{"meta":{"query_hash":"b238baad8e58","filters":{"venue":"Artificial intelligence for engineering design analysis and manufacturing"},"cohort_total":60,"direct_labels_cover":0,"predictions_cover":60,"exported":60,"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/b238baad8e58","api":"https://metacan.xera.ac/api/v1/cohort?venue=Artificial+intelligence+for+engineering+design+analysis+and+manufacturing"},"results":[{"id":"W1966606939","doi":"10.1017/s0890060407070175","title":"Using language as related stimuli for concept generation","year":2007,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Verb; Computer science; Linguistics; Psychology; Reflexive verb; Modal verb; Natural language processing; Cognitive psychology; Artificial intelligence","score_opus":0.0832191625980587,"score_gpt":0.3391932518768602,"score_spread":0.2559740892788015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1966606939","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":[0.07797486,0.00033116792,0.92069364,0.000021399952,0.0003890694,0.00037785646,0.000006373686,0.00017385356,0.000031806823],"genre_scores_gemma":[0.9234895,0.000023135926,0.07611351,0.000023667448,0.00019495182,0.000025506426,0.000044030225,0.00003720041,0.000048504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989088,0.000013240001,0.00042019467,0.00023831891,0.00009794796,0.00032149654],"domain_scores_gemma":[0.9991562,0.00047605843,0.00005865706,0.0001500124,0.000046835954,0.00011228228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007432755,0.00018813238,0.00022694265,0.00037300593,0.00012615843,0.00011806987,0.000084279,0.00010117937,0.00005006709],"category_scores_gemma":[0.00013580643,0.00020452413,0.00013293696,0.0002724914,0.000017703793,0.00020952216,0.000008013902,0.0001021692,0.000005732678],"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":[0.00001717399,0.000012042888,0.0000016236239,0.000025258447,0.0003114945,0.0000014914248,0.0008393536,0.875541,0.068930306,0.001445613,0.00002033118,0.052854355],"study_design_scores_gemma":[0.000023261498,0.00002261752,0.000009769766,0.000004626953,0.00024535228,0.0000024061444,0.0001945081,0.550125,0.44841996,0.00032201497,0.00048136,0.00014913539],"about_ca_topic_score_codex":0.000048488335,"about_ca_topic_score_gemma":0.000018046581,"teacher_disagreement_score":0.84551466,"about_ca_system_score_codex":0.00006455187,"about_ca_system_score_gemma":0.000011385383,"threshold_uncertainty_score":0.83402514},"labels":[],"label_agreement":null},{"id":"W1971466919","doi":"10.1017/s0890060401154041","title":"Extracting information from free-text aircraft repair notes","year":2001,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Natural Language Processing 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":"National Research Council Canada","funders":"","keywords":"Computer science; Technician; Crew; Process (computing); Domain (mathematical analysis); Information extraction; Natural language; Expression (computer science); Action (physics); Information retrieval; Parsing; Natural language processing; Lexical analysis; Artificial intelligence; Programming language; Engineering","score_opus":0.02463548484768225,"score_gpt":0.2577376995914167,"score_spread":0.23310221474373444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971466919","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":[0.00792411,0.00038138576,0.99038357,0.00020290013,0.00010594072,0.00018037282,0.0000030834995,0.0008119503,0.000006685157],"genre_scores_gemma":[0.5216062,0.000027462498,0.47824323,0.00003943407,0.000047156314,0.0000195134,0.0000072744283,0.0000063798825,0.0000033117851],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987146,0.000019228366,0.00044306577,0.00033166347,0.00018823888,0.00030315112],"domain_scores_gemma":[0.998719,0.00055071537,0.00013433472,0.0004419848,0.00006736017,0.00008660154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044244202,0.00019947915,0.00024882797,0.00046327015,0.00016543525,0.00043909665,0.0005043637,0.00009344407,0.0000127519515],"category_scores_gemma":[0.00036816584,0.00019038079,0.00017674023,0.000443168,0.00001768052,0.001467912,0.0001422901,0.00015553755,0.000006621749],"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":[0.000040621722,0.000041810126,0.0001303343,0.000056397563,0.0005274259,0.000018043194,0.0015118027,0.11674082,0.009858976,0.01777571,0.00004805672,0.85325],"study_design_scores_gemma":[0.000013651826,0.000023033665,0.000059270897,0.000018270424,0.000085377884,0.000002638184,0.000029215067,0.59829515,0.37625596,0.024710061,0.0003201248,0.00018723145],"about_ca_topic_score_codex":0.00027076018,"about_ca_topic_score_gemma":0.000030569965,"teacher_disagreement_score":0.85306275,"about_ca_system_score_codex":0.00004083985,"about_ca_system_score_gemma":0.0000127074145,"threshold_uncertainty_score":0.7763503},"labels":[],"label_agreement":null},{"id":"W1998248566","doi":"10.1017/s0890060407000261","title":"Making sense of engineering design review activities","year":2007,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; New product development; Process (computing); Engineering design process; Set (abstract data type); Coding (social sciences); Product design; Product (mathematics); Knowledge management; Process management; Engineering","score_opus":0.07470916172897887,"score_gpt":0.3138687174825328,"score_spread":0.23915955575355394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998248566","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":[0.005342384,0.0028109034,0.99078995,0.000031306503,0.00028927944,0.00044381173,0.000004735543,0.00023591996,0.000051689833],"genre_scores_gemma":[0.90235054,0.0013754594,0.096009046,0.00003961357,0.00010365679,0.000039462197,0.00000624009,0.000054447766,0.000021515303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983201,0.000029595409,0.0007121396,0.00028337789,0.00019825783,0.0004565473],"domain_scores_gemma":[0.9980691,0.0013632167,0.000118518095,0.00027307015,0.00005391679,0.00012217186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017851543,0.0003094566,0.00052530883,0.0006606961,0.00007186931,0.00007084582,0.0001383631,0.00009892914,0.000050077677],"category_scores_gemma":[0.00022635105,0.00033167418,0.00021545753,0.0005530389,0.000025410736,0.00028576024,0.000021541568,0.00019532366,0.000004631829],"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":[0.00003848334,0.000027412914,0.000005012688,0.0010737984,0.0007190818,0.000006789083,0.00034742823,0.9178173,0.02291023,0.0014081936,0.00008527034,0.05556102],"study_design_scores_gemma":[0.000023061133,0.000042213545,0.00007799719,0.00029714056,0.0006758275,0.000010525331,0.00013065038,0.4191355,0.5773379,0.0001681074,0.001734047,0.00036699936],"about_ca_topic_score_codex":0.000011414227,"about_ca_topic_score_gemma":0.000004723137,"teacher_disagreement_score":0.8970082,"about_ca_system_score_codex":0.00006435223,"about_ca_system_score_gemma":0.000015858286,"threshold_uncertainty_score":0.9999135},"labels":[],"label_agreement":null},{"id":"W2014020629","doi":"10.1017/s0890060400145068","title":"SEED-Config: A case-based reasoning system for conceptual building design","year":2000,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Conceptual design; Adaptation (eye); Generative Design; Process (computing); Representation (politics); Task (project management); Case-based reasoning; Object (grammar); Knowledge representation and reasoning; Human–computer interaction; Decomposition; Software engineering; Artificial intelligence; Information retrieval; Systems engineering; Programming language; Engineering","score_opus":0.03746279150245888,"score_gpt":0.25072088736310894,"score_spread":0.21325809586065006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014020629","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":[0.013757487,0.0002222096,0.9847605,0.000060857223,0.00013853083,0.00062400626,0.000011969705,0.00041528474,0.000009119579],"genre_scores_gemma":[0.5969585,0.0000033703695,0.4027842,0.000023729548,0.000070105874,0.00011397291,0.00000642676,0.000020408077,0.000019285377],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978024,0.00007922914,0.0005848779,0.0007094435,0.00018461508,0.00063947664],"domain_scores_gemma":[0.9976552,0.0015499158,0.00013808359,0.00038837665,0.00007682488,0.00019161288],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014364402,0.00034632662,0.00048441277,0.00044620055,0.00052002916,0.00049228565,0.00040392225,0.00013206653,0.000015229881],"category_scores_gemma":[0.000089932735,0.0003500094,0.0002792599,0.00045238168,0.00004207336,0.0003194626,0.000029697185,0.00016007543,0.0000051359334],"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":[0.000051995263,0.000013872856,0.0000065411923,0.00007672626,0.0002457464,0.000054171895,0.000520404,0.9320501,0.0013055847,0.00787065,0.0000101278165,0.057794046],"study_design_scores_gemma":[0.00006210734,0.00012261377,0.0000041137155,0.000088891626,0.0002453061,0.00004428773,0.00012415985,0.7466462,0.25171593,0.00034859523,0.00026934917,0.00032841813],"about_ca_topic_score_codex":0.00012027084,"about_ca_topic_score_gemma":0.000006001187,"teacher_disagreement_score":0.58320105,"about_ca_system_score_codex":0.000084193925,"about_ca_system_score_gemma":0.00005180588,"threshold_uncertainty_score":0.9998952},"labels":[],"label_agreement":null},{"id":"W2016513411","doi":"10.1017/s0890060406060057","title":"Whither design space?","year":2006,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":145,"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":"Space (punctuation); Focus (optics); Action (physics); Computer science; Process (computing); Engineering design process; Design process; Human–computer interaction; Engineering; Mechanical engineering; Programming language; Work in process","score_opus":0.032512550455895045,"score_gpt":0.2493521164959183,"score_spread":0.21683956604002325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016513411","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":[0.00525661,0.00028763706,0.99324363,0.00008801485,0.00027569936,0.00030915198,0.0000036325785,0.00033953227,0.00019610519],"genre_scores_gemma":[0.9103956,0.000055060787,0.08899666,0.00001782672,0.000196352,0.00007687673,0.000010583569,0.00004861349,0.00020242223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880475,0.000030503354,0.0003802167,0.00028005973,0.00013144476,0.00037301588],"domain_scores_gemma":[0.99911404,0.0004889912,0.000048588015,0.00021444804,0.00003603842,0.00009791814],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052992074,0.0002534291,0.0002827731,0.0004498335,0.00010728209,0.00020429402,0.00013155755,0.00009167589,0.00010165457],"category_scores_gemma":[0.000042896543,0.00026074087,0.00014119792,0.00037146427,0.000021989046,0.0002349567,0.0000119706265,0.00012992974,0.00003215424],"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":[0.000015329275,0.000021933063,0.000008776899,0.000028946302,0.00026400894,0.0000018127902,0.00010297233,0.97098434,0.0070173345,0.0037261879,0.00037638194,0.017451998],"study_design_scores_gemma":[0.000020622863,0.000024929837,0.00015945722,0.0000074171116,0.0003180863,0.000002596495,0.00005284098,0.62112814,0.37057805,0.0035162922,0.0039069704,0.0002845952],"about_ca_topic_score_codex":0.00006600236,"about_ca_topic_score_gemma":0.000016042673,"teacher_disagreement_score":0.90513897,"about_ca_system_score_codex":0.000048244277,"about_ca_system_score_gemma":0.000011238647,"threshold_uncertainty_score":0.9999845},"labels":[],"label_agreement":null},{"id":"W2020363097","doi":"10.1017/s0890060414000080","title":"The mechanical transformation and environmentally conscious behavior","year":2014,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software portability; Fuse (electrical); Reuse; Cover (algebra); Product (mathematics); Computer science; Risk analysis (engineering); Transformation (genetics); Business; Engineering; Electrical engineering; Mechanical engineering; Chemistry","score_opus":0.021119660486046844,"score_gpt":0.24483926639277206,"score_spread":0.2237196059067252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020363097","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":[0.078681074,0.000019423593,0.9206615,0.0002459504,0.00009704701,0.00021168785,6.5975496e-7,0.00007647645,0.000006193793],"genre_scores_gemma":[0.9735802,0.000028865206,0.026252959,0.000027560027,0.000020116351,0.00007405964,0.0000019529516,0.0000062319555,0.000008101094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915683,0.000024994479,0.00028145648,0.0002417869,0.00009801799,0.00019692],"domain_scores_gemma":[0.9993896,0.00029937786,0.00006547422,0.00018936009,0.00001991642,0.000036295325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056265644,0.00012208839,0.00014040795,0.00019555102,0.00029284286,0.00020522508,0.00020599607,0.000060225044,0.0000026143396],"category_scores_gemma":[0.000041452586,0.00009822992,0.000059482463,0.00013725812,0.000055401837,0.00026394767,0.00004556957,0.000114491595,0.0000030774847],"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":[0.000016610355,0.000029630946,0.00001693854,0.000011609461,0.0002186527,0.0000011784346,0.00046576737,0.015409001,0.03468895,0.43772256,0.0000035636963,0.51141554],"study_design_scores_gemma":[0.000017007133,0.000069965274,0.00025199674,0.0000031763443,0.000086155895,0.0000048326583,0.000048021633,0.5637349,0.42837676,0.0069356333,0.00036881727,0.00010277673],"about_ca_topic_score_codex":0.000009417816,"about_ca_topic_score_gemma":0.000018653223,"teacher_disagreement_score":0.8948991,"about_ca_system_score_codex":0.000027334012,"about_ca_system_score_gemma":0.0000031941206,"threshold_uncertainty_score":0.40056998},"labels":[],"label_agreement":null},{"id":"W2024610583","doi":"10.1017/s0890060415000013","title":"Analogical thinking: An introduction in the context of design","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","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 Toronto","funders":"","keywords":"Content (measure theory); Context (archaeology); Computer science; Action (physics); World Wide Web; Multimedia; Mathematics; History","score_opus":0.09327727270580408,"score_gpt":0.2960551236818955,"score_spread":0.20277785097609144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024610583","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":[0.030823572,0.0001262425,0.9681733,0.00030557267,0.00019948481,0.000283026,0.0000015856436,0.00006977012,0.000017424836],"genre_scores_gemma":[0.97677654,0.000031880572,0.022930842,0.00003408826,0.00014997626,0.000045474782,0.000010854442,0.00001528859,0.0000050834037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890274,0.000105700754,0.0003968093,0.00020889571,0.00017414393,0.00021174025],"domain_scores_gemma":[0.9991105,0.00047530927,0.000055438446,0.00022131698,0.00005269581,0.000084743886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020238077,0.00015642372,0.0002518513,0.00038065188,0.000046837806,0.000104637744,0.00020265918,0.00006983873,0.000014789845],"category_scores_gemma":[0.00019419647,0.00012695286,0.00007370044,0.00044289848,0.000028437176,0.0002866475,0.000009296366,0.00015215974,0.0000025653312],"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":[0.00004738325,0.000050500694,0.000015635238,0.000019320141,0.00013333226,0.0000014326864,0.003527549,0.9647912,0.0015447537,0.008104494,0.00014087031,0.021623546],"study_design_scores_gemma":[0.00004467537,0.00015287289,0.00021481852,0.0000057714983,0.00024676102,0.000005560553,0.003585596,0.82512075,0.16379462,0.0047857044,0.0018261825,0.00021668513],"about_ca_topic_score_codex":0.000048529553,"about_ca_topic_score_gemma":0.00002725608,"teacher_disagreement_score":0.94595295,"about_ca_system_score_codex":0.000039140825,"about_ca_system_score_gemma":0.000015678012,"threshold_uncertainty_score":0.51769876},"labels":[],"label_agreement":null},{"id":"W2040747612","doi":"10.1017/s0890060411000230","title":"Considering multiscale scenes to elucidate problems encumbering three-dimensional intellection and navigation","year":2011,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Spatial Cognition and Navigation","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":"Autodesk (Canada)","funders":"","keywords":"Computer science; Focus (optics); Context (archaeology); Representation (politics); Human–computer interaction; Virtual machine; Data