373],"study_design_scores_gemma":[0.0001740503,0.0001462805,0.00004431403,0.0002053826,0.00001097486,0.000001776864,0.1010895,0.0001922775,0.0003138408,0.1103055,0.7872268,0.0002893094],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.02757619,0.00341938,0.0005138778,0.4478033,0.001564248,0.0007932211,0.0001063071,0.0001080786,0.5181154],"genre_scores_gemma":[0.8954379,0.005354595,0.001565723,0.07323033,0.002096603,0.00002035779,0.000007930182,0.00006958648,0.02221696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8678617,"threshold_uncertainty_score":0.9998358,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Simple (philosophy); Algorithm; Machine learning; Quality (philosophy)","authors":[{"name":"Nathan Sturtevant","is_ca":true},{"name":"Jonathan Schaeffer","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0644535964446665,"gpt":0.287887594825334,"spread":0.2234339983806675,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001453342,0.00116885,0.0009983844,0.0005793878,0.0007949644,0.0008411304,0.004339583,0.001022166,0.002853851],"category_scores_gemma":[0.0007701787,0.00118059,0.000692728,0.001968345,0.0008183851,0.001179351,0.00155141,0.002854625,0.03111207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003192618,"about_ca_system_score_gemma":0.0004759273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000964825,"about_ca_topic_score_gemma":0.0001058487,"domain_scores_codex":[0.992534,0.0006411104,0.001361683,0.002125003,0.001250022,0.002088225],"domain_scores_gemma":[0.9950098,0.0005217043,0.0007176487,0.002169045,0.0007670256,0.0008147876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005469307,0.0005689755,0.001442168,0.0000726475,0.0004738848,0.0005897653,0.03325447,0.0001859625,0.0005101577,0.01962813,0.01934775,0.9238714],"study_design_scores_gemma":[0.000172219,0.001058625,0.0006410039,0.0004381833,0.0002792894,0.0001555105,0.002894315,0.1825034,0.148504,0.01477933,0.6462194,0.002354722],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001802685,0.01710528,0.933032,0.001372726,0.01185466,0.0009765996,0.00003444066,0.001270044,0.03255158],"genre_scores_gemma":[0.4531605,0.007560052,0.446201,0.004263278,0.008394563,0.0002513002,0.00009227727,0.0006685493,0.07940847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9215167,"threshold_uncertainty_score":0.9994459,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Jacky Baltes","is_ca":false},{"name":"Jonathan Bagot","is_ca":true},{"name":"Soroush Sadeghnejad","is_ca":false},{"name":"John Anderson","is_ca":true},{"name":"Chen-Hsien Hsu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1430913394168787,"gpt":0.3246507758643009,"spread":0.1815594364474221,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001012141,0.0004312508,0.0005116812,0.0003835279,0.0004087435,0.0002664407,0.001296036,0.0001711226,0.00001528262],"category_scores_gemma":[0.0004330778,0.0003365588,0.0002492935,0.0004275593,0.00009418482,0.001328258,0.0002765382,0.0001995798,0.0001343803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002168088,"about_ca_system_score_gemma":0.0001070106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002898664,"about_ca_topic_score_gemma":0.000001982238,"domain_scores_codex":[0.9968534,0.0001486214,0.000708737,0.0009239655,0.0004777123,0.0008875981],"domain_scores_gemma":[0.9974821,0.0008668269,0.0003073406,0.0009104914,0.000204634,0.000228596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005451961,0.0003784174,0.002707952,0.0001624002,0.0004596571,0.0001934849,0.008507906,0.2828833,0.2796351,0.009594477,0.002444677,0.4124874],"study_design_scores_gemma":[0.003570874,0.0005949348,0.001374991,0.0009947551,0.00008862321,0.0001952467,0.000275836,0.8123364,0.1720202,0.003923655,0.003217443,0.001407059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0377174,0.0004415793,0.958338,0.0003341701,0.001791429,0.0005393774,0.000007423642,0.0005667733,0.0002637991],"genre_scores_gemma":[0.5488717,0.00004970504,0.4489543,0.0001837831,0.001032301,0.0002302287,0.00001045243,0.00009614011,0.0005713294],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5294531,"threshold_uncertainty_score":0.9999086,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Raksha Kumaraswamy","is_ca":true},{"name":"Nandini Ramanan","is_ca":false},{"name":"Phillip Odom","is_ca":false},{"name":"Sriraam Natarajan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04656369151163966,"gpt":0.2732985312148697,"spread":0.2267348397032301,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003409257,0.0001942326,0.0002169844,0.0001665369,0.0001214967,0.000143017,0.0005698199,0.0001031174,0.0003017903],"category_scores_gemma":[0.0003422643,0.0002004692,0.0001105991,0.0007901138,0.00005258806,0.00068759,0.0001209689,0.0008281785,0.000791017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009004628,"about_ca_system_score_gemma":0.0001022873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000242559,"about_ca_topic_score_gemma":0.00002223894,"domain_scores_codex":[0.9981631,0.0002115902,0.0004207277,0.0005127848,0.0003586421,0.000333159],"domain_scores_gemma":[0.999098,0.000372411,0.00006338014,0.0001947132,0.00007814611,0.0001933479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002080933,0.0002593316,0.01524362,0.00005076897,0.0001356162,0.0002442036,0.09233861,0.1335384,0.007996093,0.5380673,0.001379712,0.2105383],"study_design_scores_gemma":[0.001125008,0.0003323131,0.01306225,0.00008765913,0.00001219923,0.00003470208,0.002496052,0.6610363,0.006855918,0.003194481,0.3109694,0.0007937226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0279112,0.000107195,0.9440012,0.005317739,0.0001988184,0.0001773846,0.0000011051,0.0003212091,0.02196412],"genre_scores_gemma":[0.982676,0.0000274771,0.01397275,0.002613943,0.0001068633,0.00001671609,0.000009231802,0.00002141417,0.000555557],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9547648,"threshold_uncertainty_score":0.999987,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"David