science","score_opus":0.0574752531943917,"score_gpt":0.24269834806869367,"score_spread":0.18522309487430197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040747612","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":[0.38061666,0.000062575,0.6187523,0.0000074973123,0.00011240092,0.0002677958,0.0000028637417,0.0001692369,0.000008666091],"genre_scores_gemma":[0.9680958,0.000026552409,0.031660922,0.000011887693,0.00005227865,0.00009712647,0.000018160576,0.0000326766,0.0000046394284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890506,0.000012118242,0.00038570756,0.0003123226,0.00011550274,0.00026925697],"domain_scores_gemma":[0.99952585,0.00012236586,0.00004104168,0.000113959,0.000053540596,0.00014324693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028476125,0.0002209881,0.00024536529,0.0004115724,0.00014140026,0.00009055826,0.000059757924,0.000080764315,0.00003153339],"category_scores_gemma":[0.000042199994,0.00023852223,0.000078654826,0.0002983615,0.000026394258,0.00022573672,0.00003180533,0.00011501523,0.00000796857],"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":[0.00005238984,0.00002082259,0.00025518163,0.00013655129,0.00034299484,0.0000027137125,0.0010763506,0.84157836,0.050825723,0.00032325092,0.0000042378865,0.105381444],"study_design_scores_gemma":[0.000023409859,0.00004617729,0.00070989766,0.00005260392,0.00012389457,0.0000039709753,0.000033251355,0.54689544,0.45088577,0.0010049917,0.000027018825,0.00019356172],"about_ca_topic_score_codex":0.000187782,"about_ca_topic_score_gemma":0.00021407487,"teacher_disagreement_score":0.58747905,"about_ca_system_score_codex":0.00003626311,"about_ca_system_score_gemma":0.0000044485837,"threshold_uncertainty_score":0.9726654},"labels":[],"label_agreement":null},{"id":"W2043379547","doi":"10.1017/s0890060405050080","title":"Functional reasoning theories: Problems and perspectives","year":2005,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Chiba University; University of Winnipeg","keywords":"Ascription; Function (biology); Computer science; Artificial intelligence; Artifact (error); Cognitive science; Epistemology; Representation (politics); Defeasible reasoning; Philosophy of science; Property (philosophy); Management science; Psychology; Engineering","score_opus":0.0366648073150374,"score_gpt":0.23460978040407846,"score_spread":0.19794497308904105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043379547","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":[0.0115487855,0.0007885234,0.9868712,0.00039992147,0.00005777022,0.00014963679,0.0000018298607,0.00016081151,0.000021544018],"genre_scores_gemma":[0.8249364,0.00005253217,0.17479624,0.000023207629,0.00010174894,0.000028439925,0.0000031708742,0.00000976235,0.000048515314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988284,0.000024634757,0.00026514818,0.00044983282,0.00013268043,0.000299313],"domain_scores_gemma":[0.9992003,0.00039000448,0.00006938691,0.00019176352,0.000046701927,0.00010188053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000655988,0.00018489569,0.00022087785,0.00031871907,0.0002661687,0.00033138937,0.00017017052,0.000058606056,0.00001243567],"category_scores_gemma":[0.00007548075,0.00017341752,0.0000936183,0.0002617853,0.000036000867,0.00040622312,0.000062983054,0.00012603358,0.0000034404782],"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":[0.000012863151,0.000018971776,0.000060426373,0.000027046444,0.00027403512,0.0000012380276,0.0035401362,0.787921,0.00089697103,0.11019006,0.000012390606,0.09704483],"study_design_scores_gemma":[0.000025292211,0.0000569822,0.00026581995,0.000029351986,0.000108657914,0.000005592249,0.00024429805,0.9428058,0.04777666,0.007971088,0.000465672,0.00024474875],"about_ca_topic_score_codex":0.000030709467,"about_ca_topic_score_gemma":0.000010772604,"teacher_disagreement_score":0.81338763,"about_ca_system_score_codex":0.00003181777,"about_ca_system_score_gemma":0.000013708253,"threshold_uncertainty_score":0.7071761},"labels":[],"label_agreement":null},{"id":"W2051029804","doi":"10.1017/s0890060409000109","title":"A dynamic knowledge modeler","year":2008,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Canada Research Chairs","keywords":"Computer science; Domain knowledge; Knowledge modeling; Ontology; Knowledge engineering; Knowledge integration; Knowledge-based systems; Software engineering; Open Knowledge Base Connectivity; Domain (mathematical analysis); Knowledge management; Personal knowledge management; Organizational learning","score_opus":0.054182130958427936,"score_gpt":0.2694206677634845,"score_spread":0.21523853680505656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051029804","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":[0.04578643,0.00032445104,0.95331466,0.00007620047,0.00012938862,0.0001459816,8.7337565e-7,0.00019889514,0.000023146817],"genre_scores_gemma":[0.83192396,0.00008120871,0.16786233,0.000014831472,0.000020095067,0.000027992095,0.0000015865603,0.000008495642,0.000059529542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988677,0.000016193297,0.00029256273,0.00039418912,0.00010364883,0.00032569488],"domain_scores_gemma":[0.9992726,0.000249369,0.00004781014,0.0003069016,0.000038856364,0.00008447188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025162633,0.00017743326,0.00028345708,0.00039992973,0.00018296335,0.00010088635,0.0003632826,0.00006186874,0.0000046696314],"category_scores_gemma":[0.000043873468,0.00016173317,0.00017031423,0.0003433223,0.000036254805,0.00025106,0.000085863554,0.000080900754,0.000010771049],"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":[0.000018944333,0.00009084633,0.00005257303,0.00005013117,0.00065103767,0.00002381016,0.0022631816,0.80387336,0.0032839514,0.037103176,0.000037381542,0.1525516],"study_design_scores_gemma":[0.000015552327,0.000036238824,0.0003733607,0.000005743104,0.00007979715,0.000008460408,0.000027028856,0.90053123,0.09429652,0.004279039,0.00015414871,0.00019286382],"about_ca_topic_score_codex":0.000032394157,"about_ca_topic_score_gemma":0.00002611098,"teacher_disagreement_score":0.7861375,"about_ca_system_score_codex":0.00002703965,"about_ca_system_score_gemma":0.000018903622,"threshold_uncertainty_score":0.65952873},"labels":[],"label_agreement":null},{"id":"W2051500251","doi":"10.1017/s0890060411000151","title":"Three-dimensional modeling of coordinate measuring machines probing accuracy and settings using fuzzy knowledge bases: Application to TP6 and TP200 triggering probes","year":2011,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Advanced Measurement and Metrology Techniques","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":"Université de Montréal; Polytechnique Montréal","funders":"Fundacja na rzecz Nauki Polskiej","keywords":"Fuzzy logic; Computer science; Algorithm; Coding (social sciences); Binary number; Knowledge base; Data mining; Artificial intelligence; Mathematics; Arithmetic","score_opus":0.07127642732229904,"score_gpt":0.27102626144494085,"score_spread":0.1997498341226418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051500251","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":[0.35591626,0.0004960518,0.6429907,0.0000042606016,0.000031366948,0.00041441445,0.0000025437873,0.00013778903,0.000006583845],"genre_scores_gemma":[0.85371256,0.000048326747,0.14606786,0.000004475692,0.000033061162,0.000091922346,0.0000029052737,0.00003797643,9.2134314e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863684,0.000013597374,0.0005132804,0.0003921151,0.00010847904,0.00033566935],"domain_scores_gemma":[0.99940735,0.0001420157,0.00008176195,0.00016527827,0.00008199782,0.00012157426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000667938,0.00029056074,0.00044782163,0.0006391714,0.00013942321,0.000040148545,0.000103704835,0.00009060678,0.0000020724692],"category_scores_gemma":[0.00008298731,0.0002937821,0.000084085565,0.00028243646,0.000034245153,0.00026225077,0.000067378256,0.00013248815,2.8315918e-7],"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":[0.000048089056,0.000020128835,0.00012631078,0.00029031938,0.0002649964,7.1364224e-7,0.0006692633,0.81763107,0.1250409,0.00038851055,5.904703e-7,0.05551913],"study_design_scores_gemma":[0.000029022947,0.000033622397,0.00004446055,0.00007996055,0.00022368584,0.0000023649961,0.000036909994,0.6260546,0.37183246,0.0014593708,0.000008309294,0.00019524797],"about_ca_topic_score_codex":0.000083648156,"about_ca_topic_score_gemma":0.000046384583,"teacher_disagreement_score":0.4977963,"about_ca_system_score_codex":0.00004259062,"about_ca_system_score_gemma":0.000009170909,"threshold_uncertainty_score":0.9999514},"labels":[],"label_agreement":null},{"id":"W2053316171","doi":"10.1017/s0890060406060136","title":"A typology of design space explorers","year":2006,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":14,"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":"Australian Research Council; RMIT University","keywords":"Space (punctuation); Context (archaeology); Typology; Computer science; Value (mathematics); Position (finance); Human–computer interaction; Feature (linguistics); Section (typography); Data science; Artificial intelligence; Sociology; Geography; Machine learning; Business","score_opus":0.04331300252508875,"score_gpt":0.2609526968930542,"score_spread":0.21763969436796549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053316171","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":[0.015624473,0.00024896197,0.98337007,0.000056343986,0.00020282365,0.00023635065,0.0000035545331,0.00014979223,0.00010766135],"genre_scores_gemma":[0.922818,0.00007183241,0.076892175,0.0000077138775,0.00007861963,0.000050906747,0.0000078171315,0.00002936453,0.0000435848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989593,0.000031706022,0.0004088914,0.00021452422,0.0001012242,0.0002843805],"domain_scores_gemma":[0.9990334,0.0006083905,0.00006712653,0.00018307852,0.000039892904,0.00006810258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004597473,0.00019282021,0.00031208125,0.00048338718,0.000055471297,0.000052241085,0.000117914686,0.00008335268,0.00005261444],"category_scores_gemma":[0.000060577728,0.0002023746,0.00012964674,0.00034328422,0.000034946093,0.00015904986,0.000011261812,0.00009903656,0.000007937464],"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":[0.000025612751,0.00002364848,0.00001102924,0.000047200178,0.00027742828,0.0000011675113,0.00017994388,0.9650048,0.017239584,0.007719946,0.00013943981,0.009330198],"study_design_scores_gemma":[0.000020106185,0.000039648905,0.000120874785,0.0000073388283,0.0002931964,0.0000019131319,0.00015379605,0.4188652,0.57667756,0.0028129553,0.000823383,0.00018403557],"about_ca_topic_score_codex":0.00008467893,"about_ca_topic_score_gemma":0.000012287407,"teacher_disagreement_score":0.90719354,"about_ca_system_score_codex":0.000031574848,"about_ca_system_score_gemma":0.000012322548,"threshold_uncertainty_score":0.8252597},"labels":[],"label_agreement":null},{"id":"W2067759000","doi":"10.1017/s0890060414000341","title":"The mechanical transformation and environmentally conscious behavior—ERRATUM","year":2015,"lang":"en","type":"erratum","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Innovative Human-Technology Interaction","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 Toronto","funders":"","keywords":"Content (measure theory); Action (physics); Transformation (genetics); Computer science; Chemistry; Mathematics; Physics","score_opus":0.035171686197774665,"score_gpt":0.265990760046035,"score_spread":0.23081907384826034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067759000","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":[0.00066185225,0.00032206884,0.995661,0.00028416203,0.0023279465,0.0005199463,0.000007907309,0.00013887986,0.000076187454],"genre_scores_gemma":[0.9492564,0.0012832112,0.04469288,0.00010842742,0.0005138215,0.0007275185,0.00017692833,0.00009641016,0.0031443718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981812,0.00004334821,0.0005927687,0.0005317016,0.00026936253,0.0003815769],"domain_scores_gemma":[0.99900097,0.00021885257,0.0002144052,0.00040687894,0.000073900344,0.00008500376],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008536461,0.00034410265,0.00039980706,0.0005072484,0.00038818174,0.0004934487,0.00050850527,0.00034529448,0.0000039321167],"category_scores_gemma":[0.00006819918,0.00028671644,0.00014572096,0.00026615115,0.00011117774,0.00038967317,0.00013220012,0.0006158975,0.0000056668036],"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":[0.00012301482,0.00021257515,0.0000050122458,0.00022309198,0.0036237896,0.00004771627,0.0031973447,0.024631642,0.010363112,0.26186472,0.022034094,0.67367387],"study_design_scores_gemma":[0.000051369523,0.00026824823,0.000046228204,0.00004648202,0.0008456846,0.000030043799,0.00024803623,0.84275746,0.109506376,0.019042661,0.026498854,0.00065855175],"about_ca_topic_score_codex":0.000025569385,"about_ca_topic_score_gemma":0.00006346185,"teacher_disagreement_score":0.95096815,"about_ca_system_score_codex":0.00012895014,"about_ca_system_score_gemma":0.000035999987,"threshold_uncertainty_score":0.9999585},"labels":[],"label_agreement":null},{"id":"W2073448498","doi":"10.1017/s0890060413000486","title":"Design problem solving with biological analogies: A verbal protocol study","year":2014,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Biomimetics; Recall; Computer science; Coding (social sciences); Cognition; Human–computer interaction; Artificial intelligence; Psychology; Cognitive psychology; Mathematics","score_opus":0.060711053370661544,"score_gpt":0.2912315540912709,"score_spread":0.23052050072060934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073448498","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":[0.0066266092,0.000011400616,0.9802811,0.000021242546,0.00006478805,0.012516817,0.0000012206561,0.00038988877,0.00008689807],"genre_scores_gemma":[0.8940267,0.0000054289244,0.09430928,0.000014915572,0.00008501017,0.01150036,0.0000040135997,0.00003976342,0.000014540051],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838746,0.00010110872,0.00046955698,0.000427236,0.00017534923,0.00043931932],"domain_scores_gemma":[0.99861395,0.0008352498,0.000079716265,0.00027440343,0.000055129025,0.0001415773],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011750096,0.00033149472,0.00041305283,0.00040271715,0.00016522552,0.00026424823,0.00020704263,0.00009037453,0.000049441256],"category_scores_gemma":[0.00010517316,0.00026986562,0.00009622391,0.00045122244,0.00003354582,0.00025347568,0.000025559111,0.00019617162,0.000010037895],"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":[0.00011998571,0.000110205474,0.000054267235,0.000068731264,0.0006406459,0.0000028453749,0.0009393524,0.9578956,0.0019363628,0.00040906714,0.000036267244,0.037786674],"study_design_scores_gemma":[0.00012070379,0.0007325571,0.00035825255,0.000025148925,0.00042295145,0.000004811651,0.000718937,0.91269827,0.08224616,0.00092221895,0.0012311408,0.0005188489],"about_ca_topic_score_codex":0.000023983483,"about_ca_topic_score_gemma":0.000014401996,"teacher_disagreement_score":0.8874001,"about_ca_system_score_codex":0.000051640654,"about_ca_system_score_gemma":0.000017193981,"threshold_uncertainty_score":0.9999754},"labels":[],"label_agreement":null},{"id":"W2075328738","doi":"10.1017/s0890060401151036","title":"Design issues in implementing a cooperative search among heterogeneous agents to aid information management","year":2001,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","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":"Concordia University","funders":"","keywords":"Computer science; Set (abstract data type); Task (project management); Transparency (behavior); Information sharing; Domain (mathematical analysis); Knowledge management; World Wide Web; Computer security; Engineering","score_opus":0.049741404147103055,"score_gpt":0.2965031881694843,"score_spread":0.24676178402238125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075328738","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":[0.089769766,0.000022580896,0.9091728,0.000065328495,0.00007418377,0.0008210615,0.0000012106374,0.00006556402,0.0000075530947],"genre_scores_gemma":[0.90164226,0.000051786235,0.09807532,0.000034829503,0.00002642311,0.0001272455,0.0000074935497,0.000008438664,0.00002618369],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983902,0.000059846523,0.0005472395,0.00033969426,0.00022527578,0.00043774938],"domain_scores_gemma":[0.9994367,0.00010027934,0.000076448334,0.00023254403,0.000047519738,0.000106514366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010054677,0.0001888096,0.00025019373,0.00088027964,0.00015640727,0.00042389263,0.00028090848,0.00004489906,0.000013724448],"category_scores_gemma":[0.000021677251,0.00018809405,0.00007946271,0.0006438246,0.000008091035,0.00072370784,0.00012269882,0.00006854356,0.000013671997],"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":[0.000016083512,0.00001854292,0.00018069362,0.000027991011,0.00015838504,0.000007735791,0.0020644758,0.93759507,0.00037574387,0.00090906734,0.000008093212,0.058638114],"study_design_scores_gemma":[0.000046019682,0.00005956293,0.0009765048,0.000029077582,0.000040792576,0.0000013774014,0.00016964466,0.8714618,0.12652709,0.00012816723,0.00036531914,0.00019461583],"about_ca_topic_score_codex":0.00026961922,"about_ca_topic_score_gemma":0.00008530752,"teacher_disagreement_score":0.8118725,"about_ca_system_score_codex":0.00007881976,"about_ca_system_score_gemma":0.000006644446,"threshold_uncertainty_score":0.7670253},"labels":[],"label_agreement":null},{"id":"W2077248744","doi":"10.1017/s0890060415000025","title":"Using analogies to explain versus inspire concepts","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Science Education and Pedagogy","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Analogy; Parallels; Concreteness; Analogical reasoning; Psychology; Process (computing); Mathematics education; Cognitive science; Epistemology; Computer science; Cognitive psychology; Engineering","score_opus":0.38800935037571616,"score_gpt":0.4554113850577344,"score_spread":0.06740203468201822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077248744","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":[0.15358335,0.00005767777,0.84476256,0.0004671287,0.0006584218,0.00021845927,0.0000023876803,0.000074213385,0.00017582694],"genre_scores_gemma":[0.9687252,0.00001654295,0.030841388,0.000058951497,0.00025849385,0.000025189232,0.0000027590222,0.000008845543,0.000062647625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987555,0.000070766815,0.0002594233,0.00029579244,0.00024628825,0.00037224195],"domain_scores_gemma":[0.99894655,0.00042492486,0.00005632936,0.00014226275,0.00009525283,0.00033470336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014591661,0.00012804728,0.00020619725,0.00043382976,0.00036654712,0.00022551809,0.00020826544,0.000064984466,0.000040967698],"category_scores_gemma":[0.00088683015,0.0001312305,0.00009184677,0.0006404167,0.00008577004,0.00023402215,0.000031155072,0.00007428107,0.000011803373],"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":[0.00009343036,0.000030183399,0.00011398153,0.00000741536,0.00018806753,0.0000019554227,0.029292107,0.87687683,0.00083936006,0.03347849,0.00013954751,0.058938634],"study_design_scores_gemma":[0.00022114356,0.00048269823,0.00048375232,0.000050272487,0.0009029906,0.0000015165623,0.15551193,0.5608059,0.21586475,0.010375778,0.05376727,0.0015320034],"about_ca_topic_score_codex":0.0015042659,"about_ca_topic_score_gemma":0.0009844358,"teacher_disagreement_score":0.81514186,"about_ca_system_score_codex":0.00012011805,"about_ca_system_score_gemma":0.00012349617,"threshold_uncertainty_score":0.5351424},"labels":[],"label_agreement":null},{"id":"W2078960088","doi":"10.1017/s0890060408000152","title":"Cooperative body–brain coevolutionary synthesis of mechatronic systems","year":2008,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":12,"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":"Université Laval; National Science Foundation","keywords":"Mechatronics; Bond graph; Flexibility (engineering); Genetic programming; Computer science; Control engineering; Computer-automated design; Distributed computing; Artificial intelligence; Systems design; Engineering; Software engineering; Mathematics","score_opus":0.03701031031788931,"score_gpt":0.2361967516323715,"score_spread":0.19918644131448218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078960088","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":[0.056315362,0.00041016104,0.9427222,0.000018200717,0.00012441221,0.0002243695,0.0000035945015,0.00015741376,0.000024265255],"genre_scores_gemma":[0.99499047,0.00012293972,0.004676226,0.0000039437773,0.00005890376,0.00006821041,0.0000102937975,0.00003043815,0.000038590082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989066,0.00003094462,0.00046204246,0.00021912024,0.00012176646,0.00025951568],"domain_scores_gemma":[0.9991451,0.00050881266,0.000061187995,0.000157089,0.00004953659,0.00007827025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029665465,0.000198435,0.00039393193,0.00044282875,0.0001341014,0.00003300106,0.000104410545,0.000080805294,0.00002662329],"category_scores_gemma":[0.000099221004,0.0002034377,0.00015381421,0.00029694533,0.00003729589,0.0001446819,0.000016134965,0.00011357174,0.000005368227],"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":[0.000010670698,0.0000104489745,0.000024857929,0.00006780201,0.00045018698,0.0000021331196,0.00022480931,0.98903704,0.0053888042,0.0030761831,0.00003102429,0.0016760257],"study_design_scores_gemma":[0.000016071193,0.000027222577,0.0002789818,0.000022806162,0.0001611933,0.000004444894,0.00011676237,0.8312579,0.16766518,0.000082060425,0.00018343439,0.00018393935],"about_ca_topic_score_codex":0.00004032213,"about_ca_topic_score_gemma":0.000004849911,"teacher_disagreement_score":0.9386751,"about_ca_system_score_codex":0.00005143967,"about_ca_system_score_gemma":0.000010618674,"threshold_uncertainty_score":0.82959485},"labels":[],"label_agreement":null},{"id":"W2106989228","doi":"10.1017/s0890060406060112","title":"Design space and typed feature logic","year":2006,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Speech and dialogue systems","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 Toronto","funders":"","keywords":"Computer science; Context (archaeology); Grammar; Space (punctuation); Feature (linguistics); Programming language; Strengths and weaknesses; Natural language processing; Natural language; Artificial intelligence; Human–computer interaction; Linguistics; Epistemology; History; Philosophy","score_opus":0.03017141613535088,"score_gpt":0.23551431497914643,"score_spread":0.20534289884379556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106989228","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":[0.0031674223,0.00042180388,0.99559385,0.00022020856,0.00014232575,0.00027865454,0.0000015129211,0.00014583852,0.000028390923],"genre_scores_gemma":[0.7635277,0.000021716043,0.23621406,0.000020182764,0.000096751646,0.00002468553,0.000003860035,0.0000101450205,0.00008091301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988286,0.000034168777,0.00024330559,0.00043009088,0.00013080153,0.00033304546],"domain_scores_gemma":[0.9992463,0.00030149225,0.0000701494,0.00025101754,0.000036272268,0.00009478895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047767864,0.000201158,0.00028584956,0.00033629817,0.00013301548,0.0003255456,0.00022058391,0.00009175196,0.00000317248],"category_scores_gemma":[0.000040496143,0.00017976273,0.00009649158,0.0003420494,0.000024714203,0.00021012376,0.00005672066,0.00008833044,0.0000059225476],"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":[0.00006329411,0.000058327452,0.00006836636,0.00008309248,0.0004187952,0.000027420636,0.0003838609,0.7795101,0.025001872,0.14813802,0.0003198396,0.045927033],"study_design_scores_gemma":[0.000029294712,0.00007040416,0.00024481435,0.00000997769,0.000112251575,0.000006600952,0.000021853648,0.6444588,0.33356437,0.020930724,0.0003014777,0.0002494475],"about_ca_topic_score_codex":0.00013033481,"about_ca_topic_score_gemma":0.000020113248,"teacher_disagreement_score":0.7603603,"about_ca_system_score_codex":0.000022842996,"about_ca_system_score_gemma":0.000012912964,"threshold_uncertainty_score":0.7330511},"labels":[],"label_agreement":null},{"id":"W2107222991","doi":"10.1017/s089006040800019x","title":"Evolving blackbox quantum algorithms using genetic programming","year":2008,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Quantum sort; Quantum computer; Quantum algorithm; Quantum; Genetic programming; Theoretical computer science; Algorithm; Quantum complexity theory; Quantum network; Quantum mechanics; Artificial intelligence; Physics","score_opus":0.039899687528056206,"score_gpt":0.2562014924601071,"score_spread":0.2163018049320509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107222991","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":[0.12658007,0.00029932897,0.8723502,0.00004269656,0.00020770932,0.00026702989,0.0000014516842,0.0002502579,0.0000012292226],"genre_scores_gemma":[0.53590375,0.000021813772,0.46391678,0.000012567176,0.000108749584,0.000013310874,0.0000014165212,0.000017166749,0.0000044399103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783164,0.000039454702,0.0005369618,0.0006918612,0.00026465414,0.00063544564],"domain_scores_gemma":[0.998901,0.00029165685,0.00013413589,0.00041268964,0.000075337775,0.00018518051],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043118594,0.00032613266,0.0004266679,0.00061031035,0.00051075546,0.0003123012,0.00049659907,0.00009007257,0.0000044680646],"category_scores_gemma":[0.000067674,0.00031400146,0.00027466964,0.0006974811,0.000061840976,0.00026588654,0.00015981327,0.00019770916,0.0000031558532],"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":[0.000003835902,0.000028744455,0.000031849988,0.000025411036,0.00022985406,0.000028486995,0.0006768118,0.86915785,0.001263618,0.0011232465,0.000002392369,0.12742792],"study_design_scores_gemma":[0.000027611555,0.00008307492,0.0003451516,0.00002507448,0.00013705634,0.000054600216,0.000031775675,0.94539213,0.051780857,0.0016279601,0.00011340159,0.0003813106],"about_ca_topic_score_codex":0.00009898795,"about_ca_topic_score_gemma":0.0000028450177,"teacher_disagreement_score":0.4093237,"about_ca_system_score_codex":0.000047055968,"about_ca_system_score_gemma":0.000037229696,"threshold_uncertainty_score":0.9999312},"labels":[],"label_agreement":null},{"id":"W2118311688","doi":"10.1017/s0890060401151024","title":"Kriging as a surrogate fitness landscape in evolutionary optimization","year":2001,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Kriging; Fitness landscape; Fitness approximation; Mathematical optimization; Surrogate model; Interpolation (computer graphics); Evolutionary algorithm; Fitness function; Computer science; Process (computing); Function (biology); Black box; Engineering design process; Mathematics; Genetic algorithm; Machine learning; Artificial intelligence; Engineering","score_opus":0.02283994912642328,"score_gpt":0.26412463775253625,"score_spread":0.24128468862611296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118311688","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":[0.003957447,0.00008063394,0.9953195,0.00009042834,0.00011189266,0.0002638158,0.0000013107128,0.00014236993,0.000032625077],"genre_scores_gemma":[0.5640389,0.00010773608,0.43571258,0.000017992328,0.000028909142,0.000046843594,0.000008856444,0.000012029266,0.000026149224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867517,0.000029835019,0.00036264004,0.00045850957,0.00014834982,0.00032548542],"domain_scores_gemma":[0.99929154,0.00024154012,0.00008771371,0.00022247655,0.00007075759,0.00008599687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032507523,0.00018655151,0.0002482425,0.0007605444,0.00012495794,0.00015172159,0.00024290747,0.00006108774,0.0000348884],"category_scores_gemma":[0.00010463844,0.00019756625,0.000093315786,0.0009433417,0.00001920705,0.0005991183,0.0000669506,0.000098537465,0.0000045612874],"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":[0.00001404219,0.000026210897,0.00009405747,0.00000660285,0.000059265964,0.000010351238,0.00018926748,0.9740255,0.0000954788,0.0027698937,8.160657e-7,0.022708531],"study_design_scores_gemma":[0.000055695167,0.00003090029,0.00022011057,0.000013185938,0.00004056461,0.000008331632,0.00007320486,0.9834166,0.013966683,0.0018919004,0.00005479871,0.00022802847],"about_ca_topic_score_codex":0.000048334816,"about_ca_topic_score_gemma":0.00001717968,"teacher_disagreement_score":0.5600814,"about_ca_system_score_codex":0.000055982928,"about_ca_system_score_gemma":0.000020652644,"threshold_uncertainty_score":0.8056518},"labels":[],"label_agreement":null},{"id":"W2119298906","doi":"10.1017/s0890060410000247","title":"Scrutinizing design educators' perceptions of the design process","year":2010,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","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 Alberta","funders":"","keywords":"Design process; Process (computing); Engineering design process; Computer science; Research design; Design education; Perception; Negotiation; Variety (cybernetics); Design brief; Iterative design; Design methods; Narrative; Design elements and principles; Management science; Psychology; Engineering; Work in process; Software engineering; Sociology; Artificial intelligence","score_opus":0.03330387175198903,"score_gpt":0.28454894753916504,"score_spread":0.25124507578717603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119298906","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":[0.057636656,0.000055397424,0.9408912,0.000078383215,0.0006527855,0.0005105287,0.0000040422415,0.00013904969,0.00003197299],"genre_scores_gemma":[0.92611235,0.000032514094,0.07352866,0.000018038218,0.0001264489,0.00010834443,0.0000027062351,0.000041160827,0.000029794324],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986512,0.000059851256,0.0004886902,0.00027829083,0.00017943933,0.00034251754],"domain_scores_gemma":[0.99846613,0.00086515176,0.00010057644,0.0003558243,0.00008630355,0.00012599236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009927106,0.00025440226,0.00030491658,0.00037856284,0.00020608904,0.00012877249,0.00031677244,0.00011938196,0.000105908395],"category_scores_gemma":[0.0002869781,0.00021654903,0.00019099737,0.0005930476,0.00006528028,0.0002460763,0.000021703301,0.00033177977,0.0000080865875],"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":[0.000017165636,0.000040948034,0.0000420366,0.00006943098,0.00030113058,2.5543994e-7,0.0011323149,0.92013574,0.060103003,0.0009978436,0.00006790707,0.017092224],"study_design_scores_gemma":[0.00001720018,0.0000219339,0.00039099925,0.000015834356,0.0003858674,0.0000037028065,0.00044186096,0.43146747,0.56554186,0.0012608774,0.00023535652,0.00021703239],"about_ca_topic_score_codex":0.000019656827,"about_ca_topic_score_gemma":0.0000106667485,"teacher_disagreement_score":0.8684757,"about_ca_system_score_codex":0.000026409649,"about_ca_system_score_gemma":0.000053667594,"threshold_uncertainty_score":0.8830613},"labels":[],"label_agreement":null},{"id":"W2138240917","doi":"10.1017/s0890060407070138","title":"Biomimetic design through natural language analysis to facilitate cross-domain information retrieval","year":2007,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ambiguity; Domain (mathematical analysis); Natural (archaeology); Natural language; Artificial intelligence; Human–computer interaction; Data science; Programming language","score_opus":0.03693632444478447,"score_gpt":0.2996638839504768,"score_spread":0.2627275595056923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138240917","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":[0.377483,0.000041487812,0.62175816,0.00003357582,0.00022046258,0.0003219255,0.000015737214,0.00011897719,0.0000066739312],"genre_scores_gemma":[0.7247005,0.0000034689406,0.27510715,0.000055429846,0.000053668464,0.000015306208,0.00001970831,0.000012517452,0.000032274358],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9973318,0.000088883804,0.0008890235,0.0005462959,0.00043040758,0.0007135779],"domain_scores_gemma":[0.9984153,0.00068920525,0.00020381543,0.00039245337,0.00011080028,0.00018844217],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0043941042,0.0003235621,0.000529247,0.0011093871,0.0003064302,0.0007207746,0.0004241435,0.0000994879,0.00015856746],"category_scores_gemma":[0.00058699056,0.0002956068,0.0002575761,0.00164128,0.0000927017,0.0007964638,0.000106621155,0.00013999344,0.00008995898],"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":[0.00019602374,0.0000115867315,0.00003162921,0.000025987863,0.00024025104,0.0000036586066,0.0026554265,0.68999916,0.3036217,0.00033776287,0.0000033710967,0.002873423],"study_design_scores_gemma":[0.000033841698,0.00008474693,0.0010272046,0.000007387563,0.00045755002,0.0000017573174,0.00028478427,0.2691159,0.72800976,0.0005326961,0.0001273674,0.0003170008],"about_ca_topic_score_codex":0.00031810606,"about_ca_topic_score_gemma":0.00004142944,"teacher_disagreement_score":0.42438805,"about_ca_system_score_codex":0.00010096057,"about_ca_system_score_gemma":0.00001953238,"threshold_uncertainty_score":0.99994963},"labels":[],"label_agreement":null},{"id":"W2143507623","doi":"10.1017/s0890060402165012","title":"Agent-based support within an interactive evolutionary design system","year":2002,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","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":"Golder Associates (Canada)","funders":"","keywords":"Preference; Computer science; Interface (matter); Function (biology); Human–computer interaction; Conceptual design; Artificial