Toman","is_ca":true},{"name":"Grant Weddell","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1349775954068925,"gpt":0.3224946006857382,"spread":0.1875170052788457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003096547,0.0001879466,0.0002072071,0.000206164,0.00008757367,0.0001919301,0.0005549041,0.0001527172,0.00001474215],"category_scores_gemma":[0.0004608433,0.0001779541,0.00007499565,0.000780084,0.00003753195,0.0004885354,0.0001455897,0.0002529405,0.0001225549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405719,"about_ca_system_score_gemma":0.0001141459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001511601,"about_ca_topic_score_gemma":0.0001415284,"domain_scores_codex":[0.9985123,0.00009998839,0.0002915771,0.000484622,0.0003089848,0.0003025368],"domain_scores_gemma":[0.9991736,0.0001864083,0.00009940047,0.0003085358,0.000113495,0.0001185473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003186394,0.0006320808,0.0528405,0.0003597542,0.000110104,0.0003692219,0.0176301,0.2383039,0.1858568,0.3530118,0.009054065,0.1415131],"study_design_scores_gemma":[0.0005387919,0.0002812741,0.06479983,0.0001612876,0.00002608612,0.00002729583,0.0008760908,0.7816945,0.1399047,0.003757133,0.007099708,0.0008332149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09213709,0.0003970511,0.8966013,0.009437168,0.0001997251,0.0002648339,0.000002966886,0.0002290437,0.0007308848],"genre_scores_gemma":[0.9002236,0.00000971125,0.09617436,0.00335712,0.0001161841,0.0000158142,0.00001055841,0.00001207983,0.00008054417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8080865,"threshold_uncertainty_score":0.7256757,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Humanities; Philosophy; Political science; Art","authors":[{"name":"Michael M. Richter","is_ca":true},{"name":"Sebastian von Mammen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05721502093420781,"gpt":0.2442313979307832,"spread":0.1870163769965754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.001699187,0.002114794,0.001919695,0.001175352,0.0005678365,0.0004622662,0.002288761,0.002048629,0.005913475],"category_scores_gemma":[0.0003359347,0.002107944,0.00101117,0.001349996,0.0005512001,0.0008532108,0.0004781662,0.002841094,0.01753452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001186058,"about_ca_system_score_gemma":0.0002701301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00496702,"about_ca_topic_score_gemma":0.0005146218,"domain_scores_codex":[0.9913417,0.0005503101,0.002750556,0.001832503,0.001092609,0.002432396],"domain_scores_gemma":[0.9942501,0.0004830672,0.0007709815,0.002901891,0.0006864985,0.0009075121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001775257,0.005136229,0.00489132,0.007208842,0.05315657,0.001214885,0.2453762,0.02759553,0.04412879,0.03105292,0.1526843,0.4257792],"study_design_scores_gemma":[0.001105585,0.0007268431,0.0009598645,0.001913318,0.002712796,0.0001220749,0.005259414,0.01133344,0.6300513,0.002481865,0.3392965,0.004037022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1414861,0.2098073,0.06274684,0.001691151,0.03181833,0.007648714,0.000428236,0.006260902,0.5381125],"genre_scores_gemma":[0.9509104,0.008655841,0.004763199,0.0007107352,0.003197066,0.0003157633,0.0002933028,0.0009118654,0.03024184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8094243,"threshold_uncertainty_score":0.9994594,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"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,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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","authors":[{"name":"Ralf Möller","is_ca":false},{"name":"Özgür Lütfü Özçep","is_ca":false},{"name":"Volker Haarslev","is_ca":true},{"name":"Anahita Nafissi","is_ca":false},{"name":"Michael Wessel","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09401379178979191,"gpt":0.2892354825306391,"spread":0.1952216907408472,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005510722,0.0003186758,0.0004108889,0.0001824154,0.0001204268,0.0002312372,0.001486068,0.000177285,0.0000193807],"category_scores_gemma":[0.0007992435,0.0002691091,0.0001306921,0.0004539104,0.0002560191,0.0008421196,0.0005570345,0.0003065516,0.0005245553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805676,"about_ca_system_score_gemma":0.0002461245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005732976,"about_ca_topic_score_gemma":0.0002356554,"domain_scores_codex":[0.9977987,0.0001271495,0.0003837581,0.0006859345,0.0004143317,0.0005901502],"domain_scores_gemma":[0.9980447,0.0003396057,0.0001489784,0.0009011438,0.0003477175,0.0002178892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000146892,0.0003794642,0.01405658,0.0000524219,0.0004057785,0.0004493367,0.02249401,0.001180924,0.001787954,0.7742763,0.03123028,0.1535401],"study_design_scores_gemma":[0.002040342,0.001901655,0.01579828,0.0002379872,0.0001278144,0.0007701563,0.03594299,0.03093809,0.2395988,0.5033481,0.1656433,0.003652526],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04826502,0.002307003,0.9108905,0.002148478,0.002813013,0.0003495701,0.000003428317,0.001260399,0.03196263],"genre_scores_gemma":[0.938817,0.00007987439,0.0590948,0.0008069365,0.0002257012,0.0000425807,0.000002855574,0.00002221766,0.0009079853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.890552,"threshold_uncertainty_score":0.9999761,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}