intelligence; Mathematics","score_opus":0.06279686457532598,"score_gpt":0.26677573219977996,"score_spread":0.203978867624454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143507623","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":[0.0048555564,0.00012460492,0.9934083,0.000043802316,0.0005290933,0.00041511268,0.000011818392,0.00053709664,0.00007460912],"genre_scores_gemma":[0.9257579,0.000021735128,0.07377681,0.00003159893,0.00011937301,0.00013387464,0.000023822602,0.00005314099,0.00008174693],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983997,0.00008111729,0.00054818176,0.00039276097,0.0001913643,0.0003868851],"domain_scores_gemma":[0.9987564,0.0005528323,0.00009542569,0.00030600675,0.00006624886,0.00022307372],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00063433626,0.00031403496,0.00034935944,0.0006253472,0.00016632152,0.00020433459,0.00019610513,0.00011040068,0.0002353066],"category_scores_gemma":[0.00007480718,0.00033314066,0.00016148791,0.00039008717,0.000028487984,0.0005280867,0.000012863885,0.00020162229,0.00006188792],"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":[0.00003612584,0.00005508425,0.000004623817,0.00008866467,0.00039869873,0.0000068580052,0.00080304884,0.98291415,0.0021892472,0.0006259861,0.00019748133,0.012680048],"study_design_scores_gemma":[0.00003552568,0.00010227991,0.000052813455,0.000020198595,0.00037287222,0.0000075919056,0.00043633382,0.8415628,0.1562528,0.000104611776,0.0007236304,0.00032854293],"about_ca_topic_score_codex":0.000028979166,"about_ca_topic_score_gemma":0.0000071360078,"teacher_disagreement_score":0.9209023,"about_ca_system_score_codex":0.00016495709,"about_ca_system_score_gemma":0.000016821834,"threshold_uncertainty_score":0.9999121},"labels":[],"label_agreement":null},{"id":"W2147955560","doi":"10.1017/s0890060409990047","title":"Software-engineering challenges of building and deploying reusable problem solvers","year":2009,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Multi-Agent Systems and Negotiation","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":"McGill University","funders":"U.S. National Library of Medicine; Centers for Disease Control and Prevention","keywords":"Software engineering; Computer science; Software; Programming language; Computational science; Systems engineering; Engineering","score_opus":0.03912924955359439,"score_gpt":0.25166881004678193,"score_spread":0.21253956049318753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147955560","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":[0.041901007,0.00065705454,0.9569024,0.00008074733,0.00007387555,0.00024398978,8.952478e-7,0.0001373705,0.0000026695598],"genre_scores_gemma":[0.7140097,0.00017173433,0.28575873,0.000005996952,0.000029237906,0.000012031027,7.0072025e-7,0.000008850015,0.0000030343283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986641,0.000015957816,0.00044257427,0.00041447429,0.00015668746,0.00030625158],"domain_scores_gemma":[0.9992378,0.00025321374,0.00012997138,0.00023982645,0.000042396707,0.00009683652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006106655,0.00019815791,0.0003492961,0.000465773,0.000102159844,0.00013225434,0.00022157562,0.00007111434,0.0000012801338],"category_scores_gemma":[0.000085804255,0.00019766188,0.000114408715,0.0002491247,0.000011101876,0.00040328116,0.00005160182,0.00008411438,4.1033715e-7],"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":[0.000011139189,0.000035570247,0.000076390315,0.00022290378,0.00031707433,0.0000029042199,0.0010932123,0.77559966,0.033961166,0.050141554,0.000003100783,0.13853532],"study_design_scores_gemma":[0.000028350925,0.000073570154,0.0006615052,0.00006583495,0.00009272779,0.0000023648427,0.000035602658,0.7232847,0.27393147,0.001585352,0.000040289113,0.00019820435],"about_ca_topic_score_codex":0.000037592632,"about_ca_topic_score_gemma":0.0000052917603,"teacher_disagreement_score":0.67210865,"about_ca_system_score_codex":0.00002754988,"about_ca_system_score_gemma":0.0000085408965,"threshold_uncertainty_score":0.8060417},"labels":[],"label_agreement":null},{"id":"W2153022398","doi":"10.1017/s0890060404040077","title":"Evaluation and selection in product design for mass customization: A knowledge decision support approach","year":2004,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":46,"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":"National Research Council Canada; National Institute of Standards and Technology","keywords":"Mass customization; Product design; Ranking (information retrieval); Personalization; Selection (genetic algorithm); Product engineering; Computer science; Fuzzy logic; Product (mathematics); New product development; Product design specification; Systems engineering; Engineering; Knowledge management; Process management; Artificial intelligence; Marketing; Business; Mathematics","score_opus":0.04859112681829944,"score_gpt":0.26886632673172345,"score_spread":0.220275199913424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153022398","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":[0.020345718,0.000103850674,0.9779747,0.000059606205,0.00009715751,0.0013260394,5.906527e-7,0.00006841896,0.00002391857],"genre_scores_gemma":[0.863267,0.000020583602,0.13607956,0.000016335987,0.0002473094,0.00029744318,0.00003628159,0.000022345514,0.000013123441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867713,0.000013098018,0.00040205146,0.00047195656,0.00018277684,0.00025301118],"domain_scores_gemma":[0.9994872,0.00009162035,0.000113011636,0.000076548284,0.00021335189,0.000018255083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023638008,0.00019405209,0.00025000938,0.0009082967,0.00017407289,0.00026287235,0.00008253571,0.0000649272,0.000009826887],"category_scores_gemma":[0.00034166875,0.00019325461,0.00006322096,0.00072932604,0.000016149417,0.00065736246,0.000024153373,0.00006268642,0.0000033928227],"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":[0.0000970361,0.000047488298,0.00006267876,0.000088016546,0.00009841966,1.632174e-7,0.00016442739,0.90343595,0.0020104493,0.0017268973,0.000020736376,0.09224772],"study_design_scores_gemma":[0.00020589404,0.000020957168,0.0005299289,0.00002347478,0.0005251191,7.578749e-7,0.00007223042,0.93349904,0.053297665,0.011305768,0.00026421872,0.00025496446],"about_ca_topic_score_codex":0.000027909728,"about_ca_topic_score_gemma":0.000038140875,"teacher_disagreement_score":0.8429213,"about_ca_system_score_codex":0.00010459142,"about_ca_system_score_gemma":0.000043446653,"threshold_uncertainty_score":0.7880695},"labels":[],"label_agreement":null},{"id":"W2158528888","doi":"10.1017/s0890060406060124","title":"Modeling dialogue with mixed initiative in design space exploration","year":2006,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":11,"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":"Simon Fraser University; RMIT University","keywords":"Notation; Computer science; Formalism (music); Interoperability; Conversation; Human–computer interaction; Grice; Generative grammar; Design space exploration; Space (punctuation); Interaction design; Programming language; Artificial intelligence; World Wide Web; Mathematics; Linguistics; Pragmatics","score_opus":0.06664706502600685,"score_gpt":0.25672650999447866,"score_spread":0.1900794449684718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158528888","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":[0.024630506,0.00012181461,0.97446156,0.000080621845,0.00011772071,0.00037880876,0.0000030373699,0.00016534652,0.000040609928],"genre_scores_gemma":[0.92925185,0.00006057066,0.07037443,0.000009558073,0.00008364128,0.00014574354,0.000025525676,0.00003721555,0.000011466168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988209,0.000049165643,0.0003985994,0.00028177633,0.00012898652,0.0003205903],"domain_scores_gemma":[0.9992652,0.00042708695,0.000044118253,0.00015123616,0.000045593853,0.00006679009],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054451264,0.00023969161,0.00028868453,0.00061294524,0.000078231504,0.00015436523,0.00009122826,0.000078886704,0.000011127552],"category_scores_gemma":[0.00005002012,0.00023758305,0.00006655494,0.00050183805,0.000016604965,0.00054420106,0.000008804938,0.00015146198,0.00000600464],"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":[0.000042539374,0.00002533587,0.000009204834,0.000025848987,0.00012351426,0.0000030921458,0.00041485083,0.9906344,0.0016394852,0.0015262997,0.00001239651,0.005543029],"study_design_scores_gemma":[0.0000398635,0.000039142647,0.0000678561,0.000019115061,0.00016164352,0.0000012984156,0.00026757934,0.82215846,0.17424518,0.0026882798,0.000056818713,0.00025476026],"about_ca_topic_score_codex":0.00017331388,"about_ca_topic_score_gemma":0.0002235563,"teacher_disagreement_score":0.90462136,"about_ca_system_score_codex":0.00007404549,"about_ca_system_score_gemma":0.000018511333,"threshold_uncertainty_score":0.9688356},"labels":[],"label_agreement":null},{"id":"W2161237822","doi":"10.1017/s0890060410000363","title":"A natural-language approach to biomimetic design","year":2010,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Directorate for Biological Sciences; University of Toronto; National Science Foundation","keywords":"Computer science; Natural language; Natural (archaeology); Task (project management); Biological engineering; Artificial intelligence; Engineering; Systems engineering; Bioinformatics","score_opus":0.02878037353777203,"score_gpt":0.26878767097368766,"score_spread":0.24000729743591565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161237822","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":[0.02455981,0.00016058386,0.97376055,0.000053115542,0.00055619085,0.0005061186,0.0000038004887,0.00030842016,0.000091416674],"genre_scores_gemma":[0.7814517,0.000015390848,0.21814133,0.000037426547,0.00012406732,0.0001176742,0.000009362494,0.000039458482,0.00006356394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986978,0.000026608797,0.0003663573,0.00035999258,0.00014280684,0.00040640112],"domain_scores_gemma":[0.99889606,0.00049685815,0.000039096918,0.0003094242,0.00003887894,0.00021969405],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007041785,0.00027552235,0.00030884772,0.0007018167,0.00010968877,0.00024053494,0.0002236384,0.00009878505,0.00004461843],"category_scores_gemma":[0.00018158944,0.00027417814,0.00014163183,0.000576195,0.000021794825,0.000205339,0.000022308282,0.00028364573,0.000033876448],"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":[0.000028746528,0.00004501527,0.0000017934333,0.000055802644,0.00044767378,0.0000015778094,0.0012328936,0.7452038,0.17390494,0.0014454403,0.0001386335,0.07749369],"study_design_scores_gemma":[0.000016135287,0.000020449928,0.000051156803,0.0000039965926,0.00022821185,0.000004253031,0.00015383378,0.58947605,0.4086938,0.00019121767,0.00090121606,0.00025969226],"about_ca_topic_score_codex":0.00002841309,"about_ca_topic_score_gemma":0.000008811956,"teacher_disagreement_score":0.7568919,"about_ca_system_score_codex":0.000028985452,"about_ca_system_score_gemma":0.000013213491,"threshold_uncertainty_score":0.99997103},"labels":[],"label_agreement":null},{"id":"W2162651210","doi":"10.1017/s0890060408000218","title":"Design rationale: Researching under uncertainty","year":2008,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Software Engineering Techniques and Practices","field":"Computer Science","cited_by":34,"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":"McGill University","keywords":"Gloom; Value (mathematics); Engineering ethics; Economic shortage; Management science; Empirical research; Public relations; Computer science; Political science; Engineering; Psychology","score_opus":0.10552812207834311,"score_gpt":0.3048532528455534,"score_spread":0.1993251307672103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162651210","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":[0.0036718012,0.00013801073,0.99506575,0.0002558022,0.00010349519,0.00033659211,0.0000015020354,0.00042177257,0.000005247751],"genre_scores_gemma":[0.55863047,0.00010262448,0.44108307,0.00003196476,0.000048109327,0.00006167472,0.0000032983585,0.000012457509,0.000026315689],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851114,0.00006010017,0.00035028422,0.0004485245,0.00025269363,0.00037725296],"domain_scores_gemma":[0.99778587,0.0015834384,0.00007798798,0.0003594199,0.00006727588,0.00012599501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010413644,0.00020879926,0.00026129806,0.00052288274,0.00031554807,0.00023146164,0.0004435623,0.000075672506,0.000011849131],"category_scores_gemma":[0.00020400457,0.00020126691,0.00013515908,0.00051077735,0.000034839755,0.00053561205,0.000097551856,0.00018589282,0.0000050919107],"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":[0.000014657368,0.000016781754,0.0000046458345,0.000012214138,0.00018999795,0.000007617942,0.00027529665,0.9682919,0.001172496,0.01441666,0.00003797094,0.015559796],"study_design_scores_gemma":[0.000017758146,0.00006845833,0.00008432787,0.000009503395,0.00006072515,0.000012668104,0.000016118613,0.86103487,0.12917702,0.00889125,0.00039598317,0.00023133976],"about_ca_topic_score_codex":0.00006650996,"about_ca_topic_score_gemma":0.0000038659978,"teacher_disagreement_score":0.5549587,"about_ca_system_score_codex":0.00004931588,"about_ca_system_score_gemma":0.00003889602,"threshold_uncertainty_score":0.82074267},"labels":[],"label_agreement":null},{"id":"W2168057526","doi":"10.1017/s0890060403172034","title":"A feature ontology to support construction cost estimating","year":2003,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"BIM and Construction Integration","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Computer science; Ontology; Feature (linguistics); Component (thermodynamics); Estimator; Product (mathematics); Similarity (geometry); Data mining; Artificial intelligence; Mathematics; Statistics","score_opus":0.025893494142698117,"score_gpt":0.25168374273809546,"score_spread":0.22579024859539734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168057526","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":[0.01603898,0.000047572434,0.9826183,0.000050564577,0.0006245587,0.00031622945,0.0000063369926,0.0002129708,0.00008452401],"genre_scores_gemma":[0.7398814,0.0000092371,0.25986302,0.000019556916,0.0000690134,0.00009880953,0.000009705739,0.000020468371,0.000028806586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990645,0.000015794378,0.00027963813,0.00025651685,0.00008883465,0.0002946993],"domain_scores_gemma":[0.99954826,0.00010831631,0.00003544906,0.00014109431,0.000042455446,0.00012440303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020283989,0.00020131744,0.00027003328,0.00042744874,0.00011220383,0.00011017925,0.00006946409,0.00010927795,0.000052288673],"category_scores_gemma":[0.00007616993,0.00020893729,0.00011347784,0.00031684383,0.000024895908,0.00012760096,0.000007301487,0.0001334109,0.000010571832],"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":[0.000008257998,0.000004628444,0.00002634152,0.0000279242,0.00021985435,0.0000012000166,0.00013967355,0.8063678,0.003762139,0.015474347,0.0000710315,0.17389677],"study_design_scores_gemma":[0.0000264664,0.00005580922,0.00004593561,0.0000124099415,0.00023486502,0.000024499748,0.00016636192,0.6500496,0.3430889,0.001990557,0.0040344694,0.00027010607],"about_ca_topic_score_codex":0.000008410077,"about_ca_topic_score_gemma":0.00004010844,"teacher_disagreement_score":0.72384244,"about_ca_system_score_codex":0.000056597553,"about_ca_system_score_gemma":0.000011102364,"threshold_uncertainty_score":0.8520215},"labels":[],"label_agreement":null},{"id":"W2205182224","doi":"10.1017/s0890060415000311","title":"A shortest path method for sequential change propagations in complex engineering design processes","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Product Development and Customization","field":"Business, Management and Accounting","cited_by":35,"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":"Shandong University of Technology","keywords":"Engineering design process; Computer science; Process (computing); Shortest path problem; Path (computing); Product (mathematics); New product development; Downstream (manufacturing); Industrial engineering; Distributed computing; Engineering; Theoretical computer science; Mathematics; Mechanical engineering; Operations management","score_opus":0.16411903670608832,"score_gpt":0.2951029859044918,"score_spread":0.13098394919840348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2205182224","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":[0.0055728666,0.00007052496,0.9926837,0.00018320128,0.00013622467,0.0011990945,0.0000038542025,0.0001443515,0.000006152362],"genre_scores_gemma":[0.81981635,0.0000098787905,0.1787487,0.000062709216,0.0005730938,0.00063100655,0.000108925684,0.000037413254,0.000011954169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985892,0.000010127705,0.00045060375,0.00041177002,0.00016269893,0.00037557763],"domain_scores_gemma":[0.9993767,0.00013998346,0.0001244227,0.00013198632,0.00018967397,0.000037189602],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011537381,0.00024909343,0.00034006947,0.00084168767,0.0001141948,0.0003217628,0.00015943883,0.00007027546,0.000009421166],"category_scores_gemma":[0.0004445126,0.0002496625,0.00008511711,0.000852141,0.000012840907,0.00076946936,0.000048154754,0.00007670213,0.0000042764386],"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":[0.000088634355,0.000058010453,0.00012884912,0.000271618,0.00018747475,0.0000024758667,0.00045287306,0.9722606,0.0022471682,0.0029662685,0.000069457856,0.021266567],"study_design_scores_gemma":[0.000088483015,0.000021859682,0.0002481093,0.000035132693,0.00030962442,7.663842e-7,0.00012775093,0.960492,0.035158217,0.0018665123,0.001313999,0.00033751485],"about_ca_topic_score_codex":0.0001484242,"about_ca_topic_score_gemma":0.00007461259,"teacher_disagreement_score":0.81424344,"about_ca_system_score_codex":0.00005981991,"about_ca_system_score_gemma":0.000035063225,"threshold_uncertainty_score":0.9999956},"labels":[],"label_agreement":null},{"id":"W2399454798","doi":"10.1017/s0890060415000219","title":"Three methods for identifying novel affordances","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Open Source Software Innovations","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Affordance; Computer science; Hacker; Product (mathematics); Human–computer interaction; Object (grammar); Natural (archaeology); Artificial intelligence; Computer security; Mathematics","score_opus":0.17155081238305683,"score_gpt":0.37254776967956144,"score_spread":0.2009969572965046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2399454798","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":[0.0009998253,0.00011981514,0.9976866,0.00018586154,0.00033947852,0.000458643,0.0000034261573,0.00020018118,0.000006128188],"genre_scores_gemma":[0.19803418,0.0000034529364,0.8016989,0.00002503062,0.000071445014,0.00013469558,0.000005014842,0.000015411884,0.000011828679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985292,0.000014850137,0.00046259945,0.00047672197,0.00014917113,0.00036747492],"domain_scores_gemma":[0.9985007,0.00071805983,0.00012376494,0.00036317573,0.00016064187,0.00013365744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017598628,0.00020018715,0.0003226164,0.0005139817,0.00018328153,0.0005079828,0.0005276932,0.0000650274,0.000002054479],"category_scores_gemma":[0.00036679916,0.00019755385,0.00018109343,0.00071134703,0.000028103488,0.00054483645,0.00012184382,0.00008667067,0.0000026577402],"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":[0.000021140444,0.000050527477,0.000024597357,0.000052937867,0.0006345136,8.318358e-7,0.0010259598,0.39767012,0.007510227,0.20979434,0.000034881457,0.3831799],"study_design_scores_gemma":[0.000031392585,0.000046018697,0.000030193263,0.000009334127,0.00012709231,0.0000015302949,0.00006343451,0.7079261,0.24788646,0.04253792,0.0011412263,0.00019929309],"about_ca_topic_score_codex":0.00004504998,"about_ca_topic_score_gemma":0.000028000639,"teacher_disagreement_score":0.3829806,"about_ca_system_score_codex":0.00004959951,"about_ca_system_score_gemma":0.00003324477,"threshold_uncertainty_score":0.8056012},"labels":[],"label_agreement":null},{"id":"W2473819074","doi":"10.1017/s089006041600024x","title":"A maintenance-focused approach to complex system design","year":2016,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Reliability and Maintenance Optimization","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":"University of Guelph","funders":"King Fahd University of Petroleum and Minerals; National Science Council; Massachusetts Institute of Technology","keywords":"Reliability engineering; Systems engineering; Computer science; Systems design; Reliability (semiconductor); Complex system; Risk analysis (engineering); Engineering; Power (physics); Artificial intelligence","score_opus":0.04493932443122554,"score_gpt":0.22620504008060002,"score_spread":0.1812657156493745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2473819074","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":[0.0024767285,0.000031836913,0.9959556,0.000064008695,0.00014604242,0.00074494624,0.000017188668,0.00048285257,0.00008078718],"genre_scores_gemma":[0.77533275,0.00003280027,0.22428055,0.0000124795715,0.00006180235,0.00019917189,0.0000056146496,0.000041674575,0.00003312112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834615,0.000030142859,0.0005081304,0.00044050347,0.0001525796,0.0005225106],"domain_scores_gemma":[0.9990848,0.00029399866,0.00004610552,0.00031363036,0.00006725988,0.00019425996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006214723,0.00030037292,0.00044502816,0.000461579,0.00010608028,0.000115459436,0.000205368,0.00010134903,0.000010941744],"category_scores_gemma":[0.000090457936,0.00022948408,0.00017963447,0.00041260515,0.000033323147,0.000181939,0.000030285837,0.00007327932,0.000013882077],"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":[0.000039193434,0.000015476402,0.0000020740738,0.000099824894,0.00026216317,9.301381e-7,0.00014276906,0.94638485,0.008167274,0.0039491374,0.00010000893,0.040836293],"study_design_scores_gemma":[0.00004986158,0.000051159102,0.000047270063,0.000056855017,0.00018519459,0.0000023184555,0.00008685717,0.8261212,0.17220649,0.000445807,0.0004315045,0.000315479],"about_ca_topic_score_codex":0.000015327554,"about_ca_topic_score_gemma":0.000003507308,"teacher_disagreement_score":0.77285606,"about_ca_system_score_codex":0.000169543,"about_ca_system_score_gemma":0.000008826591,"threshold_uncertainty_score":0.93580896},"labels":[],"label_agreement":null},{"id":"W2795687070","doi":"10.1017/s0890060417000622","title":"Segmentation of design protocol using EEG","year":2018,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Electroencephalography; Protocol (science); Artificial intelligence; Process (computing); Coding (social sciences); Pattern recognition (psychology)","score_opus":0.07885183326603978,"score_gpt":0.3324353677124894,"score_spread":0.2535835344464496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795687070","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":[0.0067685824,0.000014792755,0.98693675,0.000008680726,0.00014708049,0.005982651,0.0000027877277,0.000111266265,0.000027428548],"genre_scores_gemma":[0.78568316,0.0000059301783,0.21181485,0.000008710855,0.00009559638,0.0023452379,0.0000032871608,0.000029719842,0.00001353895],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989491,0.00003513288,0.00044403234,0.00020729515,0.00012551369,0.0002389357],"domain_scores_gemma":[0.9992849,0.00029842468,0.00008848083,0.00017038266,0.00007830276,0.00007948733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005824584,0.00018169355,0.00024755968,0.00043270728,0.00008967721,0.000078538375,0.000107383195,0.000067129055,0.00008633767],"category_scores_gemma":[0.00006466658,0.00018734617,0.00009593623,0.0003807982,0.000039985734,0.00024816068,0.000012338214,0.000075202726,0.0000064845653],"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":[0.000055300785,0.00002582104,0.00000817535,0.00010433647,0.0003798364,4.2737315e-7,0.00048309748,0.8474346,0.112689875,0.0004409452,0.000028302517,0.038349275],"study_design_scores_gemma":[0.000019485129,0.000050622613,0.000030629522,0.000010435479,0.0001719745,0.0000010075328,0.000071510745,0.49079,0.5081136,0.0003780909,0.0002486556,0.00011393901],"about_ca_topic_score_codex":0.000027275084,"about_ca_topic_score_gemma":0.0000042656534,"teacher_disagreement_score":0.7789146,"about_ca_system_score_codex":0.00004513625,"about_ca_system_score_gemma":0.000018029948,"threshold_uncertainty_score":0.76397556},"labels":[],"label_agreement":null},{"id":"W2938068717","doi":"10.1017/s0890060419000064","title":"Eco-innovation and knowledge management: issues and organizational challenges to small and medium enterprises","year":2019,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; École de Technologie Supérieure","funders":"","keywords":"TRIZ; Contradiction; Acronym; Computer science; Compromise; Management science; Dimension (graph theory); Product (mathematics); Field (mathematics); Prioritization; Interpretation (philosophy); Competition (biology); Process management; Knowledge management; Business; Engineering; Artificial intelligence; Mathematics; Sociology","score_opus":0.025158340659118546,"score_gpt":0.23823114420486638,"score_spread":0.21307280354574784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938068717","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":[0.37756824,0.0009990341,0.61892074,0.0010152801,0.00016575184,0.00095602224,0.0000018428708,0.00012442311,0.00024863982],"genre_scores_gemma":[0.9935009,0.00041244723,0.0054692174,0.00012565662,0.00018717149,0.00004956286,0.000016244396,0.000030342779,0.00020845377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882853,0.000007298925,0.0003067783,0.0004765562,0.00011092896,0.00026991483],"domain_scores_gemma":[0.999544,0.00010972715,0.00007714947,0.00014488853,0.00009552501,0.00002868239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005419053,0.00022472677,0.00026668527,0.0012191538,0.00012943447,0.00040129005,0.00010279167,0.0000494024,0.000029426259],"category_scores_gemma":[0.00006231022,0.00022749351,0.000029561275,0.0005204627,0.000025132913,0.0003491876,0.00025693807,0.000056173292,0.00001050726],"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":[0.00023025523,0.00019329698,0.009255948,0.0064308667,0.0027624534,0.000021228598,0.0030449831,0.06715624,0.0026848486,0.5955657,0.0001918563,0.31246233],"study_design_scores_gemma":[0.0006699157,0.00026051854,0.054825295,0.0004821795,0.0033581478,0.0000065815457,0.017884469,0.73860437,0.052016385,0.075949945,0.052983943,0.0029582218],"about_ca_topic_score_codex":0.000055159948,"about_ca_topic_score_gemma":0.000045717985,"teacher_disagreement_score":0.6714482,"about_ca_system_score_codex":0.000022012278,"about_ca_system_score_gemma":0.0000035454827,"threshold_uncertainty_score":0.9276916},"labels":[],"label_agreement":null},{"id":"W3088706060","doi":"10.1017/s0890060420000384","title":"Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies","year":2020,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","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":"Polytechnique Montréal","funders":"","keywords":"Greenhouse; Genetic algorithm; Quality (philosophy); Mechatronics; Computer science; Process (computing); Product (mathematics); Component (thermodynamics); Problem statement; Production (economics); Engineering design process; Product design; Industrial engineering; Process engineering; Manufacturing engineering; Engineering; Mathematics; Mechanical engineering; Artificial intelligence","score_opus":0.054027743854046684,"score_gpt":0.23194010706136972,"score_spread":0.17791236320732304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088706060","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":[0.6433568,0.00014458985,0.35583627,0.00016519682,0.000025729796,0.0002830826,0.000016456992,0.00017051358,0.0000013730921],"genre_scores_gemma":[0.98535496,0.000023212535,0.014503024,0.000018373677,0.000053448315,0.000031191863,0.00000897446,0.0000042420206,0.0000025461798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986649,0.000057393903,0.00044426916,0.0004128882,0.00012451205,0.00029599702],"domain_scores_gemma":[0.999226,0.00038305763,0.00013509841,0.00008964259,0.000052678835,0.00011351884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037338297,0.00020121239,0.00039230118,0.00008421754,0.00017807087,0.000041782077,0.00024092957,0.000121165926,0.00005461484],"category_scores_gemma":[0.00017358408,0.00011262363,0.00016663544,0.00039250214,0.0000629588,0.00018396531,0.000044741213,0.000113402915,0.000003112746],"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":[0.00013659458,0.00007859924,0.00021338844,0.000035076282,0.00053511036,0.0000068604077,0.0002624466,0.5239538,0.36358583,0.0006045671,0.000002342594,0.110585354],"study_design_scores_gemma":[0.00001256158,0.00014285256,0.0008704714,0.00000840817,0.00037542253,0.0000026895873,0.00018479607,0.5200042,0.47761953,0.00060061296,0.000012758737,0.0001656803],"about_ca_topic_score_codex":0.00014945991,"about_ca_topic_score_gemma":0.00005320946,"teacher_disagreement_score":0.3419982,"about_ca_system_score_codex":0.000020902497,"about_ca_system_score_gemma":0.000009460858,"threshold_uncertainty_score":0.45926583},"labels":[],"label_agreement":null},{"id":"W3156503367","doi":"10.1017/s089006042100007x","title":"A self-learning finite element extraction system based on reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Concordia University","funders":"","keywords":"Polygon mesh; Finite element method; Computer science; Mesh generation; Reinforcement learning; Boundary element method; Boundary (topology); Artificial neural network; Artificial intelligence; Algorithm; Engineering; Structural engineering; Mathematics; Computer graphics (images)","score_opus":0.02308148735674704,"score_gpt":0.2490084731598185,"score_spread":0.22592698580307147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156503367","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":[0.0019224774,0.00004215167,0.9973671,0.000075782715,0.00016427411,0.00016706676,3.385318e-7,0.00022104214,0.000039768038],"genre_scores_gemma":[0.852032,0.000016467155,0.14772694,0.000025227386,0.000069581816,0.00004381997,0.000023631303,0.000008477766,0.000053833854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867785,0.00006508299,0.00036317323,0.00040745636,0.00024587841,0.00024054844],"domain_scores_gemma":[0.99902946,0.00051465747,0.00010890764,0.0001697833,0.00009487661,0.00008231824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006068753,0.0001631076,0.00019724337,0.0004314804,0.00028176288,0.00030702818,0.00011723992,0.000048155773,0.00001040971],"category_scores_gemma":[0.000100271034,0.0001728994,0.00014370715,0.0005120571,0.000004517047,0.00019364046,0.000040041643,0.00015291564,0.0000071178124],"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":[0.000009770553,0.000023017066,0.0000059426634,0.00005176895,0.00013577216,0.0000065517424,0.00012805407,0.9614832,0.0010603638,0.0095946165,0.0000014171575,0.02749952],"study_design_scores_gemma":[0.000027789982,0.00009555898,0.000026121179,0.00002192158,0.000094639385,0.0000016581114,0.000061518374,0.75503707,0.24343406,0.00009692026,0.0009611006,0.00014161924],"about_ca_topic_score_codex":0.000006605986,"about_ca_topic_score_gemma":0.0000023483126,"teacher_disagreement_score":0.8501095,"about_ca_system_score_codex":0.00009866791,"about_ca_system_score_gemma":0.000035510566,"threshold_uncertainty_score":0.7050632},"labels":[],"label_agreement":null},{"id":"W4234050489","doi":"10.1017/s0890060415000517","title":"AIE volume 29 issue 4 Cover and Front matter","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Computer science; Action (physics); Content (measure theory); Environmental science; Engineering; Physics; Mathematics; Mechanical engineering","score_opus":0.05820371184066587,"score_gpt":0.22520288236031058,"score_spread":0.16699917051964472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4234050489","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":[0.018710148,0.00080013776,0.9763021,0.00017089919,0.0006221152,0.00019399874,0.00006592145,0.000037098715,0.003097614],"genre_scores_gemma":[0.9613555,0.00010522414,0.009062686,0.00009880697,0.00014233515,0.00003385553,0.000009402997,0.000021136952,0.02917107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988471,0.0000046059563,0.000415588,0.00042843487,0.000030096324,0.00027413946],"domain_scores_gemma":[0.9995228,0.000054297212,0.000103287355,0.00016850361,0.000019487532,0.00013164732],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005989353,0.00015938863,0.00039604504,0.00032787575,0.00009436895,0.00020064194,0.00010296898,0.000055095497,0.002043628],"category_scores_gemma":[0.00005090747,0.00017403316,0.00010762216,0.00009283023,0.000050602754,0.00020776395,0.00006563416,0.00005903952,0.0046723196],"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":[0.0002219837,0.00021007667,0.014988749,0.00025560035,0.00521291,0.0000148183935,0.010370218,0.7845684,0.00016615022,0.045692734,0.111337885,0.02696043],"study_design_scores_gemma":[0.00012353332,0.000086801534,0.0015782507,0.00001338025,0.0002765767,0.0000023619873,0.0008646114,0.6749048,0.008185201,0.009982781,0.3032797,0.0007019829],"about_ca_topic_score_codex":0.00017600076,"about_ca_topic_score_gemma":0.000005323303,"teacher_disagreement_score":0.9672394,"about_ca_system_score_codex":0.000045802997,"about_ca_system_score_gemma":0.0000043892273,"threshold_uncertainty_score":0.99886864},"labels":[],"label_agreement":null},{"id":"W4235232759","doi":"10.1017/s0890060418000227","title":"AIE volume 32 issue 4 Cover and Front matter","year":2018,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of Sydney; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology; Worcester Polytechnic Institute; Danmarks Tekniske Universitet; University of Southern California","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Action (physics); Content (measure theory); Environmental science; Computer science; Engineering; Physics; Mathematics; Mechanical engineering","score_opus":0.038767082575729625,"score_gpt":0.22095363229319936,"score_spread":0.18218654971746973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235232759","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":[0.017206822,0.00038439513,0.97859854,0.00016880597,0.0006103061,0.0001758313,0.00006316689,0.00003441365,0.002757708],"genre_scores_gemma":[0.96222,0.00010166952,0.006629926,0.000112787704,0.00021076802,0.00002559168,0.0000058162927,0.000018979998,0.030674426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879825,0.0000039228034,0.00041253836,0.0004685641,0.000024576775,0.00029215607],"domain_scores_gemma":[0.999554,0.000058673642,0.00010413653,0.0001812706,0.000019674206,0.00008226282],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004518213,0.00016011129,0.00037289594,0.00033810065,0.00017292176,0.00019767834,0.00010694477,0.000055967925,0.0065576993],"category_scores_gemma":[0.000034195687,0.00017500616,0.00011305486,0.00009920621,0.00009716865,0.00018507405,0.000065762564,0.000052916224,0.007165911],"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":[0.00072430377,0.00064125954,0.037971355,0.00093487016,0.020099616,0.000028938874,0.027570775,0.31484398,0.0020095229,0.17129584,0.27140453,0.15247501],"study_design_scores_gemma":[0.00008219397,0.00011519371,0.00259464,0.000015891948,0.0002680953,0.0000018382208,0.00036462286,0.6459785,0.023964778,0.006928289,0.3190137,0.00067226886],"about_ca_topic_score_codex":0.00013194759,"about_ca_topic_score_gemma":0.0000064143915,"teacher_disagreement_score":0.97196865,"about_ca_system_score_codex":0.00003146424,"about_ca_system_score_gemma":0.0000026630212,"threshold_uncertainty_score":0.99435043},"labels":[],"label_agreement":null},{"id":"W4235939205","doi":"10.1017/s089006041600010x","title":"AIE volume 30 issue 2 Cover and Front matter","year":2016,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Action (physics); Environmental science; Content (measure theory); Computer science; Engineering; Geography; Meteorology; Physics; Mathematics; Mechanical engineering","score_opus":0.03499590841965236,"score_gpt":0.21136694595805985,"score_spread":0.1763710375384075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235939205","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":[0.016930882,0.00041555235,0.9803722,0.00025937814,0.00040473588,0.00015935578,0.00009029952,0.000032002037,0.0013356261],"genre_scores_gemma":[0.95997715,0.00024216299,0.004278013,0.00007503903,0.000112787515,0.000033332064,0.00000281271,0.00001904826,0.03525963],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99881077,0.0000040750415,0.00041533692,0.000458101,0.000024356568,0.000287346],"domain_scores_gemma":[0.9995218,0.0001015355,0.00010453031,0.00017528901,0.000013071239,0.000083785795],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00042338754,0.00015853281,0.0003731399,0.00030377062,0.00011299692,0.00013505878,0.00010112767,0.000053267137,0.009871146],"category_scores_gemma":[0.000041680072,0.00013910177,0.00012080849,0.00006594379,0.00006090326,0.00020139551,0.000059811897,0.00003769237,0.0079562375],"study_design_candidate":"not_applicable","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":[0.0004741557,0.00052521715,0.04805743,0.0007869681,0.017607022,0.000035197085,0.011088984,0.22975229,0.0045874286,0.19700968,0.16757806,0.32249755],"study_design_scores_gemma":[0.00027878495,0.00015494836,0.0085882535,0.00006575319,0.0005602387,0.0000036113208,0.00049361534,0.28745678,0.056270245,0.021896927,0.6225926,0.0016381947],"about_ca_topic_score_codex":0.00007141922,"about_ca_topic_score_gemma":0.0000025509407,"teacher_disagreement_score":0.9760941,"about_ca_system_score_codex":0.000039420214,"about_ca_system_score_gemma":0.000002486977,"threshold_uncertainty_score":0.9928162},"labels":[],"label_agreement":null},{"id":"W4240731888","doi":"10.1017/s0890060420000268","title":"AIE volume 34 issue 2 Cover and Front matter","year":2020,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of Sydney; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology; Worcester Polytechnic Institute; Danmarks Tekniske Universitet; University of Southern California","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Action (physics); Content (measure theory); Computer science; Environmental science; Engineering; Geography; Physics; Mathematics; Meteorology; Mechanical engineering","score_opus":0.04705665610436991,"score_gpt":0.21281586266204403,"score_spread":0.16575920655767412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240731888","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":[0.012398046,0.00058888405,0.98429126,0.0005769297,0.00029714714,0.00018949392,0.000083980325,0.000039822444,0.0015344418],"genre_scores_gemma":[0.98469895,0.00015056599,0.0066441954,0.00029381755,0.00015292106,0.000025042842,0.000007952705,0.000019676754,0.008006899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988259,0.0000038289195,0.0004170167,0.0004735675,0.000024556348,0.00025513838],"domain_scores_gemma":[0.9995856,0.000057913763,0.00010001984,0.00012447209,0.000011201539,0.00012075331],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00027909988,0.00015944119,0.0004096086,0.00019566194,0.00011380842,0.00018767364,0.000106425054,0.000049889422,0.0048250216],"category_scores_gemma":[0.00004396695,0.0001777009,0.00012798859,0.000100635654,0.000044329605,0.00017630628,0.00006715686,0.00006546057,0.0048911516],"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":[0.00029350014,0.00017610769,0.012891052,0.00066026946,0.007634625,0.00002040456,0.014890858,0.79650664,0.0010243639,0.04766596,0.08253837,0.03569785],"study_design_scores_gemma":[0.00006745389,0.00006514775,0.0011706154,0.000008219062,0.00021320897,7.569533e-7,0.00045643654,0.78140473,0.00966125,0.0021396875,0.20431179,0.00050069677],"about_ca_topic_score_codex":0.00008389263,"about_ca_topic_score_gemma":0.0000019808733,"teacher_disagreement_score":0.97764707,"about_ca_system_score_codex":0.000026026579,"about_ca_system_score_gemma":0.0000025041882,"threshold_uncertainty_score":0.9960847},"labels":[],"label_agreement":null},{"id":"W4242441237","doi":"10.1017/s0890060415000347","title":"AIE volume 29 issue 3 Cover and Front matter","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"BIM and Construction Integration","field":"Engineering","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Cover (algebra); Scope (computer science); Front cover; Volume (thermodynamics); Computer science; Section (typography); Data science; Engineering; Artificial intelligence; Mechanical engineering","score_opus":0.023653263553407203,"score_gpt":0.2268657068199228,"score_spread":0.2032124432665156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242441237","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":[0.034374714,0.00033878777,0.9644383,0.000057957845,0.000296443,0.0001501563,0.0000065042345,0.00013583907,0.00020130182],"genre_scores_gemma":[0.9866182,0.00004942474,0.012590611,0.000027729775,0.00015151237,0.00004064118,0.000010356112,0.000025809575,0.0004857221],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916035,0.000009690793,0.00028569944,0.00021964077,0.000100993624,0.00022361134],"domain_scores_gemma":[0.999614,0.00004984695,0.000027774637,0.00013250411,0.000038191643,0.00013765563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019809746,0.00018549952,0.00023820497,0.00026526387,0.000057353285,0.00014830894,0.00006592125,0.00008429973,0.0002730007],"category_scores_gemma":[0.000017760245,0.00018187634,0.00007848063,0.000111055466,0.000029311446,0.0001962221,0.000016490114,0.00009984971,0.00018455951],"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":[0.000024605048,0.000010124767,0.00029447567,0.00006133807,0.00046792917,0.0000018881967,0.0005656821,0.92245066,0.0017009769,0.00069481076,0.0014264517,0.07230104],"study_design_scores_gemma":[0.000032172844,0.000032457134,0.00028549202,0.000011372733,0.00025248207,0.0000051562556,0.0001701291,0.87177944,0.111201815,0.0009653196,0.01500254,0.0002615983],"about_ca_topic_score_codex":0.000044546927,"about_ca_topic_score_gemma":0.000010578134,"teacher_disagreement_score":0.9522435,"about_ca_system_score_codex":0.000041099393,"about_ca_system_score_gemma":0.0000062145446,"threshold_uncertainty_score":0.7416702},"labels":[],"label_agreement":null},{"id":"W4243655611","doi":"10.1017/s0890060419000179","title":"AIE volume 33 issue 2 Cover and Front matter","year":2019,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of Sydney; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology; Worcester Polytechnic Institute; Danmarks Tekniske Universitet; University of Southern California","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Action (physics); Content (measure theory); Computer science; Environmental science; Geography; Engineering; Meteorology; Physics; Mathematics; Mechanical engineering","score_opus":0.028768905332964553,"score_gpt":0.20701138648294362,"score_spread":0.17824248114997907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243655611","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":[0.09223375,0.00065526995,0.90117145,0.00015598013,0.00090330804,0.0003682809,0.00011963166,0.00004434908,0.0043479702],"genre_scores_gemma":[0.95155036,0.00011949889,0.0038500722,0.000085663196,0.00007736815,0.000023030709,0.000007844907,0.000018341338,0.044267826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880916,0.0000037169573,0.00040737522,0.00047470583,0.000025879852,0.00027916726],"domain_scores_gemma":[0.99954706,0.00007114232,0.00010571689,0.00019136039,0.000012000467,0.00007270358],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00041678752,0.00016099405,0.00042016665,0.00032954884,0.00008963286,0.00018486699,0.00010389927,0.00005588279,0.008356855],"category_scores_gemma":[0.000020125359,0.00017647022,0.00013073791,0.000086090746,0.000035556724,0.00019948091,0.00006237791,0.00006230839,0.011685092],"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":[0.00021560291,0.0002119339,0.043241795,0.0005964671,0.006884633,0.00000890694,0.0065177013,0.80889094,0.0008423714,0.06594374,0.03803872,0.028607203],"study_design_scores_gemma":[0.0001085841,0.00007931846,0.004897771,0.000019093186,0.0002350376,0.0000016068233,0.00047862876,0.7105147,0.011642763,0.004937466,0.26635164,0.0007333639],"about_ca_topic_score_codex":0.00011983281,"about_ca_topic_score_gemma":0.0000029360606,"teacher_disagreement_score":0.8973214,"about_ca_system_score_codex":0.000033309854,"about_ca_system_score_gemma":0.000002574626,"threshold_uncertainty_score":0.99254966},"labels":[],"label_agreement":null},{"id":"W4244647496","doi":"10.1017/s0890060416000433","title":"AIE volume 30 issue 4 Cover and Front matter","year":2016,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Front (military); Cover (algebra); Volume (thermodynamics); Action (physics); Content (measure theory); Computer science; Environmental science; Engineering; Physics; Mechanical engineering; Mathematics; Thermodynamics","score_opus":0.035721437309471124,"score_gpt":0.21178488493300818,"score_spread":0.17606344762353704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244647496","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":[0.016994666,0.0004120783,0.9803399,0.00025827362,0.0004018502,0.00015915975,0.00008982759,0.000031907322,0.0013123822],"genre_scores_gemma":[0.9608278,0.000237677,0.0042718505,0.00007431101,0.0001105629,0.00003301333,0.0000027413864,0.000018875007,0.03442317],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988104,0.000004072708,0.00041544973,0.000458242,0.00002436372,0.0002874333],"domain_scores_gemma":[0.99952126,0.00010208515,0.000104446815,0.00017541277,0.00001299447,0.00008379298],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004252384,0.00015856205,0.00037316696,0.00030394993,0.00011302593,0.00013510333,0.00010117806,0.000053266893,0.009702939],"category_scores_gemma":[0.00004160642,0.00013912904,0.00012082724,0.00006595732,0.000060915445,0.00020143153,0.000059802787,0.000037692167,0.007961225],"study_design_candidate":"not_applicable","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":[0.00048576234,0.000529439,0.049747273,0.0007931399,0.01773284,0.000035169312,0.011177068,0.22833483,0.0047003636,0.20038657,0.16435096,0.3217266],"study_design_scores_gemma":[0.00028582997,0.00016035352,0.008884038,0.000067903144,0.00057398964,0.0000036907377,0.000508172,0.29657972,0.05785787,0.022486882,0.61091274,0.0016788243],"about_ca_topic_score_codex":0.000071011076,"about_ca_topic_score_gemma":0.0000025606444,"teacher_disagreement_score":0.976068,"about_ca_system_score_codex":0.000039414626,"about_ca_system_score_gemma":0.0000024892522,"threshold_uncertainty_score":0.9928112},"labels":[],"label_agreement":null},{"id":"W4246413105","doi":"10.1017/s0890060414000717","title":"AIE volume 29 issue 1 Cover and Front matter","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"BIM and Construction Integration","field":"Engineering","cited_by":0,"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":"University of South Australia; University of Cambridge; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Volume (thermodynamics); Cover (algebra); Front (military); Environmental science; Physics; Geology; Engineering; Oceanography; Mechanical engineering; Thermodynamics","score_opus":0.02401772130387798,"score_gpt":0.22715617341201194,"score_spread":0.20313845210813397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246413105","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":[0.034191947,0.00033748668,0.9646249,0.000058185506,0.00029429924,0.00015019067,0.000006586479,0.00013600462,0.00020041397],"genre_scores_gemma":[0.9864992,0.000048917736,0.012717446,0.000027770635,0.00015028496,0.00004128201,0.000010506207,0.000025833266,0.00047879346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991604,0.000009698131,0.00028559967,0.0002197161,0.0001009775,0.0002236249],"domain_scores_gemma":[0.99961376,0.000050196268,0.000027771186,0.00013266037,0.000037929072,0.00013766479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000198168,0.00018552002,0.00023818585,0.00026533342,0.00005735716,0.00014807007,0.00006595384,0.000084305364,0.00027839528],"category_scores_gemma":[0.000017763992,0.00018191275,0.00007848456,0.00011108784,0.00002930981,0.00019610359,0.000016499482,0.00009986575,0.00018298977],"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":[0.000024516512,0.000010139697,0.0002944851,0.00006059478,0.0004687489,0.0000018725543,0.00056628085,0.92312104,0.0016995082,0.00069900975,0.0014763833,0.07157743],"study_design_scores_gemma":[0.000031901305,0.000032568692,0.00028752178,0.0000114183085,0.00025487266,0.0000052065084,0.00017116444,0.87339765,0.10976688,0.00096535805,0.014813299,0.00026218628],"about_ca_topic_score_codex":0.000044153723,"about_ca_topic_score_gemma":0.00001052201,"teacher_disagreement_score":0.9523072,"about_ca_system_score_codex":0.000040971758,"about_ca_system_score_gemma":0.000006211846,"threshold_uncertainty_score":0.74181867},"labels":[],"label_agreement":null},{"id":"W4248315074","doi":"10.1017/s0890060415000542","title":"AIE volume 30 issue 1 Cover and Front matter","year":2016,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; University of Cambridge; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Action (physics); Content (measure theory); Environmental science; Computer science; Engineering; Physics; Mechanical engineering; Mathematics","score_opus":0.03602327867535659,"score_gpt":0.21175375247173334,"score_spread":0.17573047379637674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248315074","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":[0.016980236,0.00040979934,0.98035455,0.00025984584,0.00040214288,0.00015923734,0.00009164442,0.000031941418,0.0013105979],"genre_scores_gemma":[0.960122,0.00023673769,0.0043357927,0.00007479908,0.00011120075,0.00003371193,0.000002794954,0.000019008035,0.035063937],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988106,0.000004076076,0.00041519763,0.00045835818,0.00002436113,0.00028742527],"domain_scores_gemma":[0.99952054,0.00010262315,0.00010442081,0.00017554118,0.0000130930275,0.00008379488],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00042512562,0.00015857055,0.00037307153,0.00030391957,0.00011299072,0.00013483978,0.000101169826,0.000053266904,0.010011312],"category_scores_gemma":[0.000042060714,0.00013913582,0.00012082598,0.00006598219,0.00006090389,0.00020129228,0.000059818194,0.00003769887,0.008019872],"study_design_candidate":"not_applicable","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":[0.00047922242,0.00052879343,0.049553048,0.0007868006,0.017722456,0.000034719138,0.011154719,0.23043442,0.00474097,0.19573791,0.17080909,0.31801784],"study_design_scores_gemma":[0.00028136247,0.00015716231,0.008818473,0.00006732393,0.0005698607,0.000003657308,0.0005045919,0.293026,0.05734722,0.02208157,0.61547834,0.0016644268],"about_ca_topic_score_codex":0.00007088514,"about_ca_topic_score_gemma":0.000002539696,"teacher_disagreement_score":0.9760188,"about_ca_system_score_codex":0.000039411763,"about_ca_system_score_gemma":0.0000024952953,"threshold_uncertainty_score":0.9927525},"labels":[],"label_agreement":null},{"id":"W4250530910","doi":"10.1017/s0890060416000275","title":"AIE volume 30 issue 3 Cover and Front matter","year":2016,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Content (measure theory); Action (physics); Computer science; Mathematics; Engineering; Physics; Mechanical engineering; Thermodynamics","score_opus":0.03549148969088859,"score_gpt":0.21149148967511247,"score_spread":0.1759999999842239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250530910","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":[0.017062992,0.0004111643,0.980265,0.00025877132,0.00040480748,0.00015914268,0.00009050994,0.000031892076,0.0013156817],"genre_scores_gemma":[0.95976883,0.0002386106,0.0042851428,0.000074541524,0.000111846326,0.0000331411,0.000002750948,0.000018952696,0.03546617],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988106,0.0000040731106,0.00041533716,0.00045820719,0.000024364877,0.00028740856],"domain_scores_gemma":[0.9995213,0.0001019364,0.00010443333,0.00017534243,0.000013180182,0.00008378957],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004249802,0.0001585537,0.0003731003,0.00030384294,0.000112983376,0.00013504895,0.000101121754,0.000053263484,0.009826484],"category_scores_gemma":[0.00004205218,0.00013910903,0.00012082016,0.00006596369,0.00006090716,0.00020140925,0.000059785532,0.00003769305,0.008085493],"study_design_candidate":"not_applicable","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":[0.00048268077,0.00053001504,0.049736395,0.0007990496,0.017758671,0.00003512857,0.011184955,0.23110576,0.0047626183,0.19533637,0.1658791,0.32238927],"study_design_scores_gemma":[0.00028159466,0.00015550134,0.008694716,0.00006657602,0.0005605979,0.0000035969124,0.0004979951,0.29057422,0.057723016,0.021919474,0.617874,0.0016487157],"about_ca_topic_score_codex":0.000071492024,"about_ca_topic_score_gemma":0.000002552721,"teacher_disagreement_score":0.9759799,"about_ca_system_score_codex":0.000039529816,"about_ca_system_score_gemma":0.0000024963376,"threshold_uncertainty_score":0.9926868},"labels":[],"label_agreement":null},{"id":"W4251710382","doi":"10.1017/s0890060414000584","title":"AIE volume 28 issue 4 Cover and Front matter","year":2014,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Action (physics); Content (measure theory); Environmental science; Computer science; Engineering; Geography; Meteorology; Physics; Mathematics; Mechanical engineering","score_opus":0.031044609787014628,"score_gpt":0.20823742953478963,"score_spread":0.177192819747775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251710382","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":[0.014695463,0.00030903344,0.9817104,0.00014663818,0.00043249584,0.00014857789,0.000039555856,0.000031760745,0.0024860809],"genre_scores_gemma":[0.97179615,0.00010415728,0.0076464554,0.00011388267,0.00013402147,0.000028134576,0.000006967809,0.000018817782,0.020151392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988175,0.0000055912365,0.00041918905,0.00045354076,0.00002483811,0.00027931633],"domain_scores_gemma":[0.99951947,0.00009417458,0.00010768247,0.00018181127,0.000012402656,0.00008448918],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00059237116,0.00016221908,0.00041033968,0.00032158816,0.00013677815,0.0001967722,0.00010449131,0.000055257653,0.0033859399],"category_scores_gemma":[0.00004888606,0.00017739463,0.00012400403,0.000080214355,0.000050887942,0.00016713612,0.000057591198,0.000059261543,0.00418543],"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":[0.00016268174,0.00021249254,0.014652772,0.00046514394,0.0055877157,0.0000054645875,0.005981446,0.7227118,0.00048526845,0.123839185,0.05257962,0.073316395],"study_design_scores_gemma":[0.00006088691,0.00005148602,0.0022127018,0.00001045274,0.00018047252,9.524739e-7,0.00016915074,0.74663097,0.007230883,0.006766301,0.23622079,0.00046495677],"about_ca_topic_score_codex":0.00011642081,"about_ca_topic_score_gemma":0.0000043372083,"teacher_disagreement_score":0.97406393,"about_ca_system_score_codex":0.000027195998,"about_ca_system_score_gemma":0.0000018011682,"threshold_uncertainty_score":0.9975251},"labels":[],"label_agreement":null},{"id":"W4252244050","doi":"10.1017/s0890060417000555","title":"AIE volume 31 issue 4 Cover and Front matter","year":2017,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Volume (thermodynamics); Front (military); Action (physics); Computer science; Content (measure theory); Environmental science; Geography; Engineering; Physics; Mathematics; Meteorology; Mechanical engineering; Thermodynamics","score_opus":0.047548826648462175,"score_gpt":0.23125872262436478,"score_spread":0.1837098959759026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252244050","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":[0.040690526,0.0005361433,0.9517024,0.0003216985,0.0009300244,0.00025827604,0.00010342543,0.000033918932,0.005423552],"genre_scores_gemma":[0.9583167,0.00016754873,0.004764061,0.00005595079,0.00013692955,0.000028845712,0.000004862238,0.000016299886,0.036508817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988563,0.0000026004525,0.00038504959,0.00045532285,0.000023572169,0.00027715167],"domain_scores_gemma":[0.9993488,0.00005209385,0.00018252844,0.00032058122,0.000013033639,0.00008295343],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004702895,0.00016055188,0.00040332906,0.00024906837,0.000386412,0.00051681197,0.00018735949,0.00005649178,0.0025439358],"category_scores_gemma":[0.000063095955,0.00017623961,0.00013154841,0.000031358937,0.00008431788,0.00029466965,0.00010436973,0.00006146237,0.0036397604],"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":[0.00043809885,0.00045884485,0.07252709,0.00091435155,0.016267167,0.0000441456,0.01199842,0.49932107,0.0012620576,0.13750891,0.15110768,0.108152166],"study_design_scores_gemma":[0.00014550067,0.00007713496,0.018375805,0.000028043989,0.00041609813,0.0000022166341,0.00038359777,0.63724554,0.022476412,0.011412849,0.3084262,0.0010105941],"about_ca_topic_score_codex":0.00024277368,"about_ca_topic_score_gemma":0.0000074863337,"teacher_disagreement_score":0.9469384,"about_ca_system_score_codex":0.000030197589,"about_ca_system_score_gemma":0.000002942623,"threshold_uncertainty_score":0.99836785},"labels":[],"label_agreement":null},{"id":"W4254632063","doi":"10.1017/s0890060419000441","title":"AIE volume 33 issue 4 Cover and Front matter","year":2019,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of Sydney; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology; Worcester Polytechnic Institute; Danmarks Tekniske Universitet; University of Southern California","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Action (physics); Content (measure theory); Environmental science; Computer science; Geography; Engineering; Meteorology; Physics; Mathematics; Mechanical engineering; Thermodynamics","score_opus":0.02937127118496542,"score_gpt":0.20742437983459677,"score_spread":0.17805310864963136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254632063","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":[0.09180944,0.0006445549,0.9017376,0.00015406414,0.00088963995,0.0003648633,0.00011804729,0.00004386145,0.00423795],"genre_scores_gemma":[0.95249844,0.00011754941,0.0038531919,0.00008502325,0.000076013035,0.000022861885,0.000007663209,0.000018215469,0.04332104],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988088,0.0000037148288,0.00040748587,0.0004748519,0.000025887452,0.00027925207],"domain_scores_gemma":[0.9995467,0.00007152746,0.00010563248,0.00019149554,0.000011929989,0.00007270983],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00041860953,0.00016102374,0.00042019715,0.00032974334,0.00008965588,0.00018492797,0.000103951046,0.00005588253,0.008214238],"category_scores_gemma":[0.000020089794,0.00017650481,0.0001307582,0.00008610841,0.00003556384,0.00019951658,0.00006236841,0.00006230805,0.011692389],"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":[0.00022146868,0.00021420636,0.044880066,0.0006027456,0.0069522928,0.00000892361,0.006586966,0.80639696,0.000865408,0.067252874,0.037404455,0.028613664],"study_design_scores_gemma":[0.00010944102,0.00008069399,0.004980556,0.00001938326,0.00023672475,0.0000016143239,0.0004843911,0.7196716,0.011768504,0.0049845623,0.2569237,0.0007388133],"about_ca_topic_score_codex":0.00011914802,"about_ca_topic_score_gemma":0.0000029472294,"teacher_disagreement_score":0.89788437,"about_ca_system_score_codex":0.000033305132,"about_ca_system_score_gemma":0.0000025769814,"threshold_uncertainty_score":0.9926924},"labels":[],"label_agreement":null},{"id":"W4255938318","doi":"10.1017/s0890060415000104","title":"AIE volume 29 issue 2 Cover and Front matter","year":2015,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"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":"University of South Australia; George Mason University; Massachusetts Institute of Technology; Oregon State University; University of Toronto; Carnegie Mellon University; University College London; Drexel University; Boeing; Worcester Polytechnic Institute; Aalborg Universitet; Technische Universiteit Delft; Georgia Institute of Technology","keywords":"Front cover; Cover (algebra); Front (military); Volume (thermodynamics); Action (physics); Environmental science; Content (measure theory); Computer science; Engineering; Geography; Meteorology; Physics; Mathematics; Mechanical engineering","score_opus":0.05705216295712534,"score_gpt":0.22476794176693293,"score_spread":0.1677157788098076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255938318","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":[0.01869044,0.0008090719,0.97624063,0.00017209581,0.0006282826,0.00019476477,0.00006644762,0.000037309812,0.003160975],"genre_scores_gemma":[0.9604919,0.00010756979,0.009106412,0.00010010956,0.00014568592,0.000034297012,0.000009679989,0.000021402495,0.029982975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988475,0.000004608595,0.00041547514,0.00042830306,0.000030087487,0.0002740562],"domain_scores_gemma":[0.999523,0.000054004835,0.000103369886,0.00016838466,0.000019602652,0.000131636],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00059632893,0.00015935923,0.0003960163,0.00032768236,0.00009434472,0.00020057579,0.00010291769,0.00005509575,0.0020793343],"category_scores_gemma":[0.000050997587,0.00017399905,0.00010760546,0.000092811184,0.000050592633,0.00020772681,0.000065644155,0.00005903984,0.004669383],"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":[0.00021553757,0.00020730295,0.014403262,0.00025227456,0.0051486404,0.000014751921,0.010234263,0.7848774,0.0001613032,0.044685524,0.1129161,0.026883623],"study_design_scores_gemma":[0.00012231858,0.00008514962,0.0015488683,0.0000131534,0.00027405107,0.0000023462662,0.0008526016,0.66492224,0.008081364,0.00986853,0.31353396,0.00069539825],"about_ca_topic_score_codex":0.00017701223,"about_ca_topic_score_gemma":0.00000530313,"teacher_disagreement_score":0.9671342,"about_ca_system_score_codex":0.00004580949,"about_ca_system_score_gemma":0.000004385215,"threshold_uncertainty_score":0.9988329},"labels":[],"label_agreement":null},{"id":"W4313192501","doi":"10.1017/s0890060422000166","title":"Machine learning in requirements elicitation: a literature review","year":2022,"lang":"en","type":"review","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Software Engineering Research","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Requirements elicitation; Computer science; Preprocessor; Construct (python library); Process (computing); Expert elicitation; Data pre-processing; Requirements management; Artificial intelligence; Machine learning; Information retrieval; Requirements analysis; Software","score_opus":0.09816058246718821,"score_gpt":0.34776332462493664,"score_spread":0.24960274215774841,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313192501","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[3.7626828e-7,0.5113912,0.48788372,0.00001637047,0.00008589292,0.0005026188,0.000004525785,0.000113874004,0.0000014128733],"genre_scores_gemma":[0.00007831237,0.97607183,0.022942869,0.000016965529,0.000053140866,0.00063415035,0.00011298884,0.0000495873,0.00004014539],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99706084,0.0001707484,0.000961918,0.00085268344,0.00041620413,0.0005376269],"domain_scores_gemma":[0.99692166,0.0021631087,0.00021125542,0.00053657644,0.000038869162,0.00012852356],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0018655746,0.00048449374,0.0014053214,0.0018224319,0.00015405337,0.00034681,0.00088715303,0.00013781636,0.000038515678],"category_scores_gemma":[0.0009424098,0.00046423607,0.0005518387,0.002577495,0.000014426132,0.00031182126,0.00031112522,0.0008086212,0.0000061669057],"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":[0.0000016840382,0.000018799958,0.0000010972033,0.011720218,0.00036680812,0.00003649677,0.000107157095,0.028664852,8.955131e-7,0.00069020485,0.0000065005775,0.9583853],"study_design_scores_gemma":[0.00003125041,0.000106801555,0.0000031540444,0.016304558,0.0011487326,0.000027713244,0.0000066483726,0.3099988,0.00017914607,0.00036113203,0.67088586,0.0009462045],"about_ca_topic_score_codex":0.000019690318,"about_ca_topic_score_gemma":0.000004838481,"teacher_disagreement_score":0.95743906,"about_ca_system_score_codex":0.00021665574,"about_ca_system_score_gemma":0.00006283817,"threshold_uncertainty_score":0.99978095},"labels":[],"label_agreement":null},{"id":"W4313646565","doi":"10.1017/s0890060422000233","title":"Neural networks with dimensionality reduction for predicting temperature change due to plastic deformation in a cold rolling simulation","year":2023,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Artificial neural network; Nonlinear system; Curse of dimensionality; Reduction (mathematics); Dimension (graph theory); Computation; Process (computing); Principal component analysis; Deformation (meteorology); Computer science; Algorithm; Control theory (sociology); Materials science; Mathematics; Artificial intelligence; Physics; Geometry","score_opus":0.039053122886468644,"score_gpt":0.2511042695288267,"score_spread":0.21205114664235805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313646565","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":[0.4650882,0.0000136983645,0.53414345,0.000008766893,0.00020270029,0.00040380005,0.0000040678406,0.00013510809,2.221468e-7],"genre_scores_gemma":[0.9950003,0.000008772726,0.0043869386,0.0000050332574,0.00019984001,0.00030891254,0.000057552355,0.000029979312,0.000002665553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989765,0.000012299587,0.000360805,0.00022748711,0.00010025394,0.00032261913],"domain_scores_gemma":[0.99946386,0.00029202402,0.000041340783,0.00009407375,0.000035027442,0.00007365588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048939866,0.00018180735,0.00026662712,0.00051904307,0.00013508751,0.0000933201,0.000054610187,0.000088335175,0.0000022777645],"category_scores_gemma":[0.000060042243,0.00017117611,0.00006817424,0.00054923986,0.000007014281,0.00030215224,0.000014750437,0.00009454296,0.0000010551614],"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":[0.00008377561,0.000004510893,0.000012458913,0.00009694214,0.00009295522,0.000001441603,0.0003249074,0.9862445,0.008612718,0.00014699402,7.6682153e-7,0.0043780324],"study_design_scores_gemma":[0.000041700525,0.000056316825,0.0003253553,0.000054007436,0.00012061296,0.000001486854,0.00009506938,0.91288227,0.08613884,0.00008730028,0.000015858694,0.00018116714],"about_ca_topic_score_codex":0.000022375838,"about_ca_topic_score_gemma":0.00004132204,"teacher_disagreement_score":0.5299121,"about_ca_system_score_codex":0.00005051114,"about_ca_system_score_gemma":0.0000031792522,"threshold_uncertainty_score":0.6980359},"labels":[],"label_agreement":null},{"id":"W4321373549","doi":"10.1017/s0890060422000269","title":"Graph models for engineering design: Model encoding, and fidelity evaluation based on dataset and other sources of knowledge","year":2023,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Software Engineering Research","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":"Université de Montréal; Polytechnique Montréal","funders":"University of Cambridge","keywords":"Computer science; Fidelity; Machine learning; Graph; Artificial intelligence; Data modeling; Data mining; Knowledge extraction; Theoretical computer science; Software engineering","score_opus":0.1348726931625898,"score_gpt":0.33442678356376837,"score_spread":0.19955409040117858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321373549","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":[0.024997959,0.00017325456,0.97388935,0.000028498991,0.00005495656,0.0006204594,0.00007386172,0.00016081161,8.2892393e-7],"genre_scores_gemma":[0.87010866,0.000029679213,0.129586,0.00000960932,0.000025196407,0.00018409273,0.000028094379,0.00002533932,0.0000033355557],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833876,0.000036807196,0.0003968375,0.00056419364,0.00027676942,0.0003866063],"domain_scores_gemma":[0.9971504,0.002188456,0.000072167575,0.00036768423,0.000091440976,0.00012985877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023855437,0.000245348,0.00034579146,0.001048058,0.00012047106,0.00016407366,0.00028660815,0.00008588726,0.000001562451],"category_scores_gemma":[0.0004862459,0.00024493563,0.000100687306,0.0005759956,0.00003412972,0.00026519684,0.000098024095,0.00010630674,6.5547647e-7],"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":[0.000021309099,0.0000174807,0.000019911044,0.00013532532,0.000120640914,4.5936108e-7,0.0003331882,0.98171777,0.0015545612,0.0018601322,0.000021911472,0.0141973095],"study_design_scores_gemma":[0.00006832743,0.00008957957,0.00008122136,0.00004357605,0.00013352193,5.477015e-7,0.000015092882,0.889575,0.10605434,0.0036914595,0.000027819331,0.00021951158],"about_ca_topic_score_codex":0.000026549837,"about_ca_topic_score_gemma":0.000004653434,"teacher_disagreement_score":0.8451107,"about_ca_system_score_codex":0.000035723457,"about_ca_system_score_gemma":0.00003418531,"threshold_uncertainty_score":0.9988185},"labels":[],"label_agreement":null},{"id":"W4389619140","doi":"10.1017/s0890060423000203","title":"Mapping artificial intelligence-based methods to engineering design stages: a focused literature review","year":2023,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","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 Toronto","funders":"","keywords":"Engineering design process; Computer science; Context (archaeology); Categorization; Process (computing); Artificial intelligence; Software engineering; Data science; Engineering","score_opus":0.08865348405901104,"score_gpt":0.3408843748580329,"score_spread":0.2522308907990219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389619140","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":[0.00070978334,0.0024652183,0.9932019,0.0005328344,0.0006589495,0.0012730121,0.000019844296,0.0011271442,0.000011282428],"genre_scores_gemma":[0.21984541,0.004031751,0.77400464,0.0003271472,0.0004171505,0.0009634883,0.00010619101,0.00022974962,0.00007447244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99675876,0.00015844758,0.0011187366,0.0007302725,0.00033182034,0.0009019655],"domain_scores_gemma":[0.9966128,0.002152835,0.00011985965,0.0005633448,0.00013969647,0.0004114729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003174506,0.00062542735,0.0008299591,0.0021282127,0.00020421787,0.00049239147,0.00042612484,0.00020538161,0.00008781526],"category_scores_gemma":[0.00092084176,0.00066007377,0.0003927626,0.0037519622,0.000025009818,0.00037800838,0.000055406512,0.00043790924,0.00009356543],"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":[0.000025984638,0.000022535894,9.539963e-7,0.0008610279,0.0004282017,0.000012136541,0.0006672637,0.8100982,0.0105631715,0.0007938771,0.00023591163,0.17629075],"study_design_scores_gemma":[0.000016700511,0.000054080934,0.000019724752,0.0006575314,0.00035758922,0.0000033632289,0.00013042819,0.71750915,0.27647403,0.0007556198,0.0034163636,0.0006054109],"about_ca_topic_score_codex":0.000012616687,"about_ca_topic_score_gemma":0.000004555767,"teacher_disagreement_score":0.26591086,"about_ca_system_score_codex":0.00013825305,"about_ca_system_score_gemma":0.00004696891,"threshold_uncertainty_score":0.99958503},"labels":[],"label_agreement":null},{"id":"W4404252836","doi":"10.1017/s089006042400012x","title":"Analyzing problem framing in design teams: a systems mapping approach","year":2024,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","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 Waterloo; University of Toronto","funders":"","keywords":"Framing (construction); Computer science; Management science; Systems engineering; Process management; Engineering; Civil engineering","score_opus":0.04177417197638531,"score_gpt":0.26357773240557497,"score_spread":0.22180356042918967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404252836","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":[0.003849206,0.0030034017,0.99148196,0.000027650814,0.00037843786,0.00063747633,0.0000037119119,0.0005049787,0.00011318092],"genre_scores_gemma":[0.8905794,0.00025010237,0.108635016,0.000005698826,0.00014233102,0.0002580121,0.000012465519,0.00006334671,0.00005361812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981035,0.00006626565,0.00067389064,0.0004878576,0.00015819569,0.0005103092],"domain_scores_gemma":[0.99874395,0.0008336858,0.000045602425,0.00021864618,0.000029397568,0.00012874488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015531988,0.00033051573,0.0004382601,0.0015327471,0.00009843302,0.0006814501,0.00017806899,0.00013640296,0.000013376399],"category_scores_gemma":[0.000069829635,0.00034073007,0.00016044176,0.0011611291,0.000020716398,0.00047119756,0.00002147934,0.00033098742,0.000013489119],"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":[0.000007963803,0.000019730009,0.000011151883,0.0004725704,0.0005127944,0.0000066484076,0.0011165454,0.95633835,0.0031295926,0.00212209,0.000033742817,0.036228802],"study_design_scores_gemma":[0.000019939915,0.000020901323,0.000032166052,0.00013934822,0.00029218046,0.000008314808,0.0007830943,0.97328275,0.022881556,0.0006748033,0.0014738763,0.00039103877],"about_ca_topic_score_codex":0.0001000558,"about_ca_topic_score_gemma":0.0000057281122,"teacher_disagreement_score":0.8867302,"about_ca_system_score_codex":0.00016351903,"about_ca_system_score_gemma":0.00003255191,"threshold_uncertainty_score":0.99990445},"labels":[],"label_agreement":null},{"id":"W4409897988","doi":"10.1017/s0890060425000083","title":"Enhancing TRIZ through environment-based design methodology supported by a large language model","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"TRIZ; Computer science; Architectural engineering; Engineering; Systems engineering; Manufacturing engineering","score_opus":0.05799305597294572,"score_gpt":0.3134753793616325,"score_spread":0.25548232338868676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409897988","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":[0.004072884,0.0005868154,0.9943291,0.00010289394,0.000165583,0.00041978832,0.000021907928,0.00025907444,0.000041937845],"genre_scores_gemma":[0.70336527,0.000110259454,0.29596534,0.00012580688,0.000027480732,0.00013589273,0.00004387491,0.000036506204,0.00018957407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835294,0.00010504889,0.00052988064,0.00039733836,0.00012591924,0.0004888568],"domain_scores_gemma":[0.99823385,0.0012858749,0.00006935558,0.0002953726,0.00002152527,0.000094028655],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012092993,0.00030544662,0.0004389238,0.00044124466,0.00014049734,0.00011382618,0.0001822249,0.00014955067,0.00010230127],"category_scores_gemma":[0.00016759813,0.0003286392,0.00017584243,0.0003582611,0.000025117903,0.00022251206,0.00002167157,0.00020511482,0.000010610942],"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":[0.000038576345,0.00004180748,0.0000016910257,0.00007109954,0.00047558668,0.0000015187304,0.000551304,0.87814903,0.111330524,0.000981964,0.00021669676,0.008140177],"study_design_scores_gemma":[0.00003935555,0.000015266623,0.0000026620357,0.000008674603,0.0003635758,3.3680556e-7,0.00018306225,0.5104387,0.48726696,0.0006934651,0.0008198193,0.00016814037],"about_ca_topic_score_codex":0.000032575088,"about_ca_topic_score_gemma":0.000009706847,"teacher_disagreement_score":0.69929236,"about_ca_system_score_codex":0.000084134874,"about_ca_system_score_gemma":0.000033745288,"threshold_uncertainty_score":0.99991655},"labels":[],"label_agreement":null},{"id":"W4411809225","doi":"10.1017/s0890060425100048","title":"Managing combinatorial design challenges using flexibility and pathfinding algorithms","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Design Education and Practice","field":"Engineering","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":"VINNOVA","keywords":"Pathfinding; Flexibility (engineering); Computer science; Algorithm; Theoretical computer science; Mathematics; Shortest path problem","score_opus":0.08894684844875378,"score_gpt":0.3131662669326104,"score_spread":0.22421941848385663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411809225","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":[0.014051684,0.00166369,0.9827635,0.000099097066,0.0007261707,0.0003981743,0.0000025731056,0.00023624396,0.000058853464],"genre_scores_gemma":[0.9338759,0.0008913623,0.0650124,0.000016823575,0.00010076953,0.000050256578,0.0000037948266,0.00003150976,0.00001718134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855334,0.000062629806,0.0004518607,0.00042517835,0.0001234384,0.00038354372],"domain_scores_gemma":[0.99871427,0.0008285643,0.000057085774,0.00024045998,0.00004452043,0.000115121235],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011442015,0.00029020256,0.00039519407,0.0006703203,0.00021107466,0.00022861638,0.00013718414,0.00011924481,0.000011564108],"category_scores_gemma":[0.00012206019,0.00031714616,0.00011043234,0.00038179345,0.000033363212,0.00028215285,0.000039779265,0.00018861113,0.0000016919287],"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":[0.000037395897,0.000029015628,0.000010533114,0.00021584693,0.0006850087,0.000002443478,0.0005822396,0.80467147,0.0035410281,0.008205564,0.000010239462,0.1820092],"study_design_scores_gemma":[0.000047577996,0.000027585953,0.000099839555,0.000042348496,0.0005302606,0.0000022792685,0.0003787668,0.8621342,0.12167407,0.01440105,0.0003738312,0.0002881726],"about_ca_topic_score_codex":0.00003394405,"about_ca_topic_score_gemma":0.000003795801,"teacher_disagreement_score":0.91982424,"about_ca_system_score_codex":0.00009234802,"about_ca_system_score_gemma":0.000021050182,"threshold_uncertainty_score":0.99992806},"labels":[],"label_agreement":null},{"id":"W4412545183","doi":"10.1017/s0890060425100073","title":"Dynamic workload reallocation for human–robot teams based on real-time stress analysis","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Robot; Computer science; Human–robot interaction; Stress (linguistics); Human–computer interaction; Real-time computing; Artificial intelligence; Operating system","score_opus":0.024626346422115176,"score_gpt":0.34482551171357223,"score_spread":0.32019916529145703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412545183","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":[0.023523519,0.000021227595,0.9749407,0.00016664175,0.00021866923,0.00054222817,0.000028987082,0.00019070214,0.0003673156],"genre_scores_gemma":[0.9892253,0.000012012696,0.008762445,0.00006332067,0.000041535266,0.00033120916,0.00017894883,0.000022813483,0.0013624094],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982315,0.000062003404,0.0006647893,0.0005694695,0.00013670721,0.0003355795],"domain_scores_gemma":[0.9984795,0.00076010736,0.0001630885,0.00042061333,0.000093766765,0.00008295719],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058454694,0.0002597031,0.00047619516,0.0017247519,0.00028883683,0.00014920044,0.00018279588,0.0001403486,0.0004009062],"category_scores_gemma":[0.00006862689,0.0002653714,0.0004708675,0.0008335393,0.000030700623,0.000084495936,0.000015487207,0.00012524528,0.000021268846],"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":[0.00017573958,0.00012714215,0.00007189649,0.0000431253,0.0026124024,9.733674e-7,0.0002872996,0.95571053,0.002776823,0.0074685058,0.000048544214,0.03067699],"study_design_scores_gemma":[0.000087834946,0.00009484533,0.0035134237,0.00004809941,0.0022664731,1.4251157e-7,0.00021960825,0.95982546,0.03263524,0.0008605411,0.00018682014,0.00026150004],"about_ca_topic_score_codex":0.00019707403,"about_ca_topic_score_gemma":0.00017857851,"teacher_disagreement_score":0.96617824,"about_ca_system_score_codex":0.00012787414,"about_ca_system_score_gemma":0.000016895696,"threshold_uncertainty_score":0.99997985},"labels":[],"label_agreement":null},{"id":"W4414352768","doi":"10.1017/s0890060425100140","title":"Decoding the digital thread digitalization approach for product design and development: benefits, challenges, and extensions","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Manufacturing Process and Optimization","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 Toronto","funders":"University of Bristol","keywords":"Thread (computing); Implementation; Iterative design; Product design; Design flow; Engineering design process; Design methods; Data flow diagram","score_opus":0.05440190021978418,"score_gpt":0.23927386973349807,"score_spread":0.18487196951371387,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414352768","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":[0.003988512,0.005583213,0.9894374,0.000043419685,0.00006317363,0.0007100157,0.000005806938,0.00014726301,0.00002119066],"genre_scores_gemma":[0.87982357,0.001856504,0.11798511,0.000008511344,0.00003623428,0.00020446915,0.00003626382,0.000029260134,0.000020058484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892527,0.000008377833,0.0003387646,0.00037551016,0.0000810034,0.00027108073],"domain_scores_gemma":[0.99938035,0.00032098807,0.00004302748,0.00015035296,0.000044243432,0.000061052044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000350026,0.00024185434,0.0002664654,0.00030640647,0.00026813493,0.00034542303,0.00009807243,0.00006764361,8.220111e-7],"category_scores_gemma":[0.00008232693,0.0002005182,0.00005105721,0.0001506351,0.0000320575,0.00024881796,0.000041189276,0.00007421082,1.7460658e-7],"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":[0.000011474152,0.000008313502,0.0000047898084,0.0001883911,0.00021109874,8.683018e-8,0.00029705028,0.6894984,0.000036039466,0.0026980012,0.000003648573,0.30704266],"study_design_scores_gemma":[0.0000416925,0.00001806605,0.00021539275,0.000042766835,0.00021383802,0.0000015501693,0.00014656175,0.92244184,0.07461295,0.0014363828,0.0005865475,0.0002424359],"about_ca_topic_score_codex":0.0000019933002,"about_ca_topic_score_gemma":0.0000032260855,"teacher_disagreement_score":0.87583506,"about_ca_system_score_codex":0.000024773772,"about_ca_system_score_gemma":0.000011303698,"threshold_uncertainty_score":0.81768954},"labels":[],"label_agreement":null},{"id":"W4416301408","doi":"10.1017/s089006042510019x","title":"Sampling balanced high-quality data to train an automatic mesh generator","year":2025,"lang":"en","type":"article","venue":"Artificial intelligence for engineering design analysis and manufacturing","topic":"Computational Geometry and Mesh Generation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Train; Generator (circuit theory); Feed forward; Artificial neural network; Function (biology); Sampling (signal processing); Feature (linguistics)","score_opus":0.10930031470776796,"score_gpt":0.3472569671947144,"score_spread":0.23795665248694642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416301408","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":[0.08670333,0.000041837466,0.9123223,0.00025608015,0.0002370058,0.00027218097,0.000013854347,0.0001512429,0.0000021745923],"genre_scores_gemma":[0.6023629,0.000004359518,0.39737228,0.00010818424,0.00005903047,0.000028056496,0.000046143083,0.0000049459486,0.0000141346345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983878,0.00005408177,0.00047673393,0.00064639136,0.00016803341,0.00026694298],"domain_scores_gemma":[0.99872094,0.0003731179,0.00006486566,0.000649822,0.000064367065,0.00012688564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001152835,0.00017679685,0.00029792913,0.00058568467,0.00019423313,0.00040771495,0.0006577528,0.00005247492,0.000007112269],"category_scores_gemma":[0.0001704848,0.0001809866,0.000071643946,0.0008318087,0.000010646257,0.00046246612,0.00017833349,0.00007263592,0.0000029257787],"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":[0.0000065549707,0.000026615891,0.0000040530585,0.000026831854,0.00017575183,5.290018e-7,0.000139604,0.71303576,0.016962757,0.041427005,0.000016483327,0.22817805],"study_design_scores_gemma":[0.000021108834,0.00003212705,0.000732356,0.000011402979,0.00008970971,3.3600892e-7,0.000020129884,0.8071211,0.18641202,0.0051879887,0.00019885377,0.00017283196],"about_ca_topic_score_codex":0.0000503019,"about_ca_topic_score_gemma":0.00004361342,"teacher_disagreement_score":0.5156595,"about_ca_system_score_codex":0.000037945174,"about_ca_system_score_gemma":0.00004165383,"threshold_uncertainty_score":0.73804194},"labels":[],"label_agreement":null}]}